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Dynamically organized chromatin complexes often involve multiplex chromatin interactions and sometimes chromatin-associated RNA1-3. Chromatin complex compositions change during cellular differentiation and ageing, and are expected to be highly heterogeneous among terminally differentiated single cells4-7. Here we introduce the multinucleic acid interaction mapping in single cells (MUSIC) technique for concurrent profiling of multiplex chromatin interactions, gene expression and RNA-chromatin associations within individual nuclei. When applied to 14 human frontal cortex samples from older donors, MUSIC delineated diverse cortical cell types and states. We observed that nuclei exhibiting fewer short-range chromatin interactions were correlated with both an older transcriptomic signature and Alzheimers disease pathology. Furthermore, the cell type exhibiting chromatin contacts between cis expression quantitative trait loci and a promoter tends to be that in which these cis expression quantitative trait loci specifically affect the expression of their target gene. In addition, female cortical cells exhibit highly heterogeneous interactions between XIST non-coding RNA and chromosome X, along with diverse spatial organizations of the X chromosomes. MUSIC presents a potent tool for exploration of chromatin architecture and transcription at cellular resolution in complex tissues.

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.

CD4+ T cells recognize peptide antigens presented on class II major histocompatibility complex (MHC-II) molecules to carry out their function. The remarkable diversity of T cell receptor sequences and lack of antigen discovery approaches for MHC-II make profiling the specificities of CD4+ T cells challenging. We have expanded our platform of signaling and antigen-presenting bifunctional receptors to encode MHC-II molecules presenting covalently linked peptides (SABR-IIs) for CD4+ T cell antigen discovery. SABR-IIs can present epitopes to CD4+ T cells and induce signaling upon their recognition, allowing a readable output. Furthermore, the SABR-II design is modular in signaling and deployment to T cells and B cells. Here, we demonstrate that SABR-IIs libraries presenting endogenous and non-contiguous epitopes can be used for antigen discovery in the context of type 1 diabetes. SABR-II libraries provide a rapid, flexible, scalable and versatile approach for de novo identification of CD4+ T cell ligands from single-cell RNA sequencing data using experimental and computational approaches.

Dorsal interneurons (dIs) in the spinal cord encode the perception of touch, pain, heat, itchiness and proprioception. Previous studies using genetic strategies in animal models have revealed important insights into dI development, but the molecular details of how dIs arise as distinct populations of neurons remain incomplete. We have developed a resource to investigate dI fate specification by combining a single-cell RNA-Seq atlas of mouse embryonic stem cell-derived dIs with pseudotime analyses. To validate this in silico resource as a useful tool, we used it to first identify genes that are candidates for directing the transition states that lead to distinct dI lineage trajectories, and then validated them using in situ hybridization analyses in the developing mouse spinal cord in vivo. We have also identified an endpoint of the dI5 lineage trajectory and found that dIs become more transcriptionally homogeneous during terminal differentiation. This study introduces a valuable tool for further discovery about the timing of gene expression during dI differentiation and demonstrates its utility in clarifying dI lineage relationships.

Intermuscular adipose tissue (IMAT) is a relatively understudied adipose depot located between muscle fibers. IMAT content increases with age and BMI and is associated with metabolic and muscle degenerative diseases; however, an understanding of the biological properties of IMAT and its interplay with the surrounding muscle fibers is severely lacking. In recent years, single-cell and nuclei RNA sequencing have provided us with cell type-specific atlases of several human tissues. However, the cellular composition of human IMAT remains largely unexplored due to the inherent challenges of its accessibility from biopsy collection in humans. In addition to the limited amount of tissue collected, the processing of human IMAT is complicated due to its proximity to skeletal muscle tissue and fascia. The lipid-laden nature of the adipocytes makes it incompatible with single-cell isolation. Hence, single nuclei RNA sequencing is optimal for obtaining high-dimensional transcriptomics at single-cell resolution and provides the potential to uncover the biology of this depot, including the exact cellular composition of IMAT. Here, we present a detailed protocol for nuclei isolation and library preparation of frozen human IMAT for single nuclei RNA sequencing. This protocol allows for the profiling of thousands of nuclei using a droplet-based approach, thus providing the capacity to detect rare and low-abundant cell types.

In droplet-based single-cell and single-nucleus RNA-seq assays, systematic contamination of ambient RNA molecules biases the quantification of gene expression levels. Existing methods correct the contamination for all genes globally. However, there lacks specific evaluation of correction efficacy for varying contamination levels. Here, we show that DecontX and CellBender under-correct highly contaminating genes, while SoupX and scAR over-correct lowly/non-contaminating genes. Here, we develop scCDC as the first method to detect the contamination-causing genes and only correct expression levels of these genes, some of which are cell-type markers. Compared with existing decontamination methods, scCDC excels in decontaminating highly contaminating genes while avoiding over-correction of other genes.

