High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell

Quantifying common and distinct information in single-cell multimodal data with Tilted Canonical Correlation Analysis

Multimodal single-cell technologies profile multiple modalities for each cell simultaneously, enabling a more thorough characterization of cell populations. Existing dimension-reduction methods for multimodal data capture the "union of information," producing a lower-dimensional embedding that combines the information across modalities. While these tools are useful, we focus on a fundamentally different task of separating and quantifying the information among cells that is shared between the two modalities as well as unique to only one modality. Hence, we develop Tilted Canonical Correlation Analysis (Tilted-CCA), a method that decomposes a paired multimodal dataset into three lower-dimensional embeddings-one embedding captures the "intersection of information," representing the geometric relations among the cells that is common to both modalities, while the remaining two embeddings capture the "distinct information for a modality," representing the modality-specific geometric relations. We analyze single-cell multimodal datasets sequencing RNA along surface antibodies (i.e., CITE-seq) as well as RNA alongside chromatin accessibility (i.e., 10x) for blood cells and developing neurons via Tilted-CCA. These analyses show that Tilted-CCA enables meaningful visualization and quantification of the cross-modal information. Finally, Tilted-CCA's framework allows us to perform two specific downstream analyses. First, for single-cell datasets that simultaneously profile transcriptome and surface antibody markers, we show that Tilted-CCA helps design the target antibody panel to complement the transcriptome best. Second, for developmental single-cell datasets that simultaneously profile transcriptome and chromatin accessibility, we show that Tilted-CCA helps identify development-informative genes and distinguish between transient versus terminal cell types.

Omics data integration in computational biology viewed through the prism of machine learning paradigms

Important quantities of biological data can today be acquired to characterize cell types and states, from various sources and using a wide diversity of methods, providing scientists with more and more information to answer challenging biological questions. Unfortunately, working with this amount of data comes at the price of ever-increasing data complexity. This is caused by the multiplication of data types and batch effects, which hinders the joint usage of all available data within common analyses. Data integration describes a set of tasks geared towards embedding several datasets of different origins or modalities into a joint representation that can then be used to carry out downstream analyses. In the last decade, dozens of methods have been proposed to tackle the different facets of the data integration problem, relying on various paradigms. This review introduces the most common data types encountered in computational biology and provides systematic definitions of the data integration problems. We then present how machine learning innovations were leveraged to build effective data integration algorithms, that are widely used today by computational biologists. We discuss the current state of data integration and important pitfalls to consider when working with data integration tools. We eventually detail a set of challenges the field will have to overcome in the coming years.

Droplet-based bisulfite sequencing for high-throughput profiling of single-cell DNA methylomes

The genome-wide DNA methylation profile, or DNA methylome, is a critical component of the overall epigenomic landscape that modulates gene activities and cell fate. Single-cell DNA methylomic studies offer unprecedented resolution for detecting and profiling cell subsets based on methylomic features. However, existing single-cell methylomic technologies are based on use of tubes or well plates and these platforms are not easily scalable for handling a large number of single cells. Here we demonstrate a droplet-based microfluidic technology, Drop-BS, to construct single-cell bisulfite sequencing libraries for DNA methylome profiling. Drop-BS takes advantage of the ultrahigh throughput offered by droplet microfluidics to prepare bisulfite sequencing libraries of up to 10,000 single cells within 2 days. We apply the technology to profile mixed cell lines, mouse and human brain tissues to reveal cell type heterogeneity. Drop-BS offers a promising solution for single-cell methylomic studies requiring examination of a large cell population.

Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data

Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and RNA-seq data, learning such complex mechanisms from limited independent data points still presents a daunting challenge, impeding GRN inference accuracy. To overcome this challenge, we present LINGER (LIfelong neural Network for GEne Regulation), a novel deep learning-based method to infer GRNs from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference. Our results demonstrate that LINGER achieves 2-3 fold higher accuracy over existing methods. LINGER reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Additionally, following the GRN inference from a reference sc-multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies. Overall, LINGER provides a comprehensive tool for robust gene regulation inference from genomics data, empowering deeper insights into cellular mechanisms.

Methods and applications for single-cell and spatial multi-omics

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Curated single cell multimodal landmark datasets for R/Bioconductor

BACKGROUND

The majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes.

RESULTS

We collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductor's Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductor's ecosystem of hundreds of packages for single-cell and multimodal data.

CONCLUSIONS

We provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease.

