Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

Gbp2 driving macrophages dynamics in murine heart transplant

BACKGROUND AND OBJECTIVE

Organ transplantation is a vital treatment for patients with end-stage organ diseases, and macrophages play a key role in the rejection process. This study seeks to pinpoint key genes responsible for the dynamic changes in macrophages during rejection and to evaluate their impact on macrophage polarization through bioinformatics analysis.

METHODS

We selected single-cell sequencing data of mouse heart transplant models from Genome Sequence Archive to construct a dynamic landscape of immune cells during acute rejection. Key genes involved in macrophage changes were screened using pseudotime analysis and hdWGCNA. The mouse heart transplant models also were established to validate changes of the key genes during rejection.

RESULTS

Bioinformatics analysis identified Gbp2 as the key gene driving macrophage dynamics during rejection, which was also confirmed in another dataset showed Gbp2 levels increased in macrophages during acute rejection. Further experiments validated the upregulation of Gbp2 in both tissues and macrophages during acute rejection, and in vitro experiments confirmed Gbp2 increasing in M1 macrophages.

CONCLUSION

Gbp2 is a key gene that regulates macrophage polarization during acute rejection, making it a potential therapeutic target for the acute rejection.

Interpretable single-cell factor decomposition using sciRED

Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable REsidual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.

Analysis of a Single Cell RNA-seq Workflow by Random Matrix Theory Methods

Single cell RNA-seq (scRNAseq) workflows typically start with a count matrix and end with the clustering of sampled cells. While a range of methods have been developed to cluster scRNAseq datasets, no theoretical tools exist to explain why a particular cluster exists or why a hypothesized cluster is missing. Recently, several authors have shown that eigenvalues of scRNAseq count matrices can be approximated using random matrix models. In this work, we extend these previous works to the study of a scRNAseq workflow. We model scaled count matrices using random matrices with normally distributed entries. Using these random matrix models, we quantify the differential expression of a cluster and develop predictions for the workflow, and in particular clustering, as a function of the differential expression. We also use results from random matrix theory (RMT) to develop predictive formulas for portions of the scRNAseq workflow. Using simulated and real datasets, we show that our predictions are accurate if certain conditions hold on differential expression, with our RMT based predictions requiring particularly stringent condition. We find that real datasets violate these conditions, leading to bias in our predictions, but our predictions are better than a naive estimator and we point out future work that can improve the predictions. To our knowledge, our formulas represents the first predictive results for scRNAseq workflows.

Comprehensive single-cell aging atlas of healthy mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer

Aging is the greatest risk factor for breast cancer; however, how age-related cellular and molecular events impact cancer initiation is unknown. In this study, we investigated how aging rewires transcriptomic and epigenomic programs of mouse mammary glands at single-cell resolution, yielding a comprehensive resource for aging and cancer biology. Aged epithelial cells exhibit epigenetic and transcriptional changes in metabolic, pro-inflammatory and cancer-associated genes. Aged stromal cells downregulate fibroblast marker genes and upregulate markers of senescence and cancer-associated fibroblasts. Among immune cells, distinct T cell subsets (Gzmk+, memory CD4+, γδ) and M2-like macrophages expand with age. Spatial transcriptomics reveals co-localization of aged immune and epithelial cells in situ. Lastly, we found transcriptional signatures of aging mammary cells in human breast tumors, suggesting possible links between aging and cancer. Together, these data uncover that epithelial, immune and stromal cells shift in proportions and cell identity, potentially impacting cell plasticity, aged microenvironment and neoplasia risk.

All the single cells: Single-cell transcriptomics/epigenomics experimental design and analysis considerations for glial biologists

Single-cell transcriptomics, epigenomics, and other 'omics applied at single-cell resolution can significantly advance hypotheses and understanding of glial biology. Omics technologies are revealing a large and growing number of new glial cell subtypes, defined by their gene expression profile. These subtypes have significant implications for understanding glial cell function, cell-cell communications, and glia-specific changes between homeostasis and conditions such as neurological disease. For many, the training in how to analyze, interpret, and understand these large datasets has been through reading and understanding literature from other fields like biostatistics. Here, we provide a primer for glial biologists on experimental design and analysis of single-cell RNA-seq datasets. Our goal is to further the understanding of why decisions are made about datasets and to enhance biologists' ability to interpret and critique their work and the work of others. We review the steps involved in single-cell analysis with a focus on decision points and particular notes for glia. The goal of this primer is to ensure that single-cell 'omics experiments continue to advance glial biology in a rigorous and replicable way.

GraphPCA: a fast and interpretable dimension reduction algorithm for spatial transcriptomics data

The rapid advancement of spatial transcriptomics technologies has revolutionized our understanding of cell heterogeneity and intricate spatial structures within tissues and organs. However, the high dimensionality and noise in spatial transcriptomic data present significant challenges for downstream data analyses. Here, we develop GraphPCA, an interpretable and quasi-linear dimension reduction algorithm that leverages the strengths of graphical regularization and principal component analysis. Comprehensive evaluations on simulated and multi-resolution spatial transcriptomic datasets generated from various platforms demonstrate the capacity of GraphPCA to enhance downstream analysis tasks including spatial domain detection, denoising, and trajectory inference compared to other state-of-the-art methods.

