scholarly journals Quantitative assessment of cell population diversity in single-cell landscapes

2018 ◽  
Author(s):  
Qi Liu ◽  
Charles A. Herring ◽  
Quanhu Sheng ◽  
Jie Ping ◽  
Alan J. Simmons ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Systematic Investigation ◽  
Population Diversity ◽  
Cell Populations ◽  
Batch Correction ◽  
Population Structures ◽  
Altered Cell ◽  
Cell Transcriptome ◽  
Compositional Diversity

AbstractSingle-cell RNA-sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such expansion or shrinkage, or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes, identification and definition of altered cell populations, and benchmarking batch correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions, and has broad utility for gaining insight on how cell populations respond to perturbations.

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

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.

scholarly journals Building the mega single-cell transcriptome ocular meta-atlas

GigaScience ◽  
2021 ◽  
Vol 10 (10) ◽  
Author(s):  
Vinay S Swamy ◽  
Temesgen D Fufa ◽  
Robert B Hufnagel ◽  
David M McGaughey
Keyword(s):  
Single Cell ◽  
Biological Effects ◽  
R Package ◽  
Technical Noise ◽  
Batch Correction ◽  
Workflow System ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Compare And Contrast ◽  
Cluster Purity

Abstract Background: The development of highly scalable single-cell transcriptome technology has resulted in the creation of thousands of datasets, >30 in the retina alone. Analyzing the transcriptomes between different projects is highly desirable because this would allow for better assessment of which biological effects are consistent across independent studies. However it is difficult to compare and contrast data across different projects because there are substantial batch effects from computational processing, single-cell technology utilized, and the natural biological variation. While many single-cell transcriptome-specific batch correction methods purport to remove the technical noise, it is difficult to ascertain which method functions best. Results: We developed a lightweight R package (scPOP, single-cell Pick Optimal Parameters) that brings in batch integration methods and uses a simple heuristic to balance batch merging and cell type/cluster purity. We use this package along with a Snakefile-based workflow system to demonstrate how to optimally merge 766,615 cells from 33 retina datsets and 3 species to create a massive ocular single-cell transcriptome meta-atlas. Conclusions: This provides a model for how to efficiently create meta-atlases for tissues and cells of interest.

scholarly journals Quantitative assessment of cell population diversity in single-cell landscapes

PLoS Biology ◽  
2018 ◽  
Vol 16 (10) ◽  
pp. e2006687 ◽  
Author(s):  
Qi Liu ◽  
Charles A. Herring ◽  
Quanhu Sheng ◽  
Jie Ping ◽  
Alan J. Simmons ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Quantitative Assessment ◽  
Population Diversity

scholarly journals ILoReg enables high-resolution cell population identification from single-cell RNA-seq data

2020 ◽  
Author(s):  
Johannes Smolander ◽  
Sini Junttila ◽  
Mikko S Venäläinen ◽  
Laura L Elo
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Single Cell ◽  
Cell Population ◽  
R Package ◽  
High Dimensionality ◽  
Cell Populations ◽  
Rna Seq ◽  
Extraction Step ◽  
Resolution Cell

AbstractSingle-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging. We introduce ILoReg (https://github.com/elolab/iloreg), an R package implementing a new cell population identification method that achieves high differentiation resolution through a probabilistic feature extraction step that is applied before clustering and visualization.

scholarly journals Single cell immune profiling reveals a developmentally distinct CD4+ GM-CSF+ T cell lineage that induces GI tract GVHD

2022 ◽  
Author(s):  
Clint Piper ◽  
Emma Hainstock ◽  
Cheng Yin-Yuan ◽  
Yao Chen ◽  
Achia Khatun ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cell Population ◽  
Cell Populations ◽  
Sequencing Analysis ◽  
Gi Tract ◽  
Gm Csf ◽  
Mediators Of Inflammation ◽  
Ifn Γ

