Scalable, multimodal profiling of chromatin accessibility and protein levels in single cells

Abstract

Recent technological advances have enabled massively parallel chromatin profiling with single-cell Assay for Transposase Accessible Chromatin by sequencing (scATAC-seq) in thousands of individual cells. Here, we extend these approaches and present ATAC with Select Antigen Profiling by sequencing, ASAP-seq, a tool to simultaneously profile accessible chromatin and protein levels in thousands of single cells. Our approach pairs sparse scATAC-seq data with robust detection of hundreds of cell surface and intracellular protein markers and optional capture of mitochondrial DNA (mtDNA) for clonal tracking, thus concomitantly capturing three distinct modalities in single cells. Importantly, ASAP-seq uses a novel bridging approach that repurposes antibody:oligo conjugates designed for existing technologies that pair protein measurements with single cell RNA-seq. We demonstrate the utility of ASAP-seq by revealing coordinated and distinct changes in chromatin, RNA, and surface proteins during native hematopoietic differentiation, peripheral blood mononuclear cell stimulation, and as a combinatorial decoder and reporter of multiplexed perturbations in primary T cells.

scJoint: transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seq

“…We consider multi-modal measurements profiling gene expression levels or chromatin accessibility simultaneously with surface protein levels, which can be obtained via CITE-seq (28) and ASAP-seq (23). We analysed CITE-seq and ASAP-seq data from a T cell stimulation experiment in (23), which sequenced cells with these two technologies in parallel. A total of 18,088 cells were studied under two conditions: one with stimulation of anti-CD3/CD28 in the presence of IL-2 for 16 hours and the other without stimulation as control.…”

“…• Multi-modal data (CITE-seq and ASAP-seq PBMC data). The ASAP-seq and CITE-seq data were downloaded from GEO accession number GSE156478 [32], which included the fragment files and antibody-derived tags (ADTs) matrices for ASAP-seq, the raw unique molecular identifier (UMI) and ADT matrices for CITE-seq, from both control and stimulated conditions. The gene activity matrices for ASAP-seq were generated by Signac.…”

Multimodal single-cell chromatin analysis with Signac

“…Furthermore, new technologies now enable the co-assay of multiple cellular modalities in single cells, including DNA accessibility alongside mRNA abundance [Cao et al, 2018, Chen et al, 2019, Clark et al, 2018, Ludwig et al, 2019, Lareau et al, 2020, Zhu et al, 2019, Xing et al, 2020, Liu et al, 2019, Ma et al, 2020], protein abundance [Mimitou et al, 2020, Fiskin et al, 2020, Swanson et al, 2020], CRISPR guide RNAs [Rubin et al, 2019, Pierce et al, 2020], or spatial position [Thornton et al, 2019]. These datasets present unique opportunities to learn the relationships between cellular modalities [Stuart and Satija, 2019], and will be especially powerful in deciphering the regulatory roles of noncoding DNA sequences.…”

SECANT: a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics

“…Embedded in the popular scRNA-seq platform such as 10X Genomics Chromium System (Zheng et al 2017), the recently developed CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by sequencing) (Stoeckius et al 2017a) (or similar REAP-seq (RNA expression and protein sequencing) (Peterson et al 2017)), and cell hashing technologies (Stoeckius et al 2018) allow for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling and sample origin detection within a cell. Besides, more omics types of single cell data are emerging (Buenrostro et al 2015; Cusanovich et al 2015; Mimitou et al 2020). In these single cell multi-omics experiments, the abundance of different kinds of features such as mRNA or cell surface protein is converted into a quantitative and sequenceable readout through the use of DNA-barcoded antibodies and can be measured by the count of Unique Molecular Index (UMI) and Antibody-Derived Tags (ADT), respectively, simultaneously at single cell resolution.…”