A physically inspired approach to coarse-graining transcriptomes reveals the dynamics of aging

Single-cell RNA sequencing has enabled the study of aging at a molecular scale. While substantial progress has been made in measuring age-related gene expression, the underlying patterns and mechanisms of aging transcriptomes remain poorly understood. To address this gap, we propose a physics-inspired, data-analysis approach to extract additional insights from single-cell RNA sequencing data. By considering the genome as a many-body interacting system, we leverage central idea of the Renormalization Group to construct an approach to hierarchically describe aging across a spectrum of scales for the gene expresion. This framework provides a quantitative language to study the multiscale patterns of aging transcriptomes. Overall, our study demonstrates the value of leveraging theoretical physics concepts like the Renormalization Group to gain new biological insights from complex high-dimensional single-cell data.

Adding Highly Variable Genes to Spatially Variable Genes Can Improve Cell Type Clustering Performance in Spatial Transcriptomics Data

Spatial transcriptomics has allowed researchers to analyze transcriptome data in its tissue sample's spatial context. Various methods have been developed for detecting spatially variable genes (SV genes), whose gene expression over the tissue space shows strong spatial autocorrelation. Such genes are often used to define clusters in cells or spots downstream. However, highly variable (HV) genes, whose quantitative gene expressions show significant variation from cell to cell, are conventionally used in clustering analyses. In this report, we investigate whether adding highly variable genes to spatially variable genes can improve the cell type clustering performance in spatial transcriptomics data. We tested the clustering performance of HV genes, SV genes, and the union of both gene sets (concatenation) on over 50 real spatial transcriptomics datasets across multiple platforms, using a variety of spatial and non-spatial metrics. Our results show that combining HV genes and SV genes can improve overall cell-type clustering performance.

Statistically principled feature selection for single cell transcriptomics

The high dimensionality of data in single cell transcriptomics (scRNAseq) requires investigators to choose subsets of genes (feature selection) for downstream analysis (e.g., unsupervised cell clustering). The evaluation of different approaches to feature selection is hampered by the fact that, as we show here, the performance of feature selection methods varies greatly with the task being performed. For routine cell type identification, even randomly chosen features can perform well, but for cell type differences that are subtle, both number of features and selection strategy can matter strongly. Here we present a simple feature selection method grounded in an analytical model that, without resorting to arbitrary thresholds or user-defined parameters, allows for interpretable delineation of both how many and which features to choose, facilitating identification of biologically meaningful rare cell types. We compare this method to default methods in scanpy and Seurat, as well as SCTransform, showing how greater accuracy can often be achieved with surprisingly few, well-chosen features.

Mcadet: A feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection

Single-cell RNA sequencing (scRNA-seq) data analysis faces numerous challenges, including high sparsity, a high-dimensional feature space, and biological noise. These challenges hinder downstream analysis, necessitating the use of feature selection methods to identify informative genes, and reduce data dimensionality. However, existing methods for selecting highly variable genes (HVGs) exhibit limited overlap and inconsistent clustering performance across benchmark datasets. Moreover, these methods often struggle to accurately select HVGs from fine-resolution scRNA-seq datasets and minority cell types, which are more difficult to distinguish, raising concerns about the reliability of their results. To overcome these limitations, we propose a novel feature selection framework for scRNA-seq data called Mcadet. Mcadet integrates Multiple Correspondence Analysis (MCA), graph-based community detection, and a novel statistical testing approach. To assess the effectiveness of Mcadet, we conducted extensive evaluations using both simulated and real-world data, employing unbiased metrics for comparison. Our results demonstrate the superior performance of Mcadet in the selection of HVGs in scenarios involving fine-resolution scRNA-seq datasets and datasets containing minority cell populations. Overall, we demonstrate that Mcadet enhances the reliability of selected HVGs, although the impact of HVG selection on various downstream analyses varies and needs to be further investigated.

Protocol to search for genetic factors related to severe COVID-19 by analyzing publicly available genome-wide association studies

COVID-19 casualties vary among different ancestral groups due to a variety of factors. Here, we present a protocol for analyzing publicly available genome-wide association studies (GWASs) to search for ancestry-specific genetic factors related to severe COVID-19. We describe steps for downloading and comparing two COVID-19 GWASs, calculating expression quantitative trait loci, and single-cell gene expression analysis. We demonstrate this approach using GWASs from Host Genetics Initiative; however, it is applicable to other databases such as the UK Biobank. For complete details on the use and execution of this protocol, please refer to Cheng et al.1.

Leveraging gene correlations in single cell transcriptomic data

BACKGROUND

Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually).

RESULTS

We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships.

CONCLUSIONS

New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations.

