Integrative analyses of single-cell transcriptome and regulome using MAESTRO

TBX21 Methylation as a Potential Regulator of Immune Suppression in CMS1 Subtype Colorectal Cancer

Cytotoxic T lymphocyte (CTL) infiltration is associated with survival, recurrence, and therapeutic response in colorectal cancer (CRC). Immune checkpoint inhibitor (ICI) therapy, which requires CTLs for response, does not work for most CRC patients. Therefore, it is critical to improve our understanding of immune resistance in this disease. We utilized 2391 CRC patients and 7 omics datasets, integrating clinical and genomic data to determine how DNA methylation may impact survival and CTL function in CRC. Using comprehensive molecular subtype (CMS) 1 patients as reference, we found TBX21 to be the only gene with altered expression and methylation that was associated with CTL infiltration. We found that CMS1 patients with high TBX21 expression and low methylation had a significant survival advantage. To confirm the role of Tbx21 in CTL function, we utilized scRNAseq data, demonstrating the association of TBX21 with markers of enhanced CTL function. Further analysis using pathway enrichment found that the genes TBX21, MX1, and SP140 had altered expression and methylation, suggesting that the TP53/P53 pathway may modify TBX21 methylation to upregulate TBX21 expression. Together, this suggests that targeting epigenetic modification more specifically for therapy and patient stratification may provide improved outcomes in CRC.

Nonparametric single-cell multiomic characterization of trio relationships between transcription factors, target genes, and cis-regulatory regions

The epigenetic control of gene expression is highly cell-type and context specific. Yet, despite its complexity, gene regulatory logic can be broken down into modular components consisting of a transcription factor (TF) activating or repressing the target gene expression through its binding to a cis-regulatory region. We propose a nonparametric approach, TRIPOD, to detect and characterize the three-way relationships between a TF, its target gene, and the accessibility of the TF's binding site using single-cell RNA and ATAC multiomic data. We apply TRIPOD to interrogate the cell-type-specific regulatory logic in peripheral blood mononuclear cells and contrast our results to detections from enhancer databases, cis-eQTL studies, ChIP-seq experiments, and TF knockdown/knockout studies. We then apply TRIPOD to mouse embryonic brain data and identify regulatory relationships, validated by ChIP-seq and PLAC-seq. Finally, we demonstrate TRIPOD on the SHARE-seq data of differentiating mouse hair follicle cells and identify lineage-specific regulation supported by histone marks and super-enhancer annotations. A record of this paper's transparent peer review process is included in the supplemental information.

Functional inference of gene regulation using single-cell multi-omics

Cells require coordinated control over gene expression when responding to environmental stimuli. Here we apply scATAC-seq and single-cell RNA sequencing (scRNA-seq) in resting and stimulated human blood cells. Collectively, we generate ~91,000 single-cell profiles, allowing us to probe the cis-regulatory landscape of the immunological response across cell types, stimuli, and time. Advancing tools to integrate multi-omics data, we develop functional inference of gene regulation (FigR), a framework to computationally pair scA-TAC-seq with scRNA-seq cells, connect distal cis-regulatory elements to genes, and infer gene-regulatory networks (GRNs) to identify candidate transcription factor (TF) regulators. Utilizing these paired multi-omics data, we define domains of regulatory chromatin (DORCs) of immune stimulation and find that cells alter chromatin accessibility and gene expression at timescales of minutes. Construction of the stimulation GRN elucidates TF activity at disease-associated DORCs. Overall, FigR enables elucidation of regulatory interactions across single-cell data, providing new opportunities to understand the function of cells within tissues.

Single-cell methods for measuring chromatin accessibility (ATAC-seq) and gene expression (RNA-seq) are rapidly evolving, but tools to integrate data and infer gene-regulatory relationships remain limited. Here we generate multi-omics data of resting and stimulated human blood cells and present a new computational framework for constructing gene-regulatory networks (GRNs). Specifically, we describe functional inference of gene regulation (FigR), a workflow to (1) pair scATAC-seq with scRNA-seq, (2) connect cis-regulatory elements to target genes, and (3) identify TF-gene relationships.

Feasibility and Application of Cluster Nursing to the Care of Patients with Acute Oncology

Objective

To probe the utility of cluster nursing for the care of acute oncology clients.

Methods

One hundred fourteen cases of acute oncology pioneers undergoing therapy in our clinic from April 2019 to February 2021 were randomly assigned into two consecutive arms, conventional care and cluster care, in accordance with the nursing modality. Complications, satisfaction, quality of survival, and negative emotions were compared across the two parties.