Solid-phase capture and profiling of open chromatin by spatial ATAC

Current methods for epigenomic profiling are limited in their ability to obtain genome-wide information with spatial resolution. We introduce spatial ATAC, a method that integrates transposase-accessible chromatin profiling in tissue sections with barcoded solid-phase capture to perform spatially resolved epigenomics. We show that spatial ATAC enables the discovery of the regulatory programs underlying spatial gene expression during mouse organogenesis, lineage differentiation and in human pathology.

Single-cell third-generation sequencing-based multi-omics uncovers gene expression changes governed by ecDNA and structural variants in cancer cells

BACKGROUND

Cancer cells often exhibit large-scale genomic variations, such as circular extrachromosomal DNA (ecDNA) and structural variants (SVs), which have been highly correlated with the initiation and progression of cancer. Currently, no adequate method exists to unveil how these variations regulate gene expression in heterogeneous cancer cell populations at a single-cell resolution.

METHODS

Here, we developed a single-cell multi-omics sequencing method, scGTP-seq, to analyse ecDNA and SVs using long-read sequencing technologies.

RESULTS AND CONCLUSIONS

We demonstrated that our method can efficiently detect ecDNA and SVs and illustrated how these variations affect transcriptomic changes in various cell lines. Finally, we applied and validated this method in a clinical sample of hepatocellular carcinoma (HCC), demonstrating a feasible way to monitor the evolution of ecDNA and SVs during cancer progression.

Concurrent profiling of multiscale 3D genome organization and gene expression in single mammalian cells

The organization of mammalian genomes within the nucleus features a complex, multiscale three-dimensional (3D) architecture. The functional significance of these 3D genome features, however, remains largely elusive due to limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we report GAGE-seq, a highly scalable, robust single-cell co-assay that simultaneously measures 3D genome structure and transcriptome within the same cell. Employing GAGE-seq on mouse brain cortex and human bone marrow CD34+ cells, we comprehensively characterized the intricate relationships between 3D genome and gene expression. We found that these multiscale 3D genome features collectively inform cell type-specific gene expressions, hence contributing to defining cell identity at the single-cell level. Integration of GAGE-seq data with spatial transcriptomic data revealed in situ variations of the 3D genome in mouse cortex. Moreover, our observations of lineage commitment in normal human hematopoiesis unveiled notable discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level that is more nuanced than previously appreciated. Together, GAGE-seq provides a powerful, cost-effective approach for interrogating genome structure and gene expression relationships at the single-cell level across diverse biological contexts.

CMOT: Cross-Modality Optimal Transport for multimodal inference

Multimodal measurements of single-cell sequencing technologies facilitate a comprehensive understanding of specific cellular and molecular mechanisms. However, simultaneous profiling of multiple modalities of single cells is challenging, and data integration remains elusive due to missing modalities and cell-cell correspondences. To address this, we developed a computational approach, Cross-Modality Optimal Transport (CMOT), which aligns cells within available multi-modal data (source) onto a common latent space and infers missing modalities for cells from another modality (target) of mapped source cells. CMOT outperforms existing methods in various applications from developing brain, cancers to immunology, and provides biological interpretations improving cell-type or cancer classifications.

An atlas of healthy and injured cell states and niches in the human kidney

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

Chemoenzymatic Measurement of LacNAc in Single-Cell Multiomics Reveals It as a Cell-Surface Indicator of Glycolytic Activity of CD8+ T Cells

Despite the rich information about the physiological state of a cell encoded in the dynamic changes of cell-surface glycans, chemical methods to capture specific glycan epitopes at the single-cell level are quite limited. Here, we report a chemoenzymatic method for the single-cell detection of N-acetyllactosamine (LacNAc) by labeling LacNAc with a specific DNA barcode. The chemoenzymatic labeling does not alter the transcriptional status of immune cells and is compatible with multiple scRNA-seq platforms. Integrated analysis of LacNAc and the transcriptome of T cells at the single-cell level reveals that the amount of cell-surface LacNAc is significantly upregulated in activated CD8+ T cells but maintained at basal levels in resting CD8+ T cells (i.e., naive and central memory T cells). Further analysis confirms that LacNAc levels are positively correlated with the glycolytic activity of CD8+ T cells during differentiation. Taken together, our study demonstrates the feasibility of the chemoenzymatic detection of cell-surface glycan in single-cell RNA sequencing-based multiomics with TCR sequence and cell-surface epitope information (i.e., scTCR and CITE-seq), and provides a new way to characterize the biological role of glycan in diverse physiological states.

