1
|
Sun F, Gao X, Li T, Zhao X, Zhu Y. Tumor immune microenvironment remodeling after neoadjuvant therapy in gastric cancer: Update and new challenges. Biochim Biophys Acta Rev Cancer 2025; 1880:189350. [PMID: 40355011 DOI: 10.1016/j.bbcan.2025.189350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2024] [Revised: 05/05/2025] [Accepted: 05/08/2025] [Indexed: 05/14/2025]
Abstract
Gastric cancer (GC) is a malignant tumor with one of the highest morbidity and death rates in the world. Neoadjuvant therapy, including neoadjuvant chemotherapy (NAC) and NAC combined with immunotherapy, can improve the resection and long-term survival rates. However, not all patients respond well to neoadjuvant therapy. It has been confirmed that immune cells in the tumor immune microenvironment, including T cells, B cells, and natural killer cells, can affect the efficacy of neoadjuvant therapy. This paper summarizes current preclinical and clinical evidence to more fully describe the effects of neoadjuvant therapy on the immune microenvironment of GC, to provide the impetus to identify biomarkers to predict the potency of neoadjuvant therapy, and to identify the mechanisms of drug resistance, which should promote the development of individualized and accurate treatments for GC patients.
Collapse
Affiliation(s)
- Fujing Sun
- Department of Pathology, Affiliated Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University), Shenyang, China
| | - Xiaozhuo Gao
- Department of Pathology, Affiliated Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University), Shenyang, China
| | - Tianming Li
- Department of Pathology, Affiliated Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University), Shenyang, China
| | - Xiaoyan Zhao
- Graduate School, Dalian Medical University, Dalian, China
| | - Yanmei Zhu
- Department of Pathology, Affiliated Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University), Shenyang, China.
| |
Collapse
|
2
|
Wong ECN, Yang T, Li X, Liu Y, Majonis D, Abtahi M, Zhang Y, Lee ATY, Closson T, Paluan S, Hawrysh P, Winnik MA. A Potential Mercury Polymer Mass Tag Probe for Mass Cytometry. Anal Chem 2025. [PMID: 40404572 DOI: 10.1021/acs.analchem.5c00580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2025]
Abstract
A novel mercury(II) polymer with pendant dipicolylamine (DPA) chelators carrying Hg2+ ions was prepared as a potential mass tag for mass cytometry (MC) applications. The polymer itself, with DP ≈ 22 had a mixture of PEG24 (provides stealth) and PEG23-azide groups attached to the pendant groups, with ca. 3 azide groups per polymer for antibody conjugation. The initial Hg-DPA complex had 2 Cl atoms attached to each Hg2+ ion (Cl2-Hg-DPA). An approach to replace the two Hg-Cl bonds via ligand exchange reactions by treating the Hg-DPA polymer with monothiol ligands (glutathione, cysteine, and thioglycolic acid) led to a loss of Hg, while Hg polymers treated with dithiol ligands (2,3-dimercapto-1-propanol and 2,3-dimercapto-1-propanesulfonic acid sodium salt monohydrate) led to polymers 2d and 2f, respectively, in which both polymers largely retained their Hg content. These polymers showed relatively low levels of nonspecific binding to suspensions of peripheral blood mononuclear cells (PBMCs). Both polymers were conjugated to anti-CD8a via strain-promoted azide-alkyne coupling, and the conjugates were employed in a 10-plex assay of PBMCs along with Maxpar reagents. Polymer 2d-CD8a showed excellent specificity to target CD8a antigens, even at a low titer of 0.56 μg/mL, and the ability to differentiate CD8a T cells from other components of the PBMC sample. In contrast, polymer 2f-CD8a gave no Hg signal suggesting loss of Hg from the polymer during Ab conjugation or PBMC staining steps. Polymer 2d represents an important step forward in the development of Hg mass tags for MC.
Collapse
Affiliation(s)
- Edmond C N Wong
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Tianjia Yang
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Xiaochong Li
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Yang Liu
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Daniel Majonis
- Standard BioTools Canada Incorporation, 1380 Rodick Road, Suite 400, Markham, Ontario L3R 4G5, Canada
| | - Mahtab Abtahi
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Yefeng Zhang
- Standard BioTools Canada Incorporation, 1380 Rodick Road, Suite 400, Markham, Ontario L3R 4G5, Canada
| | - Andrea Tsz Yan Lee
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Taunia Closson
- Standard BioTools Canada Incorporation, 1380 Rodick Road, Suite 400, Markham, Ontario L3R 4G5, Canada
| | - Shelly Paluan
- Standard BioTools Canada Incorporation, 1380 Rodick Road, Suite 400, Markham, Ontario L3R 4G5, Canada
| | - Peter Hawrysh
- Standard BioTools Canada Incorporation, 1380 Rodick Road, Suite 400, Markham, Ontario L3R 4G5, Canada
| | - Mitchell A Winnik
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
| |
Collapse
|
3
|
Pi D, Wong JJM, Nay Yaung K, Khoo NKH, Poh SL, Wasser M, Kumar P, Arkachaisri T, Xu F, Tan HL, Mok YH, Yeo JG, Albani S. Clinical and mechanistic relevance of high-dimensionality analysis of the paediatric sepsis immunome. Front Immunol 2025; 16:1569096. [PMID: 40433376 PMCID: PMC12106532 DOI: 10.3389/fimmu.2025.1569096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2025] [Accepted: 04/15/2025] [Indexed: 05/29/2025] Open
Abstract
Background By employing a high-dimensionality approach, this study aims to identify mechanistically relevant cellular immune signatures that predict poor outcomes. Methods This prospective study recruited 39 children with sepsis admitted to the intensive care unit and 19 healthy age-matched children. Peripheral blood mononuclear cells were studied with mass cytometry. Unique cell subsets were identified in the paediatric sepsis immunome and depicted with t-distributed stochastic neighbour embedding (tSNE) plots. Network analysis was performed to quantify interactions between immune subsets. Enriched immune subsets were included in a model for distinguishing sepsis and validated by flow cytometry in an independent cohort. Results The median (interquartile range) age and paediatric sequential organ failure assessment (pSOFA) score in this cohort was 5.6(2.0, 11.3) years and 6.6 (IQR: 2.5, 10.1), respectively. High-dimensionality analyses of the immunome in sepsis revealed a loss of coordinated communication between immune subsets, particularly a loss of regulatory/inhibitory interaction between cell types, fewer interactions between cell subsets, and fewer negatively correlated edges than controls. Four independent immune subsets (CD45RA-CX3CR1+CTLA4+CD4+ T cells, CD45RA-17A+CD4+ T cells CD15+CD14+ monocytes, and Ki67+ B cells) were increased in sepsis and provide a predictive model for diagnosis with area under the receiver operating characteristic curve, AUC 0.90 (95% confidence interval, CI 0.82-0.98) in the discovery cohort and AUC 0.94 (95% CI 0.83-1.00) in the validation cohort. Conclusion The sepsis immunome is deranged with loss of regulatory/inhibitory interactions. Four immune subsets increased in sepsis could be used in a model for diagnosis and prediction of poor outcomes.
Collapse
Affiliation(s)
- Dandan Pi
- Department of Paediatric Intensive Care Unit, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
| | - Judith Ju Ming Wong
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Children’s Intensive Care Unit, Department of Pediatric Subspecialties, KK Women’s and Children’s Hospital, Singapore, Singapore
- Paediatrics Academic Clinical Programme, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
| | - Katherine Nay Yaung
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Paediatrics Academic Clinical Programme, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
| | - Nicholas Kim Huat Khoo
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Paediatrics Academic Clinical Programme, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
| | - Su Li Poh
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
| | - Martin Wasser
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Paediatrics Academic Clinical Programme, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
| | - Pavanish Kumar
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Paediatrics Academic Clinical Programme, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
| | - Thaschawee Arkachaisri
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Paediatrics Academic Clinical Programme, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Rheumatology and Immunology Service, Department of Pediatric Subspecialties, KK Women’s and Children’s Hospital, Singapore, Singapore
| | - Feng Xu
- Department of Paediatric Intensive Care Unit, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China
| | - Herng Lee Tan
- Respiratory Therapy Service, Division of Allied Health Specialties, KK Women’s and Children’s Hospital, Singapore, Singapore
| | - Yee Hui Mok
- Children’s Intensive Care Unit, Department of Pediatric Subspecialties, KK Women’s and Children’s Hospital, Singapore, Singapore
- Paediatrics Academic Clinical Programme, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
| | - Joo Guan Yeo
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Paediatrics Academic Clinical Programme, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Rheumatology and Immunology Service, Department of Pediatric Subspecialties, KK Women’s and Children’s Hospital, Singapore, Singapore
| | - Salvatore Albani
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Paediatrics Academic Clinical Programme, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Rheumatology and Immunology Service, Department of Pediatric Subspecialties, KK Women’s and Children’s Hospital, Singapore, Singapore
| |
Collapse
|
4
|
Diop M, Davidson BR, Fragiadakis GK, Sirota M, Gaudillière B, Combes AJ. Single-cell omics technologies - Fundamentals on how to create single-cell looking glasses for reproductive health. Am J Obstet Gynecol 2025; 232:S1-S20. [PMID: 40253074 PMCID: PMC12090843 DOI: 10.1016/j.ajog.2024.08.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 07/18/2024] [Accepted: 08/24/2024] [Indexed: 04/21/2025]
Abstract
Over the last decade, in line with the goals of precision medicine to offer individualized patient care, various single-cell technologies measuring gene and proteomic expression in various tissues have rapidly advanced to study health and disease at the single cell level. Precisely understanding cell composition, position within tissues, signaling pathways, and communication can reveal insights into disease mechanisms and systemic changes during development, pregnancy, and gynecologic disorders across the lifespan. Single-cell technologies dissect the complex cellular compositions of reproductive tract tissues, providing insights into mechanisms behind reproductive tract dysfunction which impact wellness and quality of life. These technologies aim to understand basic tissue and organ functions and, clinically, to develop novel diagnostics, early disease biomarkers, and cell-targeted therapies for currently suboptimally-treated disorders. Increasingly, they are applied to pregnancy and pregnancy disorders, gynecologic malignancies, and uterine and ovarian physiology and aging, which are discussed in more detail in manuscripts in this special issue of AJOG. Here, we review recent applications of single-cell technologies to the study of gynecologic disorders and systemic biological adaptations during fetal development, pregnancy, and across a woman's lifespan. We discuss sequencing- and proteomic-based single-cell methods, as well as spatial transcriptomics and high-dimensional proteomic imaging, describing each technology's mechanism, workflow, quality control, and highlighting specific benefits, drawbacks, and utility in the context of reproductive medicine. We consider analytical methods for the high-dimensional single-cell data generated, highlighting statistical constraints and recent computational techniques for downstream clinical translation. Overall, current and evolving single-cell "looking glasses", or perspectives, have the potential to transform fundamental understanding of women's health and reproductive disorders and alter the trajectory of clinical practice and patient outcomes in the future.
Collapse
Affiliation(s)
- Maïgane Diop
- Program in Immunology, Stanford University School of Medicine, Stanford, CA; Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA
| | | | - Gabriela K Fragiadakis
- UCSF CoLabs, University of California, San Francisco, CA; Bakar ImmunoX Initiative, University of California, San Francisco, CA; Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA.
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA; Department of Pediatrics, University of California, San Francisco, CA.
| | - Brice Gaudillière
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA.
| | - Alexis J Combes
- UCSF CoLabs, University of California, San Francisco, CA; Department of Pathology, University of California, San Francisco, CA; Bakar ImmunoX Initiative, University of California, San Francisco, CA; Division of Gastroenterology, Department of Medicine, University of California, San Francisco, CA.
| |
Collapse
|
5
|
Tursi AR, Lages CS, Quayle K, Koenig ZT, Loni R, Eswar S, Cobeña-Reyes J, Thornton S, Tilburgs T, Andorf S. CytoPheno: Automated descriptive cell type naming in flow and mass cytometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.11.639902. [PMID: 40161808 PMCID: PMC11952469 DOI: 10.1101/2025.03.11.639902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
Advances in cytometry have led to increases in the number of cellular markers that are routinely measured. The resulting complexity of the data has prompted a shift from manual to automated analysis methods. Currently, numerous unsupervised methods are available to cluster cells based on marker expression values. However, phenotyping the resulting clusters is typically not part of the automated process. Manually identifying both marker definitions (e.g. CD4+, CCR7+, CD45RA+, CD19-) and descriptive cell type names (e.g. naïve CD4+ T cells) based on marker expression values can be time-consuming, subjective, and error-prone. In this work we propose an algorithm that addresses these problems through the creation of an automated tool, CytoPheno, that assigns marker definitions and cell type names to unidentified clusters. First, post-clustered expression data undergoes per-marker calculations to assign markers as positive or negative. Next, marker names undergo a standardization process to match to Protein Ontology identifier terms. Finally, marker descriptions are matched to cell type names within the Cell Ontology. Each part of the tool was tested with benchmark data to demonstrate performance. Additionally, the tool is encompassed in a graphical user interface (R Shiny) to increase user accessibility and interpretability. Overall, CytoPheno can aid researchers in timely and unbiased phenotyping of post-clustered cytometry data.
Collapse
Affiliation(s)
- Amanda R Tursi
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Celine S Lages
- Division of Rheumatology, Research Flow Cytometry Core, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Kenneth Quayle
- Division of Rheumatology, Research Flow Cytometry Core, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Zachary T Koenig
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Rashi Loni
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Shruti Eswar
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pharmacology, Physiology & Neurobiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - José Cobeña-Reyes
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sherry Thornton
- Division of Rheumatology, Research Flow Cytometry Core, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Tamara Tilburgs
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sandra Andorf
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| |
Collapse
|
6
|
Vaughn N. Cytometry at the Intersection of Metabolism and Epigenetics in Lymphocyte Dynamics. Cytometry A 2025; 107:165-176. [PMID: 40052492 DOI: 10.1002/cyto.a.24919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/19/2025] [Indexed: 04/11/2025]
Abstract
Landmark studies at the turn of the century revealed metabolic reprogramming as a driving force for lymphocyte differentiation and function. In addition to metabolic changes, differentiating lymphocytes must remodel their epigenetic landscape to properly rewire their gene expression. Recent discoveries have shown that metabolic shifts can shape the fate of lymphocytes by altering their epigenetic state, bringing together these two areas of inquiry. The ongoing evolution of high-dimensional cytometry has enabled increasingly comprehensive analyses of metabolic and epigenetic landscapes in lymphocytes that transcend the technical limitations of the past. Here, we review recent insights into the interplay between metabolism and epigenetics in lymphocytes and how its dysregulation can lead to immunological dysfunction and disease. We also discuss the latest technical advances in cytometry that have enabled these discoveries and that we anticipate will advance future work in this area.
