Inositol phosphates control many central processes in eukaryotic cells including nutrient availability, growth, and motility. Kinetic resolution of a key modulator of their signaling functions, the turnover of the phosphate groups on the inositol ring, has been hampered by slow uptake, high dilution, and constraining growth conditions in radioactive pulse-labeling approaches. Here, we demonstrate a rapid (seconds to minutes) and nonradioactive labeling strategy of inositol polyphosphates through 18O-water in yeast, human cells, and amoeba, which can be applied in any media. In combination with capillary electrophoresis and mass spectrometry, 18O-water labeling simultaneously dissects the in vivo phosphate group dynamics of a broad spectrum of even rare inositol phosphates. The good temporal resolution allowed us to discover vigorous phosphate group exchanges in some inositol polyphosphates and pyrophosphates, whereas others remain remarkably inert. We propose a model in which the biosynthetic pathway of inositol polyphosphates and pyrophosphates is organized in distinct, kinetically separated pools. While transfer of compounds between those pools is slow, each pool undergoes rapid internal phosphate cycling. This might enable the pools to perform distinct signaling functions while being metabolically connected.

Single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS) is an emerging technique to investigate metal heterogeneity in individual cells. However, due to the absence of consistent calibration and suitable stabilization strategy for cells, accurate quantification of cellular heterogeneity and the content of metals remains a challenge. Herein, an accurate quantification method for the content and heterogeneous distribution of metals among individual microalgae cells was developed based on SC-ICP-MS using dual-calibration strategies and robust pretreatment methods. Gold nanoparticles (AuNPs) were used as calibration for measuring metal contents in single cells, but it would lead to a 13.6-63.1% underestimation of cell numbers due to inaccurate detection of cells' transport efficiency. To avoid this inaccuracy, we proposed an additional calibration strategy to measure cellular transport efficiency and cell numbers using endogenous Mg, enabling a more accurate assessment of cell heterogeneity. Then, an effective pretreatment method was optimized through fixation of cells with glutaraldehyde for 1 h to maintain the cellular stability and obtain accurate results, with satisfactory recoveries for cell number (98.4%) and Mg contents (91.7%), even after long-time storage. After optimization, the proposed method showed high sensitivity and repeatability in both cellular metal contents (Mg, Hg, Cd, and Co) and cell number, with detection limits (LODs) to be 0.14-0.53 fg/cell and 5.5 × 103 cells/mL, respectively. Finally, the proposed method was successfully used for detecting various metals and their heterogeneity in Synechocystis sp. PCC 6803 cells provided an accurate and robust tool for investigating the uptake and heterogeneous distribution of metals in microalgae.

Scanning ion conductance microscopy (SICM) is a noninvasive topographic mapping technique used in imaging live cells, unlike electron microscopy and certain applications of fluorescence microscopy, which can disrupt cell integrity. In this study, we used SICM to track the morphological changes of the same A549 cells to uncover the cell-to-cell heterogeneity in their response to the drug. We found that toyocamycin (TOY) induced rapid reorganization of the actin cytoskeleton in A549 cells, causing them to become circular, irregular, or ellipsoidal in shape. Mapping of the dynamic changes in morphology revealed membrane blebbing and a significant decrease in volume over time. Using high-throughput SICM, we mapped the morphology of multiple single cells treated with TOY, which revealed that A549 showed characteristics of apoptosis and necrosis. The drug treatment does not significantly change the average root-mean-square (RMS) roughness of the cells. However, the drug leads to an increase in membrane height, possibly indicating early apoptotic changes. Plotting the individual RMS roughness of the cells showed a cell with an increase in roughness and the presence of pores, which is also an indication of necrosis behavior. Our results demonstrate that SICM is an effective technique for revealing the evolution of heterogeneity in single cells in their responses to anticancer drugs over time.

Cell heterogeneity is a universal feature of life. Although biological processes affected by cell-to-cell variation are manifold, from developmental plasticity to tumour heterogeneity and differential drug responses, the sources of cell heterogeneity remain largely unclear1,2. Mutational and epigenetic signatures from cancer (epi)genomics are powerful for deducing processes that shaped cancer genome evolution3-5. However, retrospective analyses face difficulties in resolving how cellular heterogeneity emerges and is propagated to subsequent cell generations. Here, we used multigenerational single-cell tracking based on endogenously labelled proteins and custom-designed computational tools to elucidate how oncogenic perturbations induce sister cell asymmetry and phenotypic heterogeneity. Dual CRISPR-based genome editing enabled simultaneous tracking of DNA replication patterns and heritable endogenous DNA lesions. Cell lineage trees of up to four generations were tracked in asynchronously growing cells, and time-resolved lineage analyses were combined with end-point measurements of cell cycle and DNA damage markers through iterative staining. Besides revealing replication and repair dynamics, damage inheritance and emergence of sister cell heterogeneity across multiple cell generations, through combination with single-cell transcriptomics, we delineate how common oncogenic events trigger multiple routes towards polyploidization with distinct outcomes for genome integrity. Our study provides a framework to dissect phenotypic plasticity at the single-cell level and sheds light onto cellular processes that may resemble early events during cancer development.

Venoms and their associated glands and delivery structures have evolved numerous times among animals. Within these venom systems, the molecular, cellular, and morphological components interact and co-evolve to generate distinct, venom phenotypes that are increasingly recognized as models for studying adaptive evolution. However, toxins are often unevenly distributed across venom-producing tissues in patterns that are not necessarily adaptive but instead likely result from constraints associated with protein secretion.