Clustering single-cell multimodal omics data with jrSiCKLSNMF

Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise. Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") that extracts latent factors shared across omics modalities within the same set of single cells. Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data. Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

Setting the clock of neural progenitor cells during mammalian corticogenesis

Radial glial cells (RGCs) as primary neural stem cells in the developing mammalian cortex give rise to diverse types of neurons and glial cells according to sophisticated developmental programs with remarkable spatiotemporal precision. Recent studies suggest that regulation of the temporal competence of RGCs is a key mechanism for the highly conserved and predictable development of the cerebral cortex. Various types of epigenetic regulations, such as DNA methylation, histone modifications, and 3D chromatin architecture, play a key role in shaping the gene expression pattern of RGCs. In addition, epitranscriptomic modifications regulate temporal pre-patterning of RGCs by affecting the turnover rate and function of cell-type-specific transcripts. In this review, we summarize epigenetic and epitranscriptomic regulatory mechanisms that control the temporal competence of RGCs during mammalian corticogenesis. Furthermore, we discuss various developmental elements that also dynamically regulate the temporal competence of RGCs, including biochemical reaction speed, local environmental changes, and subcellular organelle remodeling. Finally, we discuss the underlying mechanisms that regulate the interspecies developmental tempo contributing to human-specific features of brain development.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data

MOTIVATION

Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in individual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterization of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy.

RESULTS

We propose SnapCCESS for clustering cells by integrating data modalities in multimodal single-cell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterization of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data.

AVAILABILITY AND IMPLEMENTATION

SnapCCESS is implemented as a Python package and is freely available from https://github.com/PYangLab/SnapCCESS under the open-source license of GPL-3. The data used in this study are publicly available (see section 'Data availability').

Droplet-based bisulfite sequencing for high-throughput profiling of single-cell DNA methylomes

Genome-wide DNA methylation profile, or DNA methylome, is a critical component of the overall epigenomic landscape that modulates gene activities and cell fate. Single-cell DNA methylomic studies offer unprecedented resolution for detecting and profiling cell subsets based on methylomic features. However, existing single-cell methylomic technologies are all based on use of tubes or well plates and these platforms are not easily scalable for handling a large number of single cells. Here we demonstrate a droplet-based microfluidic technology, Drop-BS, to construct single-cell bisulfite sequencing libraries for DNA methylome profiling. Drop-BS takes advantage of the ultrahigh throughput offered by droplet microfluidics to prepare bisulfite sequencing libraries of up to 10,000 single cells within 2 d. We applied the technology to profile mixed cell lines, mouse and human brain tissues to reveal cell type heterogeneity. Drop-BS will pave the way for single-cell methylomic studies requiring examination of a large cell population.

Epigenetic Control of Cell Potency and Fate Determination during Mammalian Gastrulation

Pluripotent embryonic stem cells have a unique and characteristic epigenetic profile, which is critical for differentiation to all embryonic germ lineages. When stem cells exit the pluripotent state and commit to lineage-specific identities during the process of gastrulation in early embryogenesis, extensive epigenetic remodelling mediates both the switch in cellular programme and the loss of potential to adopt alternative lineage programmes. However, it remains to be understood how the stem cell epigenetic profile encodes pluripotency, or how dynamic epigenetic regulation helps to direct cell fate specification. Recent advances in stem cell culture techniques, cellular reprogramming, and single-cell technologies that can quantitatively profile epigenetic marks have led to significant insights into these questions, which are important for understanding both embryonic development and cell fate engineering. This review provides an overview of key concepts and highlights exciting new advances in the field.

Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development

The brain is a complex tissue whose function relies on coordinated anatomical and molecular features. However, the molecular annotation of the spatial organization of the brain is currently insufficient. Here, we describe microfluidic indexing-based spatial assay for transposase-accessible chromatin and RNA-sequencing (MISAR-seq), a method for spatially resolved joint profiling of chromatin accessibility and gene expression. By applying MISAR-seq to the developing mouse brain, we study tissue organization and spatiotemporal regulatory logics during mouse brain development.