Spatial microenvironments tune immune response dynamics in the Drosophila larval fat body

Immune responses in tissues display intricate patterns of gene expression that vary across space and time. While such patterns have been increasingly linked to disease outcomes, the mechanisms that generate them and the logic behind them remain poorly understood. As a tractable model of spatial immune responses, we investigated heterogeneous expression of antimicrobial peptides in the larval fly fat body, an organ functionally analogous to the liver. To capture the dynamics of immune response across the full tissue at single-cell resolution, we established live light sheet fluorescence microscopy of whole larvae. We discovered that expression of antimicrobial peptides occurs in a reproducible spatial pattern, with enhanced expression in the anterior and posterior lobes of the fat body. This pattern correlates with microbial localization via blood flow but is not caused by it: loss of heartbeat suppresses microbial transport but leaves the expression pattern unchanged. This result suggests that regions of the tissue most likely to encounter microbes via blood flow are primed to produce antimicrobials. Spatial transcriptomics revealed that these immune microenvironments are defined by genes spanning multiple biological processes, including lipid-binding proteins that regulate host cell death by the immune system. In sum, the larval fly fat body exhibits spatial compartmentalization of immune activity that resembles the strategic positioning of immune cells in mammals, such as in the liver, gut, and lymph nodes. This finding suggests that tissues may share a conserved spatial organization that optimizes immune responses for antimicrobial efficacy while preventing excessive self-damage.

HLA-E and NKG2A Mediate Resistance to M. bovis BCG Immunotherapy in Non-Muscle-Invasive Bladder Cancer

Mycobacterium bovis Bacillus Calmette-Guerin (BCG) is the primary treatment for non-muscle-invasive bladder cancer (NMIBC), known to stimulate inflammatory cytokines, notably interferon (IFN)-γ. We observed that prolonged IFN-γ exposure fosters adaptive resistance in recurrent tumors, aiding immune evasion and tumor proliferation. We identify HLA-E and NKG2A, part of a novel NK and T cell checkpoint pathway, as key mediators of resistance in BCG-unresponsive NMIBC. IFN-γ enhances HLA-E and PD-L1 expression in recurrent tumors, with an enrichment of intra-tumoral NKG2A-expressing NK and CD8 T cells. CXCL9+ macrophages and dendritic cells and CXCL12-expressing stromal cells likely recruit CXCR3/CXCR4-expressing NK and T cells and CXCR7+ HLA-EHIGH tumor cells. NK and CD8 T cells remain functional within BCG-unresponsive tumors but are inhibited by HLA-E and PD-L1, providing a framework for combined NKG2A and PD-L1 blockade strategy for bladder-sparing treatment of BCG-unresponsive NMIBC.

Integrated single-cell and bulk RNA-seq analysis identifies a prognostic T-cell signature in colorectal cancer

Colorectal cancer (CRC) is a major contributor to global morbidity and mortality, necessitating more effective therapeutic approaches. T cells, prominent in the tumor microenvironment, exert a crucial role in modulating immunotherapeutic responses and clinical outcomes in CRC. This study introduces a pioneering method for characterizing the CRC immune microenvironment using single-cell sequencing data. Unlike previous approaches, which focused on individual T-cell signature genes, we utilized overall infiltration levels of colorectal cancer signature T-cells. Through weighted gene co-expression network analysis, Lasso regression, and StepCox analysis, we developed a prognostic risk model, TRGS (T-cell related genes signatures), based on six T cell-related genes. Multivariate Cox analysis identified TRGS as an independent prognostic factor for CRC, showcasing its superior predictive efficacy compared to existing immune-related prognostic models. Immunoreactivity analysis revealed higher Immunophenoscore and lower Tumor Immune Dysfunction and Exclusion scores in the low-risk group, indicating potential responsiveness to immune checkpoint inhibitor therapy. Additionally, patients in the low-risk group demonstrated heightened sensitivity to 5-fluorouracil-based chemotherapy regimens. In summary, TRGS emerges as a standalone prognostic biomarker for CRC, offering insights to optimize patient responses to immunotherapy and chemotherapy, thereby laying the groundwork for personalized tumor management strategies.

Evaluation of barley genotypes for drought adaptability: based on stress indices and comprehensive evaluation as criteria

The prevalence of drought events worldwide emphasizes the importance of screening and cultivating drought-adapted crops. In this study, 206 germplasm resources were used as materials, dry weight as target trait, and two genotyping methods as criteria to evaluate drought adaptability at the seedling establishment stage. The results showed a significant decrease in average dry weight of the tested germplasm resources (from 746.90 mg to 285.40 mg) and rich variation in the responses of dry weight among each genotype to drought (CV=61.14%). In traditional evaluation method, drought resistance coefficient (DC), geometric mean productivity index (GMP), mean productivity index (MP), stress susceptibility index (SSI), stress tolerance index (STI), and tolerance index (TOL) also exhibited diversity in tested genotypes (CV>30%). However, these indices showed varying degrees of explanation for dry weight under stress and non-stress environments and failed to differentiate drought adaptability among genotypes clearly. In new evaluation method, four stress indices were developed to quantify barley seedling production and stability capacities. Compared to traditional stress indices, the stress production index (SI) explained dry weight more comprehensively under stress conditions (R2 = 0.98), while the ideal production index (II) explained dry weight better under non-stress conditions (R2 = 0.89). Furthermore, the potential index (PI) and elasticity index (EI) eliminated disparities in traditional stress indices and comprehensively clarified the contribution of elasticity and potential to production capacity under drought stress. Ultimately, through grading evaluation and cluster analysis, the tested germplasm resources were effectively categorized, and 11 genotypes were identified as suitable for cultivation in arid areas. Overall, the comprehensive evaluation method based on the newly developed stress indices surpasses the traditional method in screening drought adaptability of crops and serves as a vital tool for identifying high-stability and high-production capacities genotypes in various environments, which is expected to provide practical guidance for barley planting and breeding in arid areas.