We generate a high-quality draft genome of the Cape coral snake (Aspidelaps lubricus) and combine analyses of venom gland single-cell RNA-seq data with spatial venom gland in situ toxin distributions. Our results reveal that while different toxin families are produced by distinct populations of cells, toxin expression is fine-tuned by regulatory modules that result in further specialization of toxin production within each cell population. We also find that the evolution of regulatory elements closely mirrors the evolution of their associated toxin genes, resulting in spatial association of closely related and functionally similar toxins in the venom gland. While this compartmentalization is non-adaptive, the modularity of the underlying regulatory network likely facilitated the repeated evolution of defensive venom in spitting cobras.

Our results provide new insight into the variability of toxin regulation across snakes, reveal the molecular mechanisms underlying the heterogeneous toxin production in snake venom glands, and provide an example of how constraints can result in non-adaptive character states that appear to be adaptive, which may nevertheless facilitate evolutionary innovation and novelty.

Traditional approaches to studying cellular morphology rely on geometric metrics from stained images. However, staining processes can disrupt the cell's natural state and diminish accuracy due to photobleaching, while conventional analysis techniques, which categorize cells based on shape to discern pathophysiological conditions, often fail to capture the continuous and asynchronous nature of biological processes such as cell differentiation, immune responses, and cancer progression. In this work, we propose the use of quantitative phase imaging for morphological assessment due to its label-free nature. For analysis, we repurposed the genomic analysis toolbox to perform trajectory inference analysis purely based on morphology information. We applied the developed framework to study the progression of leukemia and breast cancer metastasis. Applying this framework to leukemia and breast cancer metastasis, we identified key shape changes linked to disease progression, highlighting the method's potential to enhance understanding of complex biological dynamics.

Mass spectrometry (MS)-based metabolomics has emerged as a transformative tool to unraveling components and their mechanisms in traditional Chinese medicine (TCM). The integration of advanced analytical platforms, such as LC-MS and GC-MS, coupled with metabolomics, has propelled the qualitative and quantitative characterization of TCM's complex components. This review comprehensively examines the applications of MS-based metabolomics in elucidating TCM efficacy, spanning chemical composition analysis, molecular target identification, mechanism-of-action studies, and syndrome differentiation. Recent innovations in functional metabolomics, spatial metabolomics, single-cell metabolomics, and metabolic flux analysis have further expanded TCM research horizons. Artificial intelligence (AI) and bioinformatics integration offer promising avenues for overcoming analytical bottlenecks, enhancing database standardization, and driving interdisciplinary breakthroughs. However, challenges remain, including the need for improved data processing standardization, database expansion, and understanding of metabolite-gene-protein interactions. By addressing these gaps, metabolomics can bridge traditional practices and modern biomedical research, fostering global acceptance of TCM. This review highlights the synergy of advanced MS techniques, computational tools, and TCM's holistic philosophy, presenting a forward-looking perspective on its clinical translation and internationalization.

Leveraging the capacity to precisely manipulate and analyze individual cells, single-cell technology has rapidly become an indispensable tool in the advancement of cell-based drug delivery systems and innovative cell therapies. This technology offers powerful means to address cellular heterogeneity and significantly enhance therapeutic efficacy. Recent breakthroughs in techniques such as single-cell electroporation, mechanical perforation, and encapsulation, particularly when integrated with microfluidics and bioelectronics, have led to remarkable improvements in drug delivery efficiency, reductions in cytotoxicity, and more precise targeting of therapeutic effects. Moreover, single-cell analyses, including advanced sequencing and high-resolution sensing, offer profound insights into complex disease mechanisms, the development of drug resistance, and the intricate processes of stem cell differentiation. This review summarizes the most significant applications of these single-cell technologies, highlighting their impact on the landscape of modern biomedicine. Furthermore, it provides a forward-looking perspective on future research directions aimed at further optimizing drug delivery strategies and enhancing therapeutic outcomes in the treatment of various diseases.

Ischemic stroke (IS) is a highly complex and heterogeneous disease involving multiple pathophysiological events. A better understanding of the pathophysiology of IS will enhance preventive, diagnostic and therapeutic strategies. Despite significant advances in modern medicine, the molecular mechanisms of IS are still largely unknown. The high-throughput omics approach opens new avenues for identifying IS biomarkers and elucidating disease pathogenesis mechanisms. Single-cell omics enables a more thorough and in-depth analysis of the cellular interactions and properties in IS. This will lead to a better understanding of the onset, treatment and prognosis of IS. In this paper, we first reviewed the disease signatures and mechanisms research of IS. Subsequently, the use of single-cell omics to comprehend the mechanisms of IS was discussed, along with some recent developments in the field. To further delineate the upstream pathogenic alterations and downstream molecular impacts of IS, we also discussed the current use of machine learning approaches to single-cell omics data analysis. Particularly, single-cell omics is being used to inform risk assessment, early patient diagnosis and treatment strategies, and their potential impact on precision medicine. Thus, we summarized the role of single-cell omics in precision medicine. Despite the relative youth of the field, the development of single-cell omics promises to provide a powerful tool for elucidating the pathogenesis of IS.