Unsupervised neural network for single cell Multi-omics INTegration (UMINT): an application to health and disease

Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to high dimensionality and diverse nature of data, and noise associated with each platform. Sparsity in data, non-overlapping features and technical batch effects make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards due to their simplistic nature with less capacity. In addition, existing methods for single cell multi-omics integration are computationally expensive. Therefore, in this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions. It has a light-weight architecture with substantially reduced number of parameters. The proposed model is capable of learning a latent low-dimensional embedding that can extract useful features from the data facilitating further downstream analyses. UMINT has been applied to integrate healthy and disease CITE-seq (paired RNA and surface proteins) datasets including a rare disease Mucosa-Associated Lymphoid Tissue (MALT) tumor. It has been benchmarked against existing state-of-the-art methods for single cell multi-omics integration. Furthermore, UMINT is capable of integrating paired single cell gene expression and ATAC-seq (Transposase-Accessible Chromatin) assays as well.

scTIE: data integration and inference of gene regulation using single-cell temporal multimodal data

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal datasets, we demonstrate scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome dataset we generated from differentiating mouse embryonic stem cells over time, we demonstrate scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

High-throughput microfluidic droplets in biomolecular analytical system: A review

Droplet microfluidic technology has revolutionized biomolecular analytical research, as it has the capability to reserve the genotype-to-phenotype linkage and assist for revealing the heterogeneity. Massive and uniform picolitre droplets feature dividing solution to the level that single cell and single molecule in each droplet can be visualized, barcoded, and analyzed. Then, the droplet assays can unfold intensive genomic data, offer high sensitivity, and screen and sort from a large number of combinations or phenotypes. Based on these unique advantages, this review focuses on up-to-date research concerning diverse screening applications utilizing droplet microfluidic technology. The emerging progress of droplet microfluidic technology is first introduced, including efficient and scaling-up in droplets encapsulation, and prevalent batch operations. Then the new implementations of droplet-based digital detection assays and single-cell muti-omics sequencing are briefly examined, along with related applications such as drug susceptibility testing, multiplexing for cancer subtype identification, interactions of virus-to-host, and multimodal and spatiotemporal analysis. Meanwhile, we specialize in droplet-based large-scale combinational screening regarding desired phenotypes, with an emphasis on sorting for immune cells, antibodies, enzymatic properties, and proteins produced by directed evolution methods. Finally, some challenges, deployment and future perspective of droplet microfluidics technology in practice are also discussed.

Predicting cellular responses to complex perturbations in high-throughput screens

Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.

Integrative single-cell RNA and ATAC sequencing reveals that the FOXO1-PRDX2-TNF axis regulates tendinopathy

Introduction

Tendinopathy, the most common form of chronic tendon disorder, leads to persistent tendon pain and loss of function. Profiling the heterogeneous cellular composition in the tendon microenvironment helps to elucidate rational molecular mechanisms of tendinopathy.

Methods and results

In this study, through a multi-modal analysis, a single-cell RNA- and ATAC-seq integrated tendinopathy landscape was generated for the first time. We found that a specific cell subpopulation with low PRDX2 expression exhibited a higher level of inflammation, lower proliferation and migration ability, which not only promoted tendon injury but also led to microenvironment deterioration. Mechanistically, a motif enrichment analysis of chromatin accessibility showed that FOXO1 was an upstream regulator of PRDX2 transcription, and we confirmed that functional blockade of FOXO1 activity induced PRDX2 silencing. The TNF signaling pathway was significantly activated in the PRDX2-low group, and TNF inhibition effectively restored diseased cell degradation.

Discussion

We revealed an essential role of diseased cells in tendinopathy and proposed the FOXO1-PRDX2-TNF axis is a potential regulatory mechanism for the treatment of tendinopathy.

InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop

Deciphering cell-type-specific 3D structures of chromatin is challenging. Here, we present InferLoop, a novel method for inferring the strength of chromatin interaction using single-cell chromatin accessibility data. The workflow of InferLoop is, first, to conduct signal enhancement by grouping nearby cells into bins, and then, for each bin, leverage accessibility signals for loop signals using a newly constructed metric that is similar to the perturbation of the Pearson correlation coefficient. In this study, we have described three application scenarios of InferLoop, including the inference of cell-type-specific loop signals, the prediction of gene expression levels and the interpretation of intergenic loci. The effectiveness and superiority of InferLoop over other methods in those three scenarios are rigorously validated by using the single-cell 3D genome structure data of human brain cortex and human blood, the single-cell multi-omics data of human blood and mouse brain cortex, and the intergenic loci in the GWAS Catalog database as well as the GTEx database, respectively. In addition, InferLoop can be applied to predict loop signals of individual spots using the spatial chromatin accessibility data of mouse embryo. InferLoop is available at https://github.com/jumphone/inferloop.

scJVAE: A novel method for integrative analysis of multimodal single-cell data

The study of cellular decision-making can be approached comprehensively using multimodal single-cell omics technology. Recent advances in multimodal single-cell technology have enabled simultaneous profiling of more than one modality from the same cell, providing more significant insights into cell characteristics. However, learning the joint representation of multimodal single-cell data is challenging due to batch effects. Here we present a novel method, scJVAE (single-cell Joint Variational AutoEncoder), for batch effect removal and joint representation of multimodal single-cell data. The scJVAE integrates and learns joint embedding of paired scRNA-seq and scATAC-seq data modalities. We evaluate and demonstrate the ability of scJVAE to remove batch effects using various datasets with paired gene expression and open chromatin. We also consider scJVAE for downstream analysis, such as lower dimensional representation, cell-type clustering, and time and memory requirement. We find scJVAE a robust and scalable method outperforming existing state-of-the-art batch effect removal and integration methods.

Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning

Recent advances in single-cell sequencing technology have made it possible to measure multiple paired omics simultaneously in a single cell such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq). However, the widespread application of these single-cell multiomics profiling technologies has been limited by their experimental complexity, noise in nature, and high cost. In addition, single-omics sequencing technologies have generated tremendous and high-quality single-cell datasets but have yet to be fully utilized. Here, single-cell multiomics generation (scMOG), a deep learning-based framework to generate single-cell assay for transposase-accessible chromatin (ATAC) data in silico is developed from experimentally available single-cell RNA-seq measurements and vice versa. The results demonstrate that scMOG can accurately perform cross-omics generation between RNA and ATAC, and generate paired multiomics data with biological meanings when one omics is experimentally unavailable and out of training datasets. The generated ATAC, either alone or in combination with measured RNA, exhibits equivalent or superior performance to that of the experimentally measured counterparts throughout multiple downstream analyses. scMOG is also applied to human lymphoma data, which proves to be more effective in identifying tumor samples than the experimentally measured ATAC data. Finally, the performance of scMOG is investigated in other omics such as proteomics and it still shows robust performance on surface protein generation.

Integrating multiple single-cell multi-omics samples with Smmit

Multi-sample single-cell multi-omics datasets, which simultaneously measure multiple data modalities in the same cells and in multiple samples, facilitate the study of gene expression and gene regulatory activities on a population scale. Existing integration methods can integrate either multiple samples or multiple modalities, but not both simultaneously. To address this limitation, we developed Smmit, a computational pipeline that leverages existing integration methods to simultaneously integrate both samples and modalities and produces a unified representation of reduced dimensions. We demonstrate Smmit's capability to integrate information across samples and modalities while preserving cell type differences in two real datasets. Smmit is an R software package that is freely available at Github: https://github.com/zji90/Smmit.

Recent revelations and future directions using single-cell technologies in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is a clinically and biologically heterogeneous disease with varying outcomes. In the last decade, the application of next-generation sequencing technologies has allowed extensive mapping of disease-specific genomic, epigenomic, immunogenetic, and transcriptomic signatures linked to CLL pathogenesis. These technologies have improved our understanding of the impact of tumor heterogeneity and evolution on disease outcome, although they have mostly been performed on bulk preparations of nucleic acids. As a further development, new technologies have emerged in recent years that allow high-resolution mapping at the single-cell level. These include single-cell RNA sequencing for assessment of the transcriptome, both of leukemic and non-malignant cells in the tumor microenvironment; immunogenetic profiling of B and T cell receptor rearrangements; single-cell sequencing methods for investigation of methylation and chromatin accessibility across the genome; and targeted single-cell DNA sequencing for analysis of copy-number alterations and single nucleotide variants. In addition, concomitant profiling of cellular subpopulations, based on protein expression, can also be obtained by various antibody-based approaches. In this review, we discuss different single-cell sequencing technologies and how they have been applied so far to study CLL onset and progression, also in response to treatment. This latter aspect is particularly relevant considering that we are moving away from chemoimmunotherapy to targeted therapies, with a potentially distinct impact on clonal dynamics. We also discuss new possibilities, such as integrative multi-omics analysis, as well as inherent limitations of the different single-cell technologies, from sample preparation to data interpretation using available bioinformatic pipelines. Finally, we discuss future directions in this rapidly evolving field.

Multi-omic single cell sequencing: Overview and opportunities for kidney disease therapeutic development

Single cell sequencing technologies have rapidly advanced in the last decade and are increasingly applied to gain unprecedented insights by deconstructing complex biology to its fundamental unit, the individual cell. First developed for measurement of gene expression, single cell sequencing approaches have evolved to allow simultaneous profiling of multiple additional features, including chromatin accessibility within the nucleus and protein expression at the cell surface. These multi-omic approaches can now further be applied to cells in situ, capturing the spatial context within which their biology occurs. To extract insights from these complex datasets, new computational tools have facilitated the integration of information across different data types and the use of machine learning approaches. Here, we summarize current experimental and computational methods for generation and integration of single cell multi-omic datasets. We focus on opportunities for multi-omic single cell sequencing to augment therapeutic development for kidney disease, including applications for biomarkers, disease stratification and target identification.