Spatiotemporal omics for biology and medicine

The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.

Multiplexed single-cell characterization of alternative polyadenylation regulators

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice, we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle, including elongation, splicing, termination, and surveillance. We train and validate a deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis-regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation.

Interpretable single-cell factor decomposition using sciRED

Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable Residual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.

Feature selection followed by a novel residuals-based normalization that includes variance stabilization simplifies and improves single-cell gene expression analysis

Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are reduction of systematic biases primarily introduced through technical sources and transformation of counts to make them more amenable for the application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo.

A novel induced pluripotent stem cell model of Schwann cell differentiation reveals NF2 - related gene regulatory networks of the extracellular matrix

Schwann cells are vital to development and maintenance of the peripheral nervous system and their dysfunction has been implicated in a range of neurological and neoplastic disorders, including NF2 -related schwannomatosis. We developed a novel human induced pluripotent stem cell (hiPSC) model to study Schwann cell differentiation in health and disease. We performed transcriptomic, immunofluorescence, and morphological analysis of hiPSC derived Schwann cell precursors (SPCs) and terminally differentiated Schwann cells (SCs) representing distinct stages of development. To validate our findings, we performed integrated, cross-species analyses across multiple external datasets at bulk and single cell resolution. Our hiPSC model of Schwann cell development shared overlapping gene expression signatures with human amniotic mesenchymal stem cell (hAMSCs) derived SCs and in vivo mouse models, but also revealed unique features that may reflect species-specific aspects of Schwann cell biology. Moreover, we identified gene co-expression modules that are dynamically regulated during hiPSC to SC differentiation associated with ear and neural development, cell fate determination, the NF2 gene, and extracellular matrix (ECM) organization. By cross-referencing results between multiple datasets, we identified new genes potentially associated with NF2 expression. Our hiPSC model further provides a tractable platform for studying Schwann cell development in the context of human disease.

A new atlas to study embryonic cell types in Xenopus

This paper introduces a single-cell atlas for pivotal developmental stages in Xenopus, encompassing gastrulation, neurulation, and early tailbud. Notably surpassing its predecessors, the new atlas enhances gene mapping, read counts, and gene/cell type nomenclature. Leveraging the latest Xenopus tropicalis genome version, alongside advanced alignment pipelines and machine learning for cell type assignment, this release maintains consistency with previous cell type annotations while rectifying nomenclature issues. Employing an unbiased approach for cell type assignment proves especially apt for embryonic contexts, given the considerable number of non-terminally differentiated cell types. An alternative cell type attribution here adopts a fuzzy, non-deterministic stance, capturing the transient nature of early embryo progenitor cells by presenting an ensemble of types in superposition. The value of the new resource is emphasized through numerous examples, with a focus on previously unexplored germ cell populations where we uncover novel transcription onset features. Offering interactive exploration via a user-friendly web portal and facilitating complete data downloads, this atlas serves as a comprehensive and accessible reference.

STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics

Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15,000 - 30,000 dimensional gene expressions. With 4,293,195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond.

MUFFIN: a suite of tools for the analysis of functional sequencing data

The large diversity of functional genomic assays allows for the characterization of non-coding and coding events at the tissue level or at a single-cell resolution. However, this diversity also leads to protocol differences, widely varying sequencing depths, substantial disparities in sample sizes, and number of features. In this work, we have built a Python package, MUFFIN, which offers a wide variety of tools suitable for a broad range of genomic assays and brings many tools that were missing from the Python ecosystem. First, MUFFIN has specialized tools for the exploration of the non-coding regions of genomes, such as a function to identify consensus peaks in peak-called assays, as well as linking genomic regions to genes and performing Gene Set Enrichment Analyses. MUFFIN also possesses a robust and flexible count table processing pipeline, comprising normalization, count transformation, dimensionality reduction, Differential Expression, and clustering. Our tools were tested on three widely different scRNA-seq, ChIP-seq and ATAC-seq datasets. MUFFIN integrates with the popular Scanpy ecosystem and is available on Conda and at https://github.com/pdelangen/Muffin.

Multi-omic profiling reveals the endogenous and neoplastic responses to immunotherapies in cutaneous T cell lymphoma

Cutaneous T cell lymphomas (CTCLs) are skin cancers with poor survival rates and limited treatments. While immunotherapies have shown some efficacy, the immunological consequences of administering immune-activating agents to CTCL patients have not been systematically characterized. We apply a suite of high-dimensional technologies to investigate the local, cellular, and systemic responses in CTCL patients receiving either mono- or combination anti-PD-1 plus interferon-gamma (IFN-γ) therapy. Neoplastic T cells display no evidence of activation after immunotherapy. IFN-γ induces muted endogenous immunological responses, while anti-PD-1 elicits broader changes, including increased abundance of CLA+CD39+ T cells. We develop an unbiased multi-omic profiling approach enabling discovery of immune modules stratifying patients. We identify an enrichment of activated regulatory CLA+CD39+ T cells in non-responders and activated cytotoxic CLA+CD39+ T cells in leukemic patients. Our results provide insights into the effects of immunotherapy in CTCL patients and a generalizable framework for multi-omic analysis of clinical trials.

