scholarly journals Smart3-ATAC: a highly sensitive method for joint accessibility and full-length transcriptome analysis in single cells

2021 ◽  
Author(s):  
Huaitao Cheng ◽  
Han-pin Pui ◽  
Antonio Lentini ◽  
Solrún Kolbeinsdóttir ◽  
Nathanael Andrews ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Element ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Great Promise ◽  
Low Loss ◽  
Genome Wide ◽  
Highly Sensitive ◽  
Element Activity ◽  
Sensitive Method

AbstractJoint single-cell measurements of gene expression and DNA regulatory element activity holds great promise as a tool to understand transcriptional regulation. Towards this goal we have developed Smart3-ATAC, a highly sensitive method which allows joint mRNA and chromatin accessibility analysis genome wide in single cells. With Smart3-ATAC, we are able to obtain the highest possible quality measurements per cell. The method combines transcriptomic profiling based on the highly sensitive Smart-seq3 protocol on cytosolic mRNA, with a novel low-loss single-cell ATAC (scATAC) protocol to measure chromatin accessibility. Compared to current droplet multiome methods, the yield of both the scATAC protocol and mRNA-seq protocol is markedly higher.

scholarly journals Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq

2019 ◽  
Vol 47 (19) ◽  
pp. e121-e121 ◽  
Author(s):  
Weiqiang Zhou ◽  
Zhicheng Ji ◽  
Weixiang Fang ◽  
Hongkai Ji
Keyword(s):  
Single Cell ◽  
Regulatory Element ◽  
Transcriptome Profiling ◽  
Cell Number ◽  
Chromatin Accessibility ◽  
Small Cell ◽  
Rna Seq ◽  
Genomic Technologies ◽  
Genome Wide ◽  
Gene Regulatory

Abstract Conventional high-throughput genomic technologies for mapping regulatory element activities in bulk samples such as ChIP-seq, DNase-seq and FAIRE-seq cannot analyze samples with small numbers of cells. The recently developed low-input and single-cell regulome mapping technologies such as ATAC-seq and single-cell ATAC-seq (scATAC-seq) allow analyses of small-cell-number and single-cell samples, but their signals remain highly discrete or noisy. Compared to these regulome mapping technologies, transcriptome profiling by RNA-seq is more widely used. Transcriptome data in single-cell and small-cell-number samples are more continuous and often less noisy. Here, we show that one can globally predict chromatin accessibility and infer regulatory element activities using RNA-seq. Genome-wide chromatin accessibility predicted by RNA-seq from 30 cells can offer better accuracy than ATAC-seq from 500 cells. Predictions based on single-cell RNA-seq (scRNA-seq) can more accurately reconstruct bulk chromatin accessibility than using scATAC-seq. Integrating ATAC-seq with predictions from RNA-seq increases the power and value of both methods. Thus, transcriptome-based prediction provides a new tool for decoding gene regulatory circuitry in samples with limited cell numbers.

scholarly journals Global Prediction of Chromatin Accessibility Using RNA-seq from Small Number of Cells

2016 ◽  
Author(s):  
Weiqiang Zhou ◽  
Zhicheng Ji ◽  
Hongkai Ji
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Regulatory Element ◽  
Cell Number ◽  
Chromatin Accessibility ◽  
Small Cell ◽  
Rna Seq ◽  
Genome Wide ◽  
Gene Regulatory ◽  
Number Of Cells

Conventional high-throughput technologies for mapping regulatory element activities such as ChIP-seq, DNase-seq and FAIRE-seq cannot analyze samples with small number of cells. The recently developed ATAC-seq allows regulome mapping in small-cell-number samples, but its signal in single cell or samples with ≤500 cells remains discrete or noisy. Compared to these technologies, measuring transcriptome by RNA-seq in single-cell and small-cell-number samples is more mature. Here we show that one can globally predict chromatin accessibility and infer regulome using RNA-seq. Genome-wide chromatin accessibility predicted by RNA-seq from 30 cells is comparable with ATAC-seq from 500 cells. Predictions based on single-cell RNA-seq can more accurately reconstruct bulk chromatin accessibility than using single-cell ATAC-seq by pooling the same number of cells. Integrating ATAC-seq with predictions from RNA-seq increases power of both methods. Thus, transcriptome-based prediction can provide a new tool for decoding gene regulatory programs in small-cell-number samples.

scholarly journals Predicting transcription factor binding in single cells through deep learning

2020 ◽  
Vol 6 (51) ◽  
pp. eaba9031
Author(s):  
Laiyi Fu ◽  
Lihua Zhang ◽  
Emmanuel Dollinger ◽  
Qinke Peng ◽  
Qing Nie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Sequence Motifs ◽  
Genome Wide ◽  
Chromatin Immunoprecipitation Sequencing ◽  
Deep Learning Model ◽  
Accessible Chromatin

