Breast cancer is a highly heterogeneous disease where variations of biomarker expression may exist between individual foci of a cancer (intra-tumoral heterogeneity). The extent of variation of biomarker expression in the cancer cells, distribution of cell types in the local tumor microenvironment and their spatial arrangement could impact on diagnosis, treatment planning and subsequent response to treatment.

Using quantitative multiplex immunofluorescence (MxIF) imaging, we assessed the level of variations in biomarker expression levels among individual cells, density of cell cluster groups and spatial arrangement of immune subsets from regions sampled from 38 multi-focal breast cancers that were processed using whole-mount histopathology techniques. Molecular profiling was conducted to determine the intrinsic molecular subtype of each analysed region.

A subset of cancers (34.2%) showed intra-tumoral regions with more than one molecular subtype classification. High levels of intra-tumoral variations in biomarker expression levels were observed in the majority of cancers studied, particularly in Luminal A cancers. HER2 expression quantified with MxIF did not correlate well with HER2 gene expression, nor with clinical HER2 scores. Unsupervised clustering revealed the presence of various cell clusters with unique IHC4 protein co-expression patterns and the composition of these clusters were mostly similar among intra-tumoral regions. MxIF with immune markers and image patch analysis classified immune niche phenotypes and the prevalence of each phenotype in breast cancer subtypes was illustrated.

Our work illustrates the extent of spatial heterogeneity in biomarker expression and immune phenotypes, and highlights the importance of a comprehensive spatial assessment of the disease for prognosis and treatment planning.

Cells have distinct molecular repertoires on their surfaces and unique intracellular biomolecular profiles that play pivotal roles in orchestrating a myriad of biological responses in the context of growth, development and disease. A persistent challenge in the deep exploration of these cues has been in our inability to effectively and precisely capture the temporal and spatial characteristics of living cells. In this Perspective, we delve into techniques for temporal and two- and three-dimensional spatial omics analyses and underscore how their harmonious fusion promises to unlock insights into the dynamics and diversity of individual cells within biological systems such as tissues and organoids. We then explore four-dimensional profiling, a nascent but promising frontier that adds a temporal (fourth-dimension) component to three-dimensional omics; highlight the advancements, challenges and gaps in the field; and discuss potential strategies for further technological development.

Mapping the spatial organization of tissues is critical to understanding organ biology in health and disease. Developments in multiplexed antibody-based, fluorescence labelling methods have provided unique insights into tissue microenvironments. However, many current methods have a variety of limitations that reduce their practical utilization including cost, time and technical complexity. To address these drawbacks, we developed Spatial Photo-inactivation Enhanced Cyclic Target REsolved multiPlexing (SPECTRE-Plex). SPECTRE-Plex is a relatively low cost, end-to-end technique based on a series of methods that significantly improve speed, automation, and resolution of cyclic multiplex immunofluorescence imaging. We describe a representative example of the application of the method by investigating spatial cellular and neighborhood changes in the proximal small intestine between healthy tissue and active celiac disease.

Three-dimensional (3D) cell cultures have gained popularity in recent years due to their ability to represent complex tissues or organs more faithfully than conventional two-dimensional (2D) cell culture. This article reviews the application of both 2D and 3D microscopy approaches for monitoring and studying 3D cell cultures. We first summarize the most popular optical microscopy methods that have been used with 3D cell cultures. We then discuss the general advantages and disadvantages of various microscopy techniques for several broad categories of investigation involving 3D cell cultures. Finally, we provide perspectives on key areas of technical need in which there are clear opportunities for innovation. Our goal is to guide microscope engineers and biomedical end users toward optimal imaging methods for specific investigational scenarios and to identify use cases in which additional innovations in high-resolution imaging could be helpful.