Collapse
Affiliation(s)
- Nicole Vaughn
- Department of Leukemia, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| |
Collapse
|
7
|
Xu Y, Chan MTJ, Yang M, Meng H, Chen CH. Time-resolved single-cell secretion analysis via microfluidics. LAB ON A CHIP 2025; 25:1282-1295. [PMID: 39789982 DOI: 10.1039/d4lc00904e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2025]
Abstract
Revealing how individual cells alter their secretions over time is crucial for understanding their responses to environmental changes. Key questions include: When do cells modify their functions and states? What transitions occur? Insights into the kinetic secretion trajectories of various cell types are essential for unraveling complex biological systems. This review highlights seven microfluidic technologies for time-resolved single-cell secretion analysis: 1. Microwell real-time electrical detection: uses microelectrodes for precise, cell-specific, real-time measurement of secreted molecules. 2. Microwell real-time optical detection: employs advanced optical systems for real-time, multiplexed monitoring of cellular secretions. 3. Microvalve real-time optical detection: dynamically analyzes secretions under controlled in situ stimuli, enabling detailed kinetic studies at the single-cell level. 4. Droplet real-time optical detection: provides superior throughput by generating droplets containing single cells and sensors for high-throughput screening. 5. Microwell time-barcoded optical detection: utilizes sequential barcoding techniques to facilitate scalable assays for tracking multiple secretions over time. 6. Microvalve time-barcoded optical detection: incorporates automated time-barcoding via micro-valves for robust and scalable analysis. 7. Microwell time-barcoded sequencing: captures and labels secretions for sequencing, enabling multidimensional analysis, though currently limited to a few time points and extended intervals. This review specifically addresses the challenges of achieving high-resolution timing measurements with short intervals while maintaining scalability for single-cell screening. Future advancements in microfluidic devices, integrating innovative barcoding technologies, advanced imaging technologies, artificial intelligence-powered decoding and analysis, and automations are anticipated to enable highly sensitive, scalable, high-throughput single-cell dynamic analysis. These developments hold great promise for deepening our understanding of biosystems by exploring single-cell timing responses on a larger scale.
Collapse
Affiliation(s)
- Ying Xu
- Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China.
| | - Mei Tsz Jewel Chan
- Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China.
| | - Ming Yang
- Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China.
| | - Heixu Meng
- Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China.
| | - Chia-Hung Chen
- Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China.
| |
Collapse
|
8
|
Chen CJ, Yi H, Stanley N. Conditional similarity triplets enable covariate-informed representations of single-cell data. BMC Bioinformatics 2025; 26:45. [PMID: 39924480 PMCID: PMC11807331 DOI: 10.1186/s12859-025-06069-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2024] [Accepted: 01/29/2025] [Indexed: 02/11/2025] Open
Abstract
BACKGROUND Single-cell technologies enable comprehensive profiling of diverse immune cell-types through the measurement of multiple genes or proteins per individual cell. In order to translate immune signatures assayed from blood or tissue into powerful diagnostics, machine learning approaches are often employed to compute immunological summaries or per-sample featurizations, which can be used as inputs to models for outcomes of interest. Current supervised learning approaches for computing per-sample representations are trained only to accurately predict a single outcome and do not take into account relevant additional clinical features or covariates that are likely to also be measured for each sample. RESULTS Here, we introduce a novel approach for incorporating measured covariates in optimizing model parameters to ultimately specify per-sample encodings that accurately affect both immune signatures and additional clinical information. Our introduced method CytoCoSet is a set-based encoding method for learning per-sample featurizations, which formulates a loss function with an additional triplet term penalizing samples with similar covariates from having disparate embedding results in per-sample representations. CONCLUSIONS Overall, incorporating clinical covariates enables the learning of encodings for each individual sample that ultimately improve prediction of clinical outcome. This integration of information disparate more robust predictions of clinical phenotypes and holds significant potential for enhancing diagnostic and treatment strategies.
Collapse
Affiliation(s)
- Chi-Jane Chen
- Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| | - Haidong Yi
- Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Natalie Stanley
- Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Computational Medicine Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| |
Collapse
|
9
|
Min Q, Lv Q, Jiang L, Chen Q, Peng J, Zhou H, Zhou J, Dai Q, Zhou J, Huang Q. The Effect of Cryopreservation on T-Cell Subsets by Flow Cytometry Automated Algorithmic Analysis and Conventional Analysis. J Clin Lab Anal 2025; 39:e25146. [PMID: 39749863 PMCID: PMC11821723 DOI: 10.1002/jcla.25146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 11/14/2024] [Accepted: 12/22/2024] [Indexed: 01/04/2025] Open
Abstract
BACKGROUND Low-temperature cryopreservation is a common method for scientific research and clinical sample preservation when utilizing flow cytometry. In flow cytometry data analysis, traditional manual "gating" is susceptible to past experience and faces the challenge of manual subjective bias, time-consuming, and multidimensional data analysis. With the development of algorithms, the advantages of dimensionality reduction and clustering in result analysis are gradually becoming more prominent. METHODS Flow cytometry was used to detect the effects of cryopreservation and freeze-thaw cycle on T-cell subsets, and to analyze the data using automated algorithmic analysis and conventional manual "gating" methods. RESULTS The results showed that the number and viability of cells decreased slightly after one freeze-thaw within 2 weeks of cryopreservation, and there was no significant change in the subpopulation proportions and spatial locations by both analysis methods. The changes were significant with the increase of cryopreservation time and freeze-thaw cycle, which may be due to changes in the molecular conformation of the maker as a result of cryopreservation. CONCLUSION The results indicate that both analysis methods have reached similar conclusions, but the repeatability and objectivity of automated algorithmic analysis have compensated for the uncertainty brought about by the subjective discretization of traditional manual "gating." In addition, the automated algorithmic analysis more intuitively highlights the spatial positional variations in the relationships between cell populations.
Collapse
Affiliation(s)
- Qian Min
- Clinical Medical Research Center, Xinqiao HospitalArmy Medical UniversityChongqingChina
| | - Qiao Lv
- Clinical Medical Research Center, Xinqiao HospitalArmy Medical UniversityChongqingChina
| | - Lu Jiang
- Clinical Medical Research Center, Xinqiao HospitalArmy Medical UniversityChongqingChina
| | - Qian Chen
- Clinical Medical Research Center, Xinqiao HospitalArmy Medical UniversityChongqingChina
| | - Jin Peng
- Clinical Medical Research Center, Xinqiao HospitalArmy Medical UniversityChongqingChina
| | - Hongli Zhou
- Clinical Medical Research Center, Xinqiao HospitalArmy Medical UniversityChongqingChina
| | - Ju Zhou
- Clinical Medical Research Center, Xinqiao HospitalArmy Medical UniversityChongqingChina
| | - Qian Dai
- Clinical Medical Research Center, Xinqiao HospitalArmy Medical UniversityChongqingChina
| | - Jianyun Zhou
- Clinical Medical Research Center, Xinqiao HospitalArmy Medical UniversityChongqingChina
| | - Qing Huang
- Clinical Medical Research Center, Xinqiao HospitalArmy Medical UniversityChongqingChina
| |
Collapse
|
10
|
Hakala S, Hämäläinen A, Sandelin S, Giannareas N, Närvä E. Detection of Cancer Stem Cells from Patient Samples. Cells 2025; 14:148. [PMID: 39851576 PMCID: PMC11764358 DOI: 10.3390/cells14020148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2024] [Revised: 01/16/2025] [Accepted: 01/17/2025] [Indexed: 01/26/2025] Open
Abstract
The existence of cancer stem cells (CSCs) in various tumors has become increasingly clear in addition to their prominent role in therapy resistance, metastasis, and recurrence. For early diagnosis, disease progression monitoring, and targeting, there is a high demand for clinical-grade methods for quantitative measurement of CSCs from patient samples. Despite years of active research, standard measurement of CSCs has not yet reached clinical settings, especially in the case of solid tumors. This is because detecting this plastic heterogeneous population of cells is not straightforward. This review summarizes various techniques, highlighting their benefits and limitations in detecting CSCs from patient samples. In addition, methods designed to detect CSCs based on secreted and niche-associated signaling factors are reviewed. Spatial and single-cell methods for analyzing patient tumor tissues and noninvasive techniques such as liquid biopsy and in vivo imaging are discussed. Additionally, methods recently established in laboratories, preclinical studies, and clinical assays are covered. Finally, we discuss the characteristics of an ideal method as we look toward the future.
Collapse
Affiliation(s)
| | | | | | | | - Elisa Närvä
- Institute of Biomedicine and FICAN West Cancer Centre Laboratory, University of Turku and Turku University Hospital, FI-20520 Turku, Finland; (S.H.); (A.H.); (S.S.); (N.G.)
| |
Collapse
|
11
|
Zhao Q, Li S, Krall L, Li Q, Sun R, Yin Y, Fu J, Zhang X, Wang Y, Yang M. Deciphering cellular complexity: advances and future directions in single-cell protein analysis. Front Bioeng Biotechnol 2025; 12:1507460. [PMID: 39877263 PMCID: PMC11772399 DOI: 10.3389/fbioe.2024.1507460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Accepted: 12/19/2024] [Indexed: 01/31/2025] Open
Abstract
Single-cell protein analysis has emerged as a powerful tool for understanding cellular heterogeneity and deciphering the complex mechanisms governing cellular function and fate. This review provides a comprehensive examination of the latest methodologies, including sophisticated cell isolation techniques (Fluorescence-Activated Cell Sorting (FACS), Magnetic-Activated Cell Sorting (MACS), Laser Capture Microdissection (LCM), manual cell picking, and microfluidics) and advanced approaches for protein profiling and protein-protein interaction analysis. The unique strengths, limitations, and opportunities of each method are discussed, along with their contributions to unraveling gene regulatory networks, cellular states, and disease mechanisms. The importance of data analysis and computational methods in extracting meaningful biological insights from the complex data generated by these technologies is also highlighted. By discussing recent progress, technological innovations, and potential future directions, this review emphasizes the critical role of single-cell protein analysis in advancing life science research and its promising applications in precision medicine, biomarker discovery, and targeted therapeutics. Deciphering cellular complexity at the single-cell level holds immense potential for transforming our understanding of biological processes and ultimately improving human health.
Collapse
Affiliation(s)
- Qirui Zhao
- Yunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China
- State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China
| | - Shan Li
- Yunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China
- State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China
| | - Leonard Krall
- Yunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China
- State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China
| | - Qianyu Li
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China
| | - Rongyuan Sun
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China
| | - Yuqi Yin
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China
| | - Jingyi Fu
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China
| | - Xu Zhang
- Yunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China
- State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China
| | - Yonghua Wang
- Yunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China
- State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China
| | - Mei Yang
- Yunnan Key Laboratory of Cell Metabolism and Diseases, Yunnan University, Kunming, China
- State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China
| |
Collapse
|
12
|
Cai L, Lin L, Lin S, Wang X, Chen Y, Zhu H, Zhu Z, Yang L, Xu X, Yang C. Highly Multiplexing, Throughput and Efficient Single-Cell Protein Analysis with Digital Microfluidics. SMALL METHODS 2024; 8:e2400375. [PMID: 38607945 DOI: 10.1002/smtd.202400375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Indexed: 04/14/2024]
Abstract
Proteins as crucial components of cells are responsible for the majority of cellular processes. Sensitive and efficient protein detection enables a more accurate and comprehensive investigation of cellular phenotypes and life activities. Here, a protein sequencing method with high multiplexing, high throughput, high cell utilization, and integration based on digital microfluidics (DMF-Protein-seq) is proposed, which transforms protein information into DNA sequencing readout via DNA-tagged antibodies and labels single cells with unique cell barcodes. In a 184-electrode DMF-Protein-seq system, ≈1800 cells are simultaneously detected per experimental run. The digital microfluidics device harnessing low-adsorbed hydrophobic surface and contaminants-isolated reaction space supports high cell utilization (>90%) and high mapping reads (>90%) with the input cells ranging from 140 to 2000. This system leverages split&pool strategy on the DMF chip for the first time to overcome DMF platform restriction in cell analysis throughput and replace the traditionally tedious bench-top combinatorial barcoding. With the benefits of high efficiency and sensitivity in protein analysis, the system offers great potential for cell classification and drug monitoring based on protein expression at the single-cell level.
Collapse
Affiliation(s)
- Linfeng Cai
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Li Lin
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Shiyan Lin
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Xuanqun Wang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Yingwen Chen
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Huanghuang Zhu
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Zhi Zhu
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Liu Yang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Xing Xu
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Chaoyong Yang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| |
Collapse
|
13
|
Wlosik J, Granjeaud S, Gorvel L, Olive D, Chretien AS. A beginner's guide to supervised analysis for mass cytometry data in cancer biology. Cytometry A 2024; 105:853-869. [PMID: 39486897 DOI: 10.1002/cyto.a.24901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 09/16/2024] [Accepted: 10/01/2024] [Indexed: 11/04/2024]
Abstract
Mass cytometry enables deep profiling of biological samples at single-cell resolution. This technology is more than relevant in cancer research due to high cellular heterogeneity and complexity. Downstream analysis of high-dimensional datasets increasingly relies on machine learning (ML) to extract clinically relevant information, including supervised algorithms for classification and regression purposes. In cancer research, they are used to develop predictive models that will guide clinical decision making. However, the development of supervised algorithms faces major challenges, such as sufficient validation, before being translated into the clinics. In this work, we provide a framework for the analysis of mass cytometry data with a specific focus on supervised algorithms and practical examples of their applications. We also raise awareness on key issues regarding good practices for researchers curious to implement supervised ML on their mass cytometry data. Finally, we discuss the challenges of supervised ML application to cancer research.