Characterizing genome-wide binding profiles of transcription factors (TFs) is essential for understanding biological processes. Although techniques have been developed to assess binding profiles within a population of cells, determining them at a single-cell level remains elusive. Here, we report scFAN (single-cell factor analysis network), a deep learning model that predicts genome-wide TF binding profiles in individual cells. scFAN is pretrained on genome-wide bulk assay for transposase-accessible chromatin sequencing (ATAC-seq), DNA sequence, and chromatin immunoprecipitation sequencing (ChIP-seq) data and uses single-cell ATAC-seq to predict TF binding in individual cells. We demonstrate the efficacy of scFAN by both studying sequence motifs enriched within predicted binding peaks and using predicted TFs for discovering cell types. We develop a new metric “TF activity score” to characterize each cell and show that activity scores can reliably capture cell identities. scFAN allows us to discover and study cellular identities and heterogeneity based on chromatin accessibility profiles.

scholarly journals Predicting transcription factor binding in single cells through deep learning

2020 ◽  
Author(s):  
Laiyi Fu ◽  
Lihua Zhang ◽  
Emmanuel Dollinger ◽  
Qinke Peng ◽  
Qing Nie ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Deep Learning ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Biological Processes ◽  
Sequence Motifs ◽  
Genome Wide ◽  
Deep Learning Model

AbstractCharacterizing genome-wide binding profiles of transcription factor (TF) is essential for understanding many biological processes. Although techniques have been developed to assess binding profiles within a population of cells, determining binding profiles at a single cell level remains elusive. Here we report scFAN (Single Cell Factor Analysis Network), a deep learning model that predicts genome-wide TF binding profiles in individual cells. scFAN is pre-trained on genome-wide bulk ATAC-seq, DNA sequence and ChIP-seq data, and utilizes single-cell ATAC-seq to predict TF binding in individual cells. We demonstrate the efficacy of scFAN by studying sequence motifs enriched within predicted binding peaks and investigating the effectiveness of predicted TF peaks for discovering cell types. We develop a new metric “TF activity score” to characterize each cell, and show that the activity scores can reliably capture cell identities. The method allows us to discover and study cellular identities and heterogeneity based on chromatin accessibility profiles.

scholarly journals High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sarah E. Pierce ◽  
Jeffrey M. Granja ◽  
William J. Greenleaf
Keyword(s):  
Transcription Factor ◽  
Single Cell ◽  
Cancer Cells ◽  
High Throughput ◽  
Regulatory Networks ◽  
Temporal Dynamics ◽  
Chromatin Accessibility ◽  
Regulatory Regions ◽  
Factor Binding ◽  
Genome Wide

AbstractChromatin accessibility profiling can identify putative regulatory regions genome wide; however, pooled single-cell methods for assessing the effects of regulatory perturbations on accessibility are limited. Here, we report a modified droplet-based single-cell ATAC-seq protocol for perturbing and evaluating dynamic single-cell epigenetic states. This method (Spear-ATAC) enables simultaneous read-out of chromatin accessibility profiles and integrated sgRNA spacer sequences from thousands of individual cells at once. Spear-ATAC profiling of 104,592 cells representing 414 sgRNA knock-down populations reveals the temporal dynamics of epigenetic responses to regulatory perturbations in cancer cells and the associations between transcription factor binding profiles.

scholarly journals Single cell transcriptome atlas of mouse mammary epithelial cells across development

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Bhupinder Pal ◽  
Yunshun Chen ◽  
Michael J. G. Milevskiy ◽  
François Vaillant ◽  
Lexie Prokopuk ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Developmental Stages ◽  
Mammary Epithelial Cells ◽  
Expression Profiles ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Mammary Epithelial ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Background Heterogeneity within the mouse mammary epithelium and potential lineage relationships have been recently explored by single-cell RNA profiling. To further understand how cellular diversity changes during mammary ontogeny, we profiled single cells from nine different developmental stages spanning late embryogenesis, early postnatal, prepuberty, adult, mid-pregnancy, late-pregnancy, and post-involution, as well as the transcriptomes of micro-dissected terminal end buds (TEBs) and subtending ducts during puberty. Methods The single cell transcriptomes of 132,599 mammary epithelial cells from 9 different developmental stages were determined on the 10x Genomics Chromium platform, and integrative analyses were performed to compare specific time points. Results The mammary rudiment at E18.5 closely aligned with the basal lineage, while prepubertal epithelial cells exhibited lineage segregation but to a less differentiated state than their adult counterparts. Comparison of micro-dissected TEBs versus ducts showed that luminal cells within TEBs harbored intermediate expression profiles. Ductal basal cells exhibited increased chromatin accessibility of luminal genes compared to their TEB counterparts suggesting that lineage-specific chromatin is established within the subtending ducts during puberty. An integrative analysis of five stages spanning the pregnancy cycle revealed distinct stage-specific profiles and the presence of cycling basal, mixed-lineage, and 'late' alveolar intermediates in pregnancy. Moreover, a number of intermediates were uncovered along the basal-luminal progenitor cell axis, suggesting a continuum of alveolar-restricted progenitor states. Conclusions This extended single cell transcriptome atlas of mouse mammary epithelial cells provides the most complete coverage for mammary epithelial cells during morphogenesis to date. Together with chromatin accessibility analysis of TEB structures, it represents a valuable framework for understanding developmental decisions within the mouse mammary gland.