Results

The comorbidity incidence rate of the subject matter in the research cohort was 7.02%, which was below the comorbidity rate of 17.54% in the reaction cohort (P < 0.05); the percentage of satisfaction in the research cohort was 96.49%, which was higher than the satisfaction rate of 78.95% in the reaction cohort (P < 0.05); aftercare, the quality of survival was significantly higher in both groups, and the SAS and SDS scores were significantly lower, with a more pronounced trend of change in the research cohort than in the reaction cohort (P < 0.05).

Conclusion

Bundled care for casualty oncology is of major value, with a marked reduction in the incidence of postoperative complications, high quality of survival, an excellent prognosis and negative mood, high patient morale and satisfaction and compliance with curative treatment, and good support for subsequent care.

Integration of single-cell multi-omics data by regression analysis on unpaired observations

Despite recent developments, it is hard to profile all multi-omics single-cell data modalities on the same cell. Thus, huge amounts of single-cell genomics data of unpaired observations on different cells are generated. We propose a method named UnpairReg for the regression analysis on unpaired observations to integrate single-cell multi-omics data. On real and simulated data, UnpairReg provides an accurate estimation of cell gene expression where only chromatin accessibility data is available. The cis-regulatory network inferred from UnpairReg is highly consistent with eQTL mapping. UnpairReg improves cell type identification accuracy by joint analysis of single-cell gene expression and chromatin accessibility data.

Single cell cancer epigenetics

Bulk sequencing methodologies have allowed us to make great progress in cancer research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic mechanisms that govern tumor heterogeneity. Consequently, many novel single cell-sequencing methodologies have been developed over the past decade, allowing us to explore the epigenetic components that regulate different aspects of cancer heterogeneity, namely: clonal heterogeneity, tumor microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, and resistance mechanisms. In this review, we explore the different sequencing techniques that enable researchers to study different aspects of epigenetics (DNA methylation, chromatin accessibility, histone modifications, DNA-protein interactions, and chromatin 3D architecture) at the single cell level, their potential applications in cancer, and their current technical limitations.

scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously

It is a challenging task to integrate scRNA-seq and scATAC-seq data obtained from different batches. Existing methods tend to use a pre-defined gene activity matrix to convert the scATAC-seq data into scRNA-seq data. The pre-defined gene activity matrix is often of low quality and does not reflect the dataset-specific relationship between the two data modalities. We propose scDART, a deep learning framework that integrates scRNA-seq and scATAC-seq data and learns cross-modalities relationships simultaneously. Specifically, the design of scDART allows it to preserve cell trajectories in continuous cell populations and can be applied to trajectory inference on integrated data.

Leveraging Bulk and Single-Cell RNA Sequencing Data of NSCLC Tumor Microenvironment and Therapeutic Potential of NLOC-15A, A Novel Multi-Target Small Molecule

Lung cancer poses a serious threat to human health and has recently been tagged the most common malignant disease with the highest incidence and mortality rate. Although epidermal growth factor (EGFR)-tyrosine kinase inhibitors (TKIs) have significantly improved the prognosis of advanced non-small cell lung cancer (NSCLC) patients with EGFR mutations, patients often develop resistance to these drugs. There is therefore a need to identify new drug candidates with multitarget potential for treating NSCLC. We hereby provide preclinical evidence of the therapeutic efficacy of NLOC-015A a multitarget small-molecule inhibitor of EGFR/mitogen-activated protein (MAP) kinase kinase 1 (MAP2K1)/mammalian target of rapamycin (mTOR)/yes-associated protein 1 (YAP1) for the treatment NSCLC. Our multi-omics analysis of clinical data from cohorts of NSCLC revealed that dysregulation of EGFR/MAP2K1/mTOR/YAP1 signaling pathways was associated with the progression, therapeutic resistance, immune-invasive phenotypes, and worse prognoses of NSCLC patients. Analysis of single-cell RNA sequencing datasets revealed that MAP2K1, mTOR, YAP1 and EGFR were predominantly located on monocytes/macrophages, Treg and exhaustive CD8 T cell, and are involved in M2 polarization within the TME of patients with primary and metastatic NSCLC which further implied gene’s role in remodeling the tumor immune microenvironment. A molecular-docking analysis revealed that NLOC-015A bound to YAP1, EGFR, MAP kinase/extracellular signal-related kinase kinase 1 (MEK1), and mTOR with strong binding efficacies ranging –8.4 to –9.50 kcal/mol. Interestingly, compared to osimertinib, NLOC-015 bound with higher efficacy to the tyrosine kinase (TK) domains of both T790M and T790M/C797S mutant-bearing EGFR. Our in vitro studies and sequencing analysis revealed that NLOC-015A inhibited the proliferation and oncogenic phenotypes of NSCLC cell lines with concomitant downregulation of expression levels of mTOR, EGFR, YAP1, and MEK1 signaling network. We, therefore, suggest that NLOC-015A might represent a new candidate for treating NSCLC via acting as a multitarget inhibitor of EGFR, mTOR/NF-κB, YAP1, MEK1 in NSCLC.