Collapse
Affiliation(s)
- Julia Wlosik
- Team 'Immunity and Cancer', Marseille Cancer Research Center, Inserm U1068, CNRS UMR7258, Paoli-Calmettes Institute, Aix-Marseille University UM105, Marseille, France
- Immunomonitoring Department, Paoli-Calmettes Institute, Marseille, France
| | - Samuel Granjeaud
- Systems Biology Platform, Marseille Cancer Research Center, Inserm U1068, CNRS UMR7258, Paoli-Calmettes Institute, Aix-Marseille University UM105, Marseille, France
| | - Laurent Gorvel
- Team 'Immunity and Cancer', Marseille Cancer Research Center, Inserm U1068, CNRS UMR7258, Paoli-Calmettes Institute, Aix-Marseille University UM105, Marseille, France
- Immunomonitoring Department, Paoli-Calmettes Institute, Marseille, France
| | - Daniel Olive
- Team 'Immunity and Cancer', Marseille Cancer Research Center, Inserm U1068, CNRS UMR7258, Paoli-Calmettes Institute, Aix-Marseille University UM105, Marseille, France
- Immunomonitoring Department, Paoli-Calmettes Institute, Marseille, France
| | - Anne-Sophie Chretien
- Team 'Immunity and Cancer', Marseille Cancer Research Center, Inserm U1068, CNRS UMR7258, Paoli-Calmettes Institute, Aix-Marseille University UM105, Marseille, France
- Immunomonitoring Department, Paoli-Calmettes Institute, Marseille, France
| |
Collapse
|
14
|
Bakardjieva M, Pelák O, Wentink M, Glier H, Novák D, Stančíková J, Kužílková D, Mejstříková E, Janowska I, Rizzi M, van der Burg M, Stuchlý J, Kalina T. Tviblindi algorithm identifies branching developmental trajectories of human B-cell development and describes abnormalities in RAG-1 and WAS patients. Eur J Immunol 2024; 54:e2451004. [PMID: 39235410 PMCID: PMC11628918 DOI: 10.1002/eji.202451004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 08/23/2024] [Accepted: 08/27/2024] [Indexed: 09/06/2024]
Abstract
Detailed knowledge of human B-cell development is crucial for the proper interpretation of inborn errors of immunity and malignant diseases. It is of interest to understand the kinetics of protein expression changes during development, but also to properly interpret the major and possibly alternative developmental trajectories. We have investigated human samples from healthy individuals with the aim of describing all B-cell developmental trajectories. We validated a 30-parameter mass cytometry panel and demonstrated the utility of "vaevictis" visualization of B-cell developmental stages. We used the trajectory inference tool "tviblindi" to exhaustively describe all trajectories leading to all developmental ends discovered in the data. Focusing on Natural Effector B cells, we demonstrated the dynamics of expression of nuclear factors (PAX-5, TdT, Ki-67, Bcl-2), cytokine and chemokine receptors (CD127, CXCR4, CXCR5) in relation to the canonical B-cell developmental stage markers. We observed branching of the memory development, where follicular memory formation was marked by CD73 expression. Lastly, we performed an analysis of two example cases of abnormal B-cell development caused by mutations in RAG-1 and Wiskott-Aldrich syndrome gene in patients with primary immunodeficiency. In conclusion, we developed, validated, and presented a comprehensive set of tools for the investigation of B-cell development in the bone marrow compartment.
Collapse
Affiliation(s)
- Marina Bakardjieva
- CLIPDepartment of Paediatric Haematology and OncologySecond Faculty of MedicineCharles UniversityPragueCzech Republic
| | - Ondřej Pelák
- CLIPDepartment of Paediatric Haematology and OncologySecond Faculty of MedicineCharles UniversityPragueCzech Republic
| | - Marjolein Wentink
- Department of Internal MedicineErasmus MCUniversity Medical Center RotterdamRotterdamthe Netherlands
| | - Hana Glier
- CLIPDepartment of Paediatric Haematology and OncologySecond Faculty of MedicineCharles UniversityPragueCzech Republic
| | - David Novák
- CLIPDepartment of Paediatric Haematology and OncologySecond Faculty of MedicineCharles UniversityPragueCzech Republic
- Department of Applied MathematicsComputer Science and StatisticsGhent UniversityGhentBelgium
- Data Mining and Modeling for BiomedicineCenter for Inflammation ResearchVIB‐UGentGhentBelgium
| | - Jitka Stančíková
- CLIPDepartment of Paediatric Haematology and OncologySecond Faculty of MedicineCharles UniversityPragueCzech Republic
| | - Daniela Kužílková
- CLIPDepartment of Paediatric Haematology and OncologySecond Faculty of MedicineCharles UniversityPragueCzech Republic
- Department of Paediatric Haematology and OncologyUniversity Hospital MotolPragueCzech Republic
| | - Ester Mejstříková
- CLIPDepartment of Paediatric Haematology and OncologySecond Faculty of MedicineCharles UniversityPragueCzech Republic
- Department of Paediatric Haematology and OncologyUniversity Hospital MotolPragueCzech Republic
| | - Iga Janowska
- Department of Rheumatology and Clinical ImmunologyFreiburg University Medical CenterUniversity of FreiburgFreiburgGermany
- Center for Chronic ImmunodeficiencyUniversity Medical Center FreiburgFaculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Marta Rizzi
- Department of Rheumatology and Clinical ImmunologyFreiburg University Medical CenterUniversity of FreiburgFreiburgGermany
- Center for Chronic ImmunodeficiencyUniversity Medical Center FreiburgFaculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Mirjam van der Burg
- Department of PediatricsLaboratory for Pediatric ImmmunologyLeiden University Medical CenterLeidenthe Netherlands
| | - Jan Stuchlý
- CLIPDepartment of Paediatric Haematology and OncologySecond Faculty of MedicineCharles UniversityPragueCzech Republic
- Department of Paediatric Haematology and OncologyUniversity Hospital MotolPragueCzech Republic
| | - Tomáš Kalina
- CLIPDepartment of Paediatric Haematology and OncologySecond Faculty of MedicineCharles UniversityPragueCzech Republic
- Department of Paediatric Haematology and OncologyUniversity Hospital MotolPragueCzech Republic
| |
Collapse
|
15
|
Bedia JS, Huang YW, Gonzalez AD, Gonzalez VD, Funingana IG, Rahil Z, Mike A, Lowber A, Vias M, Ashworth A, Brenton JD, Fantl WJ. Coordinated protein modules define DNA damage responses to carboplatin at single cell resolution in human ovarian carcinoma models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.21.624591. [PMID: 39605494 PMCID: PMC11601625 DOI: 10.1101/2024.11.21.624591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
Tubo-ovarian high-grade serous carcinoma (HGSC) is the most lethal gynecological malignancy and frequently responds to platinum-based chemotherapy because of common genetic and somatic impairment of DNA damage repair (DDR) pathways. The mechanisms of clinical platinum resistance are diverse and poorly molecularly defined. Consequently, there are no biomarkers or medicines that improve patient outcomes. Herein we use single cell mass cytometry (CyTOF) to systematically evaluate the phosphorylation and abundance of proteins known to participate in the DNA damage response (DDR). Single cell analyses of highly characterized HGSC cell lines that phenocopy human patients show that cells with comparable levels of intranuclear platinum, a proxy for carboplatin uptake, undergo different cell fates. Unsupervised analyses revealed a continuum of DDR responses. Decompositional methods were used to identify eight distinct protein modules of carboplatin resistance and sensitivity at single cell resolution. CyTOF profiling of primary and secondary platinum-resistance patient models shows that a complex DDR sensitivity module is strongly associated with response, suggesting it as a potential tool to clinically characterize complex drug resistance phenotypes.
Collapse
Affiliation(s)
- Jacob S. Bedia
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ying-Wen Huang
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | - Veronica D. Gonzalez
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ionut-Gabriel Funingana
- Department of Oncology, University of Cambridge, Cambridgeshire, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, Cambridgeshire, CB2 0RE, UK
- Department of Oncology, Addenbrooke’s Hospital, Cambridge University Hospitals, NHS Foundation Trust, Cambridge, UK
| | - Zainab Rahil
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alyssa Mike
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alexis Lowber
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Maria Vias
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, Cambridgeshire, CB2 0RE, UK
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, 1450 Third Street, San Francisco, CA 94158, USA
| | - James D. Brenton
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, Cambridgeshire, CB2 0RE, UK
- Department of Oncology, Addenbrooke’s Hospital, Cambridge University Hospitals, NHS Foundation Trust, Cambridge, UK
| | - Wendy J. Fantl
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Stanford Comprehensive Cancer Institute
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA
| |
Collapse
|
16
|
Liu P, Pan Y, Chang HC, Wang W, Fang Y, Xue X, Zou J, Toothaker JM, Olaloye O, Santiago EG, McCourt B, Mitsialis V, Presicce P, Kallapur SG, Snapper SB, Liu JJ, Tseng GC, Konnikova L, Liu S. Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating. Brief Bioinform 2024; 26:bbae633. [PMID: 39656848 PMCID: PMC11630031 DOI: 10.1093/bib/bbae633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Revised: 11/13/2024] [Accepted: 11/25/2024] [Indexed: 12/17/2024] Open
Abstract
Cytometry is an advanced technique for simultaneously identifying and quantifying many cell surface and intracellular proteins at a single-cell resolution. Analyzing high-dimensional cytometry data involves identifying and quantifying cell populations based on their marker expressions. This study provided a quantitative review and comparison of various ways to phenotype cellular populations within the cytometry data, including manual gating, unsupervised clustering, and supervised auto-gating. Six datasets from diverse species and sample types were included in the study, and manual gating with two hierarchical layers was used as the truth for evaluation. For manual gating, results from five researchers were compared to illustrate the gating consistency among different raters. For unsupervised clustering, 23 tools were quantitatively compared in terms of accuracy with the truth and computing cost. While no method outperformed all others, several tools, including PAC-MAN, CCAST, FlowSOM, flowClust, and DEPECHE, generally demonstrated strong performance. For supervised auto-gating methods, four algorithms were evaluated, where DeepCyTOF and CyTOF Linear Classifier performed the best. We further provided practical recommendations on prioritizing gating methods based on different application scenarios. This study offers comprehensive insights for biologists to understand diverse gating methods and choose the best-suited ones for their applications.
Collapse
Affiliation(s)
- Peng Liu
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Yuchen Pan
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX 77030, US
| | - Hung-Ching Chang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Wenjia Wang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Yusi Fang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Xiangning Xue
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Jian Zou
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
| | - Jessica M Toothaker
- Department of Immunology, University of Pittsburgh, 5051 Centre Avenue, Pittsburgh, PA 15213, US
- Department of Pediatrics, Yale University, 15 York Street New Haven, CT 06510, US
| | - Oluwabunmi Olaloye
- Department of Pediatrics, Yale University, 15 York Street New Haven, CT 06510, US
| | | | - Black McCourt
- Department of Pediatrics, Yale University, 15 York Street New Haven, CT 06510, US
| | - Vanessa Mitsialis
- Department of Pediatrics, Division of Gastroenterology, Hepatology, and Nutrition, Boston Children’s Hospital and Department of Pediatrics, Harvard Medical School, 300 Longwood Ave., Boston, MA 02115, US
- Department of Medicine, Division of Gastroenterology, Hepatology, and Endoscopy, Brigham & Women’s Hospital and Department of Medicine, Harvard Medical School, 300 Longwood Ave., Boston, MA 02115, US
| | - Pietro Presicce
- Division of Neonatology and Developmental Biology, David Geffen School of Medicine at the University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA 90095, US
| | - Suhas G Kallapur
- Division of Neonatology and Developmental Biology, David Geffen School of Medicine at the University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA 90095, US
| | - Scott B Snapper
- Department of Pediatrics, Division of Gastroenterology, Hepatology, and Nutrition, Boston Children’s Hospital and Department of Pediatrics, Harvard Medical School, 300 Longwood Ave., Boston, MA 02115, US
- Department of Medicine, Division of Gastroenterology, Hepatology, and Endoscopy, Brigham & Women’s Hospital and Department of Medicine, Harvard Medical School, 300 Longwood Ave., Boston, MA 02115, US
| | - Jia-Jun Liu
- Drug Discovery Institute, School of Medicine, University of Pittsburgh, 700 Technology Dr, Pittsburgh, PA 15219, US
- Pittsburgh Liver Research Center, School of Medicine, University of Pittsburgh, 200 Lothrop Street, Pittsburgh, PA 15261, US
| | - George C Tseng
- Department of Biostatistics, School of Public Health, University of Pittsburgh, 130 De Soto St., Pittsburgh, PA 15261, US
- Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15213, US
| | - Liza Konnikova
- Department of Pediatrics, Yale University, 15 York Street New Haven, CT 06510, US
- Division of Neonatology and Developmental Biology, David Geffen School of Medicine at the University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA 90095, US
- Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University, 333 Cedar Street, New Haven, CT 06510, US
- Department of Immunobiology, Yale University, 300 Cedar Street, New Haven, CT 06520, US
- Program in Human and Translational Immunology, Yale University, 300 Cedar Street, New Haven, CT 06520, US
- Program in Translational Biomedicine, Yale University, 300 Cedar Street, New Haven, CT 06520, US
- Center for Systems and Engineering Immunology, Yale University, 100 College St., New Haven, CT 06510, US
| | - Silvia Liu
- Drug Discovery Institute, School of Medicine, University of Pittsburgh, 700 Technology Dr, Pittsburgh, PA 15219, US
- Pittsburgh Liver Research Center, School of Medicine, University of Pittsburgh, 200 Lothrop Street, Pittsburgh, PA 15261, US
- Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15213, US
- Department of Pharmacology and Chemical Biology, School of Medicine, University of Pittsburgh, 200 Lothrop St., Pittsburgh, PA 15261, US
- Hillman Cancer Center, University of Pittsburgh, 5150 Centre Ave., Pittsburgh, PA 15232, US
| |
Collapse
|
17
|
De Prado Á, Cal-Sabater P, Fiz-López A, Izquierdo S, Corrales D, Pérez-Cózar F, H-Vázquez J, Arribas-Rodríguez E, Perez-Segurado C, Muñoz ÁM, Garrote JA, Arranz E, Marañón C, Cuesta-Sancho S, Fernández-Salazar L, Bernardo D. Complex immune network and regional consistency in the human gastric mucosa revealed by high-resolution spectral cytometry. Sci Rep 2024; 14:28685. [PMID: 39562636 PMCID: PMC11577052 DOI: 10.1038/s41598-024-78908-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/05/2024] [Indexed: 11/21/2024] Open
Abstract
The immune cellular landscape from the gastric mucosa remains largely unknown despite its relevance in several inflammatory conditions. Human gastric biopsies were obtained from the antrum, body and incisura from 10 individuals to obtain lamina propria mononuclear cells that were further characterized by spectral cytometry. Phenotypic hierarchical analyses identified a total of 52 different immune cell subsets within the human gastric mucosa revealing that T-cells (> 60%) and NK cells (> 20%) were the main populations. Within T-cells, CD4+ and CD8+ were equally represented with both subsets displaying mainly a memory and effector phenotype. NK cells, on the contrary, were largely of the early phenotype. No regional differences were observed for any subsets among the 3 locations. Following unsupervised analysis, a total of 82 clusters were found. Again, no differences were observed amongst locations although a great degree of inter-individual variability was found, largely influenced by the presence of H. pylori infection and dyspepsia. We have unraveled the human gastric immune cellular subset composition and a unique interindividual immune fingerprint with no inter-regional variations.