Cellcano: supervised cell type identification for single cell ATAC-seq data

Computational cell type identification is a fundamental step in single-cell omics data analysis. Supervised celltyping methods have gained increasing popularity in single-cell RNA-seq data because of the superior performance and the availability of high-quality reference datasets. Recent technological advances in profiling chromatin accessibility at single-cell resolution (scATAC-seq) have brought new insights to the understanding of epigenetic heterogeneity. With continuous accumulation of scATAC-seq datasets, supervised celltyping method specifically designed for scATAC-seq is in urgent need. Here we develop Cellcano, a computational method based on a two-round supervised learning algorithm to identify cell types from scATAC-seq data. The method alleviates the distributional shift between reference and target data and improves the prediction performance. After systematically benchmarking Cellcano on 50 well-designed celltyping tasks from various datasets, we show that Cellcano is accurate, robust, and computationally efficient. Cellcano is well-documented and freely available at https://marvinquiet.github.io/Cellcano/ .

Nanoparticle stereochemistry-dependent endocytic processing improves in vivo mRNA delivery

Stereochemistry can alter small-molecule pharmacokinetics, safety and efficacy. However, it is unclear whether the stereochemistry of a single compound within a multicomponent colloid such as a lipid nanoparticle (LNP) can influence its activity in vivo. Here we report that LNPs containing stereopure 20α-hydroxycholesterol (20α) delivered mRNA to liver cells up to 3-fold more potently than LNPs containing a mixture of both 20α- and 20β-hydroxycholesterols (20mix). This effect was not driven by LNP physiochemical traits. Instead, in vivo single-cell RNA sequencing and imaging revealed that 20mix LNPs were sorted into phagocytic pathways more than 20α LNPs, resulting in key differences between LNP biodistribution and subsequent LNP functional delivery. These data are consistent with the fact that nanoparticle biodistribution is necessary, but not sufficient, for mRNA delivery, and that stereochemistry-dependent interactions between LNPs and target cells can improve mRNA delivery.

Spatial epigenome-transcriptome co-profiling of mammalian tissues

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

Gene expression in organoids: an expanding horizon

Recent development of human three-dimensional organoid cultures has opened new doors and opportunities ranging from modelling human development in vitro to personalised cancer therapies. These new in vitro systems are opening new horizons to the classic understanding of human development and disease. However, the complexity and heterogeneity of these models requires cutting-edge techniques to capture and trace global changes in gene expression to enable identification of key players and uncover the underlying molecular mechanisms. Rapid development of sequencing approaches made possible global transcriptome analyses and epigenetic profiling. Despite challenges in organoid culture and handling, these techniques are now being adapted to embrace organoids derived from a wide range of human tissues. Here, we review current state-of-the-art multi-omics technologies, such as single-cell transcriptomics and chromatin accessibility assays, employed to study organoids as a model for development and a platform for precision medicine.

Supervised learning of high-confidence phenotypic subpopulations from single-cell data

Accurately identifying phenotype-relevant cell subsets from heterogeneous cell populations is crucial for delineating the underlying mechanisms driving biological or clinical phenotypes. Here, by deploying a learning with rejection strategy, we developed a novel supervised learning framework called PENCIL to identify subpopulations associated with categorical or continuous phenotypes from single-cell data. By embedding a feature selection function into this flexible framework, for the first time, we were able to select informative features and identify cell subpopulations simultaneously, which enables the accurate identification of phenotypic subpopulations otherwise missed by methods incapable of concurrent gene selection. Furthermore, the regression mode of PENCIL presents a novel ability for supervised phenotypic trajectory learning of subpopulations from single-cell data. We conducted comprehensive simulations to evaluate PENCIĽs versatility in simultaneous gene selection, subpopulation identification and phenotypic trajectory prediction. PENCIL is fast and scalable to analyze 1 million cells within 1 hour. Using the classification mode, PENCIL detected T-cell subpopulations associated with melanoma immunotherapy outcomes. Moreover, when applied to scRNA-seq of a mantle cell lymphoma patient with drug treatment across multiple time points, the regression mode of PENCIL revealed a transcriptional treatment response trajectory. Collectively, our work introduces a scalable and flexible infrastructure to accurately identify phenotype-associated subpopulations from single-cell data.

