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Results for “single-cell and spatial technologies”

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PubMed2026

Deciphering CD8+ T cell exhaustion in human cancers through single-cell and spatial transcriptomics.

Exhausted CD8+ T cells (Tex) within the tumor microenvironment (TME) represents a critical barrier limiting anti-tumor immune responses. Tex cells are characterized by upregulated inhibitory immune checkpoint receptors, reduced cytotoxicity, and functional heterogeneity. Their genomic features and regulatory networks remain poorly defined, and only a minority of patients respond to immune checkpoint blockade (ICB) therapy. Single-cell RNA sequencing (scRNA-seq), through high-resolution transcriptomic profiling, has revealed diverse Tex subpopulations, identified subpopulation-specific marker genes and regulatory pathways. Spatial transcriptomics has further mapped the spatial distribution of Tex and their interaction networks with immune cells, tumor cells, and stromal cells, elucidating the impact of spatial heterogeneity on Tex functionality. Current studies indicate that the exhausted state of Tex is dynamic and modifiable, with functional differences among subpopulations closely associated with tumor progression and therapeutic response. However, the genomic characteristics, epigenetic regulation, and spatial interaction mechanisms of Tex require further exploration. This review summarizes recent advances in high-resolution omics technologies for precisely dissecting Tex heterogeneity, functional features, and interactions with other cells. It emphasizes the central value of optimizing Tex-targeted tumor immunotherapy strategies, providing theoretical foundations and directional guidance for developing more effective anti-tumor immunotherapies.

Humans↗

PubMed2026

High-Content CRISPR Screening: Methods and Applications.

Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 screening has become a central technology in functional genomics, enabling genome-scale interrogation via pooled perturbations. Early CRISPR screens employed survival or simple phenotypic readouts to identify essential genes and drug resistance mechanisms. However, as biological questions have shifted toward understanding regulatory networks, cellular heterogeneity, and context-dependent gene functions, there has been increasing demand for screening strategies capable of capturing complex cellular phenotypes beyond cell fitness. Recent advances in single-cell sequencing, high-content imaging, and spatial transcriptomics have expanded the resolution of CRISPR screening by enabling multidimensional phenotypic characterization following genetic perturbation. By integrating pooled perturbations with diverse readouts, these approaches systematically map targeted gene edits to transcriptional states, cellular phenotypes, and microenvironmental contexts. Meanwhile, innovations in library design, delivery, and computational pipelines have further improved the robustness and interpretability of high-content screening platforms. This review synthesizes the methodological evolution of CRISPR screening, emphasizing advances in perturbation strategies, delivery systems, and multimodal readouts. Representative applications spanning oncology, immunotherapy, developmental biology, neurobiology, and infectious diseases are delineated to demonstrate refined gene network annotations. Additionally, existing technical bottlenecks, such as scalability, cost constraints, and in vivo limitations, are critically assessed. Finally, future directions are proposed to facilitate the development of precise medicine.

CRISPR screening↗

PubMed2026

Unraveling lung cancer complexity: Spatial omics in tumor microenvironment characterization and precision medicine.

Heterogeneous tumor microenvironment (TME) in lung cancer plays a crucial role in disease progression and resistance to therapy. Despite advances in single-cell and bulk omics profiling, these methods often overlook spatial context, which is vital for understanding cell-cell interactions and regional heterogeneity. In recent years, spatial omics technologies-including spatial genomics, transcriptomics, proteomics, and metabolomics-have revolutionized the ability to map molecular landscapes while maintaining tissue architecture. These advancements have become essential components of next-generation lung cancer management. By providing unprecedented resolution in characterizing the lung cancer TME, spatial omics could reveal prognostic and predictive biomarkers and identify new therapeutic vulnerabilities. This review will provide the first critical evaluation of spatial multi-omics approaches for lung cancer prognosis. It will also assess various integration strategies for multi-omics data to explore the clinical translational potential of these tools for therapy selection and patient stratification. Therefore, a deeper understanding of spatial omics technologies and their application in lung cancer can significantly improve precision diagnostics and therapeutic decision-making.