Predicting the multivariate zero-inflated counts: A novel model averaging method under Pearson loss

Excessive zeros in multivariate count data are often observed in scenarios of biomedicine and public health. To provide a better analysis on this type of data, we first develop a marginalized multivariate zero-inflated Poisson (MZIP) regression model to directly interpret the overall exposure effects on marginal means. Then, we define a multiple Pearson residual for our newly developed MZIP regression model by simultaneously taking heterogeneity and correlation into consideration. Furthermore, a new model averaging prediction method is introduced based on the multiple Pearson residual, and the asymptotical optimality of this model averaging prediction is proved. Simulations and two empirical applications in medicine are used to illustrate the effectiveness of the proposed method.

Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection

Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.

Feature selection followed by a novel residuals-based normalization simplifies and improves single-cell gene expression analysis

Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are to reduce the systematic biases primarily introduced through technical sources and to transform the data to make it more amenable for application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo.

Chromatin accessibility during human first-trimester neurodevelopment

The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.

The effect of data transformation on low-dimensional integration of single-cell RNA-seq

BACKGROUND

Recent developments in single-cell RNA sequencing have opened up a multitude of possibilities to study tissues at the level of cellular populations. However, the heterogeneity in single-cell sequencing data necessitates appropriate procedures to adjust for technological limitations and various sources of noise when integrating datasets from different studies. While many analysis procedures employ various preprocessing steps, they often overlook the importance of selecting and optimizing the employed data transformation methods.

RESULTS

This work investigates data transformation approaches used in single-cell clustering analysis tools and their effects on batch integration analysis. In particular, we compare 16 transformations and their impact on the low-dimensional representations, aiming to reduce the batch effect and integrate multiple single-cell sequencing data. Our results show that data transformations strongly influence the results of single-cell clustering on low-dimensional data space, such as those generated by UMAP or PCA. Moreover, these changes in low-dimensional space significantly affect trajectory analysis using multiple datasets, as well. However, the performance of the data transformations greatly varies across datasets, and the optimal method was different for each dataset. Additionally, we explored how data transformation impacts the analysis of deep feature encodings using deep neural network-based models, including autoencoder-based models and proto-typical networks. Data transformation also strongly affects the outcome of deep neural network models.

CONCLUSIONS

Our findings suggest that the batch effect and noise in integrative analysis are highly influenced by data transformation. Low-dimensional features can integrate different batches well when proper data transformation is applied. Furthermore, we found that the batch mixing score on low-dimensional space can guide the selection of the optimal data transformation. In conclusion, data preprocessing is one of the most crucial analysis steps and needs to be cautiously considered in the integrative analysis of multiple scRNA-seq datasets.

scLENS: data-driven signal detection for unbiased scRNA-seq data analysis

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.

Modeling gene expression cascades during cell state transitions

During cellular processes such as differentiation or response to external stimuli, cells exhibit dynamic changes in their gene expression profiles. Single-cell RNA sequencing (scRNA-seq) can be used to investigate these dynamic changes. To this end, cells are typically ordered along a pseudotemporal trajectory which recapitulates the progression of cells as they transition from one cell state to another. We infer transcriptional dynamics by modeling the gene expression profiles in pseudotemporally ordered cells using a Bayesian inference approach. This enables ordering genes along transcriptional cascades, estimating differences in the timing of gene expression dynamics, and deducing regulatory gene interactions. Here, we apply this approach to scRNA-seq datasets derived from mouse embryonic forebrain and pancreas samples. This analysis demonstrates the utility of the method to derive the ordering of gene dynamics and regulatory relationships critical for proper cellular differentiation and maturation across a variety of developmental contexts.

Single cell and bulk RNA expression analyses identify enhanced hexosamine biosynthetic pathway and O-GlcNAcylation in acute myeloid leukemia blasts and stem cells

Introduction

Acute myeloid leukemia (AML) is the most common acute leukemia in adults with an overall poor prognosis and high relapse rate. Multiple factors including genetic abnormalities, differentiation defects and altered cellular metabolism contribute to AML development and progression. Though the roles of oxidative phosphorylation and glycolysis are defined in AML, the role of the hexosamine biosynthetic pathway (HBP), which regulates the O-GlcNAcylation of cytoplasmic and nuclear proteins, remains poorly defined.

Methods

We studied the expression of the key enzymes involved in the HBP in AML blasts and stem cells by RNA sequencing at the single-cell and bulk level. We performed flow cytometry to study OGT protein expression and global O-GlcNAcylation. We studied the functional effects of inhibiting O-GlcNAcylation on transcriptional activation in AML cells by Western blotting and real time PCR and on cell cycle by flow cytometry.