Single-cell transcriptomes in the heart: when every epigenome counts

The response of an organ to stimuli emerges from the actions of individual cells. Recent cardiac single-cell RNA-sequencing studies of development, injury, and reprogramming have uncovered heterogeneous populations even among previously well-defined cell types, raising questions about what level of experimental resolution corresponds to disease-relevant, tissue-level phenotypes. In this review, we explore the biological meaning behind this cellular heterogeneity by undertaking an exhaustive analysis of single-cell transcriptomics in the heart (including a comprehensive, annotated compendium of studies published to date) and evaluating new models for the cardiac function that have emerged from these studies (including discussion and schematics that depict new hypotheses in the field). We evaluate the evidence to support the biological actions of newly identified cell populations and debate questions related to the role of cell-to-cell variability in development and disease. Finally, we present emerging epigenomic approaches that, when combined with single-cell RNA-sequencing, can resolve basic mechanisms of gene regulation and variability in cell phenotype.

scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks

Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, while also incorporating technical noises. Moreover, it allows users to adjust each factor's effect easily. We validated scMultiSim's simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including cell clustering and trajectory inference, multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.

Neurodegeneration cell per cell

The clinical definition of neurodegenerative diseases is based on symptoms that reflect terminal damage of specific brain regions. This is misleading as it tells little about the initial disease processes. Circuitry failures that underlie the clinical symptomatology are themselves preceded by clinically mostly silent, slowly progressing multicellular processes that trigger or are triggered by the accumulation of abnormally folded proteins such as Aβ, Tau, TDP-43, and α-synuclein, among others. Methodological advances in single-cell omics, combined with complex genetics and novel ways to model complex cellular interactions using induced pluripotent stem (iPS) cells, make it possible to analyze the early cellular phase of neurodegenerative disorders. This will revolutionize the way we study those diseases and will translate into novel diagnostics and cell-specific therapeutic targets, stopping these disorders in their early track before they cause difficult-to-reverse damage to the brain.

Chromatin accessibility landscapes revealed the subgenome-divergent regulation networks during wheat grain development

Development of wheat (Triticum aestivum L.) grain mainly depends on the processes of starch synthesis and storage protein accumulation, which are critical for grain yield and quality. However, the regulatory network underlying the transcriptional and physiological changes of grain development is still not clear. Here, we combined ATAC-seq and RNA-seq to discover the chromatin accessibility and gene expression dynamics during these processes. We found that the chromatin accessibility changes are tightly associated with differential transcriptomic expressions, and the proportion of distal ACRs was increased gradually during grain development. Specific transcription factor (TF) binding sites were enriched at different stages and were diversified among the 3 subgenomes. We further predicted the potential interactions between key TFs and genes related with starch and storage protein biosynthesis and found different copies of some key TFs played diversified roles. Overall, our findings have provided numerous resources and illustrated the regulatory network during wheat grain development, which would shed light on the improvement of wheat yields and qualities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-023-00095-8.

Multi-omic single-cell velocity models epigenome-transcriptome interactions and improves cell fate prediction

Multi-omic single-cell datasets, in which multiple molecular modalities are profiled within the same cell, offer an opportunity to understand the temporal relationship between epigenome and transcriptome. To realize this potential, we developed MultiVelo, a differential equation model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression and improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Application to multi-omic single-cell datasets from brain, skin and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. We also find four types of cell states: two states in which epigenome and transcriptome are coupled and two distinct decoupled states. Finally, we identify time lags between transcription factor expression and binding site accessibility and between disease-associated SNP accessibility and expression of the linked genes. MultiVelo is available on PyPI, Bioconda and GitHub ( https://github.com/welch-lab/MultiVelo ).

Single-Cell Molecular Barcoding to Decode Multimodal Information Defining Cell States

Single-cell research has provided a breakthrough in biology to understand heterogeneous cell groups, such as tissues and organs, in development and disease. Molecular barcoding and subsequent sequencing technology insert a singlecell barcode into isolated single cells, allowing separation cell by cell. Given that multimodal information from a cell defines precise cellular states, recent technical advances in methods focus on simultaneously extracting multimodal data recorded in different biological materials (DNA, RNA, protein, etc.). This review summarizes recently developed singlecell multiomics approaches regarding genome, epigenome, and protein profiles with the transcriptome. In particular, we focus on how to anchor or tag molecules from a cell, improve throughputs with sample multiplexing, and record lineages, and we further discuss the future developments of the technology.

