A drop-based workflow for fast and robust joint profiling of transcriptome and chromatin accessibility in the same cell

Decision letter: The single-cell chromatin accessibility landscape in mouse perinatal testis development

Decision letter: CATaDa reveals global remodelling of chromatin accessibility during stem cell differentiation in vivo

Although critical for understanding the underlying cancer biology, the study of tumor cell phenotype and epigenetic state from bulk gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) data is often challenging because tumor samples are typically mixtures of different cell populations due to normal tissue contamination, immune cell infiltration, and tumor subclonality. These cell populations may have vastly divergent transcriptomic and epigenetic characteristics, resulting in mixtures of signals that are often impossible to deconvolute. Here we present scBayes, a computational method that allows one to characterize the transcriptional behavior and/or epigenetic state corresponding to each tumor subclone present in a tumor sample or multiple serial or multisite samples from a given cancer patient. This tool starts with genomically defined subclones reconstructed from bulk DNA sequencing data, e.g., using our own published SubcloneSeeker algorithm. scBayes then layers single-cell gene expression (scRNA-seq) and/or chromatin accessibility (scATAC-seq) data on this genomic subclone “backbone” to assign individual cells to one of the tumor subclones or normal cell compartment, facilitating subclone-specific expression or epigenetic analysis. This allows (1) the identification of tumor expression or epigenetic signals without normal tissue interference, (2) the comparison between tumor and normal cells from the same sample, (3) the comparison of expression and epigenetic state across distinct tumor subclones, and (4) the study of transcriptional/epigenetic plasticity through the comparison of the cell states of the same genomically defined subclone across different time points or metastatic sites. scBayes achieves cell-to-subclone assignment by assessing the presence of somatic variants (CNVs or SNVs) that define cancer subclones in each individual cell from the single-cell sequencing reads. Because per-cell sequencing coverage is sparse, and therefore variant dropout frequent, scBayes utilizes a Bayesian probabilistic framework to calculate the likelihood of a particular cell originating from each subclone (including the normal clone, defined as having no somatic mutations) and makes a probabilistic assignment, maximizing the utilization of the sparse single-cell sequencing data for subclone assignment. We successfully applied this method to multiple longitudinal and metastatic cancer patient datasets, representing both bulk WES and WGS DNA sequencing, as well as single-cell datasets collected using the 10X Chromium and Fluidigm C1 platforms. Our algorithmic approach enables comparative gene expression and pathway analysis, and open chromatin accessibility assessment, across subclones. scBayes is open source and freely available at https://github.com/yiq/scBayes. Citation Format: Yi Qiao, Xiaomeng Huang, Gabor Marth. scBayes: A computational method to study tumor subclone-specific gene expression and chromatin accessibility using single-cell RNA sequencing and single-cell ATAC sequencing in combination of bulk DNA sequencing [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 40.

MotivationIdentifying regulatory elements in various chromosomal regions that influence gene expression is a fundamental challenge in epigenomics, with profound implications for understanding gene regulation and disease mechanisms. The advent of paired single-cell RNA sequencing and single-cell ATAC sequencing has created unprecedented opportunities to address this challenge by enabling simultaneous profiling of gene expression and chromatin accessibility at single-cell resolution. However, the inherent signals between them are weak due to the highly sparse and noisy nature of data.ResultsThis article proposes single-cell meta-Path based Omics Embedding (scPOEM), a novel embedding method that jointly projects chromatin accessibility peaks and expressed genes into a shared low-dimensional space. By integrating the relationships among peak–peak, peak–gene, and gene–gene interactions, scPOEM assigns closer representations in the embedding space to related peak–gene pairs. Our experiments demonstrate that scPOEM generates stable representations of peaks and genes, outperforms existing methods in recovering biologically meaningful peak–gene regulatory relationships and enables new insights in subgroup and differential analysis of gene regulation. These results highlight its potential to uncover gene regulatory mechanisms and enhance the understanding of transcriptional regulation at single-cell resolution.Availability and implementationThe source code of scPOEM is available at https://github.com/Houyt23/scPOEM. The datasets can be obtained from the 10× Genomics (https://www.10xgenomics.com/datasets/pbmc-from-a-healthy-donor-granulocytes-removed-through-cell-sorting-10-k-1-standard-1-0-0) and GEO database under access codes GSE194122 and GSE239916.