The tumor microenvironment (TME) is a complex ecosystem where malignant and non-malignant cells cooperate and interact determining cancer progression. Cell abundance, phenotype and localization within the TME vary over tumor development and in response to therapeutic interventions. Therefore, increasing our knowledge of the spatiotemporal changes in the tumor ecosystem architecture is of importance to better understand the etiologic development of the neoplastic diseases. Imaging Mass Cytometry (IMC) represents the elective multiplexed imaging technology enabling the in-situ analysis of up to 43 different protein markers for in-depth phenotypic and spatial investigation of cells in their preserved microenvironment. IMC is currently applied in cancer research to define the composition of the cellular landscape and to identify biomarkers of predictive and prognostic significance with relevance in mechanisms of drug resistance. Herein, we describe the general principles and experimental workflow of IMC raising the informative potential in preclinical and clinical cancer research.

Multiplexed tissue imaging enables the simultaneous detection of dozens of proteins at single-cell resolution, providing unprecedented insights into tissue organization and disease microenvironments. However, the resulting high-dimensional, gigabyte-scale datasets pose significant computational and methodological challenges. Existing analytical workflows, often fragmented between bespoke scripts and static visualizations, lack the scalability and user-friendly interfaces required for efficient, reproducible analysis. To overcome these limitations, we developed SPAC (analysis of SPAtial single-Cell datasets), a scalable, web-based ecosystem that integrates modular pipelines, high-performance computing (HPC) connectivity, and interactive visualization to democratize end-to-end single-cell spatial analysis applied to cellular positional data and protein expression levels.

SPAC is built on a modular, layered architecture that leverages community-based and newly developed tools for single-cell and spatial proteomics analysis. A specialized Python package extends these functionalities with custom analysis routines and established software engineering practices. An Interactive Analysis Layer provides web-hosted pipelines for configuring and executing complex workflows, and scalability enhancements that support distributed or parallel execution on GPU-enabled clusters. A Real-Time Visualization Layer delivers dynamic dashboards for immediate data exploration and sharing. As a showcase of its capabilities, SPAC was applied to a 4T1 breast cancer model, analyzing a multiplex imaging dataset comprising 2.6 million cells. GPU acceleration reduced unsupervised clustering runtimes from several hours to under ten minutes, and real-time visualization enabled detailed spatial characterization of tumor subregions.

SPAC effectively overcomes key challenges in spatial single-cell analysis by streamlining high-throughput data processing and spatial profiling within an accessible and scalable framework. Its robust architecture, interactive interface and ease of access have the potential to accelerate biomedical research and clinical applications by converting complex imaging data into actionable biological and clinical insights.

Hepatitis B virus (HBV) is a global health problem with over 250 million people affected worldwide. Nucleos(t)ide analogues remain the standard of care and suppress production of progeny virions; however, they have limited effect on the viral transcriptome and long-term treatment is associated with off-target toxicities. Promising results are emerging from clinical trials and several drug classes have been evaluated, including capsid assembly modulators and RNA interfering agents. Whilst peripheral biomarkers are used to monitor responses and define treatment endpoints, they fail to reflect the full reservoir of infected hepatocytes. Given these limitations, consideration should be given to the merits of sampling liver tissue, especially in the context of clinical trials. In this review article, we will discuss methods for profiling HBV in liver tissue and their value to the HBV cure programme.

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a 3D Human Reference Atlas (HRA) of the healthy adult body. Experts from 20+ consortia collaborate to develop a Common Coordinate Framework (CCF), knowledge graphs and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes and biomarkers) and to use the HRA to characterize changes that occur with aging, disease and other perturbations. HRA v.2.0 covers 4,499 unique anatomical structures, 1,195 cell types and 2,089 biomarkers (such as genes, proteins and lipids) from 33 ASCT+B tables and 65 3D Reference Objects linked to ontologies. New experimental data can be mapped into the HRA using (1) cell type annotation tools (for example, Azimuth), (2) validated antibody panels or (3) by registering tissue data spatially. This paper describes HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interfaces, flexible hybrid cloud infrastructure and previews atlas usage applications.

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.

Identifying biomarkers of radiotherapy (RT) response is important for optimizing the treatment of early breast cancer. In this study, we tested the interaction between endothelial cell (EC) expression of phospho-Tyr397-FAK (pY397-FAK) and adjuvant-RT on clinical outcomes after breast-conserving surgery (BCS) within a randomized study. Preclinical data suggest an enhanced effect of RT on low EC_pY397-FAK expression.