Collapse
Affiliation(s)
- Ángel De Prado
- Mucosal Immunology Lab, Unit of Excellence, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid-CSIC, Sanz y Forés 3., 47003, Valladolid, Spain
| | - Paloma Cal-Sabater
- Mucosal Immunology Lab, Unit of Excellence, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid-CSIC, Sanz y Forés 3., 47003, Valladolid, Spain
| | - Aida Fiz-López
- Mucosal Immunology Lab, Unit of Excellence, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid-CSIC, Sanz y Forés 3., 47003, Valladolid, Spain
| | - Sandra Izquierdo
- Gastroenterology Department, Hospital Clínico, Universitario (HCUV-SACYL), University of Valladolid, Valladolid, Spain
| | - Daniel Corrales
- Pathology Department, Hospital Clínico, Universitario (HCUV-SACYL). Valladolid, Valladolid, Spain
| | - Francisco Pérez-Cózar
- Pfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research (GENYO), PTS, Granada, Spain
| | - Juan H-Vázquez
- Mucosal Immunology Lab, Unit of Excellence, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid-CSIC, Sanz y Forés 3., 47003, Valladolid, Spain
| | - Elisa Arribas-Rodríguez
- Mucosal Immunology Lab, Unit of Excellence, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid-CSIC, Sanz y Forés 3., 47003, Valladolid, Spain
| | - Cándido Perez-Segurado
- Mucosal Immunology Lab, Unit of Excellence, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid-CSIC, Sanz y Forés 3., 47003, Valladolid, Spain
| | - Álvaro Martín Muñoz
- Flow Cytometry Facility. Unit of Excellence Instituto de Biología y Genética Molecular (IBGM), University of Valladolid-CSIC, Valladolid, Spain
| | - José A Garrote
- Mucosal Immunology Lab, Unit of Excellence, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid-CSIC, Sanz y Forés 3., 47003, Valladolid, Spain
| | - Eduardo Arranz
- Mucosal Immunology Lab, Unit of Excellence, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid-CSIC, Sanz y Forés 3., 47003, Valladolid, Spain
| | - Concepción Marañón
- Pfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research (GENYO), PTS, Granada, Spain
| | - Sara Cuesta-Sancho
- Mucosal Immunology Lab, Unit of Excellence, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid-CSIC, Sanz y Forés 3., 47003, Valladolid, Spain
| | - Luis Fernández-Salazar
- Gastroenterology Department, Hospital Clínico, Universitario (HCUV-SACYL), University of Valladolid, Valladolid, Spain
| | - David Bernardo
- Mucosal Immunology Lab, Unit of Excellence, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid-CSIC, Sanz y Forés 3., 47003, Valladolid, Spain.
- Centro de Investigaciones Biomédicas en Red de Enfermedades Infecciosas (CIBERINFEC), Madrid, Spain.
| |
Collapse
|
18
|
Wong ECN, Zhang Y, Yang T, Liu Y, Abtahi M, Chen X, Ajayi AJ, Li X, Majonis D, Winnik MA. Optimizing the Structure of a Pt Metal-Chelating Polymer to Reduce Nonspecific Binding for Mass Cytometry. Biomacromolecules 2024; 25:6716-6726. [PMID: 39325685 DOI: 10.1021/acs.biomac.4c00937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2024]
Abstract
Mass cytometry is a bioanalytic tool based on atomic mass spectrometry for detecting biomarker expression on individual cells. Current reagents employ metal-chelating polymers binding isotopes of hard metal ions. Polymers bearing chelators for soft metal ions offer the promise for a large increase in multiplexing capabilities, but examples reported so far often have unacceptably high levels of nonspecific binding (NSB). We recently reported a new class of metal-chelating polymers with dipicolylamine (DPA) chelators that could bind Re and Pt. They also showed significant levels of NSB. Here, to reduce the NSB of the Pt-DPA polymer, we grafted water-soluble oligomers to the distal end of the dipicolylamine pendant group. Methoxy(polyethylene glycol) (DP = 24) was effective as was poly(sulfobetaine methacrylate) (DP = 29). Reacting the Pt-Cl bond of the metalated polymer with glutathione was remarkably effective at suppressing NSB. These results open the door to Pt-isotope-based metal-chelating polymers as new mass tags for mass cytometry.
Collapse
Affiliation(s)
- Edmond C N Wong
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Yefeng Zhang
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Tianjia Yang
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Yang Liu
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Mahtab Abtahi
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Xu Chen
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Ayonitemi J Ajayi
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Xiaochong Li
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | | | - Mitchell A Winnik
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
| |
Collapse
|
19
|
Almeida-da-Silva CLC, Moreira-Souza ACDA, Ojcius DM. Traditional approaches and recent tools for studying inflammasome activity. J Immunol Methods 2024; 533:113744. [PMID: 39147232 DOI: 10.1016/j.jim.2024.113744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 08/12/2024] [Accepted: 08/12/2024] [Indexed: 08/17/2024]
Abstract
Inflammasomes play a major role in the immune response to infection, development of autoimmune disease, and control of cancer. Western blots were originally used in the early 2000s to characterize inflammasome activation. Since then, a panoply of techniques has been developed to characterize and visualize inflammasome activation in cells, tissues, and animals. This review article describes the most common techniques used by researchers in the inflammasome field and proposes that cell-specific characterization of inflammasome activation in tissues or animals may soon be commonly reported.
Collapse
Affiliation(s)
| | | | - David M Ojcius
- Department of Biomedical Sciences, University of the Pacific, Arthur A. Dugoni, School of Dentistry, San Francisco, CA 94103, USA.
| |
Collapse
|
20
|
Kwon Y, Woo J, Yu F, Williams SM, Markillie LM, Moore RJ, Nakayasu ES, Chen J, Campbell-Thompson M, Mathews CE, Nesvizhskii AI, Qian WJ, Zhu Y. Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows. Mol Cell Proteomics 2024; 23:100841. [PMID: 39307423 PMCID: PMC11541776 DOI: 10.1016/j.mcpro.2024.100841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 08/19/2024] [Accepted: 08/23/2024] [Indexed: 09/25/2024] Open
Abstract
Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provides robust protein quantifications in identifying differentially abundant proteins and spatially covariable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial coexpression analysis.
Collapse
Affiliation(s)
- Yumi Kwon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Jongmin Woo
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, United States
| | - Sarah M Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Lye Meng Markillie
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, United States
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, United States
| | - Clayton E Mathews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, United States
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, United States; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States.
| | - Ying Zhu
- Department of Proteomic and Genomic Technologies, Genentech Inc, South San Francisco, California, United States.
| |
Collapse
|
21
|
Abtahi M, Gheiratmand L, Dinesh A, Liu Y, Wong ECN, Cho H, Majonis D, Jackson HW, Mrkonjic M, Winnik MA. Testing a Nanoparticle Reagent for Imaging Mass Cytometry. Biomacromolecules 2024; 25:6115-6126. [PMID: 39189480 DOI: 10.1021/acs.biomac.4c00801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/28/2024]
Abstract
Mass cytometry (MC), a powerful single-cell analysis technique, has limitations in detecting low-abundance biomarkers. Nanoparticle (NP) reagents offer the potential for enhancing sensitivity by carrying large numbers of heavy metal isotopes. Here, we report NP reporters for imaging mass cytometry (IMC) based on NaYF4:Yb3+/Er3+ NPs. A two-step ligand exchange was used to coat NP surfaces with either methoxy-PEG2K-neridronate (PEG-Ner) and/or poly(sulfobetaine methacrylate)-neridronate (PSBMA-Ner). Both modifications provided long-term colloidal stability in PBS buffer. IMC measurements on tonsil tissue showed that PSBMA-Ner or a 1:1 mixture of PSBMA-Ner + PEG-Ner effectively suppressed nonspecific binding (NSB) at 2 × 1010 NPs/mL, unlike PEG-Ner alone. However, breast cancer tissue samples showed increased NSB at titers above 2 × 1010 NPs/mL. Reduced NSB with mixed PEG-Ner and PSBMA-Ner coatings opens the door for using heterobifunctional PEGs for the development of NP conjugates with bioaffinity agents, enabling more sensitive and specific MC analyses.
Collapse
Affiliation(s)
- Mahtab Abtahi
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Ladan Gheiratmand
- Standard BioTools Canada Inc., Suite 400, 1380 Rodick Road, Markham, ON L3R 4G5, Canada
| | - Anuroopa Dinesh
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5T 3A1, Canada
| | - Yang Liu
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Edmond C N Wong
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Hyungjun Cho
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Daniel Majonis
- Standard BioTools Canada Inc., Suite 400, 1380 Rodick Road, Markham, ON L3R 4G5, Canada
| | - Hartland W Jackson
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5T 3A1, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada
- Ontario Institute of Cancer Research, Toronto, ON M5G 1M1, Canada
| | - Miralem Mrkonjic
- Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5G 1X5, Canada
| | - Mitchell A Winnik
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
| |
Collapse
|
22
|
Zhao M, Cheng Y, Gao J, Zhou F. Single-cell mass cytometry in immunological skin diseases. Front Immunol 2024; 15:1401102. [PMID: 39081313 PMCID: PMC11286489 DOI: 10.3389/fimmu.2024.1401102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 07/01/2024] [Indexed: 08/02/2024] Open
Abstract
Immune-related skin diseases represent a collective of dermatological disorders intricately linked to dysfunctional immune system processes. These conditions are primarily characterized by an immoderate activation of the immune system or deviant immune responses, involving diverse immune components including immune cells, antibodies, and inflammatory mediators. However, the precise molecular dysregulation underlying numerous individual cases of these diseases and unique subsets respond under disease conditions remains elusive. Comprehending the mechanisms and determinants governing the homeostasis and functionality of diseases could offer potential therapeutic opportunities for intervention. Mass cytometry enables precise and high-throughput quantitative measurement of proteins within individual cells by utilizing antibodies labeled with rare heavy metal isotopes. Imaging mass cytometry employs mass spectrometry to obtain spatial information on cell-to-cell interactions within tissue sections, simultaneously utilizing more than 40 markers. The application of single-cell mass cytometry presents a unique opportunity to conduct highly multiplexed analysis at the single-cell level, thereby revolutionizing our understanding of cell population heterogeneity and hierarchy, cellular states, multiplexed signaling pathways, proteolysis products, and mRNA transcripts specifically in the context of many autoimmune diseases. This information holds the potential to offer novel approaches for the diagnosis, prognostic assessment, and monitoring responses to treatment, thereby enriching our strategies in managing the respective conditions. This review summarizes the present-day utilization of single-cell mass cytometry in studying immune-related skin diseases, highlighting its advantages and limitations. This technique will become increasingly prevalent in conducting extensive investigations into these disorders, ultimately yielding significant contributions to their accurate diagnosis and efficacious therapeutic interventions.
Collapse
Affiliation(s)
- Mingming Zhao
- Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Institute of Dermatology, Anhui Medical University, Hefei, Anhui, China
- Key Laboratory of Dermatology (Anhui Medical University), Ministry of Education, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Yuqi Cheng
- Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Institute of Dermatology, Anhui Medical University, Hefei, Anhui, China
- Key Laboratory of Dermatology (Anhui Medical University), Ministry of Education, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Jinping Gao
- Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Institute of Dermatology, Anhui Medical University, Hefei, Anhui, China
- Key Laboratory of Dermatology (Anhui Medical University), Ministry of Education, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Fusheng Zhou
- Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Institute of Dermatology, Anhui Medical University, Hefei, Anhui, China
- Key Laboratory of Dermatology (Anhui Medical University), Ministry of Education, Hefei, Anhui, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| |
Collapse
|
23
|
Ask EH, Tschan-Plessl A, Hoel HJ, Kolstad A, Holte H, Malmberg KJ. MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration. PATTERNS (NEW YORK, N.Y.) 2024; 5:100989. [PMID: 39081571 PMCID: PMC11284499 DOI: 10.1016/j.patter.2024.100989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/12/2024] [Accepted: 04/15/2024] [Indexed: 08/02/2024]
Abstract
Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level. Technical advances have substantially increased data complexity, but novel bioinformatical tools often show limitations in statistical testing, data sharing, cross-experiment comparability, or clinical data integration. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of metadata. MetaGate provides a data reduction algorithm based on a combinatorial gating system that produces a small, portable, and standardized data file. This is subsequently used to produce figures and statistical analyses through a fast web-based user interface. We demonstrate the utility of MetaGate through a comprehensive mass cytometry analysis of peripheral blood immune cells from 28 patients with diffuse large B cell lymphoma along with 17 healthy controls. Through MetaGate analysis, our study identifies key immune cell population changes associated with disease progression.
Collapse
Affiliation(s)
- Eivind Heggernes Ask
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- The Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
| | - Astrid Tschan-Plessl
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Division of Hematology, University Hospital Basel, Basel, Switzerland
| | - Hanna Julie Hoel
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Arne Kolstad
- Department of Oncology, Innlandet Hospital Trust Division Gjøvik, Lillehammer, Norway
| | - Harald Holte
- Department of Oncology, Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for B Cell Malignancies, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Karl-Johan Malmberg
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- The Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
24
|
Kwon Y, Woo J, Yu F, Williams SM, Markillie LM, Moore RJ, Nakayasu ES, Chen J, Campbell-Thompson M, Mathews CE, Nesvizhskii AI, Qian WJ, Zhu Y. Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583367. [PMID: 38496682 PMCID: PMC10942300 DOI: 10.1101/2024.03.04.583367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.
Collapse
Affiliation(s)
- Yumi Kwon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jongmin Woo
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
| | - Sarah M. Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Lye Meng Markillie
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ronald J. Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Clayton E. Mathews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Alexey I. Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ying Zhu
- Department of Proteomic and Genomic Technologies, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
| |
Collapse
|
25
|
Botello-Marabotto M, Martínez-Bisbal MC, Pinazo-Durán MD, Martínez-Máñez R. Tear metabolomics for the diagnosis of primary open-angle glaucoma. Talanta 2024; 273:125826. [PMID: 38479028 DOI: 10.1016/j.talanta.2024.125826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/14/2024] [Accepted: 02/21/2024] [Indexed: 04/09/2024]
Abstract
Primary Open-Angle Glaucoma (POAG) is the most prevalent glaucoma type, and the leading cause of irreversible visual impairment and blindness worldwide. Identification of early POAG biomarkers is of enormous value, as there is not an effective treatment for the glaucomatous optic nerve degeneration (OND). In this pilot study, a metabolomic analysis, by using proton (1H) nuclear magnetic resonance (NMR) spectroscopy was conducted in tears, in order to determine the changes of specific metabolites in the initial glaucoma eyes and to discover potential diagnostic biomarkers. A classification model, based on the metabolomic fingerprint in tears was generated as a non-invasive tool to support the preclinical and clinical POAG diagnosis. 1H NMR spectra were acquired from 30 tear samples corresponding to the POAG group (n = 11) and the control group (n = 19). Data were analysed by multivariate statistics (partial least squares-discriminant analysis: PLS-DA) to determine a model capable of differentiating between groups. The whole data set was split into calibration (65%)/validation (35%), to test the performance and the ability for glaucoma discrimination. The calculated PLS-DA model showed an area under the curve (AUC) of 1, as well as a sensitivity of 100% and a specificity of 83.3% to distinguish POAG group versus control group tear data. This model included 11 metabolites, potential biomarkers of the disease. When comparing the study groups, a decrease in the tear concentration of phenylalanine, phenylacetate, leucine, n-acetylated compounds, formic acid, and uridine, was found in the POAG group. Moreover, an increase in the tear concentration of taurine, glycine, urea, glucose, and unsaturated fatty acids was observed in the POAG group. These results highlight the potential of tear metabolomics by 1H NMR spectroscopy as a non-invasive approach to support early POAG diagnosis and in order to prevent visual loss.
