Analysis of Chemically Induced Mutation in Single Cell Populations

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

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Live Flow Cytometry Analysis of c-di-GMP Levels in Single Cell Populations

c-di-GMP Signaling - Methods in Molecular Biology ◽

10.1007/978-1-4939-7240-1_10 ◽

2017 ◽

pp. 111-130 ◽

Cited By ~ 3

Author(s):

Jongchan Yeo ◽

Xin C. Wang ◽

Ming C. Hammond

Keyword(s):

Flow Cytometry ◽

Single Cell ◽

Cell Populations ◽

Flow Cytometry Analysis

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Single-Cell Transcriptomics Reveals the Expression of Aging- and Senescence-Associated Genes in Distinct Cancer Cell Populations

Cells ◽

10.3390/cells10113126 ◽

2021 ◽

Vol 10 (11) ◽

pp. 3126

Author(s):

Dominik Saul ◽

Robyn Laura Kosinsky

Keyword(s):

Single Cell ◽

Myelogenous Leukemia ◽

Ductal Adenocarcinoma ◽

Cell Populations ◽

Rna Seq ◽

Healthy Control ◽

Transcriptional Changes ◽

The Relationship ◽

Senescence Associated Genes

The human aging process is associated with molecular changes and cellular degeneration, resulting in a significant increase in cancer incidence with age. Despite their potential correlation, the relationship between cancer- and ageing-related transcriptional changes is largely unknown. In this study, we aimed to analyze aging-associated transcriptional patterns in publicly available bulk mRNA-seq and single-cell RNA-seq (scRNA-seq) datasets for chronic myelogenous leukemia (CML), colorectal cancer (CRC), hepatocellular carcinoma (HCC), lung cancer (LC), and pancreatic ductal adenocarcinoma (PDAC). Indeed, we detected that various aging/senescence-induced genes (ASIGs) were upregulated in malignant diseases compared to healthy control samples. To elucidate the importance of ASIGs during cell development, pseudotime analyses were performed, which revealed a late enrichment of distinct cancer-specific ASIG signatures. Notably, we were able to demonstrate that all cancer entities analyzed in this study comprised cell populations expressing ASIGs. While only minor correlations were detected between ASIGs and transcriptome-wide changes in PDAC, a high proportion of ASIGs was induced in CML, CRC, HCC, and LC samples. These unique cellular subpopulations could serve as a basis for future studies on the role of aging and senescence in human malignancies.

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Single-Cell Sequencing: Biological Insight and Potential Clinical Implications in Pediatric Leukemia

Cancers ◽

10.3390/cancers13225658 ◽

2021 ◽

Vol 13 (22) ◽

pp. 5658

Author(s):

Donát Alpár ◽

Bálint Egyed ◽

Csaba Bödör ◽

Gábor T. Kovács

Keyword(s):

High Resolution ◽

Single Cell ◽

Childhood Leukemia ◽

Clinical Decision Making ◽

Cellular Heterogeneity ◽

Therapeutic Interventions ◽

Cell Populations ◽

Pediatric Leukemia ◽

Single Cell Sequencing ◽

Wide Range

Single-cell sequencing (SCS) provides high-resolution insight into the genomic, epigenomic, and transcriptomic landscape of oncohematological malignancies including pediatric leukemia, the most common type of childhood cancer. Besides broadening our biological understanding of cellular heterogeneity, sub-clonal architecture, and regulatory network of tumor cell populations, SCS can offer clinically relevant, detailed characterization of distinct compartments affected by leukemia and identify therapeutically exploitable vulnerabilities. In this review, we provide an overview of SCS studies focused on the high-resolution genomic and transcriptomic scrutiny of pediatric leukemia. Our aim is to investigate and summarize how different layers of single-cell omics approaches can expectedly support clinical decision making in the future. Although the clinical management of pediatric leukemia underwent a spectacular improvement during the past decades, resistant disease is a major cause of therapy failure. Currently, only a small proportion of childhood leukemia patients benefit from genomics-driven therapy, as 15–20% of them meet the indication criteria of on-label targeted agents, and their overall response rate falls in a relatively wide range (40–85%). The in-depth scrutiny of various cell populations influencing the development, progression, and treatment resistance of different disease subtypes can potentially uncover a wider range of driver mechanisms for innovative therapeutic interventions.