Lung cancer↗

PubMed2005

Single-cell genomics.

The evolution of higher plants depended on the ability of cells to express hereditary information in many different ways and led to the development of specialized cell types, reflecting an elaborate system of control over gene expression in the individual component cells of various tissues. Bulk tissue sampling results in the loss of spatial resolution, and recent efforts have been directed toward improving access to specialized cell types in plants. Access to the contents of individual cells followed by analyses using post-genomic technologies promise to revolutionize our understanding of the differentiation of specialized cell types, and to enable downstream applications aimed at harnessing their unique biochemical properties.

Genetic Techniques↗

PubMed1998

Laser-induced native fluorescence (LINF) imaging of serotonin depletion in depolarized neurons.

Since certain neurotransmitters exhibit native fluorescence we can monitor this property to disclose intracellular changes that result from neurotransmitter release. Isolated Retzius neurons of the leech are known to release serotonin (5-HT) during depolarization. Using intensified CCD technology coupled with UV laser (305 nm) excitation we observed depolarization and calcium-dependent reductions in native fluorescence in the axon, as well as in the cortex of the cell body. When taken together with data obtained from single-cell capillary electrophoresis, we demonstrate that this laser-induced native fluorescence can be reliably used to study spatial and temporal changes in intracellular transmitter content that accompany calcium-dependent secretion.

Animals↗

PubMed2000

Single-cell MALDI: a new tool for direct peptide profiling.

Matrix-assisted laser desorption-ionization (MALDI) mass spectrometry (MS) is a rapid and sensitive analytical approach that is well suited for obtaining molecular weights of peptides and proteins from complex samples. MALDI-MS can profile the peptides and proteins from single-cell and small tissue samples without the need for extensive sample preparation, except for the cell isolation and matrix application. Strategies for peptide identification and characterization of post-translational modifications are presented. Furthermore, several recent enhancements in MALDI-MS technology, including in situ peptide sequencing as well as the direct spatial mapping of peptides in cells and tissues are discussed.

Animals↗

PubMed2002

Optical imaging fiber-based single live cell arrays: a high-density cell assay platform.

A high-density, ordered array containing thousands of microwells is fabricated on an optical imaging fiber. Each individually addressable microwell is used to accommodate a single living cell. A charged coupled device (CCD) detector is employed to monitor and spatially resolve the fluorescence signals obtained from each individual cell, allowing simultaneous monitoring of cellular responses of all the cells in the array using reporter genes (lacZ, EGFP, ECFP, DsRed) or fluorescent indicators. Yeast and bacteria cell arrays were fabricated and used to perform multiplexed cell assays with resolution at the single-cell level. Monitoring gene expression in single yeast cells carrying a two-hybrid system was used to detect in vivo protein-protein interactions. The single-cell array technology provides a new platform for monitoring the unique multiple responses of large populations of individual cells from different strains or cell lines. The rich data acquired by the cell array has the potential to be employed as a new tool for cell biology research as well as to improve cell-based high-throughput screening (HTS) applications, such as the validation of new disease-associated cellular targets and the early-stage evaluation of potential drug candidates.

Escherichia coli↗

PubMed2005

A scalable addressable positive-dielectrophoretic cell-sorting array.

We present the first known implementation of a passive, scalable architecture for trapping, imaging, and sorting individual microparticles, including cells, using a positive dielectrophoretic (p-DEP) trapping array. Our array-based technology enables "active coverslips" where, when scaled, many individually held cells can be sorted based upon imaged spatial or temporally variant characteristics. Our design incorporates a unique "ring-dot" p-DEP trap geometry organized in a row/column array format. This trap design, implemented in a two-level metal process, provides strong and highly spatially localized holding fields enabling single-cell capture for all traps in the array. We release individual trapped microparticles during sorting using a passive transistor-independent approach where we electrically ground the row and column electrodes associated with specific traps in the array. The demand for chip-to-world electrical connections in our arrays scales proportionally with the square root of the number of traps in a given array, delivering a substantial improvement over prior designs. We demonstrate capture, holding, and release operations with both beads and cells in small arrays of this new architecture.