Advances in Single-Cell Multi-Omics and Application in Cardiovascular Research

With the development of ever more powerful and versatile high-throughput sequencing techniques and innovative ways to capture single cells, mapping the multicellular tissues at the single-cell level is becoming routine practice. However, it is still challenging to depict the epigenetic landscape of a single cell, especially the genome-wide chromatin accessibility, histone modifications, and DNA methylation. We summarize the most recent methodologies to profile these epigenetic marks at the single-cell level. We also discuss the development and advancement of several multi-omics sequencing technologies from individual cells. Advantages and limitations of various methods to compare and integrate datasets obtained from different sources are also included with specific practical notes. Understanding the heart tissue at single-cell resolution and multi-modal levels will help to elucidate the cell types and states involved in physiological and pathological events during heart development and disease. The rich information produced from single-cell multi-omics studies will also promote the research of heart regeneration and precision medicine on heart diseases.

DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data

The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)–to–gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET’s predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation–specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data.

Comprehensive assessment of cellular senescence in the tumor microenvironment

Cellular senescence (CS), a state of permanent growth arrest, is intertwined with tumorigenesis. Due to the absence of specific markers, characterizing senescence levels and senescence-related phenotypes across cancer types remain unexplored. Here, we defined computational metrics of senescence levels as CS scores to delineate CS landscape across 33 cancer types and 29 normal tissues and explored CS-associated phenotypes by integrating multiplatform data from ~20 000 patients and ~212 000 single-cell profiles. CS scores showed cancer type-specific associations with genomic and immune characteristics and significantly predicted immunotherapy responses and patient prognosis in multiple cancers. Single-cell CS quantification revealed intra-tumor heterogeneity and activated immune microenvironment in senescent prostate cancer. Using machine learning algorithms, we identified three CS genes as potential prognostic predictors in prostate cancer and verified them by immunohistochemical assays in 72 patients. Our study provides a comprehensive framework for evaluating senescence levels and clinical relevance, gaining insights into CS roles in cancer- and senescence-related biomarker discovery.

Interactive single-cell data analysis using Cellar

Cell type assignment is a major challenge for all types of high throughput single cell data. In many cases such assignment requires the repeated manual use of external and complementary data sources. To improve the ability to uniformly assign cell types across large consortia, platforms and modalities, we developed Cellar, a software tool that provides interactive support to all the different steps involved in the assignment and dataset comparison process. We discuss the different methods implemented by Cellar, how these can be used with different data types, how to combine complementary data types and how to analyze and visualize spatial data. We demonstrate the advantages of Cellar by using it to annotate several HuBMAP datasets from multi-omics single-cell sequencing and spatial proteomics studies. Cellar is open-source and includes several annotated HuBMAP datasets.

Tertiary lymphoid structure and decreased CD8+ T cell infiltration in minimally invasive adenocarcinoma

Knowledge of the tumor microenvironment (TME) in patients with early lung cancer, especially in comparison with the matched adjacent tissues, remains lacking. To characterize TME of early-stage lung adenocarcinoma, we performed RNA-seq profiling on 58 pairs of minimally invasive adenocarcinoma (MIA) tumors and matched adjacent normal tissues. MIA tumors exhibited an adaptive TME characterized by high CD4⁺ T cell infiltration, high B-cell activation, and low CD8⁺ T cell infiltration. The high expression of markers for B cells, activated CD4⁺ T cells, and follicular helper T (Tfh) cells in bulk MIA samples and three independent single-cell RNA-seq datasets implied tertiary lymphoid structures (TLS) formation. Multiplex immunohistochemistry staining validated TLS formation and revealed an enrichment of follicular regulatory T cells (Tfr) in TLS follicles, which may explain the lower CD8⁺ T cell infiltration and attenuated anti-tumor immunity in MIA. Our study demonstrates how integrating transcriptome and pathology characterize TME and elucidate potential mechanisms of tumor immune evasion.

