Unsupervised clustering and epigenetic classification of single cells

Zamanighomi, Mahdi; Lin, Zhixiang; Daley, Timothy; Chen, Xi; Duren, Zhana; Schep, Alicia N.; Greenleaf, William J.; Wong, Wing Hung

doi:10.1038/s41467-018-04629-3

Cited by 107 publications

(90 citation statements)

References 55 publications

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“…S4c), also exhibited a similar pattern of preferential binding to chromatin sequences captured by SNAREseq accessibility assay ( Fig. S4d), consistent with previous observations 8 . We improved the detection sensitivity on chromatin further by using NP40 based Nuclei EZ buffer to boost tagmentation efficiency and adding RNase inhibitor combination 9 to protect RNA from degradation.…”

supporting

confidence: 90%

Linking transcriptome and chromatin accessibility in nanoliter droplets for single-cell sequencing

Chen

Lake

Zhang

2019

Preprint

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Linked profiling of transcriptome and chromatin accessibility from single cells can provide unprecedented insights into cellular status. Here we developed a droplet-based Single-Nucleus chromatin Accessibility and mRNA Expression sequencing (SNARE-seq) assay, that we used to profile neonatal and adult mouse cerebral cortices. To demonstrate the strength of single-cell dual-omics profiling, we reconstructed transcriptome and epigenetic landscapes of cell types, uncovered lineage-specific accessible sites, and connected dynamics of promoter accessibility with transcription during neurogenesis.RNA sequencing of single cells or nuclei can reveal their transcription state, while chromatin accessibility sequencing would uncover the associated upstream transcriptional regulatory landscape. Current strategies 1,2 , which involve profiling these modalities separately followed by computational integration, may not fully recapitulate the true biological state. Joint profiling of two layers of -omics information within the same cells would enable a direct matching of transcriptional regulation to its output, allowing for more accurate reconstruction of the molecular processes underlying a cell's physiology. To enable highly parallel profiling of chromatin accessibility and mRNA from individual nuclei, we developed SNARE-seq, implemented on a micro-droplet platform 3 . In this method, the accessible chromatin in permeabilized nuclei are captured by the Tn5 transposase, prior to droplet generation. We reason that, without heating or detergent treatment, binding of transposases to its DNA substrate after transposition could maintain the contiguity of the original DNA strands 4 , allowing for the copackaging of accessible genomic sites and mRNA from individual nuclei in the same droplets.

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supporting

confidence: 90%

Linking transcriptome and chromatin accessibility in nanoliter droplets for single-cell sequencing

Chen

Lake

Zhang

2019

Preprint

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“…Cells in cluster 4 and 5 were mainly derived from E9.5, suggesting that they had reached a certain level of maturity. To obtain insights into the biological processes in specific CPC populations, we identified cluster-specific peaks (Supplementary Data 4 ) 34 and performed gene ontology analysis of genes that were close to (within ± 2.5 kb to TSSs) proximal, cluster-specific peaks. Cluster 1 was enriched for GO terms related to heart development and muscle contraction suggesting advanced cardiomyocyte differentiation, whereas cluster 5 showed enrichment of GO terms related to endothelial cells (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Peaks were tested for cluster specificity using an empirical Bayes regression-based hypothesis testing procedure implemented in scABC 34 . For each peak, the cluster with lowest resulting p -value was chosen as reference cluster.…”

Section: Methodsmentioning

confidence: 99%

Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement

et al. 2018

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Formation and segregation of cell lineages forming the heart have been studied extensively but the underlying gene regulatory networks and epigenetic changes driving cell fate transitions during early cardiogenesis are still only partially understood. Here, we comprehensively characterize mouse cardiac progenitor cells (CPCs) marked by Nkx2-5 and Isl1 expression from E7.5 to E9.5 using single-cell RNA sequencing and transposase-accessible chromatin profiling (ATAC-seq). By leveraging on cell-to-cell transcriptome and chromatin accessibility heterogeneity, we identify different previously unknown cardiac subpopulations. Reconstruction of developmental trajectories reveal that multipotent Isl1+ CPC pass through an attractor state before separating into different developmental branches, whereas extended expression of Nkx2-5 commits CPC to an unidirectional cardiomyocyte fate. Furthermore, we show that CPC fate transitions are associated with distinct open chromatin states critically depending on Isl1 and Nkx2-5. Our data provide a model of transcriptional and epigenetic regulations during cardiac progenitor cell fate decisions at single-cell resolution.

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“…Second, most of the tools for single-cell analysis focus on specific analytical problems instead of providing an end-to-end workflow from alignment to post-clustering annotations. For example, SC3 [ 19 ] and SNNCliq [ 20 ] are developed for scRNA-seq clustering, scde [ 21 ] and MAST [ 22 ] for differential expression, scABC [ 23 ] and cisTopic [ 24 ] for scATAC-seq clustering, and chromVAR [ 25 ] and Cicero [ 26 ] for quantifying the chromatin accessibility at transcription regulator and gene level, respectively. Even pipelines with multiple functions, such as Monocle [ 27 ], Seurat [ 28 ], and Scanpy [ 29 ], lack the function to identify transcription regulators, which is crucial to understand the gene regulatory networks that regulate cell state transition and lineage determination [ 25 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Integrative analyses of single-cell transcriptome and regulome using MAESTRO

et al. 2020

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We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.

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Unsupervised clustering and epigenetic classification of single cells

Cited by 107 publications

References 55 publications

Linking transcriptome and chromatin accessibility in nanoliter droplets for single-cell sequencing

Linking transcriptome and chromatin accessibility in nanoliter droplets for single-cell sequencing

Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement

Integrative analyses of single-cell transcriptome and regulome using MAESTRO

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