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RNA Sequencing Allows for Differential Gene Expression Analysis of Infrequently Dividing and Frequently Dividing (IDC/FDC) Oral Epithelial Stem Cells

ABSTRACT: RNA Sequencing Allows for Differential Gene Expression Analysis of Infrequently Dividing and Frequently Dividing (IDC/FDC) Oral Epithelial Stem Cells

PROVIDER: PRJNA578638 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR10316983_1.fastq.gz	Fastqsanger.gz
	SRR10316983_2.fastq.gz	Fastqsanger.gz
	SRR10316984_1.fastq.gz	Fastqsanger.gz
	SRR10316984_2.fastq.gz	Fastqsanger.gz
	SRR10316985_1.fastq.gz	Fastqsanger.gz

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RNA Sequencing Allows for Differential Gene Expression Analysis of Infrequently Dividing and Frequently Dividing (IDC/FDC) Oral Epithelial Stem Cells

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare palatal epithelial basal cell transcriptomes between infrequently and frequently dividing cells (IDC/FDCs). Methods: RNA was isolated from 200-1000 palatal basal epithelial cells and libraries prepared using the SoLo RNA-Seq library preparation kit (NuGEN). mRNA profiles of 14-day doxycycline chased K5-tTA;tetO-H2B-GFP (containing populations of slow-cycling GFP-HI and fast-cycling GFP-LO) mice were generated with 6 matched samples using an Illumina HiSeq4000. RNA Sequencing Allows for Differential Gene Expression Analysis of infrequently dividing and frequently dividing cells (IDC/FDC). RNA-seq fastq files were trimmed and aligned to the latest mouse genome, mm10 (GRCm38/GCA_000001635.2) using BBMap. A matrix of the data was generated using featureCounts. RNAseq data was analyzed with DESeq2. All statistical analyses were performed using R version 3.4.1 and RStudio version 1.0.143. Validation was performed using lineage tracing and immunofluorescence immunohistochemistry. Results: Using an optimized data analysis workflow, approximately 2% of the transcripts showed differential expression between the GFP-HI and GFO-LO palatal basal epithelial cell with a p value <0.05. Analysis of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to oral epithelial stem cell, such as Lrig1 and Igfbp5 as followed up on in the associated manuscript. Conclusions: Our study represents the first detailed analysis of palatal basal epithelial stem cell transcriptomes, with biologic replicates, generated by RNA-seq technology. Our results show GFP-LO infrequently dividing cells are express a distinct transcriptional profile compared to frequently dividing neighbor cells such as Lrig1 and Igfbp5.

2019-10-22 | GSE139146 | GEO

Gene expression profiling of infrequently and frequently dividing cells in the mouse epidermis

Project description:Infrequently (LRCs) and frequently (non-LRCs) dividing cells in the mouse epidermis are moleculary distinct.

2016-09-01 | GSE70523 | GEO

Gene expression profiling of infrequently and frequently dividing cells in the mouse epidermis

Project description:Infrequently (LRCs) and frequently (non-LRCs) dividing cells in the mouse epidermis are moleculary distinct. Fluorescence activated cell sorting (FACS) from back skin cells of quadruple-transgenic mice harboring K5-tTA, pTRE-H2B-GFP, K14-CreER and Rosa-tdTomato transgenes.

2016-09-01 | E-GEOD-70523 | biostudies-arrayexpress

Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR.

Project description:Differential expression analysis of RNA-seq is one of the most commonly performed bioinformatics analyses. Transcript-level quantifications are inherently more uncertain than gene-level read counts because of ambiguous assignment of sequence reads to transcripts. While sequence reads can usually be assigned unambiguously to a gene, reads are very often compatible with multiple transcripts for that gene, particularly for genes with many isoforms. Software tools designed for gene-level differential expression do not perform optimally on transcript counts because the read-to-transcript ambiguity (RTA) disrupts the mean-variance relationship normally observed for gene level RNA-seq data and interferes with the efficiency of the empirical Bayes dispersion estimation procedures. The pseudoaligners kallisto and Salmon provide bootstrap samples from which quantification uncertainty can be assessed. We show that the overdispersion arising from RTA can be elegantly estimated by fitting a quasi-Poisson model to the bootstrap counts for each transcript. The technical overdispersion arising from RTA can then be divided out of the transcript counts, leading to scaled counts that can be input for analysis by established gene-level software tools with full statistical efficiency. Comprehensive simulations and test data show that an edgeR analysis of the scaled counts is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate. Simulations explore a wide range of scenarios including the effects of paired vs single-end reads, different read lengths and different numbers of replicates.

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Mex3a Marks a Slowly Dividing Subpopulation of Lgr5+ Intestinal Stem Cells

Project description:The intestinal epithelium is continuously regenerated by highly proliferative Lgr5+ intestinal stem cells (ISCs). The existence of a population of quiescent ISCs has been suggested yet its identity and features remain controversial. Here we describe that the expression of the RNA-binding protein Mex3a labels a subpopulation of Lgr5+ cells that divide less frequently and contribute to regenerate all intestinal lineages with slow kinetics. Single cell transcriptomic analysis revealed two classes of Lgr5-high cells, one of them defined by the Mex3a-expression program and by low levels of proliferation genes. Lineage tracing experiments show that large fraction of Mex3a+ cell population is continuously recalled into the rapidly dividing self-renewing ISC pool in homeostatic conditions. Chemotherapy and radiation target preferentially rapidly dividing Lgr5+ cells but spare the Mex3a-high/Lgr5+ population, which helps sustain the renewal of the intestinal epithelium during treatment.

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Transcription profiling of CD34+CD38- vs CD34+CD38+ fractions of human umbilical cord blood and comparison of slow-dividing fraction vs the fast dividing fraction of CD34+/CD38- population

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2005-09-08 | E-EMBL-1 | biostudies-arrayexpress

Expression level comparison under dividing and quiescent states in human primary fibroblasts

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2018-01-01 | GSE86867 | GEO

Comparison of rapidly vs. slowly dividing CD8 T cells during acute homeostatic proliferation

Project description:Our earlier study demonstrated that when CFSE-labeled LCMV-or Pichinde virus-immune spleen leukocytes were transferred into T cell-deficient hosts, the bona fide virus-specific memory cells underwent relatively limited cell division and were substantially diluted in frequency by other more extensively proliferating cells originating from that donor cell population. We questioned how the slowly dividing population, which contained bona fide memory cells, differed from the rapidly dividing cells, which contained “memory-like” cells. As a preliminary screen we performed a comparative genome-wide microarray analysis of genes expressed on sorted rapidly proliferating (CFSE-low) and slowly proliferating (CFSE-high) CD8 cell populations Keywords: Division profile comparison

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Transcriptional profiling of chronic myelogenous leukemia (CML) and normal, quiescent and dividing haematopoietic cells

Project description:Quiescent and dividing hemopoietic stem cells (HSC) display marked differences in their ability to move between the peripheral circulation and the bone marrow. Specifically, long-term engraftment potential predominantly resides in the quiescent HSC subfraction, and G-CSF mobilization results in the preferential accumulation of quiescent HSC in the periphery. In contrast, stem cells from chronic myeloid leukemia (CML) patients display a constitutive presence in the circulation. To understand the molecular basis for this, we have used microarray technology to analyze the transcriptional differences between dividing and quiescent, normal, and CML-derived CD34+ cells.

2014-04-25 | E-MTAB-2508 | biostudies-arrayexpress

Transcription profiling of mouse quiescent or dividing wild type and G3BP -/- MEFs

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