Cell Separation↗

PubMed2026

LAML-Pro: joint maximum likelihood inference of cell genotypes and cell lineage trees.

MOTIVATION: Recent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (i) Identify each cell's edits (genotype) from the raw sequencing or imaging data; (ii) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction (≈25%-50%) of uncertain or erroneous genotypes. RESULTS: We introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. AVAILABILITY AND IMPLEMENTATION: LAML-Pro is implemented in C++ and is available as both a command-line interface and as a Python library at: github.com/raphael-group/LAML-Pro.

Cell Lineage↗

PubMed2026

Genetic architecture of endometriosis: risk factors, comorbidities and clinical implications.

BACKGROUND: In 1999, Dr Susan Treloar and colleagues conducted a landmark twin study in Australia and reported their estimate of 51% for the heritability of endometriosis. This important result led several groups to begin mapping genetic factors contributing to increased endometriosis risk. Despite early challenges, advances in genome-wide association studies (GWAS) have identified multiple genetic risk factors and some target genes implicated in follow-up studies on genetic regulation of transcription. Access to large publicly available genetic datasets and analysis with endometriosis GWAS results is also providing new opportunities to answer important questions about comorbid conditions associated with endometriosis and their implications for clinical practice. OBJECTIVE AND RATIONALE: The objective of the review is to summarize the last 25 years of genetic studies in endometriosis, outline contributions to our understanding of the disease, and suggest future directions to accelerate biological insights from genetic studies to improve clinical outcomes. SEARCH METHODS: A comprehensive review of scientific literature on the genetics of endometriosis was conducted through searches in PubMed and Google Scholar up to June 2026. Search terms included "endometriosis AND (genetics OR GWAS OR genetic risk factors)", For studies addressing the functional characterization of genetic risk loci, additional searches employed the terms "endometriosis AND (genotype-phenotype associations OR colocalization OR eQTL OR mQTL OR multi omics methods)". To identify studies examining shared genetic risk between endometriosis and comorbid conditions, the search strategy included "endometriosis AND (genetic correlation OR colocalization OR Mendelian randomisation)". Publications reporting discoveries related to genetic risk factors for endometriosis and studies interpreting their biological and clinical significance were critically evaluated, and 144 publications were discussed in the review. OUTCOMES: Discovery of genetic risk factors started slowly and has accelerated in recent years with developments in technology and international collaborations to combine data and increase statistical power. GWAS have mapped 80 genetic risk factors that implicate gene regulation of hormonal targets, development of the reproductive tract, regulation of cell proliferation, and regulation of epithelial cell differentiation. In common with most other complex diseases, effects of individual common genetic risk factors are small. However, several examples demonstrate that small effect sizes are not a good predictor for the impact of drugs developed against genetically validated targets. Genetic risk factors implicate five genes regulating gonadotrophin release and oestrogen action, the major target pathway of current drugs for treatment of endometriosis demonstrating proof-of-principal for biologically meaningful results. Genetic correlation and Mendelian Randomization studies highlight important causal relationships between endometriosis and comorbid conditions including a possible role for testosterone during development and shared genetic risk factors for gynaecological, gastrointestinal, pain, psychiatric, and inflammatory conditions. Understanding causal relationships between endometriosis and related conditions will aid clinical management and more personalized treatments. WIDER IMPLICATIONS: Genetic studies provide novel insights into endometriosis pathogenesis and associations with related comorbid conditions. Genetic factors modifying gene regulation and disease risk likely act in specific cell types, and access to datasets from genetically informed cell-based models, single-cell and spatial omics data are needed to accelerate progress. Future studies should address critical questions of heterogeneity and disease subtypes, expand the search for genetic risk factors to non-European populations, evaluate the role of rare and structural variants, and better integrate data from functional, genomics, genetics, and clinical studies to reduce diagnostic delay, develop novel treatment strategies, and translate discoveries into personalized management strategies for affected individuals. REGISTRATION NUMBER: N/A.

comorbid conditions↗

PubMed2002

Single-cell gene expression profiling.