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

Advancements in technology over the years have propelled omics analysis to the level of single cell resolution. Following the breakthroughs in single cell transcriptomics and genomics, single cell proteomics has recently rapidly progressed, aided by highly sensitive mass spectrometry instrumentation. However, there is currently a paucity of studies and methodologies for single cell lipidomics, aside from imaging-based approaches. Profiling lipids at the single cell level holds promise for providing novel insights into the complex heterogeneity of cells in various human disorders. Further, by integrating single cell lipidomics with other single cell omics including proteomics, it becomes possible to achieve single cell multiomics, enabling the discovery of novel molecular signatures. We developed untargeted single cell lipidomics using nanoflow liquid chromatography-ion mobility spectrometry-mass spectrometry. To enhance lipid coverage at the single cell level, the method was conducted in both positive and negative ion modes. We identified an average of 161 lipids spanning phospholipids, sphingolipids, cholesteryl esters, and glycerides in positive ion mode from single cells of human cholangiocarcinoma cells based on a rule-based lipid annotation. Additionally, an average of 20 species of phospholipids was identified in the negative ion mode. These preliminary data demonstrate a new methodology to profile lipids at a single or low input of cells.

Single-cell studies of signal transduction have revealed complex temporal dynamics that determine downstream biological function. For example, the stimulus-specific dynamics of the transcription factor NFκB specify stimulus-specific gene expression programs, and loss of specificity leads to disease. Thus, it is intriguing to consider drugs that may restore signaling specificity in disease contexts, or reduce activity but maintain signaling specificity to avoid unwanted side effects. However, while steady-state dose-response relationships have been the focus of pharmacological studies, there are no established methods for quantifying drug impact on stimulus-response signaling dynamics. Here we evaluated how drug treatments affect the stimulus-specificity of NFκB activation dynamics and its ability to accurately code ligand identity and dose. Specifically, we simulated the dynamic NFκB trajectories in response to 15 stimuli representing various immune threats under treatment of 10 representative drugs across 20 dosage levels. To quantify the effects on coding capacity, we introduced a Stimulus Response Specificity (SRS) score and a stimulus confusion score. We constructed stimulus confusion maps by employing epsilon network clustering in the trajectory space and in various dimensionally reduced spaces: canonical polyadic decomposition (CPD), functional principal component analysis (fPCA), and NFκB signaling codons (i.e. established, informative dynamic features). Our results indicated that the SRS score and the stimulus confusion map based on signaling codons are best-suited to quantify stimulus-specific NFκB dynamics confusion under pharmacological perturbations. Using these tools we found that temporal coding capacity of the NFκB signaling network is generally robust to a variety of pharmacological perturbations, thereby enabling the targeting of stimulus-specific dynamics without causing broad side-effects.

More recently, single-cell encapsulation emerged as a promising field in biomedicine due to its potential applications, in cell analysis and therapy. Traditional techniques involve embedding cells in crosslinked polymers to create continuous microgels, suitable mainly for adherent cells, or encapsulating them in droplets for only short-term analysis, due to their instability. In this study, we developed a method for encapsulating single cells in liquid-core microcapsules to address these limitations. The liquid encapsulation system is generated in an all aqueous environment through polymeric electrostatic interactions. Additionally, we design an innovative and low cost sorting system utilizing magnetic nanoparticles (MNPs) to efficiently select single-cell encapsulated units for further analysis and applications. This system is tested with both suspension and adherent cell types, demonstrating cytocompatibility and no abnormal effects on cell behavior. The MNP-based sorting achieved nearly 80% purity of the single-cell population. Overall, this technology provides a highly efficient method for single-cell applications, such as cell screening, by enabling precise short to medium-term analysis, real time monitoring, and high resolution imaging of cellular behavior. Furthermore, the semipermeable membrane unlocks new potential for advancing cell therapy by offering protection for encapsulated cells while ensuring the efficient diffusion of therapeutic factors, paving the way for innovative therapeutic strategies.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular variability by capturing gene expression profiles of individual cells. The importance of cell-to-cell variability in determining and shaping cell function has been widely appreciated. Nevertheless, differential expression (DE) analysis remains a cornerstone method in analytical practice. Current computational analyses overlook the rich information encoded by variability within the single-cell gene expression data by focusing exclusively on mean expression. To offer a deeper understanding of cellular systems, there is a need for approaches to assess data variability rather than just the mean. Here we present spline-DV, a statistical framework for differential variability (DV) analysis using scRNA-seq data. The spline-DV method identifies genes exhibiting significantly increased or decreased expression variability among cells derived from two experimental conditions. Case studies show that DV genes identified using spline-DV are representative and functionally relevant to tested cellular conditions, including obesity, fibrosis, and cancer.

The cell cycle is crucial for maintaining normal cellular functions and preventing replication errors. FOXM1, a key transcription factor, plays a pivotal role in regulating cell cycle progression and is implicated in various physiological and pathological processes, including cancers like liver, prostate, breast, lung and colon cancer. Despite previous research, our understanding of FOXM1 dynamics under different cell cycle perturbations and its connection to heterogeneous cell fate decisions remains limited. In this study, we investigated FOXM1 behaviour in individual cells exposed to various perturbagens. We found that different drugs induce diverse responses due to heterogeneous FOXM1 dynamics at the single-cell level. Single-cell analysis identified six distinct cellular phenotypes: on-time cytokinesis, cytokinesis delay, cell cycle delay, G1 arrest, G2 arrest and cell death, observed across different drug types and doses. Specifically, treatments with PLK1, CDK1, CDK1/2 and Aurora kinase inhibitors revealed varied FOXM1 dynamics leading to heterogeneous cellular outcomes. Our findings affirm that the dynamics of FOXM1 are essential in shaping cellular outcomes, influencing the signals that dictate responses to various stimuli. Our results gave insights into how FOXM1 dynamics contribute to cell cycle fate decisions, especially under different cell cycle perturbations.

