MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization-based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.

YY1 knockout in pro-B cells impairs lineage commitment, enabling unusual hematopoietic lineage plasticity.

During B-cell development, cells progress through multiple developmental stages, with the pro-B-cell stage defining commitment to the B-cell lineage. YY1 is a ubiquitous transcription factor that is capable of both activation and repression functions. We found here that knockout of YY1 at the pro-B-cell stage eliminates B lineage commitment. YY1 knockout pro-B cells can generate T lineage cells in vitro using the OP9-DL4 feeder system and in vivo after injection into sublethally irradiated Rag1-/- mice. These T lineage-like cells lose their B lineage transcript profile and gain a T-cell lineage profile. Single-cell RNA-seq experiments showed that as YY1 knockout pro-B cells transition into T lineage cells in vitro, various cell clusters adopt transcript profiles representing a multiplicity of hematopoietic lineages, indicating unusual lineage plasticity. In addition, YY1 KO pro-B cells in vivo can give rise to other hematopoietic lineages in vivo. Evaluation of RNA-seq, scRNA-seq, ChIP-seq, and scATAC-seq data indicates that YY1 controls numerous chromatin-modifying proteins leading to increased accessibility of alternative lineage genes in YY1 knockout pro-B cells. Given the ubiquitous nature of YY1 and its dual activation and repression functions, YY1 may regulate commitment in multiple cell lineages.

Automated single-cell omics end-to-end framework with data-driven batch inference.

To facilitate single-cell multi-omics analysis and improve reproducibility, we present single-cell pipeline for end-to-end data integration (SPEEDI), a fully automated end-to-end framework for batch inference, data integration, and cell-type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell-type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch-inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/. A record of this paper's transparent peer review process is included in the supplemental information.

Benchmarking computational methods for single-cell chromatin data analysis.

BACKGROUND: Single-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices. RESULTS: We benchmark 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluate the performance of each method at different data processing stages. This comprehensive approach allows us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection. CONCLUSIONS: Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.

Regulatory mechanisms orchestrating cellular diversity of Cd36+ olfactory sensory neurons revealed by scRNA-seq and scATAC-seq analysis.

Recent discoveries have revealed remarkable complexity within olfactory sensory neurons (OSNs), including the existence of two OSN populations based on the expression of Cd36. However, the regulatory mechanisms governing this cellular diversity in the same cell type remain elusive. Here, we show the preferential expression of 79 olfactory receptors in Cd36+ OSNs and the anterior projection characteristics of Cd36+ OSNs, indicating the non-randomness of Cd36 expression. The integrated analysis of single-cell RNA sequencing (scRNA-seq) and scATAC-seq reveals that the differences in Cd36+/- OSNs occur at the immature OSN stage, with Mef2a and Hdac9 being important regulators of developmental divergence. We hypothesize that the absence of Hdac9 may affect the activation of Mef2a, leading to the up-regulation of Mef2a target genes, including teashirt zinc finger family member 1 (Tshz1), in the Cd36+ OSN lineage. We validate that Tshz1 directly promotes Cd36 expression through enhancer bindings. Our study unravels the intricate regulatory landscape and principles governing cellular diversity in the olfactory system.

scBlood: A comprehensive single-cell accessible chromatin database of blood cells.

The advent of single cell transposase-accessible chromatin sequencing (scATAC-seq) technology enables us to explore the genomic characteristics and chromatin accessibility of blood cells at the single-cell level. To fully make sense of the roles and regulatory complexities of blood cells, it is critical to collect and analyze these rapidly accumulating scATAC-seq datasets at a system level. Here, we present scBlood (https://bio.liclab.net/scBlood/), a comprehensive single-cell accessible chromatin database of blood cells. The current version of scBlood catalogs 770,907 blood cells and 452,247 non-blood cells from ∼400 high-quality scATAC-seq samples covering 30 tissues and 21 disease types. All data hosted on scBlood have undergone preprocessing from raw fastq files and multiple standards of quality control. Furthermore, we conducted comprehensive downstream analyses, including multi-sample integration analysis, cell clustering and annotation, differential chromatin accessibility analysis, functional enrichment analysis, co-accessibility analysis, gene activity score calculation, and transcription factor (TF) enrichment analysis. In summary, scBlood provides a user-friendly interface for searching, browsing, analyzing, visualizing, and downloading scATAC-seq data of interest. This platform facilitates insights into the functions and regulatory mechanisms of blood cells, as well as their involvement in blood-related diseases.