We analyzed tissue microarrays from the Swedish Breast Cancer Group 91 Radiotherapy (stage I-II, lymph node-negative) breast cancer cohort, consisting of 1,178 patients randomly assigned to receive either BCS alone or BCS plus adjuvant-RT. Tissue microarray sections were immunostained for pY397-FAK, CD31, α-smooth muscle actin, and pan-cytokeratin. HALO analysis scored mean pY397-FAK intensity in CD31+ ECs, pan-cytokeratin-positive tumor epithelial cells, and α-smooth muscle actin + mural/stromal cells per core. For 822 patients, multivariable Cox regression analysis was performed for the primary and secondary 5-year endpoints, locoregional recurrence and all recurrence, respectively, as dependent variables and RT and EC_pY397-FAK as independent variables.

EC_pY397-FAK expression was not predictive for the primary endpoint locoregional recurrence (P = 0.098), but the direction of the RT effect was in line with preclinical findings. For the secondary endpoint all recurrence, there was a significant interaction (P = 0.026) between EC_pY397-FAK and RT. Without RT, higher EC_pY397-FAK expression resulted in a lower risk for all recurrence (HR = 0.74 per SD; 95% confidence interval = 0.57-0.96; P = 0.026).

Within the first 5 years following BCS, patients with low EC_pY397-FAK expression derive greater benefit from RT than patients with high EC_pY397-FAK expression. However, without RT, low EC_pY397-FAK expression is associated with a higher risk of recurrence.

Spatial biology has emerged as a transformative field, offering insights into cellular interactions and organization within tissues. The field has evolved rapidly since the coining of the term "spatial omics." Now, the ability to spatially resolve proteins, RNA, chromatin, and lipids is becoming widespread, and the technologies are continually refined. Reagents to support the analysis of veterinary species are available and more are emerging. These new tools will allow pathologists and scientists to unravel the intricate interplay between tissue architecture and diverse cellular phenotypes. By integrating histological observations with spatially resolved genomic data, spatial biology holds immense potential for advancing diagnostic and therapeutic strategies in veterinary medicine. These tools will undoubtedly equip veterinary pathologists to better decipher complex disease processes and identify novel therapeutic targets.

Understanding tissue structure and function requires tools that quantify the expression of multiple proteins at single-cell resolution while preserving spatial information. Current imaging technologies use a separate channel for each protein, limiting throughput and scalability. Here, we present combinatorial multiplexing (CombPlex), a combinatorial staining platform coupled with an algorithmic framework to exponentially increase the number of measured proteins. Every protein can be imaged in several channels and every channel contains agglomerated images of several proteins. These combinatorically compressed images are then decompressed to individual protein images using deep learning. We achieve accurate reconstruction when compressing the stains of 22 proteins to five imaging channels. We demonstrate the approach both in fluorescence microscopy and in mass-based imaging and show successful application across multiple tissues and cancer types. CombPlex can escalate the number of proteins measured by any imaging modality, without the need for specialized instrumentation.

Profiling multiple enzymatic activities in tissue is crucial for understanding complex metabolic and signaling networks, yet remains a challenge with existing optical microscopies. Here, we developed a Fenton-promoted luminol electrochemiluminescence (ECL) imaging method to achieve the spatial mapping of multiple enzymatic activities within a single tissue section. This method quantitatively visualizes individual enzymatic activity by combining the enzymatic conversion of substrates with the chemical confinement of the locally produced hydrogen peroxide. To achieve high-resolution spatial imaging by limiting the diffusion (∼500 μm) of hydrogen peroxide, iron oxide nanoparticles were coated on the tissue surface to initiate the Fenton process, locally converting hydrogen peroxide into short-lived hydroxyl radicals with a nanometer-scale diffusion range. The Fenton-promoted ECL emission is confined at the enzymatic conversion sites, offering unprecedented spatial visualization of four tumor-associated oxidases within a single tissue section. Colocalization revealed a synergistic effect between lysyl oxidase and quiescin sulfhydryl oxidase on post-translational modifications of tumor extracellular matrix proteins, along with a previously undiscovered interaction with amiloride-sensitive amine oxidase, which could not be distinguished based on expressions or single enzymatic activity alone. This approach offers a novel activity-based protein profiling tool at the tissue level, providing new data for future enzynomic research and multimodal imaging.