Cell Painting is a sophisticated high-content imaging technique that has been predominantly applied to mammalian cells. Recent advancements have extended its applicability to the first insect cell line, the ovarian cell line Sf9, revealing significant insights into similarities and differences in cellular responses between different taxonomic groups. This study explores the utility of Cell Painting in Helicoverpa zea gut-derived cells, specifically the RP-HzGUT-AW1 cell line, to assess the specifics of insect epithelial cells in response to chemical treatments. Upon adaptation of the analysis pipeline to accommodate their unique morphology and characteristics, our investigations revealed distinct responses of RP-HzGUT-AW1 cells compared to the ovarian insect cell line Sf9. Variations were obtained not only in the dose-response behavior to treatments but also in the overall detectability of specific modes of action. Specifically, processes that relate to osmoregulation and the formation of epithelial structures showed the most significant and distinct responses. This suggests that the specific morphological and physiological attributes of these gut-derived insect cells contribute to unique phenotypic profiles, which enables in-depth interpretation of drug efficacy and safety in these models.

Amino acids play multiple critical roles in the regulation of various metabolic pathways and physiological processes in living organisms. Mass spectrometry (MS) has become the most pioneering platform for amino acid analysis. However, the simultaneous and sensitive determination of amino acids is still challenging because of their structural similarity and broad ranges of concentrations. To this end, a pair of isotope labeling reagents, d0/d3-2-((diazomethyl)phenyl)(9-methyl-1,3,4,9-tetrahydro-2H-pyrido[3,4-b]indol-2-yl) methanone (DMPI/d3-DMPI), were applied to label amino acid metabolites. The diazo groups on the pair of isotopomers (DMPI/d3-DMPI) can specifically react with the carboxyl groups on the amino acids. The results showed that the retention on reversed-phase column were enhanced and the detection sensitivities of 19 amino acids were increased benefiting from DMPI labeling strategy that transfers the hydrophobic indole heterocycle group of DMPI to the hydrophilic compounds of amino acids. The obtained limits of detection (LODs) of amino acids were in the range of 0.002-0.082 fmol. With this established method, we achieved the sensitive detection of amino acids in a single HUVE cell. Meanwhile, we found that the contents of amino acids in the serum of premature neonates were higher compared to normal neonates. Overall, this developed method provides great support of detection tool for the clinical metabolomic study of amino acids and the investigation of dynamic changes of amino acid metabolism in single cells.

Astrocytes, integral components of the central nervous system, are increasingly recognized for their multifaceted roles beyond support cells. Despite their acknowledged importance, understanding the intricacies of astrocyte morphological dynamics remains limited. Our study marks the first exploration of astrocytes using optical diffraction tomography (ODT), establishing a label-free, quantitative method to observe morphological changes in astrocytes over a 7-day in-vitro period. ODT offers quantitative insights into cell volume, dry mass, and area through label-free, real-time measurements-capabilities that are challenging to achieve with conventional imaging techniques. Through comprehensive analysis of 3D refractive index maps and shape characterization techniques, we capture the developmental trajectory and dynamic morphological transformations of astrocytes. Specifically, our observations reveal increased area and a transition to larger, flattened shapes, with alterations in cell volume and density, indicating shifts in cellular composition. By employing unsupervised clustering and pseudotime trajectory analysis, we introduce a novel morphological trajectory inference for neural cells, tracking the morphological evolution of astrocytes from elongated to evenly spread shapes. This analysis marks the first use of trajectory inference based solely on morphology for neural cell types, laying a foundation for future studies employing ODT to examine astrocyte dynamics and neural cell interactions across diverse substrates.

An ideal tool for the study of cellular biology would enable the measure of molecular activity nondestructively within living cells. Single-molecule localization microscopy (SMLM) techniques, such as single-molecule tracking (SMT), enable in situ measurements in cells but have historically been limited by a necessary tradeoff between spatiotemporal resolution and throughput. Here we address these limitations using oblique line scan (OLS), a robust single-objective light-sheet-based illumination and detection modality that achieves nanoscale spatial resolution and sub-millisecond temporal resolution across a large field of view. We show that OLS can be used to capture protein motion up to 14 μm2 s-1 in living cells. We further extend the utility of OLS with in-solution SMT for single-molecule measurement of ligand-protein interactions and disruption of protein-protein interactions using purified proteins. We illustrate the versatility of OLS by showcasing two-color SMT, STORM and single-molecule fluorescence recovery after photobleaching. OLS paves the way for robust, high-throughput, single-molecule investigations of protein function required for basic research, drug screening and systems biology studies.