Gastrointestinal (GI) tract involvement is a major determinant for subsequent morbidity and mortality arising during graft versus host disease (GVHD). CD4+ T cells that produce GM-CSF have emerged as central mediators of inflammation in this tissue site as GM-CSF serves as a critical cytokine link between the adaptive and innate arms of the immune system. However, cellular heterogeneity within the CD4+ GM-CSF+ T cell population due to the concurrent production of other inflammatory cytokines has raised questions as to whether these cells have a common ontology or if there exists a unique CD4+ GM-CSF+ subset that differs from other defined T helper (TH) subtypes. Using single cell RNA sequencing analysis, we identified two CD4+ GM-CSF+ T cell populations that arose during GVHD and were distinguishable by the presence or absence of IFN-γ co-expression. CD4+ GM-CSF+ IFN-γ- T cells which emerged preferentially in the colon had a distinct transcriptional profile, employed unique gene regulatory networks, and possessed a non-overlapping TCR repertoire when compared to CD4+ GM-CSF+ IFN-γ+ T cells as well as all other transcriptionally defined CD4+ T cell populations in the colon. Functionally, this CD4+ GM-CSF+ T cell population contributed to pathological damage in the GI tract which was critically dependent upon signaling through the IL-7 receptor but was independent of type 1 interferon signaling. Thus, these studies help to unravel heterogeneity within CD4+ GM-CSF+ T cells that arise during GVHD and define a developmentally distinct colitogenic TH GM-CSF+ subset that mediates immunopathology.

scholarly journals Single-cell transcriptome reveals the redifferentiation trajectories of the early stage of de novo shoot regeneration in Arabidopsis thaliana

2022 ◽  
Author(s):  
Guangyu Liu ◽  
Jie Li ◽  
Jiming Li ◽  
Zhiyong Chen ◽  
Peisi Yuan ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Shoot Regeneration ◽  
De Novo ◽  
Developmental Process ◽  
Early Stage ◽  
Cell Populations ◽  
Cell Heterogeneity ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

De novo shoot regeneration from a callus plays a crucial role in both plant biotechnology and the fundamental research of plant cell totipotency. Recent studies have revealed many regulatory factors involved in this developmental process. However. our knowledge of the cell heterogeneity and cell fate transition during de novo shoot regeneration is still limited. Here, we performed time-series single-cell transcriptome experiments to reveal the cell heterogeneity and redifferentiation trajectories during the early stage of de novo shoot regeneration. Based on the single-cell transcriptome data of 35,669 cells at five-time points, we successfully determined seven major cell populations in this developmental process and reconstructed the redifferentiation trajectories. We found that all cell populations resembled root identities and undergone gradual cell-fate transitions. In detail, the totipotent callus cells differentiated into pluripotent QC-like cells and then gradually developed into less differentiated cells that have multiple root-like cell identities, such as pericycle-like cells. According to the reconstructed redifferentiation trajectories, we discovered that the canonical regeneration-related genes were dynamically expressed at certain stages of the redifferentiation process. Moreover, we also explored potential transcription factors and regulatory networks that might be involved in this process. The transcription factors detected at the initial stage, QC-like cells, and the end stage provided a valuable resource for future functional verifications. Overall, this dataset offers a unique glimpse into the early stages of de novo shoot regeneration, providing a foundation for a comprehensive analysis of the mechanism of de novo shoot regeneration.

scholarly journals Single-cell atlas of human oral mucosa reveals a stromal-neutrophil axis regulating tissue immunity in health and inflammatory disease

2021 ◽  
Author(s):  
Drake Winslow Williams ◽  
Teresa Greenwell-Wild ◽  
Laurie Brenchley ◽  
Nicolas Dutzan ◽  
Andrew Overmiller ◽  
...  
Keyword(s):  
Single Cell ◽  
Oral Mucosa ◽  
Immune Cell ◽  
Inflammatory Diseases ◽  
Cellular Level ◽  
Cell Populations ◽  
Mucosal Tissues ◽  
Cell Transcriptome ◽  
Gingival Mucosa ◽  
Single Cell Transcriptome