Vestibular migraine (VM) is a disorder characterized by recurrent episodes of dizziness or vertigo and is often accompanied by headache. The mechanisms underlying vestibular dysfunction and pain in VM remain unclear.

Chronic migraine (CM) and VM models were induced by NTG and kainic acid, respectively. Behavioral assessments were conducted to evaluate vestibular dysfunction and pain in the VM and CM models. Transmission electron microscopy (TEM) was used to examine peripheral receptor impairment. Immunofluorescence, including staining for Cellular Proto-oncogene (c-Fos), Neuronal Nuclei (NeuN), and calcitonin gene-related peptide (CGRP), identified activated brain regions such as the cortex, midbrain, and cerebellum. Multiplex immunohistochemistry and cholera toxin subunit B (CTB) tracing were performed to analyze nuclear heterogeneity and neural communication. Additionally, RNA sequencing (RNA-Seq) and Ionized calcium-binding adapter molecule 1 (IBA1) immunostaining were used to investigate ion channel expression in the spinal trigeminal nucleus caudalis (Sp5c).

CM and VM-related behaviors, such as allodynia and balance disturbance, were successfully reproduced in mouse model. TEM revealed significant damage to peripheral sensory receptors, particularly in the trigeminal ganglion and cochlear cells. Distinct activation patterns of c-Fos and CGRP were observed in VMs and CMs. CTB tracing confirmed that signals are transmitted from the vestibulocerebellum (VbC) to the Sp5c via the vestibular nuclei (VN). Furthermore, RNA-Seq combined with coimmunostaining revealed an increased expression of transient receptor potential vanilloid 2 (TRPV2) ion channels in microglia within Sp5c, indicating their potential role in VM pathology.

This study preliminarily explored VbC-VN-Sp5c communication and identified TRPV2 ion channels in microglia as key players in neuron-glia crosstalk in VM. These findings provide new insights into the mechanisms underlying vestibular migraine and suggest potential therapeutic targets.

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis.

Immune responses require complex, spatially coordinated interactions between immune cells and their tissue environment. For decades, we have imaged tissue sections to visualize a limited number of immune-related macromolecules in situ, functioning as surrogates for cell types or processes of interest. However, this inevitably provides a limited snapshot of the tissue's immune landscape. Recent developments in high-throughput spatial -omics technologies, particularly spatial transcriptomics, and its application to human samples has facilitated a more comprehensive understanding of tissue immunity by mapping fine-grained immune cell states to their precise tissue location while providing contextual information about their immediate cellular and tissue environment. These data provide opportunities to investigate mechanisms underlying the spatial distribution of immune cells and its functional implications, including the identification of immune niches, although the criteria used to define this term have been inconsistent. Here, we review recent technological and analytic advances in multiparameter spatial profiling, focusing on how these methods have generated new insights in translational immunology. We propose a 3-step framework for the definition and characterization of immune niches, which is powerfully facilitated by new spatial profiling methodologies. Finally, we summarize current approaches to analyze adaptive immune repertoires and lymphocyte clonal expansion in a spatially resolved manner.

