Single nucleus multi-omics links human cortical cell regulatory genome diversity to disease risk variants

ABSTRACTSingle-cell technologies enable measure of unique cellular signatures, but are typically limited to a single modality. Computational approaches allow integration of diverse single-cell datasets, but their efficacy is difficult to validate in the absence of authentic multi-omic measurements. To comprehensively assess the molecular phenotypes of single cells in tissues, we devised single-nucleus methylCytosine, Chromatin accessibility and Transcriptome sequencing (snmC2T-seq) and applied it to post-mortem human frontal cortex tissue. We developed a computational framework to validate fine-grained cell types using multi-modal information and assessed the effectiveness of computational integration methods. Correlation analysis in individual cells revealed distinct relations between methylation and gene expression. Our integrative approach enabled joint analyses of the methylome, transcriptome, chromatin accessibility and conformation for 63 human cortical cell types. We reconstructed regulatory lineages for cortical cell populations and found specific enrichment of genetic risk for neuropsychiatric traits, enabling prediction of cell types with causal roles in disease.

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

10.1101/692608 ◽

2019 ◽

Cited By ~ 1

Author(s):

Song Chen ◽

Blue B Lake ◽

Kun Zhang

Keyword(s):

Mrna Expression ◽

Single Cell ◽

Adult Mouse ◽

Single Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Single Cell Sequencing ◽

Single Nucleus

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.

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Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq

eLife ◽

10.7554/elife.63632 ◽

2021 ◽

Vol 10 ◽

Author(s):

Elliott Swanson ◽

Cara Lord ◽

Julian Reading ◽

Alexander T Heubeck ◽

Palak C Genge ◽

...

Keyword(s):

Gene Regulation ◽

Single Cell ◽

Human Peripheral Blood ◽

Single Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Specific Gene ◽

Test Case ◽

Cell Assays ◽

Paired Measurement

Single-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to signals, and human disease. Recent advances have allowed paired capture of protein abundance and transcriptomic state, but a lack of epigenetic information in these assays has left a missing link to gene regulation. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases signal-to-noise and allows paired measurement of cell surface markers and chromatin accessibility: integrated cellular indexing of chromatin landscape and epitopes, called ICICLE-seq. We extended this approach using a droplet-based multiomics platform to develop a trimodal assay that simultaneously measures transcriptomics (scRNA-seq), epitopes, and chromatin accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.

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Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

PLoS ONE ◽

10.1371/journal.pone.0209648 ◽

2018 ◽

Vol 13 (12) ◽

pp. e0209648 ◽

Cited By ~ 115

Author(s):

Trygve E. Bakken ◽

Rebecca D. Hodge ◽

Jeremy A. Miller ◽

Zizhen Yao ◽

Thuc Nghi Nguyen ◽

...

Keyword(s):

Single Cell ◽

Cortical Cell ◽

Cell Types ◽

Single Nucleus

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TEA-seq: a trimodal assay for integrated single cell measurement of transcription, epitopes, and chromatin accessibility

10.1101/2020.09.04.283887 ◽

2020 ◽

Cited By ~ 3

Author(s):

Elliott Swanson ◽

Cara Lord ◽

Julian Reading ◽

Alexander T. Heubeck ◽

Adam K. Savage ◽

...

Keyword(s):

Cell Surface ◽

Single Cell ◽

Human Peripheral Blood ◽

Signal To Noise Ratio ◽

Single Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Specific Gene ◽

Test Case ◽

Cell Assays

AbstractSingle-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to extracellular signals, and human disease states. scATAC-seq has been particularly challenging due to the large size of the human genome and processing artefacts resulting from DNA damage that are an inherent source of background signal. Downstream analysis and integration of scATAC-seq with other single-cell assays is complicated by the lack of clear phenotypic information linking chromatin state and cell type. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases the signal-to-noise ratio and allows simultaneous measurement of cell surface markers: Integrated Cellular Indexing of Chromatin Landscape and Epitopes (ICICLE-seq). We extended this approach using a droplet-based multiomics platform to develop a trimodal assay to simultaneously measure Transcriptomic state (scRNA-seq), cell surface Epitopes, and chromatin Accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.

