Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing

Abstract

Technical advances have enabled the collection of genome and transcriptome datasets with single-cell resolution. However, single-cell characterization of the epigenome has remained challenging. Furthermore, because cells must be physically separated prior to biochemical processing, conventional single-cell preparatory methods scale linearly. We applied combinatorial cellular indexing to measure chromatin accessibility in thousands of single cells per assay, circumventing the need for compartmentalization of individual cells. We report chromatin accessibility profiles from over 15,000 single cells and use these data to cluster cells on the basis of chromatin accessibility landscapes. We identify modules of coordinately regulated chromatin accessibility at the level of single cells both between and within cell types, with a scalable method that may accelerate progress towards a human cell atlas.

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

“…This field has been propelled by remarkable technological advances for efficiently isolating single cells 28, 29, and the reduction of the detection levels of protocols for profiling gene expression 30, 31, histone marks 25, 26, or chromatin accessibility 32, 33 in individual cells. These techniques allow researchers to overcome the limitations of population studies, in which averaging the measurements over a heterogeneous population of cells masks the variability at the epigenetics and transcriptional levels among cells within a culture or tissue 26, 34, 35.…”

“…In contrast to previous computational methods for analyzing single cell data for identifying lineage specifiers 80, 81, 82, which rely on the identification of trends in the expression of individual genes in different subpopulations, in this study we have demonstrated that information of the interactions among genes in the subpopulation‐specific GRNs, is key for predicting the genes triggering differentiation reported experimentally 74. These network models 73, 74, 102, 103 relying solely on transcriptomics data could be significantly improved by overlaying epigenetics data 25, 32, 33, for contextualizing regulatory interactions at the transcriptional level depending on the chromatin state – e.g. open/close chromatin regions, and activating/inhibitory chromatin marks patterns 25, 26, 32, 33.…”

“…These network models 73, 74, 102, 103 relying solely on transcriptomics data could be significantly improved by overlaying epigenetics data 25, 32, 33, for contextualizing regulatory interactions at the transcriptional level depending on the chromatin state – e.g. open/close chromatin regions, and activating/inhibitory chromatin marks patterns 25, 26, 32, 33. Moreover, although it is not yet possible to perform single cell measurements of signaling pathways, the inclusion of phosphoproteomics data in these network models will include another layer of information for contextualizing network interactions in this layer, depending on the post‐translational modifications determining the activity of signalling proteins 105, 106.…”

Construction of thousands of single cell genome sequencing libraries using combinatorial indexing

“…The set of 96 wells are then pooled and then 15-25 of these randomly indexed nuclei are deposited by a second round of FANS into each well of one or more new 96-well plates. The probability of any two nuclei within the new well being derived from the same origin well is low (6-11%) 16 . Each new well is then uniquely indexed by PCR followed by pooling all individual reactions, purification, and sequencing.…”

“…The combinatorial indexing concept, which involves two tiers of library indexing (transposase stage and PCR stage) was integrated with the widely utilized assay for transposase accessible chromatin (ATAC-seq) 15 , to simultaneously produce profiles of active regulatory elements in thousands of single cells 16 (scATAC-seq, Fig. 1a).…”