Collapse
Affiliation(s)
- Marina Botello-Marabotto
- Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València - Universitat de València, Valencia, Spain; Unidad Mixta de Investigación en Nanomedicina y Sensores, Instituto de Investigación Sanitaria La Fe (IISLAFE) - Universitat Politècnica de València, Valencia, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain
| | - M Carmen Martínez-Bisbal
- Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València - Universitat de València, Valencia, Spain; Unidad Mixta de Investigación en Nanomedicina y Sensores, Instituto de Investigación Sanitaria La Fe (IISLAFE) - Universitat Politècnica de València, Valencia, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain; Departamento de Química Física, Universitat de València, Valencia, Spain.
| | - M Dolores Pinazo-Durán
- Ophthalmic Research Unit "Santiago Grisolia"/FISABIO, Valencia, Spain; Cellular and Molecular Ophthalmobiology Research Group at the University of Valencia, Valencia, Spain; Spanish Net of Inflammatory Research (REI-RICORS: RD21/0002/0032) Institute of Health Carlos III, Madrid, Spain
| | - Ramón Martínez-Máñez
- Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València - Universitat de València, Valencia, Spain; Unidad Mixta de Investigación en Nanomedicina y Sensores, Instituto de Investigación Sanitaria La Fe (IISLAFE) - Universitat Politècnica de València, Valencia, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain; Departamento de Química, Universitat Politècnica de València, Valencia, Spain; Unidad Mixta UPV-CIPF de Investigación en Mecanismos de Enfermedades y Nanomedicina, València, Universitat Politècnica de València, Centro de Investigación Príncipe Felipe, Valencia, Spain
| |
Collapse
|
26
|
Hill BD, Zak AJ, Raja S, Bugada LF, Rizvi SM, Roslan SB, Nguyen HN, Chen J, Jiang H, Ono A, Goldstein DR, Wen F. iGATE analysis improves the interpretability of single-cell immune landscape of influenza infection. JCI Insight 2024; 9:e172140. [PMID: 38814732 PMCID: PMC11383363 DOI: 10.1172/jci.insight.172140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024] Open
Abstract
Influenza poses a persistent health burden worldwide. To design equitable vaccines effective across all demographics, it is essential to better understand how host factors such as genetic background and aging affect the single-cell immune landscape of influenza infection. Cytometry by time-of-flight (CyTOF) represents a promising technique in this pursuit, but interpreting its large, high-dimensional data remains difficult. We have developed a new analytical approach, in silico gating annotating training elucidating (iGATE), based on probabilistic support vector machine classification. By rapidly and accurately "gating" tens of millions of cells in silico into user-defined types, iGATE enabled us to track 25 canonical immune cell types in mouse lung over the course of influenza infection. Applying iGATE to study effects of host genetic background, we show that the lower survival of C57BL/6 mice compared with BALB/c was associated with a more rapid accumulation of inflammatory cell types and decreased IL-10 expression. Furthermore, we demonstrate that the most prominent effect of aging is a defective T cell response, reducing survival of aged mice. Finally, iGATE reveals that the 25 canonical immune cell types exhibited differential influenza infection susceptibility and replication permissiveness in vivo, but neither property varied with host genotype or aging. The software is available at https://github.com/UmichWenLab/iGATE.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Judy Chen
- Program in Immunology
- Department of Internal Medicine
| | | | - Akira Ono
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Fei Wen
- Department of Chemical Engineering
| |
Collapse
|
27
|
G. de Castro C, G. del Hierro A, H-Vázquez J, Cuesta-Sancho S, Bernardo D. State-of-the-art cytometry in the search of novel biomarkers in digestive cancers. Front Oncol 2024; 14:1407580. [PMID: 38868532 PMCID: PMC11167087 DOI: 10.3389/fonc.2024.1407580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 05/10/2024] [Indexed: 06/14/2024] Open
Abstract
Despite that colorectal and liver cancer are among the most prevalent tumours in the world, the identification of non-invasive biomarkers to aid on their diagnose and subsequent prognosis is a current unmet need that would diminish both their incidence and mortality rates. In this context, conventional flow cytometry has been widely used in the screening of biomarkers with clinical utility in other malignant processes like leukaemia or lymphoma. Therefore, in this review, we will focus on how advanced cytometry panels covering over 40 parameters can be applied on the study of the immune system from patients with colorectal and hepatocellular carcinoma and how that can be used on the search of novel biomarkers to aid or diagnose, prognosis, and even predict clinical response to different treatments. In addition, these multiparametric and unbiased approaches can also provide novel insights into the specific immunopathogenic mechanisms governing these malignant diseases, hence potentially unravelling novel targets to perform immunotherapy or identify novel mechanisms, rendering the development of novel treatments. As a consequence, computational cytometry approaches are an emerging methodology for the early detection and predicting therapies for gastrointestinal cancers.
Collapse
Affiliation(s)
- Carolina G. de Castro
- Mucosal Immunology Lab, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid and Consejo Superior de Investigaciones Científicas (CSIC), Valladolid, Spain
| | - Alejandro G. del Hierro
- Mucosal Immunology Lab, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid and Consejo Superior de Investigaciones Científicas (CSIC), Valladolid, Spain
| | - Juan H-Vázquez
- Mucosal Immunology Lab, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid and Consejo Superior de Investigaciones Científicas (CSIC), Valladolid, Spain
| | - Sara Cuesta-Sancho
- Mucosal Immunology Lab, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid and Consejo Superior de Investigaciones Científicas (CSIC), Valladolid, Spain
| | - David Bernardo
- Mucosal Immunology Lab, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid and Consejo Superior de Investigaciones Científicas (CSIC), Valladolid, Spain
- Centro de Investigaciones Biomedicas en Red de Enfermedades Infecciosas (CIBERINFEC), Madrid, Spain
| |
Collapse
|
28
|
Mouwenda YD, Jochems SP, Van Unen V, Betouke Ongwe ME, de Steenhuijsen Piters WA, Stam KA, Massinga Loembe M, Sim BKL, Esen M, Hoffman SL, Kremsner PG, Fendel R, Mordmüller B, Yazdanbakhsh M. Immune responses associated with protection induced by chemoattenuated PfSPZ vaccine in malaria-naive Europeans. JCI Insight 2024; 9:e170210. [PMID: 38716733 PMCID: PMC11141902 DOI: 10.1172/jci.insight.170210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 03/14/2024] [Indexed: 06/02/2024] Open
Abstract
Vaccination of malaria-naive volunteers with a high dose of Plasmodium falciparum sporozoites chemoattenuated by chloroquine (CQ) (PfSPZ-CVac [CQ]) has previously demonstrated full protection against controlled human malaria infection (CHMI). However, lower doses of PfSPZ-CVac [CQ] resulted in incomplete protection. This provides the opportunity to understand the immune mechanisms needed for better vaccine-induced protection by comparing individuals who were protected with those not protected. Using mass cytometry, we characterized immune cell composition and responses of malaria-naive European volunteers who received either lower doses of PfSPZ-CVac [CQ], resulting in 50% protection irrespective of the dose, or a placebo vaccination, with everyone becoming infected following CHMI. Clusters of CD4+ and γδ T cells associated with protection were identified, consistent with their known role in malaria immunity. Additionally, EMRA CD8+ T cells and CD56+CD8+ T cell clusters were associated with protection. In a cohort from a malaria-endemic area in Gabon, these CD8+ T cell clusters were also associated with parasitemia control in individuals with lifelong exposure to malaria. Upon stimulation with P. falciparum-infected erythrocytes, CD4+, γδ, and EMRA CD8+ T cells produced IFN-γ and/or TNF, indicating their ability to mediate responses that eliminate malaria parasites.
Collapse
Affiliation(s)
- Yoanne D. Mouwenda
- Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon
- Department of Parasitology, Leiden University Medical Center (LUMC), Leiden, Netherlands
| | - Simon P. Jochems
- Department of Parasitology, Leiden University Medical Center (LUMC), Leiden, Netherlands
| | - Vincent Van Unen
- Department of Immunology, Leiden University Medical Center, Leiden, Netherlands
| | - Madeleine Eunice Betouke Ongwe
- Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon
- Department of Parasitology, Leiden University Medical Center (LUMC), Leiden, Netherlands
- Centre National de la Recherche Scientifique et Technologique, Institut De Recherche En Écologie Tropical, Libreville, Gabon
| | | | - Koen A. Stam
- Department of Parasitology, Leiden University Medical Center (LUMC), Leiden, Netherlands
| | | | - Betty Kim Lee Sim
- Sanaria Inc., Rockville, Maryland, USA
- Protein Potential LLC, Rockville, Maryland, USA
| | - Meral Esen
- Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon
- Institute of Tropical Medicine, University of Tübingen, Tübingen, Germany
- German Center for Infection Research, Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence EXC 2124, Controlling Microbes to Fight Infection, Tübingen, Germany
| | | | - Peter G. Kremsner
- Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon
- Institute of Tropical Medicine, University of Tübingen, Tübingen, Germany
| | - Rolf Fendel
- Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon
- Institute of Tropical Medicine, University of Tübingen, Tübingen, Germany
- German Center for Infection Research, Partner Site Tübingen, Tübingen, Germany
| | - Benjamin Mordmüller
- Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon
- Institute of Tropical Medicine, University of Tübingen, Tübingen, Germany
- Radboud University Medical Center (Radboudumc), Department of Medical Microbiology, Nijmegen, Netherlands
| | - Maria Yazdanbakhsh
- Department of Parasitology, Leiden University Medical Center (LUMC), Leiden, Netherlands
| |
Collapse
|
29
|
Nava AA, Arboleda VA. The omics era: a nexus of untapped potential for Mendelian chromatinopathies. Hum Genet 2024; 143:475-495. [PMID: 37115317 PMCID: PMC11078811 DOI: 10.1007/s00439-023-02560-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 04/10/2023] [Indexed: 04/29/2023]
Abstract
The OMICs cascade describes the hierarchical flow of information through biological systems. The epigenome sits at the apex of the cascade, thereby regulating the RNA and protein expression of the human genome and governs cellular identity and function. Genes that regulate the epigenome, termed epigenes, orchestrate complex biological signaling programs that drive human development. The broad expression patterns of epigenes during human development mean that pathogenic germline mutations in epigenes can lead to clinically significant multi-system malformations, developmental delay, intellectual disabilities, and stem cell dysfunction. In this review, we refer to germline developmental disorders caused by epigene mutation as "chromatinopathies". We curated the largest number of human chromatinopathies to date and our expanded approach more than doubled the number of established chromatinopathies to 179 disorders caused by 148 epigenes. Our study revealed that 20.6% (148/720) of epigenes cause at least one chromatinopathy. In this review, we highlight key examples in which OMICs approaches have been applied to chromatinopathy patient biospecimens to identify underlying disease pathogenesis. The rapidly evolving OMICs technologies that couple molecular biology with high-throughput sequencing or proteomics allow us to dissect out the causal mechanisms driving temporal-, cellular-, and tissue-specific expression. Using the full repertoire of data generated by the OMICs cascade to study chromatinopathies will provide invaluable insight into the developmental impact of these epigenes and point toward future precision targets for these rare disorders.
Collapse
Affiliation(s)
- Aileen A Nava
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Broad Stem Cell Research Center, University of California, Los Angeles, CA, USA
| | - Valerie A Arboleda
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Broad Stem Cell Research Center, University of California, Los Angeles, CA, USA.
- Molecular Biology Institute, University of California, Los Angeles, CA, USA.
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA.
| |
Collapse
|
30
|
Ermann J, Lefton M, Wei K, Gutierrez-Arcelus M. Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling. Curr Rheumatol Rep 2024; 26:144-154. [PMID: 38227172 DOI: 10.1007/s11926-023-01132-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/28/2023] [Indexed: 01/17/2024]
Abstract
PURPOSE OF REVIEW Single-cell profiling, either in suspension or within the tissue context, is a rapidly evolving field. The purpose of this review is to outline recent advancements and emerging trends with a specific focus on studies in spondyloarthritis. RECENT FINDINGS The introduction of sequencing-based approaches for the quantification of RNA, protein, or epigenetic modifications at single-cell resolution has provided a major boost to discovery-driven research. Fluorescent flow cytometry, mass cytometry, and image-based cytometry continue to evolve. Spatial transcriptomics and imaging mass cytometry have extended high-dimensional analysis to cells in tissues. Applications in spondyloarthritis include the indexing and functional characterization of cells, discovery of disease-associated cell states, and identification of signatures associated with therapeutic responses. Single-cell TCR-seq has provided evidence for clonal expansion of CD8+ T cells in spondyloarthritis. The use of single-cell profiling approaches in spondyloarthritis research is still in its early stages. Challenges include high cost and limited availability of diseased tissue samples. To harness the full potential of the rapidly expanding technical capabilities, large-scale collaborative efforts are imperative.