The oral mucosa remains an understudied barrier tissue rich in exposure to antigens, commensals and pathogens. Moreover, it is the tissue where one of the most prevalent human microbe-triggered inflammatory diseases, periodontitis, occurs. To understand this complex environment at the cellular level, we assemble herein a human single-cell transcriptome atlas of oral mucosal tissues in health and periodontitis. Our work reveals transcriptional diversity of stromal and immune cell populations, predicts intercellular communication and uncovers an altered immune responsiveness of stromal cells participating in tissue homeostasis and disease at the gingival mucosa. In health, we define unique populations of CXCL1,2,8- expressing epithelial cells and fibroblasts mediating immune homeostasis primarily through the recruitment of neutrophils. In disease, we further observe stromal, particularly fibroblast hyper-responsiveness linked to recruitment of leukocytes and neutrophil populations. Ultimately, a stromal-neutrophil axis emerges as a key regulator of mucosal immunity. Pursuant to these findings, most Mendelian forms of periodontitis were shown to be linked to genetic mutations in neutrophil and select fibroblast-expressed genes. Moreover, we document previously unappreciated expression of known pattern- and damage- recognition receptors on stromal cell populations in the setting of periodontitis, suggesting avenues for triggering stromal responses. This comprehensive atlas offers an important reference for in-depth understanding of oral mucosal homeostasis and inflammation and reveals unique stromal-immune interactions implicated in tissue immunity.

scholarly journals scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets

2021 ◽  
Author(s):  
Massimo Andreatta ◽  
Ariel J. Berenstein ◽  
Santiago J Carmona
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
R Package ◽  
Training Data ◽  
Cell Populations ◽  
Specific Cell ◽  
A Cell ◽  
Cell Data

A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. Here we present scGate, an algorithm that automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. scGate purifies a cell population of interest using a set of markers organized in a hierarchical structure, akin to gating strategies employed in flow cytometry. In our benchmark for blood-derived and tumor-infiltrating immune cells, scGate outperforms SingleR, a state-of-the-art classifier for single-cell data. scGate is implemented as an R package and integrated with the Seurat framework, providing an intuitive tool to isolate cell populations of interest from complex scRNA-seq datasets. Availability: R package source code and reproducible tutorials are available at https://github.com/carmonalab/scGate

scholarly journals Highly Multiplexed Single-Cell RNA-seq for Defining Cell Population and Transcriptional Spaces

2018 ◽  
Author(s):  
Jase Gehring ◽  
Jong Hwee Park ◽  
Sisi Chen ◽  
Matthew Thomson ◽  
Lior Pachter
Keyword(s):  
Population Structure ◽  
Single Cell ◽  
Cell Population ◽  
Effective Means ◽  
Cost Effective ◽  
Clinical Samples ◽  
Cell Populations ◽  
Rna Seq ◽  
Dna Oligonucleotides ◽  
Perturbation Experiment

AbstractWe describe a universal sample multiplexing method for single-cell RNA-seq in which cells are chemically labeled with identifying DNA oligonucleotides. Analysis of a 96-plex perturbation experiment revealed changes in cell population structure and transcriptional states that cannot be discerned from bulk measurements, establishing a cost effective means to survey cell populations from large experiments and clinical samples with the depth and resolution of single-cell RNA-seq.

scholarly journals Development of a Serial Dilution Technique for Obtaining Monoclonal Cell Populations

2021 ◽  
Author(s):  
Maryam Ranjbar ◽  
Marjan Nourigorji ◽  
Farshid Amiri ◽  
Hossein Jafari Khamirani ◽  
Sina Zoghi ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Genetic Information ◽  
Genetically Modified ◽  
Cost Effective ◽  
Serial Dilution ◽  
Cell Populations ◽  
Dilution Technique ◽  
Cell Clones ◽  
Limiting Dilution

Abstract Single cell-based techniques have drawn the attention of researchers, because they provide invaluable information of various domains ranging from genomics to epigenetics, transcriptomics, and proteomics. Single cell-derived clones provide a reliable and sustainable source of genetic information due to the homogeneity of the cell population. Aiming to obtain single-cell clones, several approaches were engineered, among which, the Limiting dilution approach stands out as a cost-effective and unsophisticated, and easy-to-apply method. Here, we demonstrate how to acquire single cell-derived clones through a simple 1:10 diluting from genetically modified heterogeneous cell populations.

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