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Whole-organism eQTL mapping at cellular resolution with single-cell sequencing

10.1101/2020.08.23.263798 ◽

2020 ◽

Author(s):

Eyal Ben-David ◽

James Boocock ◽

Longhua Guo ◽

Stefan Zdraljevic ◽

Joshua S Bloom ◽

...

Keyword(s):

Single Cell ◽

Complex Traits ◽

Disease Risk ◽

Single Cells ◽

Large Population ◽

Regulation Of Gene Expression ◽

Cell Types ◽

Cell Type ◽

One Pot ◽

Cellular Resolution

Genetic regulation of gene expression underlies variation in disease risk and other complex traits. The effect of expression quantitative trait loci (eQTLs) varies across cell types; however, the complexity of mammalian tissues makes studying cell-type eQTLs highly challenging. We developed a novel approach in the model nematode Caenorhabditis elegans that uses single cell RNA sequencing to map eQTLs at cellular resolution in a single one-pot experiment. We mapped eQTLs across cell types in an extremely large population of genetically distinct C. elegnas individuals. We found cell-type-specific trans-eQTL hotspots that affect the expression of core pathways in the relevant cell types. Finally, we found single-cell-specific eQTL effects in the nervous system, including an eQTL with opposite effects in two individual neurons. Our results show that eQTL effects can be specific down to the level of single cells.

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Single-cell regulatory landscape and disease vulnerability map of adult Macaque cortex

10.1101/2020.05.14.087601 ◽

2020 ◽

Author(s):

Ying Lei ◽

Mengnan Cheng ◽

Zihao Li ◽

Zhenkun Zhuang ◽

Liang Wu ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Primary Motor Cortex ◽

Neurological Diseases ◽

Single Cells ◽

Cell Types ◽

Regulatory Elements ◽

Chromatin Accessibility ◽

Nucleotide Polymorphisms ◽

Cell Type

Non-human primates (NHP) provide a unique opportunity to study human neurological diseases, yet detailed characterization of the cell types and transcriptional regulatory features in the NHP brain is lacking. We applied a combinatorial indexing assay, sci-ATAC-seq, as well as single-nuclei RNA-seq, to profile chromatin accessibility in 43,793 single cells and transcriptomics in 11,477 cells, respectively, from prefrontal cortex, primary motor cortex and the primary visual cortex of adult cynomolgus monkey Macaca fascularis. Integrative analysis of these two datasets, resolved regulatory elements and transcription factors that specify cell type distinctions, and discovered area-specific diversity in chromatin accessibility and gene expression within excitatory neurons. We also constructed the dynamic landscape of chromatin accessibility and gene expression of oligodendrocyte maturation to characterize adult remyelination. Furthermore, we identified cell type-specific enrichment of differentially spliced gene isoforms and disease-associated single nucleotide polymorphisms. Our datasets permit integrative exploration of complex regulatory dynamics in macaque brain tissue at single-cell resolution.

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BROCKMAN: Deciphering variance in epigenomic regulators byk-mer factorization

10.1101/129247 ◽

2017 ◽

Cited By ~ 1

Author(s):

Carl G. de Boer ◽

Aviv Regev

Keyword(s):