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling.

Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.

Single-cell chromatin accessibility and transposable element landscapes reveal shared features of tissue-residing immune cells.

Tissue adaptation is required for regulatory T (Treg) cell function within organs. Whether this program shares aspects with other tissue-localized immune populations is unclear. Here, we analyzed single-cell chromatin accessibility data, including the transposable element (TE) landscape of CD45+ immune cells from colon, skin, adipose tissue, and spleen. We identified features of organ-specific tissue adaptation across different immune cells. Focusing on tissue Treg cells, we found conservation of the Treg tissue adaptation program in other tissue-localized immune cells, such as amphiregulin-producing T helper (Th)17 cells. Accessible TEs can act as regulatory elements, but their contribution to tissue adaptation is not understood. TE landscape analysis revealed an enrichment of specific transcription factor binding motifs in TE regions within accessible chromatin peaks. TEs, specifically from the LTR family, were located in enhancer regions and associated with tissue adaptation. These findings broaden our understanding of immune tissue residency and provide an important step toward organ-specific immune interventions.

Detecting novel cell type in single-cell chromatin accessibility data via open-set domain adaptation.

Recent advances in single-cell technologies enable the rapid growth of multi-omics data. Cell type annotation is one common task in analyzing single-cell data. It is a challenge that some cell types in the testing set are not present in the training set (i.e. unknown cell types). Most scATAC-seq cell type annotation methods generally assign each cell in the testing set to one known type in the training set but neglect unknown cell types. Here, we present OVAAnno, an automatic cell types annotation method which utilizes open-set domain adaptation to detect unknown cell types in scATAC-seq data. Comprehensive experiments show that OVAAnno successfully identifies known and unknown cell types. Further experiments demonstrate that OVAAnno also performs well on scRNA-seq data. Our codes are available online at https://github.com/lisaber/OVAAnno/tree/master.

CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal.

In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.

CAraCAl: CAMML with the integration of chromatin accessibility.

BACKGROUND: A vital step in analyzing single-cell data is ascertaining which cell types are present in a dataset, and at what abundance. In many diseases, the proportions of varying cell types can have important implications for health and prognosis. Most approaches for cell type annotation have centered around cell typing for single-cell RNA-sequencing (scRNA-seq) and have had promising success. However, reliable methods are lacking for many other single-cell modalities such as single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), which quantifies the extent to which genes of interest in each cell are epigenetically "open" for expression. RESULTS: To leverage the informative potential of scATAC-seq data, we developed CAMML with the integration of chromatin accessibility (CAraCAl), a bioinformatic method that performs cell typing on scATAC-seq data. CAraCAl performs cell typing by scoring each cell for its enrichment of cell type-specific gene sets. These gene sets are composed of the most upregulated or downregulated genes present in each cell type according to projected gene activity. CONCLUSIONS: We found that CAraCAl does not improve performance beyond CAMML when scRNA-seq is present, but if only scATAC-seq is available, CAraCAl performs cell typing relatively successfully. As such, we also discuss best practices for cell typing and the strengths and weaknesses of various cell annotation options.

The single-cell chromatin landscape in gonadal cell lineage specification.

Gonad development includes sex determination and divergent maturation of the testes and ovaries. Recent advances in measuring gene expression in single cells are providing new insights into this complex process. However, the underlying epigenetic regulatory mechanisms remain unclear. Here, we profiled chromatin accessibility in mouse gonadal cells of both sexes from embryonic day 11.5 to 14.5 using single-cell assay for transposase accessible chromatin by sequencing (scATAC-seq). Our results showed that individual cell types can be inferred by the chromatin landscape, and that cells can be temporally ordered along developmental trajectories. Integrative analysis of transcriptomic and chromatin-accessibility maps identified multiple putative regulatory elements proximal to key gonadal genes Nr5a1, Sox9 and Wt1. We also uncover cell type-specific regulatory factors underlying cell type specification. Overall, our results provide a better understanding of the epigenetic landscape associated with the progressive restriction of cell fates in the gonad.