INTRODUCTION: Hypoxia is a common pathological driver contributing to various forms of pulmonary vascular diseases leading to pulmonary hypertension (PH). Pulmonary interstitial macrophages (IMs) play pivotal roles in immune and vascular dysfunction, leading to inflammation, abnormal remodeling, and fibrosis in PH. However, IMs response to hypoxia and their role in PH progression remain largely unknown. We utilized a murine model of hypoxia-induced PH to investigate the repertoire and functional profiles of IMs in response to acute and prolonged hypoxia, aiming to elucidate their contributions to PH development. METHODS: We conducted single-cell transcriptomic analyses to characterize the repertoire and functional profiles of murine pulmonary IMs following exposure to hypobaric hypoxia for varying durations (0, 1, 3, 7, and 21 days). Hallmark pathways from the mouse Molecular Signatures Database were utilized to characterize the molecular function of the IM subpopulation in response to hypoxia. RESULTS: Our analysis revealed an early acute inflammatory phase during acute hypoxia exposure (Days 1-3), which was resolved by Day 7, followed by a pro-remodeling phase during prolonged hypoxia (Days 7-21). These phases were marked by distinct subpopulations of IMs: MHCIIhiCCR2+EAR2+ cells characterized the acute inflammatory phase, while TLF+VCAM1hi cells dominated the pro-remodeling phase. The acute inflammatory phase exhibited enrichment in interferon-gamma, IL-2, and IL-6 pathways, while the pro-remodeling phase showed dysregulated chemokine production, hemoglobin clearance, and tissue repair profiles, along with activation of distinct complement pathways. DISCUSSION: Our findings demonstrate the existence of distinct populations of pulmonary interstitial macrophages corresponding to acute and prolonged hypoxia exposure, pivotal in regulating the inflammatory and remodeling phases of PH pathogenesis. This understanding offers potential avenues for targeted interventions

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

In vivo molecular imaging tools are crucially important for elucidating how cells move through complex biological systems; however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upward of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a statistical tracking algorithm (PEPT-EM) to achieve a sensitivity of 4 becquerel per cell and a streamlined workflow to reliably label single cells with over 50 becquerel per cell of 18F-fluorodeoxyglucose (FDG). To demonstrate the potential of the method, we tracked the fate of more than 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.

As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tauFisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher's performance in adding timestamps to both bulk and single-cell transcriptomic samples collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNAseq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNAseq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.

Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) are powerful in vitro models to study the mechanisms underlying cardiomyopathies and cardiotoxicity. Quantification of the contractile function in single hiPSC-CMs at high-throughput and over time is essential to disentangle how cellular mechanisms affect heart function. Here, we present CONTRAX, an open-access, versatile, and streamlined pipeline for quantitative tracking of the contractile dynamics of single hiPSC-CMs over time. Three software modules enable: parameter-based identification of single hiPSC-CMs; automated video acquisition of >200 cells/hour; and contractility measurements via traction force microscopy. We analyze >4,500 hiPSC-CMs over time in the same cells under orthogonal conditions of culture media and substrate stiffnesses; +/- drug treatment; +/- cardiac mutations. Using undirected clustering, we reveal converging maturation patterns, quantifiable drug response to Mavacamten and significant deficiencies in hiPSC-CMs with disease mutations. CONTRAX empowers researchers with a potent quantitative approach to develop cardiac therapies.

Facioscapulohumeral muscular dystrophy (FSHD) is linked to abnormal derepression of the transcription activator DUX4. This effect is localized to a low percentage of cells, requiring single-cell analysis. However, single-cell/nucleus RNA-seq cannot fully capture the transcriptome of multinucleated large myotubes. To circumvent these issues, we use multiplexed error-robust fluorescent in situ hybridization (MERFISH) spatial transcriptomics that allows profiling of RNA transcripts at a subcellular resolution. We simultaneously examined spatial distributions of 140 genes, including 24 direct DUX4 targets, in in vitro differentiated myotubes and unfused mononuclear cells (MNCs) of control, isogenic D4Z4 contraction mutant and FSHD patient samples, as well as the individual nuclei within them. We find myocyte nuclei segregate into two clusters defined by the expression of DUX4 target genes, which is exclusively found in patient/mutant nuclei, whereas MNCs cluster based on developmental states. Patient/mutant myotubes are found in FSHD-hi and FSHD-lo states with the former signified by high DUX4 target expression and decreased muscle gene expression. Pseudotime analyses reveal a clear bifurcation of myoblast differentiation into control and FSHD-hi myotube branches, with variable numbers of DUX4 target-expressing nuclei found in multinucleated FSHD-hi myotubes. Gene coexpression modules related to extracellular matrix and stress gene ontologies are significantly altered in patient/mutant myotubes compared with the control. We also identify distinct subpathways within the DUX4 gene network that may differentially contribute to the disease transcriptomic phenotype. Taken together, our MERFISH-based study provides effective gene network profiling of multinucleated cells and identifies FSHD-induced transcriptomic alterations during myoblast differentiation.

Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.

Debate remains around the anatomical origins of specific brain cell subtypes and lineage relationships within the human forebrain1-7. Thus, direct observation in the mature human brain is critical for a complete understanding of its structural organization and cellular origins. Here we utilize brain mosaic variation within specific cell types as distinct indicators for clonal dynamics, denoted as cell-type-specific mosaic variant barcode analysis. From four hemispheres and two different human neurotypical donors, we identified 287 and 780 mosaic variants, respectively, that were used to deconvolve clonal dynamics. Clonal spread and allele fractions within the brain reveal that local hippocampal excitatory neurons are more lineage-restricted than resident neocortical excitatory neurons or resident basal ganglia GABAergic inhibitory neurons. Furthermore, simultaneous genome transcriptome analysis at both a cell-type-specific and a single-cell level suggests a dorsal neocortical origin for a subgroup of DLX1+ inhibitory neurons that disperse radially from an origin shared with excitatory neurons. Finally, the distribution of mosaic variants across 17 locations within one parietal lobe reveals that restriction of clonal spread in the anterior-posterior axis precedes restriction in the dorsal-ventral axis for both excitatory and inhibitory neurons. Thus, cell-type-resolved somatic mosaicism can uncover lineage relationships governing the development of the human forebrain.

This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function.

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

The circadian clock is a critical regulator of immunity, and this circadian control of immune modulation has an essential function in host defense and tumor immunosurveillance. Here we use a single-cell RNA sequencing approach and a genetic model of colorectal cancer to identify clock-dependent changes to the immune landscape that control the abundance of immunosuppressive cells and consequent suppression of cytotoxic CD8+ T cells. Of these immunosuppressive cell types, PD-L1-expressing myeloid-derived suppressor cells (MDSCs) peak in abundance in a rhythmic manner. Disruption of the epithelial cell clock regulates the secretion of cytokines that promote heightened inflammation, recruitment of neutrophils and the subsequent development of MDSCs. We also show that time-of-day anti-PD-L1 delivery is most effective when synchronized with the abundance of immunosuppressive MDSCs. Collectively, these data indicate that circadian gating of tumor immunosuppression informs the timing and efficacy of immune checkpoint inhibitors.

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

Melanoma clinical outcomes emerge from incompletely understood genetic mechanisms operating within the tumor and its microenvironment. Here, we used single-cell RNA-based spatial molecular imaging (RNA-SMI) in patient-derived archival tumors to reveal clinically relevant markers of malignancy progression and prognosis. We examined spatial gene expression of 203,472 cells inside benign and malignant melanocytic neoplasms, including melanocytic nevi and primary invasive and metastatic melanomas. Algorithmic cell clustering paired with intratumoral comparative two-dimensional analyses visualized synergistic, spatial gene signatures linking cellular proliferation, metabolism, and malignancy, validated by protein expression. Metastatic niches included up-regulation of CDK2 and FABP5, which independently predicted poor clinical outcome in 473 patients with melanoma via Cox regression analysis. More generally, our work demonstrates a framework for applying single-cell RNA-SMI technology toward identifying gene regulatory landscapes pertinent to cancer progression and patient survival.