New treatment approaches are warranted for patients with advanced melanoma refractory to immune checkpoint blockade (ICB) or BRAF-targeted therapy. We designed BNT221, a personalized, neoantigen-specific autologous T cell product derived from peripheral blood, and tested this in a 3 + 3 dose-finding study with two dose levels (DLs) in patients with locally advanced or metastatic melanoma, disease progression after ICB, measurable disease (Response Evaluation Criteria in Solid Tumors version 1.1) and, where appropriate, BRAF-targeted therapy. Primary and secondary objectives were evaluation of safety, highest tolerated dose and anti-tumor activity. We report here the non-pre-specified, final results of the completed monotherapy arm consisting of nine patients: three at DL1 (1 × 108-1 × 109 cells) and six at DL2 (2 × 109-1 × 1010 cells). Drug products (DPs) were generated for all enrolled patients. BNT221 was well tolerated across both DLs, with no dose-limiting toxicities of grade 3 or higher attributed to the T cell product observed. Specifically, no cytokine release, immune effector cell-associated neurotoxicity or macrophage activation syndromes were reported. A dose of 5.0 × 108-1.0 × 1010 cells was identified for further study conduct. Six patients showed stable disease as best overall response, and tumor reductions (≤20%) were reported for four of these patients. In exploratory analyses, multiple mutant-specific CD4+ and CD8+ T cell responses were generated in each DP. These were cytotoxic, polyfunctional and expressed T cell receptors with broad functional avidities. Neoantigen-specific clonotypes were detected after treatment in blood and tumor. Our results provide key insights into this neoantigen-specific adoptive T cell therapy and demonstrate proof of concept for this new therapeutic approach. ClinicalTrials.gov registration: NCT04625205 .

Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant global health challenge due to its high mortality rate and late-stage diagnosis. The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes. This review provides a comprehensive overview of current and emerging biomarkers for HCC, including alpha-fetoprotein, des-gamma-carboxy prothrombin, glypican-3, Golgi protein 73, osteopontin, and microRNAs. Despite advancements, the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages. The review emphasizes the importance of integrating multi-omics approaches, combining genomics, proteomics, and metabolomics, to develop more robust biomarker panels. Such integrative methods have the potential to capture the complex molecular landscape of HCC, offering insights into disease mechanisms and identifying targets for personalized therapies. The significance of large-scale validation studies, collaboration between research institutions and clinical settings, and consideration of regulatory pathways for clinical implementation is also discussed. In conclusion, while substantial progress has been made in biomarker discovery, continued research and innovation are essential to address the remaining challenges. The successful translation of these discoveries into clinical practice will require rigorous validation, standardization of protocols, and cross-disciplinary collaboration. By advancing the development and application of novel biomarkers, we can improve the early detection and management of HCC, ultimately enhancing patient survival and quality of life.

Lung cancer remains the deadliest cancer in the United States, with lung adenocarcinoma (LUAD) as its most prevalent subtype. While computed tomography (CT)-based screening has improved early detection and enabled curative surgeries, the molecular and cellular dynamics driving early-stage LUAD progression remain poorly understood, limiting non-surgical treatment options. To address this gap, we profiled 2.24 million cells from 122 early-stage LUAD patients using multiplexed imaging mass cytometry (IMC). This analysis revealed the molecular, spatial, and temporal dynamics of LUAD development. Our findings uncover a binary progression model. LUAD advances through either inflammation, driven by a balance of cytotoxic and regulatory immune activity, or fibrosis, characterized by stromal activation. Surprisingly, tumor cell populations did not increase significantly. Instead, they displayed a mixed phenotypic profile consistent with epithelial-to-mesenchymal transition (EMT), effectively masking the expansion of malignant cells. Furthermore, we addressed discrepancies between CT-based and histology-based subtyping. CT scans, while non-invasive, often mischaracterize invasive fibrotic tumors-which account for 20.5% of LUAD cases-as mild, non-solid ground glass opacities (GGOs). Using high-content IMC imaging, we demonstrate that these tumors harbor significant risks and advocate for improved diagnostic strategies. These strategies should integrate molecular profiling to refine patient stratification and therapeutic decision-making. Altogether, our study provides a high-resolution, systems-level view of the tumor microenvironment in early-stage LUAD. We characterize key transitions in oncogenesis and propose a precision-driven framework to enhance the detection and management of aggressive disease subtypes.