Collapse
Affiliation(s)
- Joerg Ermann
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Micah Lefton
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Maria Gutierrez-Arcelus
- Harvard Medical School, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| |
Collapse
|
31
|
Bongiovanni D, Novelli L, Condello F, Kirmes K, Han J, Wein B, Elvinger S, Viggiani G, von Scheidt M, Laugwitz KL, Raake PWJ, Kastrati A, Chiarito M, Bernlochner I. Reticulated Platelets Predict Cardiovascular Death and Adverse Events in Coronary Artery Disease: A Systematic Review and Meta-analysis. Thromb Haemost 2024; 124:310-319. [PMID: 37696301 DOI: 10.1055/s-0043-1773763] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
BACKGROUND The pro-thrombotic immature or reticulated platelets (RPs) are known to be elevated in high-risk patients and in different pathological settings. It has been shown that RPs correlate with an insufficient antiplatelet response to antiplatelet agents. RPs are emerging novel predictors of adverse cardiovascular events in cardiovascular disease. This study, using the totality of existing evidence, evaluated the prognostic role of RPs in patients with coronary artery disease. METHODS We performed a systematic review and meta-analysis including trials of acute and chronic coronary syndrome reporting clinical outcomes according to RPs levels in the peripheral blood. We compared patients with elevated RPs (RPshigh) to patients without elevated RPs (RPslow). Odds ratios (ORs) and 95% CIs were used as metric of choice for treatment effects with random-effects models. The primary endpoint was major adverse cardiovascular and cerebrovascular events (MACCE). Secondary endpoints were cardiovascular death, myocardial infarction, ischemic stroke, urgent coronary revascularization and bleedings. RESULTS A total of 7 studies, including 2213 patients, were included. The risk for MACCE was significantly higher in RPshigh compared to RPslow patients (OR 2.67 [1.87; 3.81], I2 = 43.8%). RPshigh were associated with cardiovascular death (OR 2.09 [1.36; 3.22], I2 = 40.4%). No associations for RPshigh were detected with the other singular components of MACCE: myocardial infarction (OR 1.73 [0.89; 3.38] I2 = 60.5%) and stroke (OR 1.72 [0.59; 4.96] I2 = 21%). The risk of bleeding did not differ between groups(OR 0.58 [0.15; 2.22] I2 = 86.1%). CONCLUSION Elevated RPs are significantly associated with increased risk of cardiovascular events and cardiovascular death.
Collapse
Affiliation(s)
- Dario Bongiovanni
- Department of Internal Medicine I, Cardiology, University Hospital Augsburg, University of Augsburg, Germany
- Department of Cardiovascular Medicine, Humanitas Clinical and Research Center IRCCS and Humanitas University, Rozzano, Milan, Italy
- Department of Internal Medicine I, School of Medicine, University hospital rechts der Isar, Technical University of Munich, Munich, Germany
| | - Laura Novelli
- Department of Cardiovascular Medicine, Humanitas Clinical and Research Center IRCCS and Humanitas University, Rozzano, Milan, Italy
| | - Francesco Condello
- Department of Cardiovascular Medicine, Humanitas Clinical and Research Center IRCCS and Humanitas University, Rozzano, Milan, Italy
| | - Kilian Kirmes
- Department of Internal Medicine I, School of Medicine, University hospital rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jiaying Han
- Department of Internal Medicine I, School of Medicine, University hospital rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bastian Wein
- Department of Internal Medicine I, Cardiology, University Hospital Augsburg, University of Augsburg, Germany
| | - Sébastien Elvinger
- Department of Internal Medicine I, Cardiology, University Hospital Augsburg, University of Augsburg, Germany
| | - Giacomo Viggiani
- Department of Internal Medicine I, School of Medicine, University hospital rechts der Isar, Technical University of Munich, Munich, Germany
| | - Moritz von Scheidt
- Department of Cardiology, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany
| | - Karl-Ludwig Laugwitz
- Department of Internal Medicine I, School of Medicine, University hospital rechts der Isar, Technical University of Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Germany
| | - Philip W J Raake
- Department of Internal Medicine I, Cardiology, University Hospital Augsburg, University of Augsburg, Germany
| | - Adnan Kastrati
- Department of Cardiology, Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Germany
| | - Mauro Chiarito
- Department of Cardiovascular Medicine, Humanitas Clinical and Research Center IRCCS and Humanitas University, Rozzano, Milan, Italy
| | - Isabell Bernlochner
- Department of Internal Medicine I, School of Medicine, University hospital rechts der Isar, Technical University of Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Germany
| |
Collapse
|
32
|
Yan H, Ju X, Huang A, Yuan J. Advancements in technology for characterizing the tumor immune microenvironment. Int J Biol Sci 2024; 20:2151-2167. [PMID: 38617534 PMCID: PMC11008272 DOI: 10.7150/ijbs.92525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 03/12/2024] [Indexed: 04/16/2024] Open
Abstract
Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number of patients. The biological basis for the success of immunotherapy is the complex interaction between tumor cells and tumor immune microenvironment (TIME). Historically, research on tumor immune constitution was limited to the analysis of one or two markers, more novel technologies are needed to interpret the complex interactions between tumor cells and TIME. In recent years, major advances have already been made in depicting TIME at a considerably elevated degree of throughput, dimensionality and resolution, allowing dozens of markers to be labeled simultaneously, and analyzing the heterogeneity of tumour-immune infiltrates in detail at the single cell level, depicting the spatial landscape of the entire microenvironment, as well as applying artificial intelligence (AI) to interpret a large amount of complex data from TIME. In this review, we summarized emerging technologies that have made contributions to the field of TIME, and provided prospects for future research.
Collapse
Affiliation(s)
- Honglin Yan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, P.R. China
| | | | | | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, P.R. China
| |
Collapse
|
33
|
Scheuermann S, Kristmann B, Engelmann F, Nuernbergk A, Scheuermann D, Koloseus M, Abed T, Solass W, Seitz CM. Unveiling spatial complexity in solid tumor immune microenvironments through multiplexed imaging. Front Immunol 2024; 15:1383932. [PMID: 38566984 PMCID: PMC10985204 DOI: 10.3389/fimmu.2024.1383932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 02/29/2024] [Indexed: 04/04/2024] Open
Abstract
Deciphering cellular components and the spatial interaction network of the tumor immune microenvironment (TIME) of solid tumors is pivotal for understanding biologically relevant cross-talks and, ultimately, advancing therapies. Multiplexed tissue imaging provides a powerful tool to elucidate spatial complexity in a holistic manner. We established and cross-validated a comprehensive immunophenotyping panel comprising over 121 markers for multiplexed tissue imaging using MACSima™ imaging cyclic staining (MICS) alongside an end-to-end analysis workflow. Applying this panel and workflow to primary cancer tissues, we characterized tumor heterogeneity, investigated potential therapeutical targets, conducted in-depth profiling of cell types and states, sub-phenotyped T cells within the TIME, and scrutinized cellular neighborhoods of diverse T cell subsets. Our findings highlight the advantage of spatial profiling, revealing immunosuppressive molecular signatures of tumor-associated myeloid cells interacting with neighboring exhausted, PD1high T cells in the TIME of hepatocellular carcinoma (HCC). This study establishes a robust framework for spatial exploration of TIMEs in solid tumors and underscores the potency of multiplexed tissue imaging and ultra-deep cell phenotyping in unraveling clinically relevant tumor components.
Collapse
Affiliation(s)
- Sophia Scheuermann
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
- iFIT Cluster of Excellence EXC 2180 ‘Image-Guided and Functionally Instructed Tumor Therapies’, University of Tuebingen, Tuebingen, Germany
- German Cancer Consortium (DKTK), partner site Tuebingen, a partnership between German Cancer Research Center (DKFZ) and University Hospital Tuebingen, Tuebingen, Germany
| | - Beate Kristmann
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - Fabienne Engelmann
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - Alice Nuernbergk
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - David Scheuermann
- School of Business and Economics, Faculty of Economics and Social Sciences, University of Tuebingen, Tuebingen, Germany
| | - Marie Koloseus
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - Tayeb Abed
- Institute of Pathology and Neuropathology, University Hospital Tuebingen and Comprehensive Cancer Center, Tuebingen, Germany
| | - Wiebke Solass
- Institute of Tissue Medicine and Pathology (ITMP), University of Bern, Bern, Switzerland
| | - Christian M. Seitz
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
- iFIT Cluster of Excellence EXC 2180 ‘Image-Guided and Functionally Instructed Tumor Therapies’, University of Tuebingen, Tuebingen, Germany
- German Cancer Consortium (DKTK), partner site Tuebingen, a partnership between German Cancer Research Center (DKFZ) and University Hospital Tuebingen, Tuebingen, Germany
| |
Collapse
|
34
|
Nalwoga A, Nakibuule M, Roshan R, Kwizera Mbonye M, Miley W, Whitby D, Newton R, Rochford R, Cose S. Immune cell phenotype and function patterns across the life course in individuals from rural Uganda. Front Immunol 2024; 15:1356635. [PMID: 38562926 PMCID: PMC10982424 DOI: 10.3389/fimmu.2024.1356635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/28/2024] [Indexed: 04/04/2024] Open
Abstract
Background To determine the pattern of immune cell subsets across the life span in rural sub-Saharan Africa (SSA), and to set a reference standard for cell subsets amongst Africans, we characterised the major immune cell subsets in peripheral blood including T cells, B cells, monocytes, NK cells, neutrophils and eosinophils, in individuals aged 3 to 89 years from Uganda. Methods Immune phenotypes were measured using both conventional flow cytometry in 72 individuals, and full spectrum flow cytometry in 80 individuals. Epstein-Barr virus (EBV) IFN-γ T cell responses were quantified in 332 individuals using an ELISpot assay. Full blood counts of all study participants were also obtained. Results The percentages of central memory (TCM) and senescent CD4+ and CD8+ T cell subsets, effector memory (TEM) CD8+ T cells and neutrophils increased with increasing age. On the other hand, the percentages of naïve T (TN) and B (BN) cells, atypical B cells (BA), total lymphocytes, eosinophils and basophils decreased with increasing age. There was no change in CD4+ or CD8+ T effector memory RA (TEMRA) cells, exhausted T cells, NK cells and monocytes with age. Higher eosinophil and basophil percentages were observed in males compared to females. T cell function as measured by IFN-γ responses to EBV increased with increasing age, peaking at 31-55 years. Conclusion The percentages of cell subsets differ between individuals from SSA compared to those elsewhere, perhaps reflecting a different antigenic milieu. These results serve as a reference for normal values in this population.
Collapse
Affiliation(s)
- Angela Nalwoga
- Department of Immunology and Microbiology, University of Colorado, Aurora, CO, United States
- Medical Research Council/ Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine, Entebbe, Uganda
| | - Marjorie Nakibuule
- Medical Research Council/ Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine, Entebbe, Uganda
| | - Romin Roshan
- Frederick National Laboratory for Cancer Research, Viral Oncology Section, AIDS and Cancer Virus Program, Leidos Biomedical Research, Inc., Frederick, MD, United States
| | - Moses Kwizera Mbonye
- Medical Research Council/ Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine, Entebbe, Uganda
| | - Wendell Miley
- Frederick National Laboratory for Cancer Research, Viral Oncology Section, AIDS and Cancer Virus Program, Leidos Biomedical Research, Inc., Frederick, MD, United States
| | - Denise Whitby
- Frederick National Laboratory for Cancer Research, Viral Oncology Section, AIDS and Cancer Virus Program, Leidos Biomedical Research, Inc., Frederick, MD, United States
| | - Robert Newton
- Medical Research Council/ Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine, Entebbe, Uganda
- Department of Health Sciences, University of York, York, United Kingdom
| | - Rosemary Rochford
- Department of Immunology and Microbiology, University of Colorado, Aurora, CO, United States
| | - Stephen Cose
- Medical Research Council/ Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine, Entebbe, Uganda
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, United Kingdom
| |
Collapse
|
35
|
Yang Z, Chen J, Xiao Y, Yang C, Zhao CX, Chen D, Weitz DA. Digital Barcodes for High-Throughput Screening. CHEM & BIO ENGINEERING 2024; 1:2-12. [PMID: 39973970 PMCID: PMC11835184 DOI: 10.1021/cbe.3c00085] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2025]
Abstract
High-throughput screening is an indispensable technology in drug discovery, cancer therapy, and disease diagnosis, and it could greatly reduce time cost, reagent consumption, and labor expense. Here, four high-throughput screening methods with high sensitivity and accessibility are discussed in detail. Fluorescence, DNA, heavy metal, and nonmetal isotope barcodes, which generally label antibodies, proteins, and saccharides to identify cells, are detected by flow cytometry, second-generation DNA sequencing, mass cytometry, and second-ion mass spectrometry, respectively. Encoding binary information in barcodes, labeling individual cells by barcodes, performing the characterization of cells together, and identifying the result belonging to individual cells via barcodes are the main steps for high-throughput screening. Applications of the four digital barcodes in high-throughput screening for both in vitro and in vivo tests are described in detail, and their advantages and disadvantages are also summarized. High-throughput screening has provided a powerful platform widely accessible for multidisciplinary studies and has greatly sped up the progress of drug discovery, disease diagnosis, and cancer therapy.
Collapse
Affiliation(s)
- Ze Yang
- College
of Energy Engineering and State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310003, Zhejiang
Province, People’s Republic
of China
- Zhejiang
Key Laboratory of Smart Biomaterials, College of Chemical and Biological
Engineering, Zhejiang University, Hangzhou 310027, Zhejiang Province, People’s Republic of China
| | - Jingyi Chen
- John
A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Yao Xiao
- College
of Energy Engineering and State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310003, Zhejiang
Province, People’s Republic
of China
| | - Chenjing Yang
- John
A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
- Wenzhou
Institute, University of Chinese Academy
of Sciences, Wenzhou, Zhejiang 325001, People’s Republic of China
| | - Chun-Xia Zhao
- School
of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Dong Chen
- College
of Energy Engineering and State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310003, Zhejiang
Province, People’s Republic
of China
- Zhejiang
Key Laboratory of Smart Biomaterials, College of Chemical and Biological
Engineering, Zhejiang University, Hangzhou 310027, Zhejiang Province, People’s Republic of China
- Department
of Medical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang
Province, People’s Republic
of China
| | - David A. Weitz
- John
A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| |
Collapse
|
36
|
Potter N, Latour S, Wong ECN, Winnik MA, Jackson HW, McGuigan AP, Nitz M. Design Parameters for a Mass Cytometry Detectable HaloTag Ligand. Bioconjug Chem 2024; 35:80-91. [PMID: 38112314 DOI: 10.1021/acs.bioconjchem.3c00434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Mass cytometry permits the high dimensional analysis of complex biological samples; however, some techniques are not yet integrated into the mass cytometry workflow due to reagent availability. The use of self-labeling protein systems, such as HaloTag, are one such application. Here, we describe the design and implementation of the first mass cytometry ligands for use with HaloTag. "Click"-amenable HaloTag warheads were first conjugated onto poly(l-lysine) or poly(acrylic acid) polymers that were then functionalized with diethylenetriaminepentaacetic acid (DTPA) lutetium metal chelates. Kinetic analysis of the HaloTag labeling rates demonstrated that the structure appended to the 1-chlorohexyl warhead was key to success. A construct with a diethylene glycol spacer appended to a benzamide gave similar rates (kobs ∼ 102 M-1 s-1), regardless of the nature of the polymer. Comparison of the polymer with a small molecule chelate having rapid HaloTag labeling kinetics (kobs ∼ 104 M-1 s-1) suggests the polymers significantly reduced the HaloTag labeling rate. HEK293T cells expressing surface-exposed GFP-HaloTag fusions were labeled with the polymeric constructs and 175Lu content measured by cytometry by time-of-flight (CyTOF). Robust labeling was observed; however, significant nonspecific binding of the constructs to cells was also present. Heavily pegylated polymers demonstrated that nonspecific binding could be reduced to allow cells bearing the HaloTag protein to be distinguished from nonexpressing cells.