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Axes of inter-sample variability among transcriptional neighborhoods reveal disease associated cell states in single-cell data

10.1101/2021.04.19.440534 ◽

2021 ◽

Author(s):

Yakir A Reshef ◽

Laurie Rumker ◽

Joyce B Kang ◽

Aparna Nathan ◽

Megan B Murray ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Single Cell ◽

Active Tuberculosis ◽

Cell Populations ◽

Cell Type ◽

Accurate Identification ◽

Shared Function ◽

Statistical Approaches ◽

Cell Data ◽

Notch Activation

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes like clinical phenotypes. Current statistical approaches typically map cells to cell-type clusters and examine sample differences through that lens alone. Here we present covarying neighborhood analysis (CNA), an unbiased method to identify cell populations of interest with greater flexibility and granularity. CNA characterizes dominant axes of variation across samples by identifying groups of very small regions in transcriptional space, termed neighborhoods, that covary in abundance across samples, suggesting shared function or regulation. CNA can then rigorously test for associations between any sample-level attribute and the abundances of these covarying neighborhood groups. We show in simulation that CNA enables more powerful and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, redefines monocyte populations expanded in sepsis, and identifies a previously undiscovered T-cell population associated with progression to active tuberculosis.

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Single Cell, Single Nucleus and Spatial RNA Sequencing of the Human Liver Identifies Hepatic Stellate Cell and Cholangiocyte Heterogeneity

10.1101/2021.03.27.436882 ◽

2021 ◽

Author(s):

Tallulah S Andrews ◽

Jawairia Atif ◽

Jeff C Liu ◽

Catia T Perciani ◽

Xue-Zhong Ma ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Human Liver ◽

Stellate Cell ◽

Parenchymal Cell ◽

Cell Types ◽

Cell Populations ◽

Healthy Human ◽

Single Nucleus

The critical functions of the human liver are coordinated through the interactions of hepatic parenchymal and non-parenchymal cells. Recent advances in single cell transcriptional approaches have enabled an examination of the human liver with unprecedented resolution. However, dissociation related cell perturbation can limit the ability to fully capture the human liver's parenchymal cell fraction, which limits the ability to comprehensively profile this organ. Here, we report the transcriptional landscape of 73,295 cells from the human liver using matched single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq). The addition of snRNA-seq enabled the characterization of interzonal hepatocytes at single-cell resolution, revealed the presence of rare subtypes of hepatic stellate cells previously only seen in disease, and detection of cholangiocyte progenitors that had only been observed during in vitro differentiation experiments. However, T and B lymphocytes and NK cells were only distinguishable using scRNA-seq, highlighting the importance of applying both technologies to obtain a complete map of tissue-resident cell-types. We validated the distinct spatial distribution of the hepatocyte, cholangiocyte and stellate cell populations by an independent spatial transcriptomics dataset and immunohistochemistry. Our study provides a systematic comparison of the transcriptomes captured by scRNA-seq and snRNA-seq and delivers a high-resolution map of the parenchymal cell populations in the healthy human liver.

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Super-cells untangle large and complex single-cell transcriptome networks

10.1101/2021.06.07.447430 ◽

2021 ◽

Author(s):

Mariia Bilous ◽

Loc Tran ◽

Chiara Cianciaruso ◽

Santiago J Carmona ◽

Mikael J Pittet ◽

...

Keyword(s):

Single Cell ◽

Coarse Graining ◽

Cell Populations ◽

Cell Type ◽

Large Numbers ◽

Single Cell Rna Sequencing ◽

Gene Correlation ◽

Cell Transcriptome ◽

Single Cell Transcriptome

Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Here we develop a network-based coarse-graining framework where highly similar cells are merged into super-cells. We demonstrate that super-cells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, super-cells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

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