Sequential multiplex methodologies such as Akoya CODEX, Miltenyi MACSima, Rarecyte Orion, and others require modification of the antibodies by conjugation to an oligo or a specific fluorophore which means the use of off-the-shelf reagents is not possible. Modifications of these antibodies are typically performed via reduction chemistry and thus require verification and validation post-modification. Fixed panels are therefore developed due to various limitations including spectral overlap that creates spectral unmixing issues, steric hindrance, harsh antibody removal, and tissue degradation throughout the labeling. As such, a complex interrogation evaluating multiple study hypotheses and/or endpoints requires the development of sequential panels, reconstruction, and realignment of the tissue that necessitate a z-stack strategy. Standardized antibody panels are typically fixed and require substantial validation efforts to modify a single target and thus do not evolve with the pace of research interests. To increase the throughput of profiling cells within the human central nervous system (CNS), we developed and validated a CNS-specific library with an associated analysis platform using the newly developed Lunaphore COMET TM platform. The COMET TM is an automated staining/imaging instrument integrating a reagent deck for staining buffers and off-the-shelf label-free primary antibodies and fluorophore-labeled secondary antibodies, which feed into a circular plate holding up to 4 slides that are automatically imaged in microscope-operated control software. For this study, standard formalin fixed paraffin embedded histology slides are used. However, the COMET is capable of imaging fresh-frozen samples using specialized settings. Our methodologies address an unmet need in the neuroscience field while leveraging prior developmental efforts in the domain of immunology spatial profiling. Cataloging and validating a large series of antibodies on the COMET™ along with developing CNS autofluorescence management strategies while optimizing standard operating procedures have allowed for the visualization at the subcellular level. Forty analytes can be used to analyze one specimen which has clinical utility in cases in which the CNS can only be sampled by biopsy. CNS biopsies, depending on the anatomical location, can have limited available volume to a degree that requires prioritization and restriction to select analysis. In-depth bioinformatic imaging analysis can be done using standard bioinformatic tools and software such as Visiopharm®. These results establish a general framework for imaging and quantifying cell populations and networks within the CNS while providing the scientific community with standard operating procedures.

Cell segmentation and classification are critical tasks in spatial omics data analysis. Here we introduce CelloType, an end-to-end model designed for cell segmentation and classification for image-based spatial omics data. Unlike the traditional two-stage approach of segmentation followed by classification, CelloType adopts a multitask learning strategy that integrates these tasks, simultaneously enhancing the performance of both. CelloType leverages transformer-based deep learning techniques for improved accuracy in object detection, segmentation and classification. It outperforms existing segmentation methods on a variety of multiplexed fluorescence and spatial transcriptomic images. In terms of cell type classification, CelloType surpasses a model composed of state-of-the-art methods for individual tasks and a high-performance instance segmentation model. Using multiplexed tissue images, we further demonstrate the utility of CelloType for multiscale segmentation and classification of both cellular and noncellular elements in a tissue. The enhanced accuracy and multitask learning ability of CelloType facilitate automated annotation of rapidly growing spatial omics data.

Neutrophils are vital for immunity against Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), yet their heterogeneous nature suggests a complex role in TB pathogenesis. Here, we identify two distinct neutrophil populations based on CD101 expression, highlighting their divergent roles in TB. CD101-negative (CD101-ve) neutrophils, which resemble immature, pro-inflammatory granulocytes, exhibit reduced Mtb phagocytosis compared to their mature, CD101-positive (CD101+ve) counterparts. Our findings reveal that type I interferons (IFN-Is) suppress neutrophil Mtb uptake and drive the recruitment of CD101-ve neutrophils to the lungs. Infiltration of these cells promotes Mtb extracellular persistence, exacerbates epithelial damage, and impairs surfactant production. Furthermore, we demonstrate that granulocyte colony-stimulating factor (G-CSF) and chemokine receptor CXCR2 are essential for the pulmonary accumulation of CD101-ve neutrophils. Our study uncovers a pathogenic role for CD101-ve neutrophils in TB and highlights the IFN-I-dependent recruitment of this functionally compromised immature neutrophil as a driver of TB immunopathogenesis.

By simultaneously staining multiple immunomarkers on a single tissue section, multiplexed immunohistochemistry (mIHC) enhances the amount of information that can be observed in a single tissue section and thus can be a powerful tool to visualise cellular interactions directly in the tumour microenvironment. Performing mIHC remains technically and practically challenging, and this technique has many limitations if not properly validated. However, with proper validation, heterogeneity between histopathological images can be avoided.