Results

We found higher expression levels of the key enzymes in the HBP in AML as compared to healthy donors in whole blood. We observed elevated O-GlcNAc Transferase (OGT) and O-GlcNAcase (OGA) expression in AML stem and bulk cells as compared to normal hematopoietic stem and progenitor cells (HSPCs). We also found that both AML bulk cells and stem cells show significantly enhanced OGT protein expression and global O-GlcNAcylation as compared to normal HSPCs, validating our in silico findings. Gene set analysis showed substantial enrichment of the NF-κB pathway in AML cells expressing high OGT levels. Inhibition of O-GlcNAcylation decreased NF-κB nuclear translocation and the expression of selected NF-κB-dependent genes controlling cell cycle. It also blocked cell cycle progression suggesting a link between enhanced O-GlcNAcylation and NF-κB activation in AML cell survival and proliferation.

Discussion

Our study suggests the HBP may prove a potential target, alone or in combination with other therapeutic approaches, to impact both AML blasts and stem cells. Moreover, as insufficient targeting of AML stem cells by traditional chemotherapy is thought to lead to relapse, blocking HBP and O-GlcNAcylation in AML stem cells may represent a novel promising target to control relapse.

Mapping human tissues with highly multiplexed RNA in situ hybridization

In situ transcriptomic techniques promise a holistic view of tissue organization and cell-cell interactions. There has been a surge of multiplexed RNA in situ mapping techniques but their application to human tissues has been limited due to their large size, general lower tissue quality and high autofluorescence. Here we report DART-FISH, a padlock probe-based technology capable of profiling hundreds to thousands of genes in centimeter-sized human tissue sections. We introduce an omni-cell type cytoplasmic stain that substantially improves the segmentation of cell bodies. Our enzyme-free isothermal decoding procedure allows us to image 121 genes in large sections from the human neocortex in <10 h. We successfully recapitulated the cytoarchitecture of 20 neuronal and non-neuronal subclasses. We further performed in situ mapping of 300 genes on a diseased human kidney, profiled >20 healthy and pathological cell states, and identified diseased niches enriched in transcriptionally altered epithelial cells and myofibroblasts.

Long-read single-cell RNA sequencing enables the study of cancer subclone-specific genotype and phenotype in chronic lymphocytic leukemia

Bruton's tyrosine kinase (BTK) inhibitors are effective for the treatment of chronic lymphocytic leukemia (CLL) due to BTK's role in B cell survival and proliferation. Treatment resistance is most commonly caused by the emergence of the hallmark BTKC481S mutation that inhibits drug binding. In this study, we aimed to investigate whether the presence of additional CLL driver mutations in cancer subclones harboring a BTKC481S mutation accelerates subclone expansion. In addition, we sought to determine whether BTK-mutated subclones exhibit distinct transcriptomic behavior when compared to other cancer subclones. To achieve these goals, we employ our recently published method (Qiao et al. 2024) that combines bulk DNA sequencing and single-cell RNA sequencing (scRNA-seq) data to genotype individual cells for the presence or absence of subclone-defining mutations. While the most common approach for scRNA-seq includes short-read sequencing, transcript coverage is limited due to the vast majority of the reads being concentrated at the priming end of the transcript. Here, we utilized MAS-seq, a long-read scRNAseq technology, to substantially increase transcript coverage across the entire length of the transcripts and expand the set of informative mutations to link cells to cancer subclones in six CLL patients who acquired BTKC481S mutations during BTK inhibitor treatment. We found that BTK-mutated subclones often acquire additional mutations in CLL driver genes, leading to faster subclone proliferation. When examining subclone-specific gene expression, we found that in one patient, BTK-mutated subclones are transcriptionally distinct from the rest of the malignant B cell population with an overexpression of CLL-relevant genes.

eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

BACKGROUND

Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes.

RESULTS

We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression.

CONCLUSIONS

eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

Single-cell time series analysis reveals the dynamics of HSPC response to inflammation

Hematopoietic stem and progenitor cells (HSPCs) are known to respond to acute inflammation; however, little is understood about the dynamics and heterogeneity of these stress responses in HSPCs. Here, we performed single-cell sequencing during the sensing, response, and recovery phases of the inflammatory response of HSPCs to treatment (a total of 10,046 cells from four time points spanning the first 72 h of response) with the pro-inflammatory cytokine IFNα to investigate the HSPCs' dynamic changes during acute inflammation. We developed the essential novel computational approaches to process and analyze the resulting single-cell time series dataset. This includes an unbiased cell type annotation and abundance analysis post inflammation, tools for identification of global and cell type-specific responding genes, and a semi-supervised linear regression approach for response pseudotime reconstruction. We discovered a variety of different gene responses of the HSPCs to the treatment. Interestingly, we were able to associate a global reduced myeloid differentiation program and a locally enhanced pyroptosis activity with reduced myeloid progenitor and differentiated cells after IFNα treatment. Altogether, the single-cell time series analyses have allowed us to unbiasedly study the heterogeneous and dynamic impact of IFNα on the HSPCs.