Skeletal muscle is a heterogenous tissue comprised primarily of myofibers, commonly classified into three fiber types in humans: one "slow" (type 1) and two "fast" (type 2A and type 2X). However, heterogeneity between and within traditional fiber types remains underexplored. We applied transcriptomic and proteomic workflows to 1050 and 1038 single myofibers from human vastus lateralis, respectively. Proteomics was conducted in males, while transcriptomics included ten males and two females. We identify metabolic, ribosomal, and cell junction proteins, in addition to myosin heavy chain isoforms, as sources of multi-dimensional variation between myofibers. Furthermore, whilst slow and fast fiber clusters are identified, our data suggests that type 2X fibers are not phenotypically distinct to other fast fibers. Moreover, myosin heavy chain-based classifications do not adequately describe the phenotype of myofibers in nemaline myopathy. Overall, our data indicates that myofiber heterogeneity is multi-dimensional with sources of variation beyond myosin heavy chain isoforms.

Recent advancements in methodologies and technologies have enabled the simultaneous measurement of multiple omics data, which provides a comprehensive understanding of cellular heterogeneity. However, existing methods have limitations in accurately identifying cell types while maintaining model interpretability, especially in the presence of noise.

We propose a novel method called scMFG, which leverages feature grouping and group integration techniques for the integration of single-cell multi-omics data. By organizing features with similar characteristics within each omics layer through feature grouping. Furthermore, scMFG ensures a consistent feature grouping approach across different omics layers, promoting comparability of diverse data types. Additionally, scMFG incorporates a matrix factorization-based approach to enable the integrated results remain interpretable.

We comprehensively evaluated scMFG's performance on four complex real-world datasets generated using diverse sequencing technologies, highlighting its robustness in accurately identifying cell types. Notably, scMFG exhibited superior performance in deciphering cellular heterogeneity at a finer resolution compared to existing methods when applied to simulated datasets. Furthermore, our method proved highly effective in identifying rare cell types, showcasing its robust performance and suitability for detecting low-abundance cellular populations. The interpretability of scMFG was successfully validated through its specific association of outputs with specific cell types or states observed in the neonatal mouse cerebral cortices dataset. Moreover, we demonstrated that scMFG is capable of identifying cell developmental trajectories even in datasets with batch effects.

Our work presents a robust framework for the analysis of single-cell multi-omics data, advancing our understanding of cellular heterogeneity in a comprehensive and interpretable manner.

Spatially resolved transcriptomics (SRT) has transformed tissue biology by linking gene expression profiles with spatial information. However, sequencing-based SRT methods aggregate signals from multiple cell types within capture locations ("spots"), masking cell-type-specific gene expression patterns. Traditional cell-type deconvolution methods estimate cell compositions within spots but fail to resolve cell-type-specific gene expression, limiting their ability to uncover critical biological processes such as cellular interactions and microenvironmental dynamics. Here, we present STged (spatial transcriptomic gene expression deconvolution), a novel computational framework that goes beyond traditional deconvolution by reconstructing cell-type-specific gene expression profiles from mixed spots. STged integrates graph-based spatial correlations and reference-derived gene signatures using a non-negative least-squares regression framework, achieving precise and biologically meaningful deconvolution. Comprehensive simulations show that STged consistently outperforms existing methods in accuracy and robustness. Applications to human pancreatic ductal adenocarcinoma and human squamous cell carcinoma datasets reveal its capacity to identify microenvironment-specific highly variable genes, reconstruct spatial cell-cell communication networks, and resolve tissue architecture at near-single-cell resolution. In mouse kidney tissues, STged uncovers dynamic spatial gene expression patterns and distinct gene programs, advancing our understanding of tissue heterogeneity and cellular dynamics.

Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell's state, introduced in 2013 to optimize and standardize image-based profiling. Cell Painting's ability to capture cellular responses to various perturbations has expanded owing to improvements in the protocol, adaptations for different perturbations, and enhanced methodologies for feature extraction, quality control, and batch-effect correction. Cell Painting is a versatile tool that has been used in various applications, alone or with other -omics data, to decipher the mechanism of action of a compound, its toxicity profile, and other biological effects. Future advances will likely involve computational and experimental techniques, new publicly available datasets, and integration with other high-content data types.

Single-cell analysis is critical for advancing personalized medicine, as it reveals cell population heterogeneity that influences disease outcomes. We present a microwell-assembled aluminum substrate platform that enhances single-cell Raman spectroscopy in liquid suspension by isolating individual cells and preventing stacking and movement, which significantly improves signal stability and the signal-to-noise ratio (SNR). We applied this novel platform to analyze PC-9 lung cancer cells and BEAS-2B normal bronchial epithelial cells, identifying distinct biochemical differences. Notably, cancer cells showed higher levels of adenine, cytochromes, DNA/RNA, and unsaturated lipids, along with an increased unsaturation ratio and protein content. These findings were further validated using machine learning models. An eXtreme Gradient Boosting (XGBoost) model achieved perfect classification accuracy of 100 %, underscoring the robustness of the spectral features identified by our platform. Our platform not only enhances single-cell Raman signal detection but also holds promise for biomedical applications, including early cancer detection, treatment monitoring, and drug development. The high-throughput capacity of this platform featuring over 120,000 wells, along with its compatibility with techniques such as Raman-activated cell sorting (RACS) further extends its potential for clinical diagnostics and personalized medicine.

