Profiling of transcribed cis-regulatory elements in single cells

Profiling of cis-regulatory elements (CREs, mostly promoters and enhancers) in single cells allows the interrogation of the cell-type and -state specific contexts of gene regulation and genetic predisposition to diseases. Here we demonstrate single-cell RNA-5′end-sequencing (sc-end5-seq) methods can detect transcribed CREs (tCREs), enabling simultaneous quantification of gene expression and enhancer activities in a single assay with no extra cost. We show enhancer RNAs can be effectively detected using sc-end5-seq methods with either random or oligo(dT) priming. To analyze tCREs in single cells, we developed SCAFE (Single Cell Analysis of Five-prime Ends) to identify genuine tCREs and analyze their activities (https://github.com/chung-lab/scafe). As compared to accessible CRE (aCRE, based on chromatin accessibility), tCREs are more accurate in predicting CRE interactions by co-activity, more sensitive in detecting shifts in alternative promoter usage and more enriched in diseases heritability. Our results highlight additional dimensions within sc-end5-seq data which can be used for interrogating gene regulation and disease heritability.

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SIMBA: SIngle-cell eMBedding Along with features

10.1101/2021.10.17.464750 ◽

2021 ◽

Author(s):

Huidong Chen ◽

Jayoung Ryu ◽

Michael Edward Vinyard ◽

Adam Lerer ◽

Luca Pinello

Keyword(s):

Gene Regulation ◽

Single Cell ◽

Dna Sequences ◽

Single Cell Analysis ◽

Single Cells ◽

Cellular Heterogeneity ◽

Omics Data ◽

Cell Analysis ◽

Genomic Features ◽

The Individual

Recent advances in single cell omics technologies enable the individual or joint profiling of cellular measurements including gene expression, epigenetic features, chromatin structure and DNA sequences. Currently, most single-cell analysis pipelines are cluster-centric, i.e., they first cluster cells into non-overlapping cellular states and then extract their defining genomic features. These approaches assume that discrete clusters correspond to biologically relevant subpopulations and do not explicitly model the interactions between different feature types. However, cellular processes are defined in individual cells and inherently involve multiple genomic features that interact with each other and together provide complementary views on principles of gene regulation. In addition, single-cell methods are generally designed for a particular task as distinct single-cell problems are formulated differently. To address these current shortcomings, we present SIMBA, a single-cell embedding method that embeds single cells along with their defining features, such as genes, chromatin accessible regions, and transcription factor binding sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for cellular heterogeneity study, clustering-free marker discovery, gene regulation inference, batch effect removal, and omics data integration. SIMBA has been extensively applied to scRNA-seq, scATAC-seq, and dual-omics data. We show that SIMBA provides a single framework that allows diverse single-cell analysis problems to be formulated in a common way and thus simplifies the development of new analyses and integration of other single-cell modalities.

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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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Single-cell analysis of chromatin silencing programs in development and tumor progression

10.1101/2020.09.04.282418 ◽

2020 ◽

Author(s):

Steven J. Wu ◽

Scott N. Furlan ◽

Anca B. Mihalas ◽

Hatice S. Kaya-Okur ◽

Abdullah H. Feroze ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Polycomb Group ◽

Cell Analysis ◽

Tumor Patient ◽

Primary Sample ◽

Before And After

Single-cell analysis has become a powerful approach for the molecular characterization of complex tissues. Methods for quantifying gene expression1 and chromatin accessibility2 of single cells are now well-established, but analysis of chromatin regions with specific histone modifications has been technically challenging. Here, we adapt the recently published CUT&Tag method3 to scalable single-cell platforms to profile chromatin landscapes in single cells (scCUT&Tag) from complex tissues. We focus on profiling Polycomb Group (PcG) silenced regions marked by H3K27 trimethylation (H3K27me3) in single cells as an orthogonal approach to chromatin accessibility for identifying cell states. We show that scCUT&Tag profiling of H3K27me3 distinguishes cell types in human blood and allows the generation of cell-type-specific PcG landscapes from heterogeneous tissues. Furthermore, we use scCUT&Tag to profile H3K27me3 in a brain tumor patient before and after treatment, identifying cell types in the tumor microenvironment and heterogeneity in PcG activity in the primary sample and after treatment.

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Total mRNA Quantification in Single Cells: Sarcoma Cell Heterogeneity

Cells ◽

10.3390/cells9030759 ◽

2020 ◽

Vol 9 (3) ◽

pp. 759 ◽

Cited By ~ 1

Author(s):

Emma Jonasson ◽

Lisa Andersson ◽

Soheila Dolatabadi ◽

Salim Ghannoum ◽

Pierre Åman ◽

...

Keyword(s):

Single Cell ◽

Dynamic Range ◽

Single Cell Analysis ◽

Single Cells ◽

Human Fibroblasts ◽

Mrna Levels ◽

Cell Analysis ◽

Cell Heterogeneity ◽

Cell Type ◽

Sarcoma Cell

Single-cell analysis enables detailed molecular characterization of cells in relation to cell type, genotype, cell state, temporal variations, and microenvironment. These studies often include the analysis of individual genes and networks of genes. The total amount of RNA also varies between cells due to important factors, such as cell type, cell size, and cell cycle state. However, there is a lack of simple and sensitive methods to quantify the total amount of RNA, especially mRNA. Here, we developed a method to quantify total mRNA levels in single cells based on global reverse transcription followed by quantitative PCR. Standard curve analyses of diluted RNA and sorted cells showed a wide dynamic range, high reproducibility, and excellent sensitivity. Single-cell analysis of three sarcoma cell lines and human fibroblasts revealed cell type variations, a lognormal distribution of total mRNA levels, and up to an eight-fold difference in total mRNA levels among the cells. The approach can easily be combined with targeted or global gene expression profiling, providing new means to study cell heterogeneity at an individual gene level and at a global level. This method can be used to investigate the biological importance of variations in the total amount of mRNA in healthy as well as pathological conditions.