Deep generative models in single-cell omics.

Deep Generative Models (DGMs) are becoming instrumental for inferring probability distributions inherent to complex processes, such as most questions in biomedical research. For many years, there was a lack of mathematical methods that would allow this inference in the scarce data scenario of biomedical research. The advent of single-cell omics has finally made square the so-called "skinny matrix", allowing to apply mathematical methods already extensively used in other areas. Moreover, it is now possible to integrate data at different molecular levels in thousands or even millions of samples, thanks to the number of single-cell atlases being collaboratively generated. Additionally, DGMs have proven useful in other frequent tasks in single-cell analysis pipelines, from dimensionality reduction, cell type annotation to RNA velocity inference. In spite of its promise, DGMs need to be used with caution in biomedical research, paying special attention to its use to answer the right questions and the definition of appropriate error metrics and validation check points that confirm not only its correct use but also its relevance. All in all, DGMs provide an exciting tool that opens a bright future for the integrative analysis of single-cell -omics to understand health and disease.

Kidney Organoid Modeling of WT1 Mutations Reveals Key Regulatory Paths Underlying Podocyte Development.

Wilms tumor-1(WT1) is a crucial transcription factor that regulates podocyte development. However, the epigenomic mechanism underlying the function of WT1 during podocyte development has yet to be fully elucidated. Here, single-cell chromatin accessibility and gene expression maps of foetal kidneys and kidney organoids are generated. Functional implications of WT1-targeted genes, which are crucial for the development of podocytes and the maintenance of their structure, including BMPER/PAX2/MAGI2 that regulates WNT signaling pathway, MYH9 that maintains actin filament organization and NPHS1 that modulates cell junction assembly are identified. To further illustrate the functional importance of WT1-mediated transcriptional regulation during podocyte development, cultured and implanted patient-derived kidney organoids derived from the Induced Pluripotent Stem Cell (iPSCs) of a patient with a heterozygous missense mutation in WT1 are generated. Results from single-cell RNA sequencing (scRNA-seq) and functional assays confirm that the WT1 mutation leads to delays in podocyte development and causes damage to cell structures, due to its failure to activate the targeting genes MAGI2, MYH9, and NPHS1. Notably, correcting the mutation in the patient iPSCs using CRISPR-Cas9 gene editing rescues the podocyte phenotype. Collectively, this work elucidates the WT1-related epigenomic landscape with respect to human podocyte development and identifies the disease-causing role of a WT1 mutation.

A review of single-cell transcriptomics and epigenomics studies in maternal and child health.

Single-cell sequencing technologies enhance our understanding of cellular dynamics throughout pregnancy. We outlined the workflow of single-cell sequencing techniques and reviewed single-cell studies in maternal and child health. We conducted a literature review of single cell studies on maternal and child health using PubMed. We summarized the findings from 16 single-cell atlases of the human and mammalian placenta across gestational stages and 31 single-cell studies on maternal exposures and complications including infection, obesity, diet, gestational diabetes, pre-eclampsia, environmental exposure and preterm birth. Single-cell studies provides insights on novel cell types in placenta and cell type-specific marks associated with maternal exposures and complications.

Single-cell sequencing technologies offer new biological insights on pregnancy at the cellular level. We reviewed these technologies and their applications in maternal and child health studies, including 16 placenta cell databases and 31 studies on health challenges during pregnancy such as COVID infection. New cell types and biological pathways among specific groups of cells were found.

Single-cell insights: pioneering an integrated atlas of chromatin accessibility and transcriptomic landscapes in diabetic cardiomyopathy.