A key goal of biology is to relate the expression of specific genes to a particular cellular phenotype. However, current assays for gene expression destroy the structural context. By combining advances in computational fluorescence microscopy with multiplex probe design, we devised technology in which the expression of many genes can be visualized simultaneously inside single cells with high spatial and temporal resolution. Analysis of 11 genes in serum-stimulated cultured cells revealed unique patterns of gene expression within individual cells. Using the nucleus as the substrate for parallel gene analysis, we provide a platform for the fusion of genomics and cell biology: "cellular genomics."

Adenocarcinoma↗

PubMed2005

Mitochondrial correlation microscopy and nanolaser spectroscopy - new tools for biophotonic detection of cancer in single cells.

Currently, pathologists rely on labor-intensive microscopic examination of tumor cells using century-old staining methods that can give false readings. Emerging BioMicroNano-technologies have the potential to provide accurate, realtime, high-throughput screening of tumor cells without the need for time-consuming sample preparation. These rapid, nano-optical techniques may play an important role in advancing early detection, diagnosis, and treatment of disease. In this report, we show that laser scanning confocal microscopy can be used to identify a previously unknown property of certain cancer cells that distinguishes them, with single-cell resolution, from closely related normal cells. This property is the correlation of light scattering and the spatial organization of mitochondria. In normal liver cells, mitochondria are highly organized within the cytoplasm and highly scattering, yielding a highly correlated signal. In cancer cells, mitochondria are more chaotically organized and poorly scattering. These differences correlate with important bioenergetic disturbances that are hallmarks of many types of cancer. In addition, we review recent work that exploits the new technology of nanolaser spectroscopy using the biocavity laser to characterize the unique spectral signatures of normal and transformed cells. These optical methods represent powerful new tools that hold promise for detecting cancer at an early stage and may help to limit delays in diagnosis and treatment.

Animals↗

PubMed2026

Cell-type specific activation of the cGAS-STING pathway in tumor immunotherapy: mechanisms and therapeutic implications.

BACKGROUND: The cyclic GMP–AMP synthase–stimulator of interferon genes (cGAS–STING) pathway acts as a pivotal innate immune sensor that detects cytosolic DNA and links genomic instability to antitumor immune activation. Therapeutic activation of this pathway has garnered substantial interest as a strategy to enhance cancer immunotherapy by promoting dendritic cell maturation, augmenting antigen presentation, and facilitating cytotoxic lymphocyte infiltration. However, the functional outcomes of cGAS–STING signaling are highly context dependent and influenced by both cell type and tumor microenvironmental (TME) conditions. MAIN BODY: Recent advances in single-cell and spatial transcriptomic profiling have revealed profound heterogeneity in cGAS–STING activation across distinct cellular and regional compartments within tumors. Acute and spatially restricted activation of the pathway can elicit potent antitumor immune responses, whereas chronic or dysregulated signaling may promote immune tolerance and tumor progression. Moreover, metabolic stress, epigenetic silencing, and microenvironmental immunosuppressive factors such as TGF-β and IL-10 can further modulate STING activity, leading to resistance to immunotherapy. Current translational efforts focus on next-generation STING agonists, nanoparticle-based delivery systems, and rational combination strategies with immune checkpoint blockade and metabolic modulators to overcome tumor-intrinsic resistance and minimize systemic toxicity. CONCLUSIONS: Understanding the cell-type-specific and spatial dynamics of cGAS–STING signaling is crucial for the rational design of precision immunotherapies. Future research should emphasize context-dependent modulation of STING activity to maximize therapeutic benefit while limiting adverse effects. Integrating multi-omics technologies and spatially guided drug delivery may ultimately enable personalized modulation of the cGAS–STING axis, transforming it into a clinically effective and safe strategy for cancer immunotherapy.

Humans↗

PubMed2026

LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.

MOTIVATION: Recent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (1) Identify each cell's edits (genotype) from the raw sequencing or imaging data; (2) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction (≈ 25-50%) of uncertain or erroneous genotypes. RESULTS: We introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. AVAILABILITY AND IMPLEMENTATION: LAML-Pro is freely available at: github.com/raphael-group/LAML-Pro.

Journal Article↗