This review aimed to summarize the currently used methods and to propose a standardised method for effective mIHC.

An extensive literature review was conducted to identify different methods currently in use for mIHC.

Guidelines for antibody selection, panel design, antibody validation and analytical strategies are given. The advantages and disadvantages of each method are discussed.

This review summarizes widely used pathology imaging software and discusses the potential for automation of pathology image analysis so that mIHC technology can be a truly powerful tool for research as well as clinical use.

Triple-negative breast cancer (TNBC) is an aggressive and complex subtype of breast cancer characterized by a lack of targeted treatment options. Intratumoral heterogeneity significantly drives disease progression and complicates therapeutic responses, necessitating advanced analytical approaches to understand its underlying biology. This review aims to explore the advancements in single-cell proteomics and their application in uncovering cellular diversity in TNBC. It highlights innovations in sample preparation, mass spectrometry-based techniques, and the potential for integrating proteomics into multi-omics platforms.

The review discusses the combination of improved sample preparation methods and cutting-edge mass spectrometry techniques in single-cell proteomics. It emphasizes the challenges associated with protein analysis, such as the inability to amplify proteins akin to transcripts, and examines strategies to overcome these limitations.

Single-cell proteomics provides a direct link to phenotype and cell behavior, complementing transcriptomic approaches and offering new insights into the mechanisms driving TNBC. The integration of advanced techniques has enabled deeper exploration of cellular heterogeneity and disease mechanisms.

Despite the challenges, single-cell proteomics holds immense potential to evolve into a high-throughput and scalable multi-omics platform. Addressing existing hurdles will enable deeper biological insights, ultimately enhancing the diagnosis and treatment of TNBC.

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, e.g., primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data.

Current studies of biological tissues require visualizing diverse cell types and molecular interactions, creating a growing need for versatile techniques to simultaneously probe numerous targets. Traditional multiplexed imaging is limited to around five targets at once. Emerging methods using sequential rounds of staining, imaging, and signal removal can probe tens of targets but require specialized hardware and time-consuming workflows and face challenges with sample distortion and artifacts. We present a highly multiplexed fluorescence microscopy method using semiconducting polymer dots (Pdots) in a single round of staining and imaging. Pdots are small, bright, and photostable fluorescent probes with a wide range of tunable Stokes shifts (20 to 450 nanometers). Multiple series of Pdots with varying excitation wavelengths allow for fast (<1 minute) and single-round imaging of up to 21 targets in the brain and kidney. This method is based on a simple immunofluorescence workflow, efficient use of spectral space, standard hardware, and straightforward analysis, making it widely applicable for bioimaging laboratories.

Tumours are composed of tumour cells and the surrounding tumour microenvironment (TME), and the molecular characterisation of the various elements of the TME and their interactions is essential for elucidating the mechanisms of tumour progression and developing better therapeutic strategies. Multiplex imaging is a technique that can quantify the expression of multiple protein markers on the same tissue section while maintaining spatial positioning, and this method has been rapidly developed in cancer research in recent years. Many multiplex imaging technologies and spatial analysis methods are emerging, and the elucidation of their principles and features is essential. In this review, we provide an overview of the latest multiplex imaging techniques by type of imaging and staining method and an introduction to image analysis methods, primarily focusing on spatial cellular properties, providing deeper insight into tumour organisation and spatial molecular biology in the TME.

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artifacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly and feature extraction. Here we show that these artifacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artifacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years before data collection, such as those from clinical trials.

The most common methods for multiplexed immunohistochemistry rely on cyclic procedures, whereby cells or tissues are repeatedly stained, imaged, and regenerated. Here, we present a simple and inexpensive approach for amine-targeted labeling of antibodies using a linker that can be easily cleaved by a mild reducing agent. This method requires only inexpensive and readily-available reagents, and can be carried out without synthetic experience in a simple one-pot reaction. We demonstrate the applicability of this approach by performing repeated staining-imaging-removal cycles on isolated cells and tissue sections, finding that over 94% of the labels can be removed within 30 minutes using only the gentle application of reducing agent, increasing up to 99% by extending the incubation duration to 1 hour. By providing a convenient way to introduce cleavable linkers, our method simplifies methodologies such as high-content imaging or multiplexed immunohistochemistry.