BACKGROUND: Diabetic cardiomyopathy (DCM) poses a growing health threat, elevating heart failure risk in diabetic individuals. Understanding DCM is crucial, with fibroblasts and endothelial cells playing pivotal roles in driving myocardial fibrosis and contributing to cardiac dysfunction. Advances in Multimodal single-cell profiling, such as scRNA-seq and scATAC-seq, provide deeper insights into DCM's unique cell states and molecular landscape for targeted therapeutic interventions. METHODS: Single-cell RNA and ATAC data from 10x Multiome libraries were processed using Cell Ranger ARC v2.0.1. Gene expression and ATAC data underwent Seurat and Signac filtration. Differential gene expression and accessible chromatin regions were identified. Transcription factor activity was estimated with chromVAR, and Cis-coaccessibility networks were calculated using Cicero. Coaccessibility connections were compared to the GeneHancer database. Gene Ontology analysis, biological process scoring, cell-cell communication analysis, and gene-motif correlation was performed to reveal intricate molecular changes. Immunofluorescent staining utilized various antibodies on paraffin-embedded tissues to verify the findings. RESULTS: This study integrated scRNA-seq and scATAC-seq data obtained from hearts of WT and DCM mice, elucidating molecular changes at the single-cell level throughout the diabetic cardiomyopathy progression. Robust and accurate clustering analysis of the integrated data revealed altered cell proportions, showcasing decreased endothelial cells and macrophages, coupled with increased fibroblasts and myocardial cells in the DCM group, indicating enhanced fibrosis and endothelial damage. Chromatin accessibility analysis unveiled unique patterns in cell types, with heightened transcriptional activity in myocardial cells. Subpopulation analysis highlighted distinct changes in cardiomyocytes and fibroblasts, emphasizing pathways related to fatty acid metabolism and cardiac contraction. Fibroblast-centered communication analysis identified interactions with endothelial cells, implicating VEGF receptors. Endothelial cell subpopulations exhibited altered gene expressions, emphasizing contraction and growth-related pathways. Candidate regulators, including Tcf21, Arnt, Stat5a, and Stat5b, were identified, suggesting their pivotal roles in DCM development. Immunofluorescence staining validated marker genes of cell subpopulations, confirming PDK4, PPARγ and Tpm1 as markers for metabolic pattern-altered cardiomyocytes, activated fibroblasts and endothelial cells with compromised proliferation. CONCLUSION: Our integrated scRNA-seq and scATAC-seq analysis unveils intricate cell states and molecular alterations in diabetic cardiomyopathy. Identified cell type-specific changes, transcription factors, and marker genes offer valuable insights. The study sheds light on potential therapeutic targets for DCM.

Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms.

There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omics data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.

Type I interferons induce an epigenetically distinct memory B cell subset in chronic viral infection.

Memory B cells (MBCs) are key providers of long-lived immunity against infectious disease, yet in chronic viral infection, they do not produce effective protection. How chronic viral infection disrupts MBC development and whether such changes are reversible remain unknown. Through single-cell (sc)ATAC-seq and scRNA-seq during acute versus chronic lymphocytic choriomeningitis viral infection, we identified a memory subset enriched for interferon (IFN)-stimulated genes (ISGs) during chronic infection that was distinct from the T-bet+ subset normally associated with chronic infection. Blockade of IFNAR-1 early in infection transformed the chromatin landscape of chronic MBCs, decreasing accessibility at ISG-inducing transcription factor binding motifs and inducing phenotypic changes in the dominating MBC subset, with a decrease in the ISG subset and an increase in CD11c+CD80+ cells. However, timing was critical, with MBCs resistant to intervention at 4 weeks post-infection. Together, our research identifies a key mechanism to instruct MBC identity during viral infection.

Single-cell approaches define two groups of mammalian oligodendrocyte precursor cells and their evolution over developmental time.

Here, we used single-cell RNA sequencing (scRNA-seq), single-cell ATAC sequencing (scATAC-seq), and single-cell spatial transcriptomics to characterize murine cortical OPCs throughout postnatal life. During development, we identified two groups of differentially localized PDGFRα+ OPCs that are transcriptionally and epigenetically distinct. One group (active, or actOPCs) is metabolically active and enriched in white matter. The second (homeostatic, or hOPCs) is less active, enriched in gray matter, and predicted to derive from actOPCs. In adulthood, these two groups are transcriptionally but not epigenetically distinct, and relative to developing OPCs are less active metabolically and have less open chromatin. When adult oligodendrogenesis is enhanced during experimentally induced remyelination, adult OPCs do not reacquire a developmental open chromatin state, and the oligodendrogenesis trajectory is distinct from that seen neonatally. These data suggest that there are two OPC groups subserving distinct postnatal functions and that neonatal and adult OPC-mediated oligodendrogenesis are fundamentally different.