Single-cell technology (SCT), which enables the examination of the fundamental units comprising biological organs, tissues, and cells, has emerged as a powerful tool, particularly in the field of biology, with a profound impact on stem cell research. This innovative technology opens new pathways for acquiring cell-specific data and gaining insights into the molecular pathways governing organ function and biology. SCT is not only frequently used to explore rare and diverse cell types, including stem cells, but it also unveils the intricacies of cellular diversity and dynamics. This perspective, crucial for advancing stem cell research, facilitates non-invasive analyses of molecular dynamics and cellular functions over time. Despite numerous investigations into potential stem cell therapies for genetic disorders, degenerative conditions, and severe injuries, the number of approved stem cell-based treatments remains limited. This limitation is attributed to the various heterogeneities present among stem cell sources, hindering their widespread clinical utilization. Furthermore, stem cell research is intimately connected with cutting-edge technologies, such as microfluidic organoids, CRISPR technology, and cell/tissue engineering. Each strategy developed to overcome the constraints of stem cell research has the potential to significantly impact advanced stem cell therapies. Drawing on the advantages and progress achieved through SCT-based approaches, this study aims to provide an overview of the advancements and concepts associated with the utilization of SCT in stem cell research and its related fields.

Epithelial to mesenchymal transition (EMT) is highly plastic with a programme where cells lose adhesion and become more motile. EMT heterogeneity is one of the factors for disease progression and chemoresistance in cancer. Omics characterisations are costly and challenging to use. We developed single cell phenomics with easy to use wide-field fluorescence microscopy. We analyse over 70,000 cells and combined 53 features. Our simplistic pipeline allows efficient tracking of EMT plasticity, with a single statistical metric. We discriminate four high EMT plasticity cancer cell lines along the EMT spectrum. We test two cytokines, inducing EMT in all cell lines, alone or in combination. The single cell EMT metrics demonstrate the additive effect of cytokines combination on EMT independently of cell line EMT spectrum. The effects of cytokines are also observed at the front of migration during wound healing assay. Single cell phenomics is uniquely suited to characterise the cellular heterogeneity in response to complex microenvironment and show potential for drug testing assays.

CDK4/6 inhibitors such as palbociclib block cell cycle progression and improve outcomes for many ER+/HER2- breast cancer patients. Unfortunately, many patients are initially resistant to the drug or develop resistance over time in part due to heterogeneity among individual tumor cells. To better understand these mechanisms of resistance, we used multiplex, single-cell imaging to profile cell cycle proteins in ER+ breast tumor cells under increasing palbociclib concentrations. We then applied spherical principal component analysis (SPCA), a dimensionality reduction method that leverages the inherently cyclical nature of the high-dimensional imaging data, to look for changes in cell cycle behavior in resistant cells. SPCA characterizes data as a hypersphere and provides a framework for visualizing and quantifying differences in cell cycles across treatment-induced perturbations. The hypersphere representations revealed shifts in the mean cell state and population heterogeneity. SPCA validated expected trends of CDK4/6 inhibitor response such as decreased expression of proliferation markers (Ki67, pRB), but also revealed potential mechanisms of resistance including increased expression of cyclin D1 and CDK2. Understanding the molecular mechanisms that allow treated tumor cells to evade arrest is critical for identifying targets of future therapies. Ultimately, we seek to further SPCA as a tool of precision medicine, targeting treatments by individual tumors, and extending this computational framework to interpret other cyclical biological processes represented by high-dimensional data.

Enzyme activity plays a key role in cell heterogeneity. Its spatially quantitative imaging in a living cell not only directly displays but also helps people to understand cell heterogeneity. Current methods are hard to achieve due to the short intracellular retention or lack of internal reference of the imaging probes. Herein, we rationally designed a self-referenced Raman probe Val-Cit-Cys(StBu)-Pra-Gly-CBT (Yne-CBT) which takes an intracellular cathepsin B (CTSB)-initiated CBT-Cys click reaction to yield a long-retained cyclic dimer in cell. In the meantime, Raman signal changes of its two chemical bonds (C≡C and C≡N) after the reaction are used for self-referencing and quantitative Raman imaging of CTSB activity. In vitro experiments demonstrated that, with shell-isolated nanoparticle-enhanced Raman spectroscopy technique, 20 μM Yne-CBT was able to quantitatively detect CTSB activity with a limit of detection of 61.4 U L-1. Under a homemade microfluidic channel, Yne-CBT was successfully applied for spatially quantitative imaging CTSB activity in a living cell. Our strategy provides people with a facile method to directly and quantitatively display cell heterogeneity.

Raman-activated cell sorting isolates single cells in a nondestructive and label-free manner, but its throughput is limited by small spontaneous Raman scattering cross section. Coherent Raman scattering integrated with microfluidics enables high-throughput cell analysis, but faces challenges with small cells (<3 μm) and tissue sections. Here, we report stimulated Raman-activated cell ejection (S-RACE) that enables high-throughput single-cell sorting by integrating stimulated Raman imaging, in situ image decomposition, and laser-induced cell ejection. S-RACE allows ejection of live bacteria or fungi guided by their Raman signatures. Furthermore, S-RACE successfully sorted lipid-rich Rhodotorula glutinis cells from a cell mixture with a throughput of ~13 cells per second, and the sorting results were confirmed by downstream quantitative polymerase chain reaction. Beyond single cells, S-RACE shows high compatibility with tissue sections. Incorporating a closed-loop feedback control circuit further enables real-time SRS imaging-identification-ejection. In summary, S-RACE opens exciting opportunities for diverse single-cell sorting applications.