Molecularly stratified hypothalamic astrocytes are cellular foci for obesity

Astrocytes safeguard the homeostasis of the central nervous system1,2. Despite their prominent morphological plasticity under conditions that challenge the brain's adaptive capacity3-5, the classification of astrocytes, and relating their molecular make-up to spatially devolved neuronal operations that specify behavior or metabolism, remained mostly futile6,7. Although it seems unexpected in the era of single-cell biology, the lack of a major advance in stratifying astrocytes under physiological conditions rests on the incompatibility of 'neurocentric' algorithms that rely on stable developmental endpoints, lifelong transcriptional, neurotransmitter, and neuropeptide signatures for classification6-8 with the dynamic functional states, anatomic allocation, and allostatic plasticity of astrocytes1. Simplistically, therefore, astrocytes are still grouped as 'resting' vs. 'reactive', the latter referring to pathological states marked by various inducible genes3,9,10. Here, we introduced a machine learning-based feature recognition algorithm that benefits from the cumulative power of published single-cell RNA-seq data on astrocytes as a reference map to stepwise eliminate pleiotropic and inducible cellular features. For the healthy hypothalamus, this walk-back approach revealed gene regulatory networks (GRNs) that specified subsets of astrocytes, and could be used as landmarking tools for their anatomical assignment. The core molecular censuses retained by astrocyte subsets were sufficient to stratify them by allostatic competence, chiefly their signaling and metabolic interplay with neurons. Particularly, we found differentially expressed mitochondrial genes in insulin-sensing astrocytes and demonstrated their reciprocal signaling with neurons that work antagonistically within the food intake circuitry. As a proof-of-concept, we showed that disrupting Mfn2 expression in astrocytes reduced their ability to support dynamic circuit reorganization, a time-locked feature of satiety in the hypothalamus, thus leading to obesity in mice. Overall, our results suggest that astrocytes in the healthy brain are fundamentally more heterogeneous than previously thought and topologically mirror the specificity of local neurocircuits.

deMULTIplex2: robust sample demultiplexing for scRNA-seq

Sample multiplexing enables pooled analysis during single-cell RNA sequencing workflows, thereby increasing throughput and reducing batch effects. A challenge for all multiplexing techniques is to link sample-specific barcodes with cell-specific barcodes, then demultiplex sample identity post-sequencing. However, existing demultiplexing tools fail under many real-world conditions where barcode cross-contamination is an issue. We therefore developed deMULTIplex2, an algorithm inspired by a mechanistic model of barcode cross-contamination. deMULTIplex2 employs generalized linear models and expectation-maximization to probabilistically determine the sample identity of each cell. Benchmarking reveals superior performance across various experimental conditions, particularly on large or noisy datasets with unbalanced sample compositions.

Heterogeneity and transcriptional drivers of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a heterogeneous disease with limited treatment options. To characterize TNBC heterogeneity, we defined transcriptional, epigenetic, and metabolic subtypes and subtype-driving super-enhancers and transcription factors by combining functional and molecular profiling with computational analyses. Single-cell RNA sequencing revealed relative homogeneity of the major transcriptional subtypes (luminal, basal, and mesenchymal) within samples. We found that mesenchymal TNBCs share features with mesenchymal neuroblastoma and rhabdoid tumors and that the PRRX1 transcription factor is a key driver of these tumors. PRRX1 is sufficient for inducing mesenchymal features in basal but not in luminal TNBC cells via reprogramming super-enhancer landscapes, but it is not required for mesenchymal state maintenance or for cellular viability. Our comprehensive, large-scale, multiplatform, multiomics study of both experimental and clinical TNBC is an important resource for the scientific and clinical research communities and opens venues for future investigation.

Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics

MOTIVATION

The recent development of spatially resolved transcriptomics (SRT) technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data. They first conduct clustering analysis and then aggregate cluster-level expression based on the clustering results. This workflow fails to leverage the marker gene information efficiently. On the other hand, other cell annotation methods designed for single-cell RNA-seq data utilize the cell-type marker genes information but fail to use spatial information in SRT data.

RESULTS

We introduce a statistical spatial transcriptomics cell assignment model, SPAN, to annotate clusters of cells or spots into known types in SRT data with prior knowledge of predefined marker genes and spatial information. The SPAN model annotates cells or spots from SRT data using predefined overexpressed marker genes and combines a mixture model with a hidden Markov random field to model the spatial dependency between neighboring spots. We demonstrate the effectiveness of SPAN against spatial and nonspatial clustering algorithms through extensive simulation and real data experiments.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ChengZ352/SPAN.

Leveraging gene correlations in single cell transcriptomic data

BACKGROUND

Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data when ground truth about biological variation is unknown (i.e., usually).

RESULTS

We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p-values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships.

CONCLUSIONS

New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations.