Single Cell ◽

Dna Sequences ◽

Single Cells ◽

K562 Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Variable Component ◽

Variable Expression ◽

Diverse Interactions ◽

Word Frequencies

AbstractBackgroundVariation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin byK-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.ResultsBROCKMAN represents each sample as a vector of epigenomic-mark-associated DNA word frequencies, and decomposes the resulting matrix to find hidden structure in the data, followed by unsupervised grouping of samples and identification of the TFs that distinguish groups. Applied to single cell ATAC-seq, BROCKMAN readily distinguished cell types, treatments, batch effects, experimental artifacts, and cycling cells. We show that each variable component in thek-mer landscape reflects a set of co-varying TFs, which are often known to physically interact. For example, in K562 cells, AP-1 TFs were central determinant of variability in chromatin accessibility through their variable expression levels and diverse interactions with other TFs. We provide a theoretical basis for why cooperative TF binding – and any associated epigenomic mark – is inherently more variable than non-cooperative binding.ConclusionsBROCKMAN and related approaches will help gain a mechanistic understanding of thetransdeterminants of chromatin variability between cells, treatments, and individuals.

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Multi-omic profiling of transcriptome and DNA methylome in single nuclei with molecular partitioning

10.1101/434845 ◽

2018 ◽

Cited By ~ 1

Author(s):

Chongyuan Luo ◽

Hanqing Liu ◽

Bang-An Wang ◽

Anna Bartlett ◽

Angeline Rivkin ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Cell Types ◽

Cell Type ◽

Data Types ◽

Rna Molecules ◽

Dna And Rna ◽

Multiple Data ◽

Mammalian Tissues ◽

Single Nucleus

AbstractSingle-cell transcriptomic and epigenomic analyses provide powerful strategies for unbiased determination of cell types in mammalian tissues. Although previous studies have identified cell types using individual molecular signatures, the generation of consensus cell type classification requires the integration of multiple data types. Most existing single-cell techniques can only make one type of molecular measurement. Here we describe single-nucleus methylcytosine and transcriptome sequencing (snmCT-seq), a multi-omic method that requires no physical separation of DNA and RNA molecules. We demonstrated that snmCT-seq profiles generated from single cells or nuclei robustly distinguish human cell types and accurately measures cytosine DNA methylation and gene expression signatures of each cell type.

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Faculty Opinions recommendation of Single-nucleus and single-cell transcriptomes compared in matched cortical cell types.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.734689485.793579538 ◽

2020 ◽

Author(s):

Eloisa Herrera ◽

Augusto Escalante

Keyword(s):

Single Cell ◽

Cortical Cell ◽

Cell Types ◽

Single Nucleus

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Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq

10.1101/2021.04.26.441522 ◽

2021 ◽

Author(s):

Elisabeth Rebboah ◽

Fairlie Reese ◽

Katherine Williams ◽

Gabriela Balderrama-Gutierrez ◽

Cassandra McGill ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Rna Seq ◽

Short Read ◽

Whole Cells ◽

Long Read ◽

Isoform Expression ◽

Differential Isoform Expression

AbstractAlternative RNA isoforms are defined by promoter choice, alternative splicing, and polyA site selection. Although differential isoform expression is known to play a large regulatory role in eukaryotes, it has proved challenging to study with standard short-read RNA-seq because of the uncertainties it leaves about the full-length structure and precise termini of transcripts. The rise in throughput and quality of long-read sequencing now makes it possible, in principle, to unambiguously identify most transcript isoforms from beginning to end. However, its application to single-cell RNA-seq has been limited by throughput and expense. Here, we develop and characterize long-read Split-seq (LR-Split-seq), which uses a combinatorial barcoding-based method for sequencing single cells and nuclei with long reads. We show that LR-Split-seq can associate isoforms with cell types with relative economy and design flexibility. We characterize LR-Split-seq for whole cells and nuclei by using the well-studied mouse C2C12 system in which mononucleated myoblast cells differentiate and fuse into multinucleated myotubes. We show that the overall results are reproducible when comparing long- and short-read data from the same cell or nucleus. We find substantial evidence of differential isoform expression during differentiation including alternative transcription start site (TSS) usage. We integrate the resulting isoform expression dynamics with snATAC-seq chromatin accessibility to validate TSS-driven isoform choices. LR-Split-seq provides an affordable method for identifying cluster-specific isoforms in single cells that can be further quantified with companion deep short-read scRNA-seq from the same cell populations.

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