Collapse
Affiliation(s)
- Nicole Potter
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
| | - Simon Latour
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, Ontario M5S 3G9, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario M5S 3E5, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada
| | - Edmond C N Wong
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
| | - Mitchell A Winnik
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
| | - Hartland W Jackson
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada
- Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Toronto, Ontario M5S 1A8, Canada
- Ontario Institute of Cancer Research, 661 University Avenue, Toronto, Ontario M5S 0A3, Canada
| | - Alison P McGuigan
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, Ontario M5S 3G9, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ontario M5S 3E5, Canada
| | - Mark Nitz
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada
| |
Collapse
|
37
|
Rybakowska P, Alarcón-Riquelme ME, Marañón C. Approaching Mass Cytometry Translational Studies by Experimental and Data Curation Settings. Methods Mol Biol 2024; 2779:369-394. [PMID: 38526795 DOI: 10.1007/978-1-0716-3738-8_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Clinical studies are conducted to better understand the pathological mechanism of diseases and to find biomarkers associated with disease activity, drug response, or outcome prediction. Mass cytometry (MC) is a high-throughput single-cell technology that measures hundreds of cells per second with more than 40 markers per cell. Thus, it is a suitable tool for immune monitoring and biomarker discovery studies. Working in translational and clinical settings requires a careful experimental design to minimize, monitor, and correct the variations introduced during sample collection, preparation, acquisition, and analysis. In this review, we will focus on these important aspects of MC-related experiments and data curation in the context of translational clinical research projects.
Collapse
Affiliation(s)
- Paulina Rybakowska
- Pfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research (GENYO), Granada, Spain
| | - Marta E Alarcón-Riquelme
- Pfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research (GENYO), Granada, Spain
- Institute for Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Concepción Marañón
- Pfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research (GENYO), Granada, Spain.
| |
Collapse
|
38
|
Ali MU, Chaudhary BN, Panja S, Gendelman HE. Theranostic Diagnostics. Results Probl Cell Differ 2024; 73:551-578. [PMID: 39242393 DOI: 10.1007/978-3-031-62036-2_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2024]
Abstract
Diagnosing and then treating disease defines theranostics. The approach holds promise by facilitating targeted disease outcomes. The simultaneous analysis of finding the presence of disease pathophysiology while providing a parallel in treatment is a novel and effective strategy for seeking improved medical care. We discuss how theranostics improves disease outcomes is discussed. The chapter reviews the delivery of targeted therapies. Bioimaging techniques are highlighted as early detection and tracking systems for microbial infections, degenerative diseases, and cancers.
Collapse
Affiliation(s)
- Mohammad Uzair Ali
- Department of Pharmacology and Experimental Neuroscience, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Bharat N Chaudhary
- Department of Pharmacology and Experimental Neuroscience, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Sudipta Panja
- Department of Pharmacology and Experimental Neuroscience, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Howard E Gendelman
- Department of Pharmacology and Experimental Neuroscience, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA.
| |
Collapse
|
39
|
Pérez-Lanzón M, Plantureux C, Paillet J, Sotty J, Soussan P, Kroemer G, Maiuri MC, Pol J. Flow Cytometry Assessment of Lymphocyte Populations Infiltrating Liver Tumors. Methods Mol Biol 2024; 2769:129-141. [PMID: 38315394 DOI: 10.1007/978-1-0716-3694-7_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Tissue-resident and recruited immune cells are essential mediators of natural and therapy-induced immunosurveillance of liver neoplasia. This idea has been recently reinforced by the clinical approval of immune checkpoint inhibitors for the immunotherapy of hepatocellular carcinoma and cholangiocarcinoma. Such research progress relies on the in-depth characterization of the immune populations that are present in pre-neoplastic and neoplastic hepatic lesions. A convenient technology for advancing along this path is high-dimensional cytometry.In this chapter, we present a protocol to assess the subtype and differentiation state of hepatic lymphocyte populations by multicolor immunofluorescence staining and flow cytometry. We detail the steps required for viability assessment and immune cell phenotyping of single-cell suspensions of liver cells by means of surface and intracellular staining of more than a dozen markers of interest. This protocol does not require prior removal of debris and dead cells and allows to process multiple samples in parallel. The procedure includes the use of a fixative-resistant viability dye that allows cell fixation and permeabilization after cell surface staining and before intracellular staining and data acquisition on a flow cytometer. Moreover, we provide a panel of fluorochrome-labeled antibodies designed for the characterization of lymphocytic subsets that can be adapted to distinct experimental settings. Finally, we present an overview of the post-staining pipeline, including data acquisition on a flow cytometer and tools for post-acquisition analyses.
Collapse
Affiliation(s)
- Maria Pérez-Lanzón
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Inserm U1138, Université Paris Cité, Sorbonne Université, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, UMS AMMICa, Gustave Roussy, Villejuif, France
| | - Céleste Plantureux
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Inserm U1138, Université Paris Cité, Sorbonne Université, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, UMS AMMICa, Gustave Roussy, Villejuif, France
- Faculté de Médecine, Université Paris-Saclay, Kremlin-Bicêtre, France
| | - Juliette Paillet
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Inserm U1138, Université Paris Cité, Sorbonne Université, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, UMS AMMICa, Gustave Roussy, Villejuif, France
- Laboratory of Human Lymphohematopoieisis, Imagine Institute, INSERM UMR 1163, Université Paris Cité, Paris, France
- Smart Immune, Paris, France
| | - Jules Sotty
- Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherche de Saint Antoine (CRSA), Paris, France
| | - Patrick Soussan
- Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherche de Saint Antoine (CRSA), Paris, France
- Assistance Publique - Hôpitaux de Paris (AP-HP). Sorbonne Université, Département de Virologie, GHU Paris-Est, Paris, France
| | - Guido Kroemer
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Inserm U1138, Université Paris Cité, Sorbonne Université, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, UMS AMMICa, Gustave Roussy, Villejuif, France
- Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Européen Georges Pompidou, AP-HP, Paris, France
| | - Maria Chiara Maiuri
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Inserm U1138, Université Paris Cité, Sorbonne Université, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, UMS AMMICa, Gustave Roussy, Villejuif, France
- Department of Molecular Medicine and Medical Biotechnologies, University of Napoli Federico II, Naples, Italy
| | - Jonathan Pol
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Inserm U1138, Université Paris Cité, Sorbonne Université, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, UMS AMMICa, Gustave Roussy, Villejuif, France
| |
Collapse
|
40
|
Muftuoglu M, Andreeff M. Sample multiplexing in CyTOF: Path to optimize single-cell proteomic profiling. CELL SIGNALING 2024; 2:113-119. [PMID: 40182016 PMCID: PMC11967567 DOI: 10.46439/signaling.2.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/05/2025]
Abstract
Sample multiplexing significantly enhanced the depth of single-cell proteomic analysis in CyTOF (Cytometry by Time-Of-Flight). New polymer-based chelators have broadened the utility of metal isotopes, enabling improved tagging and simultaneous analysis of multiple samples. These approaches minimize batch effects, streamline experiments, conserve valuable samples, reduce costs, enhance throughput, and increase the accuracy of biological data, thereby facilitating novel discoveries.
Collapse
Affiliation(s)
- Muharrem Muftuoglu
- Section of Molecular Hematology and Therapy, Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Michael Andreeff
- Section of Molecular Hematology and Therapy, Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| |
Collapse
|
41
|
Gu Y, Hu Y, Zhang H, Wang S, Xu K, Su J. Single-cell RNA sequencing in osteoarthritis. Cell Prolif 2023; 56:e13517. [PMID: 37317049 PMCID: PMC10693192 DOI: 10.1111/cpr.13517] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/30/2023] [Accepted: 05/26/2023] [Indexed: 06/16/2023] Open
Abstract
Osteoarthritis is a progressive and heterogeneous joint disease with complex pathogenesis. The various phenotypes associated with each patient suggest that better subgrouping of tissues associated with genotypes in different phases of osteoarthritis may provide new insights into the onset and progression of the disease. Recently, single-cell RNA sequencing was used to describe osteoarthritis pathogenesis on a high-resolution view surpassing traditional technologies. Herein, this review summarizes the microstructural changes in articular cartilage, meniscus, synovium and subchondral bone that are mainly due to crosstalk amongst chondrocytes, osteoblasts, fibroblasts and endothelial cells during osteoarthritis progression. Next, we focus on the promising targets discovered by single-cell RNA sequencing and its potential applications in target drugs and tissue engineering. Additionally, the limited amount of research on the evaluation of bone-related biomaterials is reviewed. Based on the pre-clinical findings, we elaborate on the potential clinical values of single-cell RNA sequencing for the therapeutic strategies of osteoarthritis. Finally, a perspective on the future development of patient-centred medicine for osteoarthritis therapy combining other single-cell multi-omics technologies is discussed. This review will provide new insights into osteoarthritis pathogenesis on a cellular level and the field of applications of single-cell RNA sequencing in personalized therapeutics for osteoarthritis in the future.
Collapse
Affiliation(s)
- Yuyuan Gu
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
- School of MedicineShanghai UniversityShanghaiChina
| | - Yan Hu
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
| | - Hao Zhang
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
| | - Sicheng Wang
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
- Department of OrthopedicsShanghai Zhongye HospitalShanghaiChina
| | - Ke Xu
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
- Wenzhou Institute of Shanghai UniversityWenzhouChina
| | - Jiacan Su
- Institute of Translational MedicineShanghai UniversityShanghaiChina
- Organoid Research CenterShanghai UniversityShanghaiChina
| |
Collapse
|
42
|
Na S, Choo Y, Yoon TH, Paek E. CyGate Provides a Robust Solution for Automatic Gating of Single Cell Cytometry Data. Anal Chem 2023; 95:16918-16926. [PMID: 37946317 PMCID: PMC10666088 DOI: 10.1021/acs.analchem.3c03006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 10/12/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023]
Abstract
To gain a better understanding of the complex human immune system, it is necessary to measure and interpret numerous cellular protein expressions at the single cell level. Mass cytometry is a relatively new technology that offers unprecedented information about the protein expression of a single cell. Conversely, the analysis of high-dimensional and multiparametric mass cytometric data sets presents a new computational challenge. For instance, conventional "manual gating" analysis was inefficient and unreliable for multiparametric phenotyping of the heterogeneous immune cellular system; consequently, automated methods have been developed to address the high dimensionality of mass cytometry data and enhance the reproducibility of the analysis. Here, we present CyGate, a semiautomated method for classifying single cells into their respective cell types. CyGate learns a gating strategy from a reference data set, trains a model for cell classification, and then automatically analyzes additional data sets using the trained model. CyGate also supports the machine learning framework for the classification of "ungated" cells, which are typically disregarded by automated methods. CyGate's utility was demonstrated by its high performance in cell type classification and the lowest generalization error on various public data sets when compared to the state-of-the-art semiautomated methods. Notably, CyGate had the shortest execution time, allowing it to scale with a growing number of samples. CyGate is available at https://github.com/seungjinna/cygate.
Collapse
Affiliation(s)
- Seungjin Na
- Institute
for Artificial Intelligence Research, Hanyang
University, Seoul 04763, Republic
of Korea
- Department
of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Yujin Choo
- Department
of Artificial Intelligence, Hanyang University, Seoul 04763, Republic of Korea
| | - Tae Hyun Yoon
- Department
of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Republic
of Korea
- Institute
of Next Generation Material Design, Hanyang
University, Seoul 04763, Republic of Korea
- Yoon
Idea
Lab Co., Ltd., Seoul 04763, Republic of Korea
| | - Eunok Paek
- Institute
for Artificial Intelligence Research, Hanyang
University, Seoul 04763, Republic
of Korea
- Department
of Computer Science, Hanyang University, Seoul 04763, Republic of Korea
- Department
of Artificial Intelligence, Hanyang University, Seoul 04763, Republic of Korea
| |
Collapse
|
43
|
Guo X, Lin CY, Alavi S, You L, Mostaghimi J. Investigation of calcium variations in single cells and the impact of Yoda1 on osteocytes by ICP-OES. Anal Chim Acta 2023; 1281:341906. [PMID: 38783744 DOI: 10.1016/j.aca.2023.341906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 08/16/2023] [Accepted: 10/10/2023] [Indexed: 05/25/2024]
Abstract
BACKGROUND Detection of elements in individual cells by inductively coupled plasma (ICP) spectrometry has recently attracted significant interest in biological research, due to the unique ability of ICP spectrometry for trace element analysis. However, performing single-cell analysis using ICP optical emission spectrometry (ICP-OES) remains a challenge due to the small size and discrete nature of cells. This is while ICP-OES can serve as a cost-effective and label-free method for this purpose. Therefore, it is necessary to improve the current ICP-OES technique to facilitate the detection of elements in single cells, thereby unlocking novel applications. RESULTS A new conical ICP torch, which has been illustrated to offer better analytical performance than the conventional ones, was applied to achieve the detection of calcium in single micro-sized cells. A new heated chamber was designed and coupled with a high-efficiency nebulizer as the sample introduction system. For the detection of single SiO2 particles, the number of particle events obtained by the new sample introduction system was found to be up to 9 times higher than that of the conventional system without sacrificing the signal intensity. Subsequently, calcium in human breast cancer cells (MDA-MB-231), mice breast cancer cells (Py8119), and mice osteocytes (MLO-Y4) was successfully detected using the new ICP-OES system. The cell detection efficiency turned out to be around 2%-3% which is much higher than that the reported values in previous single-cell ICP-OES research. Finally, as a new application, the effect of Yoda1, a recently identified activator of Piezo1 calcium channel, on osteocytes was investigated. The calcium content in Yoda1-treated MLO-Y4 cells was seen increase by 36% compared to the control sample. SIGNIFICANCE This research reveals the capability of ICP-OES in single-cell analysis for micro-sized cells which was made possible by the new conical ICP torch and the new sample introduction system. The ability to detect calcium in single mammalian cells enables the first ever application of this technique to assess the impact of the Yoda1 activator on the calcium level in osteocytes.