Stomach encyclopedia: Combined single-cell and spatial transcriptomics reveal cell diversity and homeostatic regulation of human stomach

The stomach is an important digestive organ with various biological functions. However, because of the complexity of its cellular and glandular composition, its precise cellular biology has yet to be elucidated. In this study, we conducted single-cell RNA sequencing (scRNA-seq) and subcellular-level spatial transcriptomics analysis of the human stomach and constructed the largest dataset to date: a stomach encyclopedia. This dataset consists of approximately 380,000 cells from scRNA-seq and the spatial transcriptome, enabling integrated analyses of transcriptional and spatial information of gastric and metaplastic cells. This analysis identified LEFTY1 as an uncharacterized stem cell marker, which was confirmed through lineage tracing analysis. A wide variety of cell-cell interactions between epithelial and stromal cells, including PDGFRA+BMP4+WNT5A+ fibroblasts, was highlighted in the developmental switch of intestinal metaplasia. Our extensive dataset will function as a fundamental resource in investigations of the stomach, including studies of development, aging, and carcinogenesis.

Comprehensive single cell aging atlas of mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer

Aging is the greatest risk factor for breast cancer; however, how age-related cellular and molecular events impact cancer initiation is unknown. We investigate how aging rewires transcriptomic and epigenomic programs of mouse mammary glands at single cell resolution, yielding a comprehensive resource for aging and cancer biology. Aged epithelial cells exhibit epigenetic and transcriptional changes in metabolic, pro-inflammatory, or cancer-associated genes. Aged stromal cells downregulate fibroblast marker genes and upregulate markers of senescence and cancer-associated fibroblasts. Among immune cells, distinct T cell subsets (Gzmk+, memory CD4+, γδ) and M2-like macrophages expand with age. Spatial transcriptomics reveal co-localization of aged immune and epithelial cells in situ. Lastly, transcriptional signatures of aging mammary cells are found in human breast tumors, suggesting mechanistic links between aging and cancer. Together, these data uncover that epithelial, immune, and stromal cells shift in proportions and cell identity, potentially impacting cell plasticity, aged microenvironment, and neoplasia risk.

scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning

Annotation of cell-types is a critical step in the analysis of single-cell RNA sequencing (scRNA-seq) data that allows the study of heterogeneity across multiple cell populations. Currently, this is most commonly done using unsupervised clustering algorithms, which project single-cell expression data into a lower dimensional space and then cluster cells based on their distances from each other. However, as these methods do not use reference datasets, they can only achieve a rough classification of cell-types, and it is difficult to improve the recognition accuracy further. To effectively solve this issue, we propose a novel supervised annotation method, scDeepInsight. The scDeepInsight method is capable of performing manifold assignments. It is competent in executing data integration through batch normalization, performing supervised training on the reference dataset, doing outlier detection and annotating cell-types on query datasets. Moreover, it can help identify active genes or marker genes related to cell-types. The training of the scDeepInsight model is performed in a unique way. Tabular scRNA-seq data are first converted to corresponding images through the DeepInsight methodology. DeepInsight can create a trainable image transformer to convert non-image RNA data to images by comprehensively comparing interrelationships among multiple genes. Subsequently, the converted images are fed into convolutional neural networks such as EfficientNet-b3. This enables automatic feature extraction to identify the cell-types of scRNA-seq samples. We benchmarked scDeepInsight with six other mainstream cell annotation methods. The average accuracy rate of scDeepInsight reached 87.5%, which is more than 7% higher compared with the state-of-the-art methods.

Individual cell types in C. elegans age differently and activate distinct cell-protective responses

Aging is characterized by a global decline in physiological function. However, by constructing a complete single-cell gene expression atlas, we find that Caenorhabditis elegans aging is not random in nature but instead is characterized by coordinated changes in functionally related metabolic, proteostasis, and stress-response genes in a cell-type-specific fashion, with downregulation of energy metabolism being the only nearly universal change. Similarly, the rates at which cells age differ significantly between cell types. In some cell types, aging is characterized by an increase in cell-to-cell variance, whereas in others, variance actually decreases. Remarkably, multiple resilience-enhancing transcription factors known to extend lifespan are activated across many cell types with age; we discovered new longevity candidates, such as GEI-3, among these. Together, our findings suggest that cells do not age passively but instead react strongly, and individualistically, to events that occur during aging. This atlas can be queried through a public interface.

Cell-type-specific co-expression inference from single cell RNA-sequencing data

The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. For this task, the high sequencing depth variations and measurement errors in scRNA-seq data present two significant challenges, and they have not been adequately addressed by existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, that explicitly models sequencing depth variations and measurement errors in scRNA-seq data. Systematic evaluations show that most existing methods suffered from inflated false positives as well as biased co-expression estimates and clustering analysis, whereas CS-CORE gave accurate estimates in these experiments. When applied to scRNA-seq data from postmortem brain samples from Alzheimer's disease patients/controls and blood samples from COVID-19 patients/controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from existing methods.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Comparison of transformations for single-cell RNA-seq data

The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range. These steps are intended to make subsequent application of generic statistical methods more palatable. Here, we describe four transformation approaches based on the delta method, model residuals, inferred latent expression state and factor analysis. We compare their strengths and weaknesses and find that the latter three have appealing theoretical properties; however, in benchmarks using simulated and real-world data, it turns out that a rather simple approach, namely, the logarithm with a pseudo-count followed by principal-component analysis, performs as well or better than the more sophisticated alternatives. This result highlights limitations of current theoretical analysis as assessed by bottom-line performance benchmarks.