scholarly journals RA3 is a reference-guided approach for epigenetic characterization of single cells

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shengquan Chen ◽  
Guanao Yan ◽  
Wenyu Zhang ◽  
Jinzhao Li ◽  
Rui Jiang ◽  
...  
Keyword(s):  
Single Cell ◽  
Computational Analysis ◽  
Reference Data ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Biological Variation ◽  
Superior Performance ◽  
High Dimensionality ◽  
High Degree

AbstractThe recent advancements in single-cell technologies, including single-cell chromatin accessibility sequencing (scCAS), have enabled profiling the epigenetic landscapes for thousands of individual cells. However, the characteristics of scCAS data, including high dimensionality, high degree of sparsity and high technical variation, make the computational analysis challenging. Reference-guided approaches, which utilize the information in existing datasets, may facilitate the analysis of scCAS data. Here, we present RA3 (Reference-guided Approach for the Analysis of single-cell chromatin Accessibility data), which utilizes the information in massive existing bulk chromatin accessibility and annotated scCAS data. RA3 simultaneously models (1) the shared biological variation among scCAS data and the reference data, and (2) the unique biological variation in scCAS data that identifies distinct subpopulations. We show that RA3 achieves superior performance when used on several scCAS datasets, and on references constructed using various approaches. Altogether, these analyses demonstrate the wide applicability of RA3 in analyzing scCAS data.

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

eLife ◽  
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.

scholarly journals Enhancement and Imputation of Peak Signal Enables Accurate Cell-Type Classification in scATAC-seq

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhe Cui ◽  
Ya Cui ◽  
Yan Gao ◽  
Tao Jiang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Support Vector ◽  
Cell Type ◽  
Peripheral Blood Mononuclear ◽  
Copy Numbers ◽  
Genome Wide ◽  
Accessible Chromatin

Single-cell Assay Transposase Accessible Chromatin sequencing (scATAC-seq) has been widely used in profiling genome-wide chromatin accessibility in thousands of individual cells. However, compared with single-cell RNA-seq, the peaks of scATAC-seq are much sparser due to the lower copy numbers (diploid in humans) and the inherent missing signals, which makes it more challenging to classify cell type based on specific expressed gene or other canonical markers. Here, we present svmATAC, a support vector machine (SVM)-based method for accurately identifying cell types in scATAC-seq datasets by enhancing peak signal strength and imputing signals through patterns of co-accessibility. We applied svmATAC to several scATAC-seq data from human immune cells, human hematopoietic system cells, and peripheral blood mononuclear cells. The benchmark results showed that svmATAC is free of literature-based markers and robust across datasets in different libraries and platforms. The source code of svmATAC is available at https://github.com/mrcuizhe/svmATAC under the MIT license.

scholarly journals Single-cell analysis of the epigenomic and transcriptional landscape of innate immunity to seasonal and adjuvanted pandemic influenza vaccination in humans

2021 ◽  
Author(s):  
Florian Wimmers ◽  
Michele Donato ◽  
Alex Kuo ◽  
Tal Ashuach ◽  
Shakti Gupta ◽  
...  
Keyword(s):  
Influenza Vaccination ◽  
Single Cell ◽  
Pandemic Influenza ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Interferon Response ◽  
Myeloid Dendritic Cells ◽  
Response Factors ◽  
Transcriptional Landscape

Emerging evidence indicates a fundamental role for the epigenome in immunity. Here, we used a systems biology approach to map the epigenomic and transcriptional landscape of immunity to influenza vaccination in humans at the single-cell level. Vaccination against seasonal influenza resulted in persistently reduced H3K27ac in monocytes and myeloid dendritic cells, which was associated with impaired cytokine responses to TLR stimulation. Single cell ATAC-seq analysis of 120,305 single cells revealed an epigenomically distinct subcluster of monocytes with reduced chromatin accessibility at AP-1-targeted loci after vaccination. Similar effects were also observed in response to vaccination with the AS03-adjuvanted H5N1 pandemic influenza vaccine. However, this vaccine also stimulated persistently increased chromatin accessibility at loci targeted by interferon response factors (IRFs). This was associated with elevated expression of antiviral genes and type 1 IFN production and heightened resistance to infection with the heterologous viruses Zika and Dengue. These results demonstrate that influenza vaccines stimulate persistent epigenomic remodeling of the innate immune system. Notably, AS03-adjuvanted vaccination remodeled the epigenome of myeloid cells to confer heightened resistance against heterologous viruses, revealing its potentially unappreciated role as an epigenetic adjuvant.

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