Electrochemiluminescence (ECL) is one of the most powerful techniques that meet the needs of analysis and detection in a variety of scenarios, because of its highly analytical sensitivity and excellent spatiotemporal controllability. ECL combined with microscopy (ECLM) offers a promising approach for quantifying and mapping a wide range of analytes. To date, ECLM has been widely used to image biological entities and processes, such as cells, subcellular structures, proteins and membrane transport properties. In this review, we first introduced the mechanisms of several classic ECL systems, then highlighted the progress of visual biosensing and bioimaging by ECLM in the last decade. Finally, the characteristics of ECLM were summarized, as well as some of the current challenges. The future research interests and potential directions for the application of ECLM were also outlooked.

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

Ischemic stroke is a significant threat to human health. Currently, there is a lack of effective treatments for stroke, and progress in new neuron-centered drug target development is relatively slow. On the other hand, studies have demonstrated that brain microvascular endothelial cells (BMECs) are crucial components of the neurovascular unit and play pivotal roles in ischemic stroke progression. To better understand the complex multifaceted roles of BMECs in the regulation of ischemic stroke pathophysiology and facilitate BMEC-based drug target discovery, we utilized a transcriptomics-informed systems biology modeling approach and constructed a mechanism-based computational multipathway model to systematically investigate BMEC function and its modulatory potential. Extensive multilevel data regarding complex BMEC pathway signal transduction and biomarker expression under various pathophysiological conditions were used for quantitative model calibration and validation, and we generated dynamic BMEC phenotype maps in response to various stroke-related stimuli to identify potential determinants of BMEC fate under stress conditions. Through high-throughput model sensitivity analyses and virtual target perturbations in model-based single cells, our model predicted that targeting succinate could effectively reverse the detrimental cell phenotype of BMECs under oxygen and glucose deprivation/reoxygenation, a condition that mimics stroke pathogenesis, and we experimentally validated the utility of this new target in terms of regulating inflammatory factor production, free radical generation and tight junction protection in vitro and in vivo. Our work is the first that complementarily couples transcriptomic analysis with mechanistic systems-level pathway modeling in the study of BMEC function and endothelium-based therapeutic targets in ischemic stroke.

Transcriptome analysis in single cells, enabled by single-cell RNA sequencing, has become a prevalent approach in biomedical research, ranging from investigations of gene regulation to the characterization of tissue organization. Over the past decade, advances in single-cell RNA sequencing technology, including its underlying chemistry, have significantly enhanced its performance, marking notable improvements in methodology. A recent development in the field, which integrates RNA metabolic labeling with single-cell RNA sequencing, has enabled the profiling of temporal transcriptomes in individual cells, offering new insights into dynamic biological processes involving RNA kinetics and cell fate determination. In this review, we explore the chemical principles and design improvements that have enhanced single-molecule capture efficiency, improved RNA quantification accuracy, and increased cellular throughput in single-cell transcriptome analysis. We also illustrate the concept of RNA metabolic labeling for detecting newly synthesized transcripts and summarize recent advancements that enable single-cell temporal transcriptome analysis. Additionally, we examine data analysis strategies for the precise quantification of newly synthesized transcripts and highlight key applications of transcriptome and temporal transcriptome analyses in single cells.

There is an increasing need for simple-to-use, noninvasive, and rapid tools to identify and separate various cell types or subtypes at the single-cell level with sufficient throughput. Often, the selection of cells based on their direct biological activity would be advantageous. These steps are critical in immune therapy, regenerative medicine, cancer diagnostics, and effective treatment. Today, live cell selection procedures incorporate some kind of biomolecular labeling or other invasive measures, which may impact cellular functionality or cause damage to the cells. In this study, we first introduce a highly accurate single-cell segmentation methodology by combining the high spatial resolution of a phase-contrast microscope with the adhesion kinetic recording capability of a resonant waveguide grating (RWG) biosensor. We present a classification workflow that incorporates the semiautomatic separation and classification of single cells from the measurement data captured by an RWG-based biosensor for adhesion kinetics data and a phase-contrast microscope for highly accurate spatial resolution. The methodology was tested with one healthy and six cancer cell types recorded with two functionalized coatings. The data set contains over 5000 single-cell samples for each surface and over 12,000 samples in total. We compare and evaluate the classification using these two types of surfaces (fibronectin and noncoated) with different segmentation strategies and measurement timespans applied to our classifiers. The overall classification performance reached nearly 95% with the best models showing that our proof-of-concept methodology could be adapted for real-life automatic diagnostics use cases. The label-free measurement technique has no impact on cellular functionality, directly measures cellular activity, and can be easily tuned to a specific application by varying the sensor coating. These features make it suitable for applications requiring further processing of selected cells.

High-throughput single-cell heterogeneity imaging and analysis is essential for understanding complex biological systems and for advancing personalized precision disease diagnosis and treatment. Here, we present a miniaturized lensless chemiluminescence chip for high-throughput single-cell functional imaging with subcellular resolution. With the sensitive chemiluminescence sensing and wide field of view of contact lensless imaging, we demonstrated the chemiluminescent imaging of over 1000 single cells, and their membrane glycoprotein and the high-throughput single-cell heterogeneity of membrane protein imaging were examined for precision analysis. Furthermore, the functional adhesion and heterogeneity of single live cells were imaged and explored. This miniaturized lensless on-chip CL-CMOS imaging platform enables high-throughput single-cell imaging and analysis with high sensitivity and subcellular resolution, providing new techniques for the cellular study of biological heterogeneity and has potential application in precision disease diagnosis and treatment at the point-of-care settings.