Single Nuclei Multiomics Analysis of Transcriptional and Chromatin Accessibility of Tumor Cells Uncovers Molecular Signatures and Regulatory Elements of Malignant Clonal Evolution in Multiple Myeloma

Simple SummaryMitochondria, the powerhouse of the cell, exist in the range of 100 s–1000 s of copies in almost every cell in the body, each with their own mitochondrial DNA, called mtDNA. When the healthy operation of a significant proportion of these mitochondria is disrupted, it can lead to dysfunction and by extension disease. One source of dysfunction arises due to mutations in the mtDNA, resulting in individual cells harbouring multiple versions of mtDNA—a “standard” wild type and a variant—a state called heteroplasmy. Heteroplasmy is a state that can arise either through inheritance or by mutations that occur through life, resulting in a new mitochondrial allele within a cell. The proportion of mitochondria that have a wild type and that have a variant allele differs between individuals, tissues within an individual, and even cells within a tissue. Historically, heteroplasmy has mainly been studied with bulk sequencing technologies, which miss variation within a tissue. The cellular variation in heteroplasmy throughout the body and its implications for pathology is not fully understood. In this review article we outline recent developments in scRNA-seq and scATAC-seq techniques which allow researchers to discover the extent of this cellular variation and further uncover the role heteroplasmy plays in disease at the cellular level.Next-generation sequencing technologies have revolutionised the study of biological systems by enabling the examination of a broad range of tissues. Its application to single-cell genomics has generated a dynamic and evolving field with a vast amount of research highlighting heterogeneity in transcriptional, genetic and epigenomic state between cells. However, compared to these aspects of cellular heterogeneity, relatively little has been gleaned from single-cell datasets regarding cellular mitochondrial heterogeneity. Single-cell sequencing techniques can provide coverage of the mitochondrial genome which allows researchers to probe heteroplasmies at the level of the single cell, and observe interactions with cellular function. In this review, we give an overview of two popular single-cell modalities—single-cell RNA sequencing and single-cell ATAC sequencing—whose throughput and widespread usage offers researchers the chance to probe heteroplasmy combined with cell state in detailed resolution across thousands of cells. After summarising these technologies in the context of mitochondrial research, we give an overview of recent methods which have used these approaches for discovering mitochondrial heterogeneity. We conclude by highlighting current limitations of these approaches and open problems for future consideration.

Lymphoblasts from Minimal Residual Disease in B-ALL Have a Transcriptional and Chromatin Accessibility Profile Shifted Towards Earlier Hematopoietic Progenitor Biology

Single cell chromatin accessibility profiling and transcriptome sequencing are the most widely used technologies for single-cell genomics. Here, we present Microwell-seq3, a high-throughput and facile platform for high-sensitivity single-nucleus chromatin accessibility or full-length transcriptome profiling. The method combines a preindexing strategy and a penetrable chip-in-a-tube for single nucleus loading and DNA amplification and therefore does not require specialized equipment. We used Microwell-seq3 to profile chromatin accessibility in more than 200,000 single nuclei and the full-length transcriptome in ~50,000 nuclei from multiple adult mouse tissues. Compared with the existing polyadenylated transcript capture methods, integrative analysis of cell type-specific regulatory elements and total RNA expression uncovered comprehensive cell type heterogeneity in the brain. Gene regulatory networks based on chromatin accessibility profiling provided an improved cell type communication model. Finally, we demonstrated that Microwell-seq3 can identify malignant cells and their specific regulons in spontaneous lung tumors of aged mice. We envision a broad application of Microwell-seq3 in many areas of research.