Collapse
Affiliation(s)
- Xiaoman Guo
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, M5S 3G8, Canada
| | - Chun-Yu Lin
- Institute of Biomedical Engineering, University of Toronto, Toronto, M5S 3G9, Canada
| | - Sina Alavi
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, M5S 3G8, Canada.
| | - Lidan You
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, M5S 3G8, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, M5S 3G9, Canada
| | - Javad Mostaghimi
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, M5S 3G8, Canada.
| |
Collapse
|
44
|
Ask EH, Tschan-Plessl A, Hoel HJ, Kolstad A, Holte H, Malmberg KJ. MetaGate: Interactive Analysis of High-Dimensional Cytometry Data with Meta Data Integration. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564454. [PMID: 37961421 PMCID: PMC10634916 DOI: 10.1101/2023.10.27.564454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level, widely used in basic research and routine clinical diagnostics. Traditionally, data analysis is carried out using manual gating, in which cut-offs are defined manually for each marker. Recent technical advances, including the introduction of mass cytometry, have increased the number of proteins that can be simultaneously assessed in each cell. To tackle the resulting escalation in data complexity, numerous new analysis algorithms have been developed. However, many of these show limitations in terms of providing statistical testing, data sharing, cross-experiment comparability integration with clinical data. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of clinical meta data. MetaGate allows manual gating to take place in traditional cytometry analysis software, while providing a combinatorial gating system for simple and transparent definition of biologically relevant cell populations. We demonstrate the utility of MetaGate through a comprehensive analysis of peripheral blood immune cells from 28 patients with diffuse large B-cell lymphoma (DLBCL) along with 17 age- and sex-matched healthy controls using two mass cytometry panels made of a total of 55 phenotypic markers. In a two-step process, raw data from 143 FCS files is first condensed through a data reduction algorithm and combined with information from manual gates, user-defined cellular populations and clinical meta data. This results in one single small project file containing all relevant information to allow rapid statistical calculation and visualization of any desired comparison, including box plots, heatmaps and volcano plots. Our detailed characterization of the peripheral blood immune cell repertoire in patients with DLBCL corroborate previous reports showing expansion of monocytic myeloid-derived suppressor cells, as well as an inverse correlation between NK cell numbers and disease progression.
Collapse
Affiliation(s)
- Eivind Heggernes Ask
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- The Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
| | - Astrid Tschan-Plessl
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Division of Hematology, University Hospital Basel, Basel, Switzerland
| | - Hanna Julie Hoel
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Arne Kolstad
- Department of Oncology, Innlandet Hospital Trust Division Gjøvik, Lillehammer, Norway
| | - Harald Holte
- Department of Oncology, Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for B cell malignancies, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Karl-Johan Malmberg
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- The Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
45
|
Kare AJ, Nichols L, Zermeno R, Raie MN, Tumbale SK, Ferrara KW. OMIP-095: 40-Color spectral flow cytometry delineates all major leukocyte populations in murine lymphoid tissues. Cytometry A 2023; 103:839-850. [PMID: 37768325 PMCID: PMC10843696 DOI: 10.1002/cyto.a.24788] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 07/26/2023] [Accepted: 08/18/2023] [Indexed: 09/29/2023]
Abstract
High-dimensional immunoprofiling is essential for studying host response to immunotherapy, infection, and disease in murine model systems. However, the difficulty of multiparameter panel design combined with a lack of existing murine tools has prevented the comprehensive study of all major leukocyte phenotypes in a single assay. Herein, we present a 40-color flow cytometry panel for deep immunophenotyping of murine lymphoid tissues, including the spleen, blood, Peyer's patches, inguinal lymph nodes, bone marrow, and thymus. This panel uses a robust set of surface markers capable of differentiating leukocyte subsets without the use of intracellular staining, thus allowing for the use of cells in downstream functional experiments or multiomic analyses. Our panel classifies T cells, B cells, natural killer cells, innate lymphoid cells, monocytes, macrophages, dendritic cells, basophils, neutrophils, eosinophils, progenitors, and their functional subsets by using a series of co-stimulatory, checkpoint, activation, migration, and maturation markers. This tool has a multitude of systems immunology applications ranging from serial monitoring of circulating blood signatures to complex endpoint analysis, especially in pre-clinical settings where treatments can modulate leukocyte abundance and/or function. Ultimately, this 40-color panel resolves a diverse array of immune cells on the axes of time, tissue, and treatment, filling the niche for a modern tool dedicated to murine immunophenotyping.
Collapse
Affiliation(s)
- Aris J. Kare
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
| | - Lisa Nichols
- Stanford Shared FACS Facility, Stanford University, Stanford, CA 94305, USA
| | - Ricardo Zermeno
- Stanford Shared FACS Facility, Stanford University, Stanford, CA 94305, USA
| | - Marina N. Raie
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
| | | | | |
Collapse
|
46
|
Bhattacharyya S, Ehsan SF, Karacosta LG. Phenotypic maps for precision medicine: a promising systems biology tool for assessing therapy response and resistance at a personalized level. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1256104. [PMID: 37964768 PMCID: PMC10642209 DOI: 10.3389/fnetp.2023.1256104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/28/2023] [Indexed: 11/16/2023]
Abstract
In this perspective we discuss how tumor heterogeneity and therapy resistance necessitate a focus on more personalized approaches, prompting a shift toward precision medicine. At the heart of the shift towards personalized medicine, omics-driven systems biology becomes a driving force as it leverages high-throughput technologies and novel bioinformatics tools. These enable the creation of systems-based maps, providing a comprehensive view of individual tumor's functional plasticity. We highlight the innovative PHENOSTAMP program, which leverages high-dimensional data to construct a visually intuitive and user-friendly map. This map was created to encapsulate complex transitional states in cancer cells, such as Epithelial-Mesenchymal Transition (EMT) and Mesenchymal-Epithelial Transition (MET), offering a visually intuitive way to understand disease progression and therapeutic responses at single-cell resolution in relation to EMT-related single-cell phenotypes. Most importantly, PHENOSTAMP functions as a reference map, which allows researchers and clinicians to assess one clinical specimen at a time in relation to their phenotypic heterogeneity, setting the foundation on constructing phenotypic maps for personalized medicine. This perspective argues that such dynamic predictive maps could also catalyze the development of personalized cancer treatment. They hold the potential to transform our understanding of cancer biology, providing a foundation for a future where therapy is tailored to each patient's unique molecular and cellular tumor profile. As our knowledge of cancer expands, these maps can be continually refined, ensuring they remain a valuable tool in precision oncology.
Collapse
Affiliation(s)
- Sayantan Bhattacharyya
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Shafqat F. Ehsan
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Loukia G. Karacosta
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| |
Collapse
|
47
|
Li Y, Wang B, Ahmad Khan Z, He J, Cheung E, Su W, Wang A, Jiang H, Jiang L, Ding X. Platinum-Chimeric Carrier Cells for Ultratrace Cell Analysis in Mass Cytometry. Anal Chem 2023; 95:14998-15007. [PMID: 37767956 DOI: 10.1021/acs.analchem.3c02706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Mass cytometry by time-of-flight (CyTOF), a high-dimensional single-cell analysis platform, detects up to 50 biomarkers at single-cell resolution. However, CyTOF analysis of biological samples with a minimal number of available cells or rare cell subsets remains a major technical challenge due to the extensive loss of cells during cell recovery, staining, and acquisition. Here, we introduce a platinum-chimeric carrier cell strategy for mass cytometry profiling of ultratrace cell samples. Cisplatin can rapidly enter broken plasma membranes of dead cells and form a chimeric interaction with cellular proteins, peptides, and amino acids. Thus, 198Pt-cisplatin is adopted to tag carrier cells in the pretreatment stage. We investigated 8 cell lines that are commonly accessible in laboratories for their potential as carrier cells to preserve rare target cells for CyTOF analysis. We designed a panel of 35 protein biomarkers to evaluate the comprehensive single-cell subtype classification capability with or without the carrier cell strategy. We further demonstrated the detection and analysis of as few as 1 × 104 immune cells using our method. The proposed method thus allows CyTOF analysis on precious clinical samples with less abundant cells.
Collapse
Affiliation(s)
- Yiyang Li
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Boqian Wang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Zara Ahmad Khan
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Jie He
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Edwin Cheung
- Cancer Centre, University of Macau, Taipa 999078, Macau SAR
- Centre for Precision Medicine Research and Training, University of Macau, Taipa 999078, Macau SAR
- MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa 999078, Macau SAR
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau SAR
| | - Wenqiong Su
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Aiting Wang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Hui Jiang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Lai Jiang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Xianting Ding
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| |
Collapse
|
48
|
Edwards JM, Andrews MC, Burridge H, Smith R, Owens C, Edinger M, Pilkington K, Desfrancois J, Shackleton M, Senthi S, van Zelm MC. Design, optimisation and standardisation of a high-dimensional spectral flow cytometry workflow assessing T-cell immunophenotype in patients with melanoma. Clin Transl Immunology 2023; 12:e1466. [PMID: 37692904 PMCID: PMC10484688 DOI: 10.1002/cti2.1466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/26/2023] [Accepted: 08/18/2023] [Indexed: 09/12/2023] Open
Abstract
Objectives Despite the success of immune checkpoint blockade, most metastatic melanoma patients fail to respond to therapy or experience severe toxicity. Assessment of biomarkers and immunophenotypes before or early into treatment will help to understand favourable responses and improve therapeutic outcomes. Methods We present a high-dimensional approach for blood T-cell profiling using three multi-parameter cytometry panels: (1) a TruCount panel for absolute cell counts, (2) a 27-colour spectral panel assessing T-cell markers and (3) a 20-colour spectral panel evaluating intracellular cytokine expression. Pre-treatment blood mononuclear cells from patients and healthy controls were cryopreserved before staining across 11 batches. Batch effects were tracked using a single-donor control and the suitability of normalisation was assessed. The data were analysed using manual gating and high-dimensional strategies. Results Batch-to-batch variation was minimal, as demonstrated by the dimensionality reduction of batch-control samples, and normalisation did not improve manual or high-dimensional analysis. Application of the workflow demonstrated the capacity of the panels and showed that patients had fewer lymphocytes than controls (P = 0.0027), due to lower naive CD4+ (P = 0.015) and CD8+ (P = 0.011) T cells and follicular helper T cells (P = 0.00076). Patients showed trends for higher proportions of Ki67 and IL-2-expressing cells within CD4+ and CD8+ memory subsets, and increased CD57 and EOMES expression within TCRγδ+ T cells. Conclusion Our optimised high-parameter spectral cytometry approach provided in-depth profiling of blood T cells and found differences in patient immunophenotype at baseline. The robustness of our workflow, as demonstrated by minimal batch effects, makes this approach highly suitable for the longitudinal evaluation of immunotherapy effects.
Collapse
Affiliation(s)
- Jack M Edwards
- Alfred Health Radiation OncologyThe Alfred HospitalMelbourneVICAustralia
- Department of Immunology, Central Clinical SchoolMonash University and Alfred HospitalMelbourneVICAustralia
| | - Miles C Andrews
- Department of Medicine, Central Clinical SchoolMonash UniversityMelbourneVICAustralia
- Department of Medical OncologyThe Alfred HospitalMelbourneVICAustralia
| | - Hayley Burridge
- Department of Medical OncologyThe Alfred HospitalMelbourneVICAustralia
| | - Robin Smith
- Alfred Health Radiation OncologyThe Alfred HospitalMelbourneVICAustralia
| | - Carole Owens
- Alfred Health Radiation OncologyThe Alfred HospitalMelbourneVICAustralia
| | | | | | | | - Mark Shackleton
- Department of Medicine, Central Clinical SchoolMonash UniversityMelbourneVICAustralia
- Department of Medical OncologyThe Alfred HospitalMelbourneVICAustralia
| | - Sashendra Senthi
- Alfred Health Radiation OncologyThe Alfred HospitalMelbourneVICAustralia
| | - Menno C van Zelm
- Department of Immunology, Central Clinical SchoolMonash University and Alfred HospitalMelbourneVICAustralia
| |
Collapse
|
49
|
Wu C, Men X, Liu M, Wei Y, Wei X, Yu YL, Xu ZR, Chen ML, Wang JH. Two-Dimensional Multi-parameter Cytometry Platform for Single-Cell Analysis. Anal Chem 2023; 95:13297-13304. [PMID: 37610312 DOI: 10.1021/acs.analchem.3c02457] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
A 2D flow cytometry platform, known as CytoLM Plus, was developed for multi-parameter single-cell analysis. Single particles or cells after hydrodynamic alignment in a microfluidic unit undergo first-dimension fluorescence and side scattering dual-channel optical detection. They were thereafter immediately directed to ICP-MS by connecting the microfluidic unit with a high-efficiency nebulizer to facilitate the second-dimension ICP-MS detection. Flow cytometry measurements of fluorescent microspheres evaluated the performance of CytoLM Plus for optical detection. 6434 fluorescence bursts were observed with a valid signal proportion as high as 99.7%. After signal unification and gating analysis, 6067 sets of single-particle signals were obtained with 6.6 and 6.2% deviations for fluorescence burst area and height, respectively. This is fairly comparable with that achieved by a commercial flow cytometer. Afterward, CytoLM Plus was evaluated by 2D flow cytometry measurement of Ag+-incubated and AO-stained MCF-7 cells. A program for 2D single-cell signal unification was developed based on the algorithm of screening in lag time window. In the present case, a lag time window of -4.2 ± 0.09 s was determined by cross-correlation analysis and two-parameter optimization, which efficiently unified the concurrent single-cell signals from fluorescence, side scattering, and ICP-MS. A total of 495 sets of concurrent 2D signals were screened out, and the statistical analysis of these single-cell signals ensured 2D multi-parameter single-cell analysis and data elucidation.
Collapse
Affiliation(s)
- Chengxin Wu
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, China
| | - Xue Men
- School of Chemistry and Environment, Yunnan Minzu University, Kunming 650504, China
| | - Meijun Liu
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, China
| | - Yujia Wei
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, China
| | - Xing Wei
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, China
| | - Yong-Liang Yu
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, China
| | - Zhang-Run Xu
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, China
| | - Ming-Li Chen
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, China
| | - Jian-Hua Wang
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang 110819, China
| |
Collapse
|
50
|
Abstract
The advent of high-dimensional single-cell technologies has enabled detection of cellular heterogeneity and functional diversity of immune cells during health and disease conditions. Because of its multiplexing capabilities and limited compensation requirements, mass cytometry or cytometry by time of flight (CyTOF) has played a superior role in immune monitoring compared with flow cytometry. Further, it has higher throughput and lower cost compared with other single-cell techniques. Several published articles have utilized CyTOF to identify cellular phenotypes and features associated with disease outcomes. This article introduces CyTOF-based assays to profile immune cell-types, cell-states, and their applications in clinical research.
Collapse
Affiliation(s)
- Abhishek Koladiya
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Kara L Davis
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Center for Cancer Cell Therapy, Stanford University, Stanford, CA, USA.
| |
Collapse
|