The long noncoding RNA (lncRNA) HOTAIR has been shown to act as an oncogene in a variety of cancers, including hepatocellular carcinoma (HCC). MicroRNA-122 (miR-122) is a key liver-specific miRNA that is frequently inhibited in HCC and is associated with poor prognosis. However, a potential relationship between HOTAIR and miR-122 in individual hepatocytes has not been explored. To this end, we propose here an intracellular catalytic hairpin self-assembly-CRISPR/Cas12a tandem multiplexed signal amplification strategy for the simultaneous quantification of HOTAIR and miRNA-122 in a single hepatocyte. We applied this method to analyze both normal HL-7702 liver cells and HepG2 HCC cells, and found that HL-7702 cells contained large amounts of miRNA-122, while the content of miRNA-122 in HepG2 cells was low. However, the level of HOTAIR in HepG2 cells was much higher than that in HL-7702 cells, confirming the overexpression of HOTAIR in HCC cells. We achieved the simultaneous absolute quantification of HOTAIR and miRNA-122 in single cells, providing an important method to study the relationships between these two RNA molecules in individual cells.

With the aim of efficiently sorting rare circulating tumor cells (CTCs) from blood and minimizing damage to CTCs during isolation, we constructed an inertia-assisted single-cell focusing generator (I-SCF) and a water droplet deterministic lateral displacement cell sorting (D-DLD) microfluidic system (IDIC) based on different sizes, the device is initially sorted by a continuous fluid swing and Dean flow-assisted helical micromixers, then flows through a droplet shaped DLD region, enabling single-cell focused sequencing and precise separation, improving cell separation efficiency (>95%) and purity, while ensuring a high single cells survival rate of more than 98.6%. Subsequently, breast cancer cell lines were run through our chip, and then the downstream epithelial-mesenchymal transition (EMT) process induced by TGF-β was detected, and the levels of three proteins, EpCAM, PD-L1, and N-cadherin, were analyzed to establish the relationship between PD-L1 and the EMT process. Compared with other analytical techniques such as the filtration method, the enrichment method and immunoaffinity capture methods, this method not only ensures the separation efficiency and purity, but also ensures the cell activity, and avoids missing the different results caused by the heterogeneity of CTCs due to the isolation of high purity (84.01%). The device has a high throughput processing capacity (5 mL of diluted whole blood/∼2.8 h). By using the chip, we can more easily and conveniently predict tumor stage and carry out cancer prevention and treatment in advance, and it is expected to be further developed into a clinical liquid biopsy technology in the future.

Image-based cell profiling is a powerful tool that compares perturbed cell populations by measuring thousands of single-cell features and summarizing them into profiles. Typically a sample is represented by averaging across cells, but this fails to capture the heterogeneity within cell populations. We introduce CytoSummaryNet: a Deep Sets-based approach that improves mechanism of action prediction by 30-68% in mean average precision compared to average profiling on a public dataset. CytoSummaryNet uses self-supervised contrastive learning in a multiple-instance learning framework, providing an easier-to-apply method for aggregating single-cell feature data than previously published strategies. Interpretability analysis suggests that the model achieves this improvement by downweighting small mitotic cells or those with debris and prioritizing large uncrowded cells. The approach requires only perturbation labels for training, which are readily available in all cell profiling datasets. CytoSummaryNet offers a straightforward post-processing step for single-cell profiles that can significantly boost retrieval performance on image-based profiling datasets.

Understanding the dynamic states and transitions of heterogeneous cell populations is crucial for addressing fundamental biological questions. High-content imaging provides rich datasets, but it remains increasingly difficult to integrate and annotate high-dimensional and time-resolved datasets to profile heterogeneous cell population dynamics in different microenvironments. Using hepatic stellate cells (HSCs) LX-2 as model, we proposed a novel analytical strategy for image-based integration and annotation to profile dynamics of heterogeneous cell populations in 2D/3D microenvironments. High-dimensional features were extracted from extensive image datasets, and cellular states were identified based on feature profiles. Time-series clustering revealed distinct temporal patterns of cell shape and actin cytoskeleton reorganization. We found LX-2 showed more complex membrane dynamics and contractile systems with an M-shaped actin compactness trend in 3D culture, while they displayed rapid spreading in early 2D culture. This image-based integration and annotation strategy enhances our understanding of HSCs heterogeneity and dynamics in complex extracellular microenvironments.

Uncovering fine-grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological processes. However, this endeavor poses significant technical challenges for unsupervised machine learning, requiring the extraction of features that not only faithfully preserve this heterogeneity but also effectively discriminate between established biological states, all while remaining interpretable. To tackle these challenges, a self-training deep learning framework designed for fine-grained and interpretable phenotyping is presented. This framework incorporates an unsupervised teacher model with interpretable features to facilitate feature learning in a student deep neural network (DNN). Significantly, an autoencoder-based regularizer is designed to encourage the student DNN to maximize the heterogeneity associated with molecular perturbations. This method enables the acquisition of features with enhanced discriminatory power, while simultaneously preserving the heterogeneity associated with molecular perturbations. This study successfully delineated fine-grained phenotypes within the heterogeneous protrusion dynamics of migrating epithelial cells, revealing specific responses to pharmacological perturbations. Remarkably, this framework adeptly captured a concise set of highly interpretable features uniquely linked to these fine-grained phenotypes, each corresponding to specific temporal intervals crucial for their manifestation. This unique capability establishes it as a valuable tool for investigating diverse cellular dynamics and their heterogeneity.