We present a novel approach to cell phenotyping for spatial proteomics that addresses the challenge of generalization across diverse datasets with varying marker panels. Our approach utilizes a transformer with channel-wise attention to create a language-informed vision model; this model's semantic understanding of the underlying marker panel enables it to learn from and adapt to heterogeneous datasets. Leveraging a curated, diverse dataset with cell type labels spanning the literature and the NIH Human BioMolecular Atlas Program (HuBMAP) consortium, our model demonstrates robust performance across various cell types, tissues, and imaging modalities. Comprehensive benchmarking shows superior accuracy and generalizability of our method compared to existing methods. This work significantly advances automated spatial proteomics analysis, offering a generalizable and scalable solution for cell phenotyping that meets the demands of multiplexed imaging data.

Proteins work together in nanostructures in many physiological contexts and disease states. We recently developed expansion revealing (ExR), which expands proteins away from each other, in order to support better labeling with antibody tags and nanoscale imaging on conventional microscopes. Here, we report multiplexed expansion revealing (multiExR), which enables high-fidelity antibody visualization of >20 proteins in the same specimen, over serial rounds of staining and imaging. Across all datasets examined, multiExR exhibits a median round-to-round registration error of 39 nm, with a median registration error of 25 nm when the most stringent form of the protocol is used. We precisely map 23 proteins in the brain of 5xFAD Alzheimer's model mice, and find reductions in synaptic protein cluster volume, and co-localization of specific AMPA receptor subunits with amyloid-beta nanoclusters. We visualize 20 synaptic proteins in specimens of mouse primary somatosensory cortex. multiExR may be of broad use in analyzing how different kinds of protein are organized amidst normal and pathological processes in biology.

Recent advances in multiplexed fluorescence imaging have provided new opportunities for deciphering the complex spatial relationships among various cell types across diverse tissues. We introduce CytoSpatio, open-source software that constructs generative, multirange, and multitype point process models that capture interactions among multiple cell types at various distances simultaneously. On analyzing five cell types across five tissues, our software showed consistent spatial relationships within the same tissue type, with certain cell types like proliferating T cells consistently clustering across tissue types. It also revealed that the attraction-repulsion relationships between cell types like B cells and CD4-positive T cells vary with tissue type. CytoSpatio can also generate synthetic tissue structures that preserve the spatial relationships seen in training images, a capability not provided by previous descriptive, motif-based approaches. This potentially allows spatially realistic simulations of how cell relationships affect tissue biochemistry.

Multiplex immunofluorescence (mIF) staining plays an important role in profiling biomarkers and allows investigation of co-relationships between multiple biomarkers in the same tissue section. The Cell DIVE mIF platform (Leica Microsystems) employs an alkaline solution of hydrogen peroxide as a fluorophore inactivation reagent in the sequential staining, imaging, and bleaching protocol for use on FFPE sections. Suboptimal bleaching efficiency, degradation of tissue structure, and loss of antigen immunogenicity occasionally are encountered with the standard bleaching process. To overcome these impediments, we adopted a modified photochemical bleaching method, which utilizes an intense LED light exposure concurrent with the application of hydrogen peroxide. Repeated stain/bleach rounds with different antibodies were performed on breast tissue and other tissue sections. Residual signal after conventional bleaching and the modified technique were compared and tissue integrity and antigen immunogenicity were assessed. The modified technique effectively eliminates fluorescence signal from previous staining rounds and produces consistent results for multiple rounds of staining and imaging. With the modified method, photochemical treatments did not destroy tissue sub-cellular contents, and the tissue antigenicity was well preserved during the entire mIF process. Overall processing time was reduced from 36 to 30 hours in an mIF procedure with 8 rounds. With the conventional method, tissue quality was highly degraded after 8 rounds. The new technique allows reduced turn-around time, provides reliable fluorophore removal in mIF with excellent maintenance of tissue integrity, facilitating studies of the co-localization of multiple biomarkers in tissues of interest.