Fibroblasts in the skin are highly heterogeneous, both in vivo and in vitro. One difference between follicular (dermal papilla fibroblasts [DP]) and interfollicular fibroblasts (papillary fibroblasts [PFi]) in vitro is their ability to differentiate in response to osteogenic media (OM), or mechanical stimulation. Here, we asked whether differences in the ability of DP and PFi to respond to differentiation stimuli are due to differences in chromatin accessibility. We performed chromatin accessibility and transcriptional profiling of DP and PFi in human skin, which arise from a common progenitor during development, yet display distinct characteristics in adult tissue and in vitro. We found that cells cultured in growth media had unique chromatin accessibility profiles; however, these profiles control similar functional networks. Upon introduction of a chemical perturbation (OM) to promote differentiation, we observed a divergence not only in the accessible chromatin signatures but also in the functional networks controlled by these signatures. The biggest divergence between DP and PFi was observed when we applied 2 perturbations to cells: growth in OM and mechanical stimulation (a shock wave [OMSW]). DP readily differentiate into bone in OMSW conditions, while PFi lack differentiation capability in vitro. In the DP we found a number of uniquely accessible promoters that controlled osteogenic interaction networks associated with bone and differentiation functions. Using ATAC-seq and RNA-seq we found that the combination of 2 stimuli (OMSW) could result in significant changes in chromatin accessibility associated with osteogenic differentiation, but only within the DP (capable of osteogenic differentiation). De novo motif analysis identified enrichment of motifs bound by the TEA domain (TEAD) family of transcription factors, and inter-cell comparisons (UpSet analysis) displayed large groups of genes to be unique to single cell types and conditions. Our results suggest that these 2 stimuli (OMSW) elicit cell-specific responses by modifying chromatin accessibility of osteogenic-related gene promoters.

Protocol for scChaRM-seq: Simultaneous profiling of gene expression, DNA methylation, and chromatin accessibility in single cells

Inter- and intra-tumor heterogeneity is considered a significant factor contributing to the development of endocrine resistance in breast cancer. Recent advances in single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) allow us to explore inter- and intra-tumor heterogeneity at single-cell resolution. However, such integrated single-cell analysis has not yet been demonstrated to characterize the transcriptome and chromatin accessibility in breast cancer endocrine resistance. In this study, we conducted an integrated analysis combining scRNA-seq and scATAC-seq on more than 80,000 breast tissue cells from two normal tissues (NTs), three primary tumors (PTs), and three tamoxifen-treated recurrent tumors (RTs). A variety of cell types among breast tumor tissues were identified, PT- and RT-specific cancer cell states (CSs) were defined, and a heterogeneity-guided core signature (HCS) was derived through such integrated analysis. Functional experiments were performed to validate the oncogenic role of BMP7, a key gene within the core signature. We observed a striking level of cell-to-cell heterogeneity among six tumor tissues and delineated the primary to recurrent tumor progression, underscoring the significance of these single-cell level tumor cell clusters classified from scRNA-seq data. We defined nine CSs, including five PT-specific, three RT-specific, and one PT-RT-shared CSs, and identified distinct open chromatin regions of CSs, as well as a HCS of 137 genes. In addition, we predicted specific transcription factors (TFs) associated with the core signature and novel biological/metabolism pathways that mediate the communications between CSs and the tumor microenvironment (TME). We finally demonstrated that BMP7 plays an oncogenic role in tamoxifen-resistant breast cancer cells through modulating MAPK signaling pathways. Our integrated single-cell analysis provides a comprehensive understanding of the tumor heterogeneity in tamoxifen resistance. We envision this integrated single-cell epigenomic and transcriptomic measure will become a powerful approach to unravel how epigenetic factors and the tumor microenvironment govern the development of tumor heterogeneity and to uncover potential therapeutic targets that circumvent heterogeneity-related failures.

Integrated Epigenetic and Transcriptional Single Cell Analysis of t(11;14) Myeloma and Its BCL2 Dependency

Profiling chromatin accessibility at the single-cell level provides critical information about cell type composition and cell-to-cell variation within a complex tissue. Emerging techniques for the interrogation of chromatin accessibility in individual cells allow investigation of the fundamental mechanisms that lead to the variability of different cells. This protocol describes a fast and robust method for single-cell chromatin accessibility profiling based on the assay for transposase-accessible chromatin using sequencing (ATAC-seq). The method combines up-front bulk Tn5 tagging of chromatin with flow cytometry to isolate single nuclei or cells. Reagents required to generate sequencing libraries are added to the same well in the plate where cells are sorted. The protocol described here generates data of high complexity and excellent signal-to-noise ratio and can be combined with index sorting for in-depth characterization of cell types. The whole experimental procedure can be finished within 1 or 2 d with a throughput of hundreds to thousands of nuclei, and the data can be processed by the provided computational pipeline. The execution of the protocol only requires basic techniques and equipment in a molecular biology laboratory with flow cytometry support.

Profiling Chromatin Accessibility at Single-cell Resolution

Runx2 regulates chromatin accessibility to direct the osteoblast program at neonatal stages.