MOTIVATION: Recent advances in spatial transcriptomics allow spatially resolved gene expression measurements with cellular or even sub-cellular resolution, directly characterizing the complex spatiotemporal gene expression landscape and cell-to-cell interactions in their native microenvironments. Due to technology limitations, most spatial transcriptomic technologies still yield incomplete expression measurements with excessive missing values. Therefore, gene imputation is critical to filling in missing data, enhancing resolution, and improving overall interpretability. However, existing methods either require additional matched single-cell RNA-seq data, which is rarely available, or ignore spatial proximity or expression similarity information. RESULTS: To address these issues, we introduce Impeller, a path-based heterogeneous graph learning method for spatial transcriptomic data imputation. Impeller has two unique characteristics distinct from existing approaches. First, it builds a heterogeneous graph with two types of edges representing spatial proximity and expression similarity. Therefore, Impeller can simultaneously model smooth gene expression changes across spatial dimensions and capture similar gene expression signatures of faraway cells from the same type. Moreover, Impeller incorporates both short- and long-range cell-to-cell interactions (e.g. via paracrine and endocrine) by stacking multiple GNN layers. We use a learnable path operator in Impeller to avoid the over-smoothing issue of the traditional Laplacian matrices. Extensive experiments on diverse datasets from three popular platforms and two species demonstrate the superiority of Impeller over various state-of-the-art imputation methods. AVAILABILITY AND IMPLEMENTATION: The code and preprocessed data used in this study are available at https://github.com/aicb-ZhangLabs/Impeller and https://zenodo.org/records/11212604.

Intratumor heterogeneity underlies cancer evolution and treatment resistance, but targetable mechanisms driving intratumor heterogeneity are poorly understood. Meningiomas are the most common primary intracranial tumors and are resistant to all medical therapies, and high-grade meningiomas have significant intratumor heterogeneity. Here we use spatial approaches to identify genomic, biochemical and cellular mechanisms linking intratumor heterogeneity to the molecular, temporal and spatial evolution of high-grade meningiomas. We show that divergent intratumor gene and protein expression programs distinguish high-grade meningiomas that are otherwise grouped together by current classification systems. Analyses of matched pairs of primary and recurrent meningiomas reveal spatial expansion of subclonal copy number variants associated with treatment resistance. Multiplexed sequential immunofluorescence and deconvolution of meningioma spatial transcriptomes using cell types from single-cell RNA sequencing show decreased immune infiltration, decreased MAPK signaling, increased PI3K-AKT signaling and increased cell proliferation, which are associated with meningioma recurrence. To translate these findings to preclinical models, we use CRISPR interference and lineage tracing approaches to identify combination therapies that target intratumor heterogeneity in meningioma cell co-cultures.

Natural killer (NK) cells are innate lymphoid cells (ILCs) contributing to immune responses to microbes and tumors. Historically, their classification hinged on a limited array of surface protein markers. Here, we used single-cell RNA sequencing (scRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to dissect the heterogeneity of NK cells. We identified three prominent NK cell subsets in healthy human blood: NK1, NK2 and NK3, further differentiated into six distinct subgroups. Our findings delineate the molecular characteristics, key transcription factors, biological functions, metabolic traits and cytokine responses of each subgroup. These data also suggest two separate ontogenetic origins for NK cells, leading to divergent transcriptional trajectories. Furthermore, we analyzed the distribution of NK cell subsets in the lung, tonsils and intraepithelial lymphocytes isolated from healthy individuals and in 22 tumor types. This standardized terminology aims at fostering clarity and consistency in future research, thereby improving cross-study comparisons.

Bone marrow aspirate concentrate (BMAC) and adipose-derived stromal vascular fraction (ADSVF) are the most marketed stem cell therapies to treat a variety of conditions in the general population and elite athletes. Both tissues have been used interchangeably clinically even though their detailed composition, heterogeneity, and mechanisms of action have neither been rigorously inventoried nor compared. This lack of information has prevented investigations into ideal dosages and has facilitated anecdata and misinformation. Here, we analyzed single-cell transcriptomes, proteomes, and flow cytometry profiles from paired clinical-grade BMAC and ADSVF. This comparative transcriptional atlas challenges the prevalent notion that there is one therapeutic cell type present in both tissues. We also provide data of surface markers that may enable isolation and investigation of cell (sub)populations. Furthermore, the proteome atlas highlights intertissue and interpatient heterogeneity of injected proteins with potentially regenerative or immunomodulatory capacities. An interactive webtool is available online.

OBJECTIVES: To determine the dysregulated signaling pathways of head and neck squamous cell carcinoma associated with circulating tumor cells (CTCs) via single-cell molecular characterization. INTRODUCTION: Head and neck squamous cell carcinoma (HNSCC) has a significant global burden and is a disease with poor survival. Despite trials exploring new treatment modalities to improve disease control rates, the 5 year survival rate remains low at only 60%. Most cancer malignancies are reported to progress to a fatal phase due to the metastatic activity derived from treatment-resistant cancer cells, regarded as one of the most significant obstacles to develope effective cancer treatment options. However, the molecular profiles of cancer cells have not been thoroughly studied. METHODS: Here, we examined in-situ HNSCC tumors and pairwisely followed up with the downstream circulating tumor cells (CTCs)-based on the surrogate biomarkers to detect metastasis that is established in other cancers - not yet being fully adopted in HNSCC treatment algorithms. RESULTS: Specifically, we revealed metastatic HNSCC patients have complex CTCs that could be defined through gene expression and mutational gene profiling derived from completed single-cell RNASeq (scRNASeq) that served to confirm molecular pathways inherent in these CTCs. To enhance the reliability of our findings, we cross-validated those molecular profiles with results from previously published studies. CONCLUSION: Thus, we identified 5 dysregulated signaling pathways in CTCs to derive HNSCC biomarker panels for screening HNSCC in situ tumors.

