Stephen J. Clark

Bio: Stephen J. Clark is an academic researcher from Babraham Institute. The author has contributed to research in topics: DNA methylation & Epigenetics. The author has an hindex of 13, co-authored 28 publications receiving 1759 citations. Previous affiliations of Stephen J. Clark include Imperial College London.

Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity

Abstract: We report scM&T-seq, a method for parallel single-cell genome-wide methylome and transcriptome sequencing that allows for the discovery of associations between transcriptional and epigenetic variation. Profiling of 61 mouse embryonic stem cells confirmed known links between DNA methylation and transcription. Notably, the method revealed previously unrecognized associations between heterogeneously methylated distal regulatory elements and transcription of key pluripotency genes.

scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells.

Abstract: Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, methylation and transcription sequencing) uses a GpC methyltransferase to label open chromatin followed by bisulfite and RNA sequencing. We validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.

Multi-omics profiling of mouse gastrulation at single-cell resolution.

Abstract: Formation of the three primary germ layers during gastrulation is an essential step in the establishment of the vertebrate body plan and is associated with major transcriptional changes1-5. Global epigenetic reprogramming accompanies these changes6-8, but the role of the epigenome in regulating early cell-fate choice remains unresolved, and the coordination between different molecular layers is unclear. Here we describe a single-cell multi-omics map of chromatin accessibility, DNA methylation and RNA expression during the onset of gastrulation in mouse embryos. The initial exit from pluripotency coincides with the establishment of a global repressive epigenetic landscape, followed by the emergence of lineage-specific epigenetic patterns during gastrulation. Notably, cells committed to mesoderm and endoderm undergo widespread coordinated epigenetic rearrangements at enhancer marks, driven by ten-eleven translocation (TET)-mediated demethylation and a concomitant increase of accessibility. By contrast, the methylation and accessibility landscape of ectodermal cells is already established in the early epiblast. Hence, regulatory elements associated with each germ layer are either epigenetically primed or remodelled before cell-fate decisions, providing the molecular framework for a hierarchical emergence of the primary germ layers.

Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity

Abstract: Emerging single-cell epigenomic methods are being developed with the exciting potential to transform our knowledge of gene regulation. Here we review available techniques and future possibilities, arguing that the full potential of single-cell epigenetic studies will be realized through parallel profiling of genomic, transcriptional, and epigenetic information.

Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq)

Abstract: DNA methylation (DNAme) is an important epigenetic mark in diverse species. Our current understanding of DNAme is based on measurements from bulk cell samples, which obscures intercellular differences and prevents analyses of rare cell types. Thus, the ability to measure DNAme in single cells has the potential to make important contributions to the understanding of several key biological processes, such as embryonic development, disease progression and aging. We have recently reported a method for generating genome-wide DNAme maps from single cells, using single-cell bisulfite sequencing (scBS-seq), allowing the quantitative measurement of DNAme at up to 50% of CpG dinucleotides throughout the mouse genome. Here we present a detailed protocol for scBS-seq that includes our most recent developments to optimize recovery of CpGs, mapping efficiency and success rate; reduce hands-on time; and increase sample throughput with the option of using an automated liquid handler. We provide step-by-step instructions for each stage of the method, comprising cell lysis and bisulfite (BS) conversion, preamplification and adaptor tagging, library amplification, sequencing and, lastly, alignment and methylation calling. An individual with relevant molecular biology expertise can complete library preparation within 3 d. Subsequent computational steps require 1-3 d for someone with bioinformatics expertise.

Integrated analysis of multimodal single-cell data

Abstract: The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce ‘weighted-nearest neighbor’ analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. Availability Installation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

Opportunities and obstacles for deep learning in biology and medicine.

Abstract: Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

The Human Cell Atlas

Abstract: The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

A rapid and robust method for single cell chromatin accessibility profiling

Abstract: The assay for transposase-accessible chromatin using sequencing (ATAC-seq) is widely used to identify regulatory regions throughout the genome. However, very few studies have been performed at the single cell level (scATAC-seq) due to technical challenges. Here we developed a simple and robust plate-based scATAC-seq method, combining upfront bulk Tn5 tagging with single-nuclei sorting. We demonstrate that our method works robustly across various systems, including fresh and cryopreserved cells from primary tissues. By profiling over 3000 splenocytes, we identify distinct immune cell types and reveal cell type-specific regulatory regions and related transcription factors. ATAC-seq is widely used to identify regulatory regions in the genome. Here the authors develop a simple and robust plate-based single-cell ATAC-seq method that works in fresh and cryopreserved cells.