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ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

Nature Genetics ◽

10.1038/s41588-021-00790-6 ◽

2021 ◽

Vol 53 (3) ◽

pp. 403-411 ◽

Cited By ~ 1

Author(s):

Jeffrey M. Granja ◽

M. Ryan Corces ◽

Sarah E. Pierce ◽

S. Tansu Bagdatli ◽

Hani Choudhry ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Chromatin Accessibility ◽

Cell Analysis ◽

Software Suite ◽

Cell Clustering ◽

Gene Regulatory ◽

Regulatory Landscapes ◽

Scalable Software

AbstractThe advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://www.archrproject.com/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.

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Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models

Nature Communications ◽

10.1038/s41467-021-23238-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Jeremy A. Lombardo ◽

Marzieh Aliaghaei ◽

Quy H. Nguyen ◽

Kai Kessenbrock ◽

Jered B. Haun

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Cell Analysis ◽

Processing Technologies ◽

Microfluidic Platform ◽

Tissue Processing ◽

Single Cell Rna Sequencing ◽

Culture Models ◽

Tissue Digestion

AbstractTissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Nature Communications ◽

10.1038/s41467-021-21583-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Rongxin Fang ◽

Sebastian Preissl ◽

Yang Li ◽

Xiaomeng Hou ◽

Jacinta Lucero ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Expression Patterns ◽

Regulatory Elements ◽

Cellular Heterogeneity ◽

Specific Gene ◽

Open Chromatin ◽

Cell Type ◽

Process Data ◽

Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

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Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes

Cell Research ◽

10.1038/s41422-018-0125-4 ◽

2018 ◽

Vol 29 (2) ◽

pp. 110-123 ◽

Cited By ~ 25

Author(s):

Chan Gu ◽

Shanling Liu ◽

Qihong Wu ◽

Lin Zhang ◽

Fan Guo

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Chromatin Accessibility ◽

Cell Analysis ◽

Dna Methylome ◽

Mouse Oocytes

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More than efficacy revealed by single-cell analysis of antiviral therapeutics

10.1101/606715 ◽

2019 ◽

Author(s):

Wu Liu ◽

Mehmet U. Caglar ◽

Zhangming Mao ◽

Andrew Woodman ◽

Jamie J. Arnold ◽

...

Keyword(s):

Single Cell ◽

Drug Targets ◽

Single Cell Analysis ◽

Single Cells ◽

Principal Component ◽

Viral Population ◽

Cell Analysis ◽

Toxic Dose ◽

Time Required

SUMMARYDevelopment of antiviral therapeutics emphasizes minimization of the effective dose and maximization of the toxic dose, first in cell culture and later in animal models. Long-term success of an antiviral therapeutic is determined not only by its efficacy but also by the duration of time required for drug-resistance to evolve. We have developed a microfluidic device comprised of ~6000 wells, with each well containing a microstructure to capture single cells. We have used this device to characterize enterovirus inhibitors with distinct mechanisms of action. In contrast to population methods, single-cell analysis reveals that each class of inhibitor interferes with the viral infection cycle in a manner that can be distinguished by principal component analysis. Single-cell analysis of antiviral candidates reveals not only efficacy but also properties of the members of the viral population most sensitive to the drug, the stage of the lifecycle most affected by the drug, and perhaps even if the drug targets an interaction of the virus with its host.

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μCB-seq: Microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells

10.1101/2020.02.18.954974 ◽

2020 ◽

Author(s):

Tyler N. Chen ◽

Anushka Gupta ◽

Mansi Zalavadia ◽

Aaron M. Streets

Keyword(s):

High Resolution ◽

Single Cell ◽

Single Cell Analysis ◽

Protein Localization ◽

Single Cells ◽

Optical Measurements ◽

Detection Sensitivity ◽

High Resolution Imaging ◽

Cell Analysis ◽

Resolution Imaging

AbstractSingle-cell RNA sequencing (scRNA-seq) enables the investigation of complex biological processes in multicellular organisms with high resolution. However, many phenotypic features that are critical to understanding the functional role of cells in a heterogeneous tissue or organ are not directly encoded in the genome and therefore cannot be profiled with scRNA-seq. Quantitative optical microscopy has long been a powerful approach for characterizing diverse cellular phenotypes including cell morphology, protein localization, and chemical composition. Combining scRNA-seq with optical imaging has the potential to provide comprehensive single-cell analysis, allowing for functional integration of gene expression profiling and cell-state characterization. However, it is difficult to track single cells through both measurements; therefore, coupling current scRNA-seq protocols with optical measurements remains a challenge. Here, we report Microfluidic Cell Barcoding and Sequencing (μCB-seq), a microfluidic platform that combines high-resolution imaging and sequencing of single cells. μCB-seq is enabled by a novel fabrication method that preloads primers with known barcode sequences inside addressable reaction chambers of a microfluidic device. In addition to enabling multi-modal single-cell analysis, μCB-seq improves gene detection sensitivity, providing a scalable and accurate method for information-rich characterization of single cells.

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