With the increasing incidence of kidney diseases, there is an urgent need to develop therapeutic strategies to combat post-injury fibrosis. Immune cells, including platelets, play a pivotal role in this repair process, primarily through their released cytokines. However, the specific role of platelets in kidney injury and subsequent repair remains underexplored. Here, the detrimental role of platelets in renal recovery following ischemia/reperfusion injury and its contribution to acute kidney injury  to chronic kidney disease transition is aimed to investigated. In this study, it is shown that depleting platelets accelerates injury resolution and significantly reduces fibrosis. Employing advanced single-cell and spatial transcriptomic techniques, macrophages as the primary mediators modulated by platelet signals is identified. A novel subset of macrophages, termed cycling M2, which exhibit an M2 phenotype combined with enhanced proliferative activity is uncovered. This subset emerges in the injured kidney during the resolution phase and is modulated by platelet-derived thrombospondin 1 (THBS1) signaling, acquiring profibrotic characteristics. Conversely, targeted inhibition of THBS1 markedly downregulates the cycling M2 macrophage, thereby mitigating fibrotic progression. Overall, this findings highlight the adverse role of platelet THBS1-boosted cycling M2 macrophages in renal injury repair and suggest platelet THBS1 as a promising therapeutic target for alleviating inflammation and kidney fibrosis.

The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell-cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene-gene colocalization. We demonstrate MERFISH, seqFISH + , Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.

Glioblastoma (GBM) is the most common malignant primary brain tumor and remains incurable. Previous work has shown that systemic administration of Decitabine (DAC) induces sufficient expression of cancer-testis antigens (CTA) in GBM for targeting by adoptive T-cell therapy in vivo. However, the mechanisms by which DAC enhances immunogenicity in GBM remain to be elucidated. Using New York esophageal squamous cell carcinoma 1 (NY-ESO-1) as a representative inducible CTA, we demonstrate in patient tissue, immortalized glioma cells, and primary patient-derived gliomaspheres that basal CTA expression is restricted by promoter hypermethylation in gliomas. DAC treatment of glioma cells specifically inhibits DNA methylation silencing to render NY-ESO-1 and other CTA into inducible tumor antigens at single-cell resolution. Functionally, NY-ESO-1 T-cell receptor-engineered effector cell targeting of DAC-induced antigen in primary glioma cells promotes specific and polyfunctional T-cell cytokine profiles. In addition to induction of CTA, DAC concomitantly reactivates tumor-intrinsic human endogenous retroviruses, interferon response signatures, and MHC-I. Overall, we demonstrate that DAC induces targetable tumor antigen and enhances T-cell functionality against GBM, ultimately contributing to the improvement of targeted immune therapies in glioma.SignificanceThis study dissects the tumor-intrinsic epigenetic and transcriptional mechanisms underlying enhanced T-cell functionality targeting decitabine-induced cancer-testis antigens in glioma. Our findings demonstrate concomitant induction of tumor antigens, reactivation of human endogenous retroviruses, and stimulation of interferon signaling as a mechanistic rationale to epigenetically prime human gliomas to immunotherapeutic targeting.

The development of the retina is under tight temporal and spatial control. To gain insights into the molecular basis of this process, we generate a single-nuclei dual-omic atlas of the human developing retina with approximately 220,000 nuclei from 14 human embryos and fetuses aged between 8 and 23-weeks post-conception with matched macular and peripheral tissues. This atlas captures all major cell classes in the retina, along with a large proportion of progenitors and cell-type-specific precursors. Cell trajectory analysis reveals a transition from continuous progression in early progenitors to a hierarchical development during the later stages of cell type specification. Both known and unrecorded candidate transcription factors, along with gene regulatory networks that drive the transitions of various cell fates, are identified. Comparisons between the macular and peripheral retinae indicate a largely consistent yet distinct developmental pattern. This atlas offers unparalleled resolution into the transcriptional and chromatin accessibility landscapes during development, providing an invaluable resource for deeper insights into retinal development and associated diseases.