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Higher infiltration and activation of B and CD4⁺ T cell characterize MIA tumors

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MIA tumors are infiltrated with lower CD8⁺ T cells than normal tissues

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TLS constituted by B, CD4⁺ T cells, and CD35⁺ FDC is validated in MIA tumors

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Decreased CD8⁺ T is associated with Tfr-mediated immunosuppression in MIA tumor

Dihydroartemisinin beneficially regulates splenic immune cell heterogeneity through the SOD3-JNK-AP-1 axis

The immunomodulatory potential of dihydroartemisinin (DHA) has recently been highlighted; however, the potential mechanism remains to be clarified. Single-cell RNA sequencing was explored in combination with cellular and biochemical approaches to elucidate the immunomodulatory mechanisms of DHA. In this study, we found that DHA induced both spleen enlargement and rearrangement of splenic immune cell subsets in mice. It was revealed that DHA promoted the reversible expansion of effective regulatory T cells and interferon-γ⁺ cytotoxic CD8⁺ T cells in the spleen via induction of superoxide dismutase 3 (SOD3) expression and increased phosphorylation of c-Jun N-terminal kinases (JNK) and its downstream activator protein 1 (AP-1) transcription factors. Further, SOD3 knockout mice were resistant to the regulatory effect of DHA. Thus, DHA, through the activation of the SOD3-JNK-AP-1 axis, beneficially regulated immune cell heterogeneity and splenic immune cell homeostasis to treat autoimmune diseases.

Accurate and fast cell marker gene identification with COSG

Accurate cell classification is the groundwork for downstream analysis of single-cell sequencing data, yet how to identify true marker genes for different cell types still remains a big challenge. Here, we report COSine similarity-based marker Gene identification (COSG) as a cosine similarity-based method for more accurate and scalable marker gene identification. COSG is applicable to single-cell RNA sequencing data, single-cell ATAC sequencing data and spatially resolved transcriptome data. COSG is fast and scalable for ultra-large datasets of million-scale cells. Application on both simulated and real experimental datasets showed that the marker genes or genomic regions identified by COSG have greater cell-type specificity, demonstrating the superior performance of COSG in terms of both accuracy and efficiency as compared with other available methods.

SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging

High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using linear models challenging. We present SIGNET, a deep learning-based framework for capturing complex regulatory relationships between genes under the assumption that the expression levels of transcription factors participating in gene regulation are strong predictors of the expression of their target genes. Evaluations based on a variety of real and simulated scRNA-seq datasets showed that SIGNET is more sensitive to ChIP-seq validated regulatory interactions in different types of cells, particularly rare cells. Therefore, this process is more effective for various downstream analyses, such as cell clustering and gene regulatory network inference. We demonstrated that SIGNET is a useful tool for identifying important regulatory modules driving various biological processes.

Deep learning-based advances and applications for single-cell RNA-sequencing data analysis

The rapid development of single-cell RNA-sequencing (scRNA-seq) technology has raised significant computational and analytical challenges. The application of deep learning to scRNA-seq data analysis is rapidly evolving and can overcome the unique challenges in upstream (quality control and normalization) and downstream (cell-, gene- and pathway-level) analysis of scRNA-seq data. In the present study, recent advances and applications of deep learning-based methods, together with specific tools for scRNA-seq data analysis, were summarized. Moreover, the future perspectives and challenges of deep-learning techniques regarding the appropriate analysis and interpretation of scRNA-seq data were investigated. The present study aimed to provide evidence supporting the biomedical application of deep learning-based tools and may aid biologists and bioinformaticians in navigating this exciting and fast-moving area.

Fast alignment and preprocessing of chromatin profiles with Chromap

As sequencing depth of chromatin studies continually grows deeper for sensitive profiling of regulatory elements or chromatin spatial structures, aligning and preprocessing of these sequencing data have become the bottleneck for analysis. Here we present Chromap, an ultrafast method for aligning and preprocessing high throughput chromatin profiles. Chromap is comparable to BWA-MEM and Bowtie2 in alignment accuracy and is over 10 times faster than traditional workflows on bulk ChIP-seq/Hi-C profiles and than 10x Genomics' CellRanger v2.0.0 pipeline on single-cell ATAC-seq profiles.

Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects

Single-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.

Multi-omics integration in the age of million single-cell data

An explosion in single-cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell-state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single-cell techniques have extended to multi-omics approaches and now enable the simultaneous measurement of data modalities and spatial cellular context. Data are now available for millions of cells, for whole-genome measurements and for multiple modalities. Although analyses of such multimodal datasets have the potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single-cell dataset. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell-type classification, regulatory network modelling and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multi-omics data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.

TIMEx: tumor-immune microenvironment deconvolution web-portal for bulk transcriptomics using pan-cancer scRNA-seq signatures

SUMMARY

The heterogeneous cell types of the tumor-immune microenvironment (TIME) play key roles in determining cancer progression, metastasis and response to treatment. We report the development of TIMEx, a novel TIME deconvolution method emphasizing on estimating infiltrating immune cells for bulk transcriptomics using pan-cancer single-cell RNA-seq signatures. We also implemented a comprehensive, user-friendly web-portal for users to evaluate TIMEx and other deconvolution methods with bulk transcriptomic profiles.