Tn5 DNA Transposase in Multi-Omics Research

Tn5 transposase use in biotechnology has substantially advanced the sequencing applications of genome-wide analysis of cells. This is mainly due to the ability of Tn5 transposase to efficiently transpose DNA essentially randomly into any target DNA without the aid of other factors. This concise review is focused on the advances in Tn5 applications in multi-omics technologies, genome-wide profiling, and Tn5 hybrid molecule creation. The possibilities of other transposase uses are also discussed.

Epigenetic Regulations in Mammalian Cells: Roles and Profiling Techniques

The genome is almost identical in all the cells of the body. However, the functions and morphologies of each cell are different, and the factors that determine them are the genes and proteins expressed in the cells. Over the past decades, studies on epigenetic information, such as DNA methylation, histone modifications, chromatin accessibility, and chromatin conformation have shown that these properties play a fundamental role in gene regulation. Furthermore, various diseases such as cancer have been found to be associated with epigenetic mechanisms. In this study, we summarized the biological properties of epigenetics and single-cell epigenomic profiling techniques, and discussed future challenges in the field of epigenetics.

Computational approaches to understand transcription regulation in development

Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional dynamics in developmental systems. Computational prediction of GRNs has been successfully applied to genome-wide gene expression measurements with the advent of microarrays and RNA-sequencing. However, these inferred networks are inaccurate and mostly based on correlative rather than causative interactions. In this review, we highlight three approaches that significantly impact GRN inference: (1) moving from one genome-wide functional modality, gene expression, to multi-omics, (2) single cell sequencing, to measure cell type-specific signals and predict context-specific GRNs, and (3) neural networks as flexible models. Together, these experimental and computational developments have the potential to significantly impact the quality of inferred GRNs. Ultimately, accurately modeling the regulatory interactions between transcription factors and their target genes will be essential to understand the role of transcription factors in driving developmental gene expression programs and to derive testable hypotheses for validation.

Simultaneous Measurement of DNA Methylation and Nucleosome Occupancy in Single Cells Using scNOMe-Seq

Single-cell Nucleosome Occupancy and Methylome sequencing (scNOMe-seq) is a multimodal assay that simultaneously measures endogenous DNA methylation and nucleosome occupancy (i.e., chromatin accessibility) in single cells. scNOMe-seq combines the activity of a GpC Methyltransferase, an enzyme which methylates cytosines in GpC dinucleotides, with bisulfite conversion, whereby unmethylated cytosines are converted into thymines. Because GpC Methyltransferase acts only on cytosines present in non-nucleosomal regions of the genome, the subsequent bisulfite conversion step not only detects the endogenous DNA methylation, but also reveals the genome-wide pattern of chromatin accessibility. Implementing this technology at the single-cell level helps to capture the dynamics governing methylation and accessibility vary across individual cells and cell types. Here, we provide a scalable plate-based protocol for preparing scNOMe-seq libraries from single nucleus suspensions.

Simultaneous Single-Cell Profiling of the Transcriptome and Accessible Chromatin Using SHARE-seq

The ability to analyze the transcriptomic and epigenomic states of individual single cells has in recent years transformed our ability to measure and understand biological processes. Recent advancements have focused on increasing sensitivity and throughput to provide richer and deeper biological insights at the cellular level. The next frontier is the development of multiomic methods capable of analyzing multiple features from the same cell, such as the simultaneous measurement of the transcriptome and the chromatin accessibility of candidate regulatory elements. In this chapter, we discuss and describe SHARE-seq (Simultaneous high-throughput ATAC, and RNA expression with sequencing) for carrying out simultaneous chromatin accessibility and transcriptome measurements in single cells, together with the experimental and analytical considerations for achieving optimal results.

siVAE: interpretable deep generative models for single-cell transcriptomes

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.

Using single cell atlas data to reconstruct regulatory networks

Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)-gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.

Lung Cell Atlases in Health and Disease

The human lung cellular portfolio, traditionally characterized by cellular morphology and individual markers, is highly diverse, with over 40 cell types and a complex branching structure highly adapted for agile airflow and gas exchange. While constant during adulthood, lung cellular content changes in response to exposure, injury, and infection. Some changes are temporary, but others are persistent, leading to structural changes and progressive lung disease. The recent advance of single-cell profiling technologies allows an unprecedented level of detail and scale to cellular measurements, leading to the rise of comprehensive cell atlas styles of reporting. In this review, we chronical the rise of cell atlases and explore their contributions to human lung biology in health and disease.