Standard single-cell (sc) proteomics of disease states inferred from multicellular organs or organoids cannot currently be related to single-cell physiology. Here, a scPatch-Clamp/Proteomics platform is developed on single neurons generated from hiPSCs bearing an Alzheimer's disease (AD) genetic mutation and compares them to isogenic wild-type controls. This approach provides both current and voltage electrophysiological data plus detailed proteomics information on single-cells. With this new method, the authors are able to observe hyperelectrical activity in the AD hiPSC-neurons, similar to that observed in the human AD brain, and correlate it to ≈1400 proteins detected at the single neuron level. Using linear regression and mediation analyses to explore the relationship between the abundance of individual proteins and the neuron's mutational and electrophysiological status, this approach yields new information on therapeutic targets in excitatory neurons not attainable by traditional methods. This combined patch-proteomics technique creates a new proteogenetic-therapeutic strategy to correlate genotypic alterations to physiology with protein expression in single-cells.

The contrast between the disruption of genome topology after cohesin loss and the lack of downstream gene expression changes instigates intense debates regarding the structure-function relationship between genome and gene regulation. Here, by analyzing transcriptome and chromatin accessibility at the single-cell level, we discover that, instead of dictating population-wide gene expression levels, cohesin supplies a general function to neutralize stochastic coexpression tendencies of cis-linked genes in single cells. Notably, cohesin loss induces widespread gene coactivation and chromatin co-opening tens of million bases apart in cis. Spatial genome and protein imaging reveals that cohesin prevents gene co-bursting along the chromosome and blocks spatial mixing of transcriptional hubs. Single-molecule imaging shows that cohesin confines the exploration of diverse enhancer and core promoter binding transcriptional regulators. Together, these results support that cohesin arranges nuclear topology to control gene coexpression in single cells.

Understanding how organisms respond to oxygen (O2) fluctuations in their aquatic microenvironments and studying O2 consumption as an indicator of metabolism is crucial for advancing ecology, toxicology, tissue engineering and environmental sciences. However, despite their importance across scientific disciplines, traditional O2 measurements often lack the sensitivity to capture rapid or localized changes occurring at the microscale. This PhD thesis addresses this challenge by developing novel microfluidic techniques and systems to sense O2. To investigate advective and diffusive O2 fluxes at the microscale, this thesis developed chemical sensing particle image velocimetry (sensPIV), a technique that combines optode microparticles and rapid microscope imaging. Specifically, I contributed by testing the performance of sensPIV particles within O2-permeable polydimethylsiloxane (PDMS) devices. This demonstrated the effectiveness of sensPIV in visualizing microscale O2 fluxes, with potential applications around complex biological structures such as coral segments. To characterize single-cell O2 respiration rates this thesis developed a micro-respiration chamber device. This device consists of gastight microwells that isolate single-cells, allowing for measuring their O2 consumption via immobilized O2-sensitive optodes. When applied to human hepatic cells, this device revealed size-related respiration kinetics in single-cells and adaptively changing respiration rates due to O2 limitations. The micro-respiration device was then refined into SlipO2Chip, a microfluidic platform that quantifies single-cell O2 respiration rates before and after chemical exposures. This was achieved by adding a mechanism for opening and closing microwells via slipping a dedicated channel that introduces chemical solutions. SlipO2Chip demonstrated a dose-dependent decrease in diatom respiration when exposed to the bacterial infochemical 2-Heptyl-4-Quinolone (HHQ), thus enhancing our understandin

Archived patient-derived tissue specimens play a central role in understanding disease and developing therapies. To address specificity and sensitivity shortcomings of existing single-cell resolution proteoform analysis tools, we introduce a hybrid microfluidic platform (DropBlot) designed for proteoform analyses in chemically fixed single cells. DropBlot serially integrates droplet-based encapsulation and lysis of single fixed cells, with on-chip microwell-based antigen retrieval, with single-cell western blotting of target antigens. A water-in-oil droplet formulation withstands the harsh chemical (SDS, 6 M urea) and thermal conditions (98 °C, 1-2 hr) required for effective antigen retrieval, and supports analysis of retrieved protein targets by single-cell electrophoresis. We demonstrate protein-target retrieval from unfixed, paraformaldehyde-fixed (PFA), and methanol-fixed cells. Key protein targets (HER2, GAPDH, EpCAM, Vimentin) retrieved from PFA-fixed cells were resolved and immunoreactive. Relevant to biorepositories, DropBlot profiled targets retrieved from human-derived breast tumor specimens archived for six years, offering a workflow for single-cell protein-biomarker analysis of sparing biospecimens.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