AVAILABILITY AND IMPLEMENTATION

TIMEx web-portal is freely accessible at http://timex.moffitt.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target

Despite remarkable clinical efficacy of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits for triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that deletion of the E3 ubiquitin ligase Cop1 in cancer cells decreases secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, enhances anti-tumor immunity, and strengthens ICB response. Transcriptomics, epigenomics, and proteomics analyses revealed that Cop1 functions through proteasomal degradation of the C/ebpδ protein. The Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebpδ, which leads to polyubiquitination of C/ebpδ. In addition, deletion of the E3 ubiquitin ligase Cop1 in cancer cells stabilizes C/ebpδ to suppress expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy in TNBC by regulating chemokine secretion and macrophage infiltration in the tumor microenvironment.

Intricacies of single-cell multi-omics data integration

A wealth of single-cell protocols makes it possible to characterize different molecular layers at unprecedented resolution. Integrating the resulting multimodal single-cell data to find cell-to-cell correspondences remains a challenge. We argue that data integration needs to happen at a meaningful biological level of abstraction and that it is necessary to consider the inherent discrepancies between modalities to strike a balance between biological discovery and noise removal. A survey of current methods reveals that a distinction between technical and biological origins of presumed unwanted variation between datasets is not yet commonly considered. The increasing availability of paired multimodal data will aid the development of improved methods by providing a ground truth on cell-to-cell matches.

CellCall: integrating paired ligand-receptor and transcription factor activities for cell-cell communication

With the dramatic development of single-cell RNA sequencing (scRNA-seq) technologies, the systematic decoding of cell-cell communication has received great research interest. To date, several in-silico methods have been developed, but most of them lack the ability to predict the communication pathways connecting the insides and outsides of cells. Here, we developed CellCall, a toolkit to infer inter- and intracellular communication pathways by integrating paired ligand-receptor and transcription factor (TF) activity. Moreover, CellCall uses an embedded pathway activity analysis method to identify the significantly activated pathways involved in intercellular crosstalk between certain cell types. Additionally, CellCall offers a rich suite of visualization options (Circos plot, Sankey plot, bubble plot, ridge plot, etc.) to present the analysis results. Case studies on scRNA-seq datasets of human testicular cells and the tumor immune microenvironment demonstrated the reliable and unique functionality of CellCall in intercellular communication analysis and internal TF activity exploration, which were further validated experimentally. Comparative analysis of CellCall and other tools indicated that CellCall was more accurate and offered more functions. In summary, CellCall provides a sophisticated and practical tool allowing researchers to decipher intercellular communication and related internal regulatory signals based on scRNA-seq data. CellCall is freely available at https://github.com/ShellyCoder/cellcall.

EpiScanpy: integrated single-cell epigenomic analysis

EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types.

Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona

MOTIVATION

Single-cell multi-omics sequencing data can provide a comprehensive molecular view of cells. However, effective approaches for the integrative analysis of such data are challenging. Existing manifold alignment methods demonstrated the state-of-the-art performance on single-cell multi-omics data integration, but they are often limited by requiring that single-cell datasets be derived from the same underlying cellular structure.

RESULTS

In this study, we present Pamona, a partial Gromov-Wasserstein distance-based manifold alignment framework that integrates heterogeneous single-cell multi-omics datasets with the aim of delineating and representing the shared and dataset-specific cellular structures across modalities. We formulate this task as a partial manifold alignment problem and develop a partial Gromov-Wasserstein optimal transport framework to solve it. Pamona identifies both shared and dataset-specific cells based on the computed probabilistic couplings of cells across datasets, and it aligns cellular modalities in a common low-dimensional space, while simultaneously preserving both shared and dataset-specific structures. Our framework can easily incorporate prior information, such as cell type annotations or cell-cell correspondence, to further improve alignment quality. We evaluated Pamona on a comprehensive set of publicly available benchmark datasets. We demonstrated that Pamona can accurately identify shared and dataset-specific cells, as well as faithfully recover and align cellular structures of heterogeneous single-cell modalities in a common space, outperforming the comparable existing methods.

AVAILABILITYAND IMPLEMENTATION

Pamona software is available at https://github.com/caokai1073/Pamona.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data

The comparison of gene regulatory networks between diseased versus healthy individuals or between two different treatments is an important scientific problem. Here, we propose sc-compReg as a method for the comparative analysis of gene expression regulatory networks between two conditions using single cell gene expression (scRNA-seq) and single cell chromatin accessibility data (scATAC-seq). Our software, sc-compReg, can be used as a stand-alone package that provides joint clustering and embedding of the cells from both scRNA-seq and scATAC-seq, and the construction of differential regulatory networks across two conditions. We apply the method to compare the gene regulatory networks of an individual with chronic lymphocytic leukemia (CLL) versus a healthy control. The analysis reveals a tumor-specific B cell subpopulation in the CLL patient and identifies TOX2 as a potential regulator of this subpopulation.

