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

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.

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A reference-guided approach for epigenetic characterization of single cells

10.1101/2020.06.16.155010 ◽

2020 ◽

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 approach, which utilizes the information in existing datasets, may facilitate the analysis of scCAS data. We present RA3 (Reference-guided Approach for the Analysis of single-cell chromatin Acessibility 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 in many scCAS datasets. We also present several approaches to construct the reference data to demonstrate the wide applicability of RA3.

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ChromVAR: Inferring transcription factor variation from single-cell epigenomic data

10.1101/110346 ◽

2017 ◽

Cited By ~ 8

Author(s):

Alicia N. Schep ◽

Beijing Wu ◽

Jason D. Buenrostro ◽

William J. Greenleaf

Keyword(s):

Transcription Factor ◽

Single Cell ◽

De Novo ◽

Single Cells ◽

R Package ◽

Chromatin Accessibility ◽

Data Sets ◽

Sequence Motifs ◽

Computational Approaches

AbstractSingle cell ATAC-seq (scATAC) yields sparse data that makes application of conventional computational approaches for data analysis challenging or impossible. We developed chromVAR, an R package for analyzing sparse chromatin accessibility data by estimating the gain or loss of accessibility within sets of peaks sharing the same motif or annotation while controlling for known technical biases. chromVAR enables accurate clustering of scATAC-seq profiles and enables characterization of known, or the de novo identification of novel, sequence motifs associated with variation in chromatin accessibility across single cells or other sparse epigenomic data sets.

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Single cell transcriptome atlas of mouse mammary epithelial cells across development

Breast Cancer Research ◽

10.1186/s13058-021-01445-4 ◽

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.

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Characterization of CRISPR/Cas9 RANKL knockout mesenchymal stem cell clones based on single-cell printing technology and emulsion coupling assay as a low-cellularity workflow for single-cell cloning

10.1101/2020.08.17.253559 ◽

2020 ◽

Author(s):

Tobias Groß ◽

Csaba Jeney ◽

Darius Halm ◽

Günter Finkenzeller ◽

G. Björn Stark ◽

...

Keyword(s):

Single Cell ◽

Cell Line ◽

Large Scale ◽

Single Cells ◽

Protein Detection ◽

Cell Line Development ◽

Cell Printing ◽

Cell Clones ◽

The University

AbstractThe homogeneity of the genetically modified single-cells is a necessity for many applications such as cell line development, gene therapy, and tissue engineering and in particular for regenerative medical applications. The lack of tools to effectively isolate and characterize CRISPR/Cas9 engineered cells is considered as a significant bottleneck in these applications. Especially the incompatibility of protein detection technologies to confirm protein expression changes without a preconditional large-scale clonal expansion, creates a gridlock in many applications. To ameliorate the characterization of engineered cells, we propose an improved workflow, including single-cell printing/isolation technology based on fluorescent properties with high yield, a genomic edit screen (surveyor assay), mRNA rtPCR assessing altered gene expression and a versatile protein detection tool called emulsion-coupling to deliver a high-content, unified single-cell workflow. The workflow was exemplified by engineering and functionally validating RANKL knockout immortalized mesenchymal stem cells showing altered bone formation capacity of these cells. The resulting workflow is economical, without the requirement of large-scale clonal expansions of the cells with overall cloning efficiency above 30% of CRISPR/Cas9 edited cells. Nevertheless, as the single-cell clones are comprehensively characterized at an early, highly parallel phase of the development of cells including DNA, RNA, and protein levels, the workflow delivers a higher number of successfully edited cells for further characterization, lowering the chance of late failures in the development process.Author summaryI completed my undergraduate degree in biochemistry at the University of Ulm and finished my master's degree in pharmaceutical biotechnology at the University of Ulm and University of applied science of Biberach with a focus on biotechnology, toxicology and molecular biology. For my master thesis, I went to the University of Freiburg to the department of microsystems engineering, where I developed a novel workflow for cell line development. I stayed at the institute for my doctorate, but changed my scientific focus to the development of the emulsion coupling technology, which is a powerful tool for the quantitative and highly parallel measurement of protein and protein interactions. I am generally interested in being involved in the development of innovative molecular biological methods that can be used to gain new insights about biological issues. I am particularly curious to unravel the complex and often poorly understood protein interaction pathways that are the cornerstone of understanding cellular functionality and are a fundamental necessity to describe life mechanistically.

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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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Evaluation of single-cell classifiers for single-cell RNA sequencing data sets

Briefings in Bioinformatics ◽

10.1093/bib/bbz096 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1581-1595 ◽

Cited By ~ 6

Author(s):

Xinlei Zhao ◽

Shuang Wu ◽

Nan Fang ◽

Xiao Sun ◽

Jue Fan

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Reference Data ◽

Predictive Accuracy ◽

Cell Types ◽

Superior Performance ◽

Marker Genes ◽

Data Sets ◽

Sequencing Data ◽

Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

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Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data

10.1101/2020.04.10.035865 ◽

2020 ◽

Author(s):

Gregor Sturm ◽

Tamas Szabo ◽

Georgios Fotakis ◽

Marlene Haider ◽

Dietmar Rieder ◽

...

Keyword(s):

T Cell ◽

Single Cell ◽

Large Scale ◽

Single Cells ◽

Cell Receptor ◽

Sequencing Data ◽

Seamless Integration ◽

T Cell Phenotypes ◽

Cell Phenotypes

AbstractSummaryAdvances in single-cell technologies have enabled the investigation of T cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses in cancer, but also in infectious diseases like COVID-19. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T cell receptors. Here we propose Scirpy, a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data.Availability and implementationScirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.

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Using Single Nucleotide Variations in Single-Cell RNA-Seq to Identify Subpopulations and Genotype-phenotype Linkage

10.1101/095810 ◽

2016 ◽

Cited By ~ 4

Author(s):

Olivier Poirion ◽

Xun Zhu ◽

Travers Ching ◽

Lana X. Garmire

Keyword(s):

Gene Expression ◽

Single Cell ◽

Single Cells ◽

Transcript Abundance ◽

Rna Seq ◽

Linear Modeling ◽

Modeling Framework ◽

Single Nucleotide ◽

Single Nucleotide Variations

AbstractDespite its popularity, characterization of subpopulations with transcript abundance is subject to a significant amount of noise. We propose to use effective and expressed nucleotide variations (eeSNVs) from scRNA-seq as alternative features for tumor subpopulation identification. We developed a linear modeling framework, SSrGE, to link eeSNVs associated with gene expression. In all the datasets tested, eeSNVs achieve better accuracies than gene expression for identifying subpopulations. Previously validated cancer-relevant genes are also highly ranked, confirming the significance of the method. Moreover, SSrGE is capable of analyzing coupled DNA-seq and RNA-seq data from the same single cells, demonstrating its value in integrating multi-omics single cell techniques. In summary, SNV features from scRNA-seq data have merits for both subpopulation identification and linkage of genotype-phenotype relationship. The method SSrGE is available at https://github.com/lanagarmire/SSrGE.

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

10.1101/2021.05.24.21253087 ◽

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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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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