Recent innovations in single-cell RNA-sequencing (scRNA-seq) provide the technology to investigate biological questions at cellular resolution. Pooling cells from multiple individuals has become a common strategy, and droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences. An implicit challenge with scRNA-seq is the occurrence of doublets—droplets containing two or more cells. We develop Demuxafy, a framework to enhance donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. Demuxafy significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual., This work was funded by the National Health and Medical Research Council (NHMRC) Investigator grant (1175781), and funding from the Goodridge foundation. J.E.P is also supported by a fellowship from the Fok Foundation., Peer Reviewed, "Article signat per 13 autors/es: Drew Neavin, Anne Senabouth, Himanshi Arora, Jimmy Tsz Hang Lee, Aida Ripoll-Cladellas, sc-eQTLGen Consortium, Lude Franke, Shyam Prabhakar, Chun Jimmie Ye, Davis J. McCarthy, Marta Melé, Martin Hemberg & Joseph E. Powel", Postprint (published version)

We analyzed >700,000 single-nucleus RNA sequencing profiles from 106 donors during prenatal and postnatal developmental stages and identified lineage-specific programs that underlie the development of specific subtypes of excitatory cortical neurons, interneurons, glial cell types, and brain vasculature. By leveraging single-nucleus chromatin accessibility data, we delineated enhancer gene regulatory networks and transcription factors that control commitment of specific cortical lineages. By intersecting our results with genetic risk factors for human brain diseases, we identified the cortical cell types and lineages most vulnerable to genetic insults of different brain disorders, especially autism. We find that lineage-specific gene expression programs up-regulated in female cells are especially enriched for the genetic risk factors of autism. Our study captures the molecular progression of cortical lineages across human development.

Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and transcriptomic levels fail to address information on proteins that are central to many important biological processes. Single-cell proteomics enables understanding of the functional status of individual cells and is particularly crucial when the specimen is composed of heterogeneous entities of cells. With the growing importance of this field, significant methodological advancements have emerged recently. These include miniaturized and automated sample preparation, multi-omics analyses, and combined analyses of multiple techniques such as mass spectrometry and microscopy. Moreover, artificial intelligence and single-molecule detection technologies have advanced throughput and improved sensitivity limitations, respectively, over conventional methods. In this review, we summarize cutting-edge methodologies for single-cell proteomics and relevant emerging technologies that have been reported in the last 5 years, and provide an outlook on this research field.

Oxygen-deficient marine waters referred to as oxygen minimum zones (OMZs) or anoxic marine zones (AMZs) are common oceanographic features. They host both cosmopolitan and endemic microorganisms adapted to low oxygen conditions. Microbial metabolic interactions within OMZs and AMZs drive coupled biogeochemical cycles resulting in nitrogen loss and climate active trace gas production and consumption. Global warming is causing oxygen-deficient waters to expand and intensify. Therefore, studies focused on microbial communities inhabiting oxygen-deficient regions are necessary to both monitor and model the impacts of climate change on marine ecosystem functions and services. Here we present a compendium of 5,129 single-cell amplified genomes (SAGs) from marine environments encompassing representative OMZ and AMZ geochemical profiles. Of these, 3,570 SAGs have been sequenced to different levels of completion, providing a strain-resolved perspective on the genomic content and potential metabolic interactions within OMZ and AMZ microbiomes. Hierarchical clustering confirmed that samples from similar oxygen concentrations and geographic regions also had analogous taxonomic compositions, providing a coherent framework for comparative community analysis.

UNLABELLED: RNA editing modifies single nucleotides of RNAs, regulating primary protein structure and protein abundance. In recent years, the diversity of proteins and complexity of gene regulation associated with RNA editing dysregulation has been increasingly appreciated in oncology. Large-scale shifts in editing have been observed in bulk tumors across various cancer types. However, RNA editing in single cells and individual cell types within tumors has not been explored. By profiling editing in single cells from lung adenocarcinoma biopsies, we found that the increased editing trend of bulk lung tumors was unique to cancer cells. Elevated editing levels were observed in cancer cells resistant to targeted therapy, and editing sites associated with drug response were enriched. Consistent with the regulation of antiviral pathways by RNA editing, higher editing levels in cancer cells were associated with reduced antitumor innate immune response, especially levels of natural killer cell infiltration. In addition, the level of RNA editing in cancer cells was positively associated with somatic point mutation burden. This observation motivated the definition of a new metric, RNA editing load, reflecting the amount of RNA mutations created by RNA editing. Importantly, in lung cancer, RNA editing load was a stronger predictor of patient survival than DNA mutations. This study provides the first single cell dissection of editing in cancer and highlights the significance of RNA editing load in cancer prognosis. SIGNIFICANCE: RNA editing analysis in single lung adenocarcinoma cells uncovers RNA mutations that correlate with tumor mutation burden and cancer innate immunity and reveals the amount of RNA mutations that strongly predicts patient survival. See related commentary by Luo and Liang, p. 351.

Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.

Spatial transcriptomics extends single-cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as multiplexed error-robust fluorescence in situ hybridization (MERFISH) can achieve single-cell resolution, directly mapping single-cell identities to spatial positions. MERFISH produces a different data type than scRNA-seq, and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on the mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas and from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both the liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that MERFISH provides a quantitatively comparable method for single-cell gene expression and can identify cell types without the need for computational integration with scRNA-seq atlases.

Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell-cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development.

MOTIVATION: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in individual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterization of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy. RESULTS: We propose SnapCCESS for clustering cells by integrating data modalities in multimodal single-cell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterization of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data. AVAILABILITY AND IMPLEMENTATION: SnapCCESS is implemented as a Python package and is freely available from https://github.com/PYangLab/SnapCCESS under the open-source license of GPL-3. The data used in this study are publicly available (see section Data availability).

Ischemic stroke (IS) is the leading cause of acquired disability and the second leading cause of dementia and mortality. Current treatments for IS are primarily focused on revascularization of the occluded artery. However, only 10% of patients are eligible for revascularization and 50% of revascularized patients remain disabled at 3 months. Accumulating evidence highlight the prognostic significance of the neuro- and thrombo-inflammatory response after IS. However, several randomized trials of promising immunosuppressive or immunomodulatory drugs failed to show positive results. Insufficient understanding of inter-patient variability in the cellular, functional, and spatial organization of the inflammatory response to IS likely contributed to the failure to translate preclinical findings into successful clinical trials. The inflammatory response to IS involves complex interactions between neuronal, glial, and immune cell subsets across multiple immunological compartments, including the blood-brain barrier, the meningeal lymphatic vessels, the choroid plexus, and the skull bone marrow. Here, we review the neuro- and thrombo-inflammatory responses to IS. We discuss how clinical imaging and single-cell omic technologies have refined our understanding of the spatial organization of pathobiological processes driving clinical outcomes in patients with an IS. We also introduce recent developments in machine learning statistical methods for the integration of multi-omic data (biological and radiological) to identify patient-specific inflammatory states predictive of IS clinical outcomes.

The enteric nervous system (ENS) consists of glial cells (EGCs) and neurons derived from neural crest precursors. EGCs retain capacity for large-scale neurogenesis in culture, and in vivo lineage tracing has identified neurons derived from glial cells in response to inflammation. We thus hypothesize that EGCs possess a chromatin structure poised for neurogenesis. We use single-cell multiome sequencing to simultaneously assess transcription and chromatin accessibility in EGCs undergoing spontaneous neurogenesis in culture, as well as small intestine myenteric plexus EGCs. Cultured EGCs maintain open chromatin at genomic loci accessible in neurons, and neurogenesis from EGCs involves dynamic chromatin rearrangements with a net decrease in accessible chromatin. A subset of in vivo EGCs, highly enriched within the myenteric ganglia and that persist into adulthood, have a gene expression program and chromatin state consistent with neurogenic potential. These results clarify the mechanisms underlying EGC potential for neuronal fate transition.

Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context1-5. However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research.

Bispecific antibodies (BsAbs) represent an emerging class of immunotherapy, but inefficiency in the current discovery has limited their broad clinical availability. Here we report a high throughput, agnostic, single-cell-based functional screening pipeline, comprising molecular and cell engineering for efficient generation of BsAb library cells, followed by functional interrogation at the single-cell level to identify and sort positive clones and downstream sequence identification and functionality characterization. Using a CD19xCD3 bispecific T cell engager (BiTE) as a model, we demonstrate that our single-cell platform possesses a high throughput screening efficiency of up to one and a half million variant library cells per run and can isolate rare functional clones at a low abundance of 0.008%. Using a complex CD19xCD3 BiTE-expressing cell library with approximately 22,300 unique variants comprising combinatorially varied scFvs, connecting linkers and VL/VH orientations, we have identified 98 unique clones, including extremely rare ones (~ 0.001% abundance). We also discovered BiTEs that exhibit novel properties and insights to design variable preferences for functionality. We expect our single-cell platform to not only increase the discovery efficiency of new immunotherapeutics, but also enable identifying generalizable design principles based on an in-depth understanding of the inter-relationships between sequence, structure, and function.

Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.