Changes in cell state underlie the difference between health and disease. Here, the authors propose a computational framework for the integration of gene expression and chromatin-accessibility data from single cells to identify differences in gene regulation in cell types across two conditions.

Review of multi-omics data resources and integrative analysis for human brain disorders

In the last decade, massive omics datasets have been generated for human brain research. It is evolving so fast that a timely update is urgently needed. In this review, we summarize the main multi-omics data resources for the human brains of both healthy controls and neuropsychiatric disorders, including schizophrenia, autism, bipolar disorder, Alzheimer's disease, Parkinson's disease, progressive supranuclear palsy, etc. We also review the recent development of single-cell omics in brain research, such as single-nucleus RNA-seq, single-cell ATAC-seq and spatial transcriptomics. We further investigate the integrative multi-omics analysis methods for both tissue and single-cell data. Finally, we discuss the limitations and future directions of the multi-omics study of human brain disorders.

The metabolic-epigenetic nexus in type 2 diabetes mellitus

The prevalence of type 2 diabetes mellitus (T2DM) continues to rise globally. Yet the aetiology and pathophysiology of this noncommunicable, polygenic disease, is poorly understood. Lifestyle factors, such as poor dietary intake, lack of exercise, and abnormal glycaemia, are purported to play a role in disease onset and progression, and these environmental factors may disrupt specific epigenetic mechanisms, leading to a reprogramming of gene transcription. The hyperglycaemic cell per se, alters epigenetics through chemical modifications to DNA and histones via metabolic intermediates such as succinate, α-ketoglutarate and O-GlcNAc. To illustrate, α-ketoglutarate is considered a salient co-factor in the activation of the ten-eleven translocation (TET) dioxygenases, which drives DNA demethylation. On the contrary, succinate and other mitochondrial tricarboxylic acid cycle intermediates, inhibit TET activity predisposing to a state of hypermethylation. Hyperglycaemia depletes intracellular ascorbic acid, and damages DNA by enhancing the production of reactive oxygen species (ROS); this compromised cell milieu exacerbates the oxidation of 5-methylcytosine alongside a destabilisation of TET. These metabolic connections may regulate DNA methylation, affecting gene transcription and pancreatic islet β-cell function in T2DM. This complex interrelationship between metabolism and epigenetic alterations may provide a conceptual foundation for understanding how pathologic stimuli modify and control the intricacies of T2DM. As such, this narrative review will comprehensively evaluate and detail the interplay between metabolism and epigenetic modifications in T2DM.

Leveraging Novel Integrated Single-Cell Analyses to Define HIV-1 Latency Reversal

While suppressive antiretroviral therapy can effectively limit HIV-1 replication and evolution, it leaves behind a residual pool of integrated viral genomes that persist in a state of reversible nonproductive infection, referred to as the HIV-1 reservoir. HIV-1 infection models were established to investigate HIV-1 latency and its reversal; recent work began to probe the dynamics of HIV-1 latency reversal at single-cell resolution. Signals that establish HIV-1 latency and govern its reactivation are complex and may not be completely resolved at the cellular and regulatory levels by the aggregated measurements of bulk cellular-sequencing methods. High-throughput single-cell technologies that characterize and quantify changes to the epigenome, transcriptome, and proteome continue to rapidly evolve. Combinations of single-cell techniques, in conjunction with novel computational approaches to analyze these data, were developed and provide an opportunity to improve the resolution of the heterogeneity that may exist in HIV-1 reactivation. In this review, we summarize the published single-cell HIV-1 transcriptomic work and explore how cutting-edge advances in single-cell techniques and integrative data-analysis tools may be leveraged to define the mechanisms that control the reversal of HIV-1 latency.

Applications of Single-Cell Omics in Tumor Immunology

The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem's complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques.

scAMACE: Model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation

MOTIVATION

The advancement in technologies and the growth of available single-cell datasets motivate integrative analysis of multiple single-cell genomic datasets. Integrative analysis of multimodal single-cell datasets combines complementary information offered by single-omic datasets and can offer deeper insights on complex biological process. Clustering methods that identify the unknown cell types are among the first few steps in the analysis of single-cell datasets, and they are important for downstream analysis built upon the identified cell types.

RESULTS

We propose scAMACE for the integrative analysis and clustering of single-cell data on chromatin accessibility, gene expression and methylation. We demonstrate that cell types are better identified and characterized through analyzing the three data types jointly. We develop an efficient expectationmaximization (EM) algorithm to perform statistical inference, and evaluate our methods on both simulation study and real data applications. We also provide the GPU implementation of scAMACE, making it scalable to large datasets.

