Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

Hsiao, Chiaowen Joyce; Tung, Po-Yuan; Blischak, John; Burnett, Jonathan E.; Barr, Kenneth; Dey, Kushal K.; Stephens, Matthew; Gilad, Yoav

doi:10.1101/526848

Cited by 25 publications

(30 citation statements)

References 52 publications

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“…Further, the lineage annotations were confirmed by Single-R [ 24 ], which compares each cell against a reference dataset of population-level transcriptional profiles (in this case, the Immgen database [ 25 ]) ( S3C Fig ). Lastly, one cluster in each patient expressed high levels of cell cycle genes [ 30 ], which would indicate dividing cells ( S3D Fig ). T and NK cells were the most abundant immune cell population in all samples, comprising 73%, 48%, and 54% of total immune cells in ALGS, BASM, and iBA respectively ( Fig 2D ).…”

Section: Resultsmentioning

confidence: 99%

Transcriptional profiling of pediatric cholestatic livers identifies three distinct macrophage populations

et al. 2021

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Background & aims Limited understanding of the role for specific macrophage subsets in the pathogenesis of cholestatic liver injury is a barrier to advancing medical therapy. Macrophages have previously been implicated in both the mal-adaptive and protective responses in obstructive cholestasis. Recently two macrophage subsets were identified in non-diseased human liver; however, no studies to date fully define the heterogeneous macrophage subsets during the pathogenesis of cholestasis. Here, we aim to further characterize the transcriptional profile of macrophages in pediatric cholestatic liver disease. Methods We isolated live hepatic immune cells from patients with biliary atresia (BA), Alagille syndrome (ALGS), and non-cholestatic pediatric liver by fluorescence activated cell sorting. Through single-cell RNA sequencing analysis and immunofluorescence, we characterized cholestatic macrophages. We next compared the transcriptional profile of pediatric cholestatic and non-cholestatic macrophage populations to previously published data on normal adult hepatic macrophages. Results We identified 3 distinct macrophage populations across cholestatic liver samples and annotated them as lipid-associated macrophages, monocyte-like macrophages, and adaptive macrophages based on their transcriptional profile. Immunofluorescence of liver tissue using markers for each subset confirmed their presence across BA (n = 6) and ALGS (n = 6) patients. Cholestatic macrophages demonstrated reduced expression of immune regulatory genes as compared to normal hepatic macrophages and were distinct from macrophage populations defined in either healthy adult or pediatric non-cholestatic liver. Conclusions We are the first to perform single-cell RNA sequencing on human pediatric cholestatic liver and identified three macrophage subsets with distinct transcriptional signatures from healthy liver macrophages. Further analyses will identify similarities and differences in these macrophage sub-populations across etiologies of cholestatic liver disease. Taken together, these findings may allow for future development of targeted therapeutic strategies to reprogram macrophages to an immune regulatory phenotype and reduce cholestatic liver injury.

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Section: Resultsmentioning

confidence: 99%

Transcriptional profiling of pediatric cholestatic livers identifies three distinct macrophage populations

et al. 2021

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“…To assess whether any of the 38 high-confidence cell cycle genes identified by our approach, but not in the prior synchronized cell culture studies, exhibit cell cycle–regulated expression, we turned to a new single-cell RNASeq data set GSE121265 (Hsiao et al , 2019). This data set used iPSC lines that were genetically engineered to express the FUCCI (fluorescent ubiquitination cell cycle indicator) reporters to indicate cell cycle status.…”

Section: Resultsmentioning

confidence: 99%

“…Gene expression summarization was performed by normalizing each Affymetrix platform by Robust Multichip Average (Irizarry et al , 2003). A single-cell RNASeq data set that quantifies continuous cell cycle phase using single-cell gene expression data (GSE121265) was used for validation (Hsiao et al , 2019). We considered all gene names annotated at NCBI (ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene_info.gz, downloaded on July 12, 2018) in our comparisons with prior work.…”

Section: Methodsmentioning

confidence: 99%

Unbiased Boolean analysis of public gene expression data for cell cycle gene identification

Dabydeen

Desai

Sahoo

2019

MBoC

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Cell proliferation is essential for the development and maintenance of all organisms and is dysregulated in cancer. Using synchronized cells progressing through the cell cycle, pioneering microarray studies defined cell cycle genes based on cyclic variation in their expression. However, the concordance of the small number of synchronized cell studies has been limited, leading to discrepancies in definition of the transcriptionally regulated set of cell cycle genes within and between species. Here we present an informatics approach based on Boolean logic to identify cell cycle genes. This approach used the vast array of publicly available gene expression data sets to query similarity to CCNB1, which encodes the cyclin subunit of the Cdk1-cyclin B complex that triggers the G2-to-M transition. In addition to highlighting conservation of cell cycle genes across large evolutionary distances, this approach identified contexts where well-studied genes known to act during the cell cycle are expressed and potentially acting in nondivision contexts. An accessible web platform enables a detailed exploration of the cell cycle gene lists generated using the Boolean logic approach. The methods employed are straightforward to extend to processes other than the cell cycle.

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“…Note that if cell cycle signals are of marked interest then equalization may not be appropriate. However, reduction of cell-cycle signals has been implemented in most scRNA-seq analysis pipelines as it is considered a hindrance in downstream analysis (26,27).…”

Section: Discussionmentioning

confidence: 99%

Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization

Bacher

Chu

Argus

et al. 2020

Preprint

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Considerable effort has been devoted to refining experimental protocols having reduced levels of technical variability and artifacts in single-cell RNA-sequencing data (scRNA-seq). We here present evidence that equalizing the concentration of cDNA libraries prior to pooling, a step not consistently performed in single-cell experiments, improves gene detection rates, enhances biological signals, and reduces technical artifacts in scRNA-seq data. To evaluate the effect of equalization on various protocols, we developed Scaffold, a simulation framework that models each step of an scRNA-seq experiment. Numerical experiments demonstrate that equalization reduces variation in sequencing depth and gene-specific expression variability. We then performed a set of experiments in vitro with and without the equalization step and found that equalization increases the number of genes that are detected in every cell by 17-31%, improves discovery of biologically relevant genes, and reduces nuisance signals associated with cell cycle. Further support is provided in an analysis of publicly available data.

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Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

Cited by 25 publications

References 52 publications

Transcriptional profiling of pediatric cholestatic livers identifies three distinct macrophage populations

Transcriptional profiling of pediatric cholestatic livers identifies three distinct macrophage populations

Unbiased Boolean analysis of public gene expression data for cell cycle gene identification

Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization

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