AVAILABILITY

The software and datasets are available at https://github.com/cuhklinlab/scAMACE_py (python implementation) and https://github.com/cuhklinlab/scAMACE (R implementation).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mammary gland development from a single cell 'omics view

Understanding the complexity and heterogeneity of mammary cell subpopulations is vital to delineate the mechanisms behind breast cancer development, progression and prevention. Increasingly sophisticated tools for investigating these cell subtypes has led to the development of a greater understanding of these cell subtypes, complex interplay of certain subtypes and their developmental potential. Of note, increasing accessibility and affordability of single cell technologies has led to a plethora of studies being published containing data from mammary cell subtypes and their differentiation potential in both mice and human data sets. Here, we review the different types of single cell technologies and how they have been used to improve our understanding of mammary gland development.

Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable insights for clinical and translational research. In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis. We will start with the pre-processing of single-cell RNA sequencing (scRNA-seq) data, including data imputation, cross-platform batch effect removal, and cell cycle and cell-type identification. Next, we will introduce advanced data analysis tools and methods used for copy number variance estimate, single-cell pseudo-time trajectory analysis, phylogenetic tree inference, cell-cell interaction, regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data. Finally, we will present the latest analyzing challenges, such as multi-omics integration and integrated analysis of scRNA-seq data.

Computational principles and challenges in single-cell data integration

The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term 'data integration' has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods.

Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis

Calcific aortic valve disease sits at the confluence of multiple world-wide epidemics of aging, obesity, diabetes, and renal dysfunction, and its prevalence is expected to nearly triple over the next 3 decades. This is of particularly dire clinical relevance, as calcific aortic valve disease can progress rapidly to aortic stenosis, heart failure, and eventually premature death. Unlike in atherosclerosis, and despite the heavy clinical toll, to date, no pharmacotherapy has proven effective to halt calcific aortic valve disease progression, with invasive and costly aortic valve replacement representing the only treatment option currently available. This substantial gap in care is largely because of our still-limited understanding of both normal aortic valve biology and the key regulatory mechanisms that drive disease initiation and progression. Drug discovery is further hampered by the inherent intricacy of the valvular microenvironment: a unique anatomic structure, a complex mixture of dynamic biomechanical forces, and diverse and multipotent cell populations collectively contributing to this currently intractable problem. One promising and rapidly evolving tactic is the application of multiomics approaches to fully define disease pathogenesis. Herein, we summarize the application of (epi)genomics, transcriptomics, proteomics, and metabolomics to the study of valvular heart disease. We also discuss recent forays toward the omics-based characterization of valvular (patho)biology at single-cell resolution; these efforts promise to shed new light on cellular heterogeneity in healthy and diseased valvular tissues and represent the potential to efficaciously target and treat key cell subpopulations. Last, we discuss systems biology- and network medicine-based strategies to extract meaning, mechanisms, and prioritized drug targets from multiomics datasets.

BABEL enables cross-modality translation between multiomic profiles at single-cell resolution

Simultaneous profiling of multiomic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility-for example, generating paired measurements of single-cell transcriptome (single-cell RNA sequencing [scRNA-seq]) and chromatin accessibility (single-cell assay for transposase-accessible chromatin using sequencing [scATAC-seq])-widespread application of joint profiling is challenging due to its experimental complexity, noise, and cost. Here, we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging an interoperable neural network model, BABEL can predict single-cell expression directly from a cell's scATAC-seq and vice versa after training on relevant data. This makes it possible to computationally synthesize paired multiomic measurements when only one modality is experimentally available. Across several paired single-cell ATAC and gene expression datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to cell types within new biological contexts not seen during training. Starting from scATAC-seq of patient-derived basal cell carcinoma (BCC), BABEL generated single-cell expression that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of experimental BCC scRNA-seq data for diverse cell types related to BABEL's training data. We further show that BABEL can incorporate additional single-cell data modalities, such as protein epitope profiling, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

Epigenetic Landscapes of Single-Cell Chromatin Accessibility and Transcriptomic Immune Profiles of T Cells in COVID-19 Patients

T cells play a critical role in coronavirus diseases. How they do so in COVID-19 may be revealed by analyzing the epigenetic chromatin accessibility of cis- and trans-regulatory elements and creating transcriptomic immune profiles. We performed single-cell assay for transposase-accessible chromatin (scATAC) and single-cell RNA (scRNA) sequencing (seq) on the peripheral blood mononuclear cells (PBMCs) of severely ill/critical patients (SCPs) infected with COVID-19, moderate patients (MPs), and healthy volunteer controls (HCs). About 76,570 and 107,862 single cells were used, respectively, for analyzing the characteristics of chromatin accessibility and transcriptomic immune profiles by the application of scATAC-seq (nine cases) and scRNA-seq (15 cases). The scATAC-seq detected 28,535 different peaks in the three groups; among these peaks, 41.6 and 10.7% were located in the promoter and enhancer regions, respectively. Compared to HCs, among the peak-located genes in the total T cells and its subsets, CD4+ T and CD8+ T cells, from SCPs and MPs were enriched with inflammatory pathways, such as mitogen-activated protein kinase (MAPK) signaling pathway and tumor necrosis factor (TNF) signaling pathway. The motifs of TBX21 were less accessible in the CD4+ T cells of SCPs compared with those in MPs. Furthermore, the scRNA-seq showed that the proportion of T cells, especially the CD4+ T cells, was decreased in SCPs and MPs compared with those in HCs. Transcriptomic results revealed that histone-related genes, and inflammatory genes, such as NFKBIA, S100A9, and PIK3R1, were highly expressed in the total T cells, CD4+ T and CD8+ T cells, both in the cases of SCPs and MPs. In the CD4+ T cells, decreased T helper-1 (Th1) cells were observed in SCPs and MPs. In the CD8+T cells, activation markers, such as CD69 and HLA class II genes (HLA-DRA, HLA-DRB1, and HLA-DRB5), were significantly upregulated in SCPs. An integrated analysis of the data from scATAC-seq and scRNA-seq showed some consistency between the approaches. Cumulatively, we have generated a landscape of chromatin epigenetic status and transcriptomic immune profiles of T cells in patients with COVID-19. This has provided a deeper dissection of the characteristics of the T cells involved at a higher resolution than from previously obtained data merely by the scRNA-seq analysis. Our data led us to suggest that the T-cell inflammatory states accompanied with defective functions in the CD4+ T cells of SCPs may be the key factors for determining the pathogenesis of and recovery from COVID-19.

Harnessing Single-Cell RNA Sequencing to Better Understand How Diseased Cells Behave the Way They Do in Cardiovascular Disease

The transition of healthy arteries and cardiac valves into dense, cell-rich, calcified, and fibrotic tissues is driven by a complex interplay of both cellular and molecular mechanisms. Specific cell types in these cardiovascular tissues become activated following the exposure to systemic stimuli including circulating lipoproteins or inflammatory mediators. This activation induces multiple cascades of events where changes in cell phenotypes and activation of certain receptors may trigger multiple pathways and specific alterations to the transcriptome. Modifications to the transcriptome and proteome can give rise to pathological cell phenotypes and trigger mechanisms that exacerbate inflammation, proliferation, calcification, and recruitment of resident or distant cells. Accumulating evidence suggests that each cell type involved in vascular and valvular diseases is heterogeneous. Single-cell RNA sequencing is a transforming medical research tool that enables the profiling of the unique fingerprints at single-cell levels. Its applications have allowed the construction of cell atlases including the mammalian heart and tissue vasculature and the discovery of new cell types implicated in cardiovascular disease. Recent advances in single-cell RNA sequencing have facilitated the identification of novel resident cell populations that become activated during disease and has allowed tracing the transition of healthy cells into pathological phenotypes. Furthermore, single-cell RNA sequencing has permitted the characterization of heterogeneous cell subpopulations with unique genetic profiles in healthy and pathological cardiovascular tissues. In this review, we highlight the latest groundbreaking research that has improved our understanding of the pathological mechanisms of atherosclerosis and future directions for calcific aortic valve disease.

TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

Exploring Additional Valuable Information From Single-Cell RNA-Seq Data

Single-cell RNA-seq (scRNA-seq) technologies are broadly applied to dissect the cellular heterogeneity and expression dynamics, providing unprecedented insights into single-cell biology. Most of the scRNA-seq studies mainly focused on the dissection of cell types/states, developmental trajectory, gene regulatory network, and alternative splicing. However, besides these routine analyses, many other valuable scRNA-seq investigations can be conducted. Here, we first review cell-to-cell communication exploration, RNA velocity inference, identification of large-scale copy number variations and single nucleotide changes, and chromatin accessibility prediction based on single-cell transcriptomics data. Next, we discuss the identification of novel genes/transcripts through transcriptome reconstruction approaches, as well as the profiling of long non-coding RNAs and circular RNAs. Additionally, we survey the integration of single-cell and bulk RNA-seq datasets for deconvoluting the cell composition of large-scale bulk samples and linking single-cell signatures to patient outcomes. These additional analyses could largely facilitate corresponding basic science and clinical applications.

Integrative analyses of single-cell transcriptome and regulome using MAESTRO

We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.