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RNA-seq Analysis of bone marrow mesenchymal stromal cells from DPBS-treated vs FAP inhibitor-treated mice

ABSTRACT: Purpose: The goal of this study is to compare the transcriptomic differences between bone marrow mesenchymal stromal cells (BMSCs) from DPBS-treated mice and FAP inhibitor (FAPi)-treated mice, in order to find out the differentially expressed genes and pathways underlying the bone phenotypes after FAPi treatment. Methods: The mRNA profiles of BMSCs from DPBS treated mice (n=3) and FAPi treated mice (n=4) were generated by deep sequencing on a HiSeq X Ten system (Illumina) as paired-end 150-bp reads. The sequence reads that passed quality filters were analyzed by STAR and HTSeq. Raw gene counts for each gene was determined by UMI numbers. Results: Using an optimized data analysis workflow, we mapped more than 40 million sequence reads per sample to the mouse genome (mm10) and identified about 9 million raw counts (removing the duplicates by UMIs from Read2) in the BMSCs of DPBS treated mice and FAPi treated mice with HTSeq. Approximately 3% of the genes showed differential expression between BMSCs from DPBS treated mice and FAPi treated mice (fold change > 2.0 or < -2.0 and p value <0.05). Conclusions: Our study revealed the molecular mechanisms underlying the bone-forming activity of FAPi in BMSCs after in vivo administration.

ORGANISM(S): Mus musculus

PROVIDER: GSE145473 | GEO | 2020/10/10

REPOSITORIES: GEO

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Project description:Purpose: The goal of this study is to unravel the differential gene expression profiles (RNA-Seq) during the early differentiation of bone marrow mesenchymal stem cells (BMSCs) into neural progenitor-like stem cells (NPCs) under the influence of different growth factor combinations using high-throughput RNA sequencing method; Group A: EGF and bFGF, Group B: EGF + bFGF and IGF-1, and Control: untreated BMSCs. Methodology: mRNA profiles of BMSCs (Control) and BMSCs-derived NPCs under the influence of growth factor combinations (Group A and Group B) were generated by deep sequencing, in triplicate, using Illumina NextSeq500. Raw reads were evaluated for quality checks in terms of sequencing quality and contamination. Reads were trimmed and filtered using BBDuk. Reads were then aligned to the latest reference genome (rn6) with GTF from Ensembl (v99) using STAR aligner. Aligned reads were transformed into read counts per gene using the RSEM tool. Pairwise differential expression analysis was performed using the DESeq2 R package. qRT–PCR validation was performed using the RT² Profiler PCR Array Rat Custom array (Array ID: CAPR13765E). Results: Using an optimized data analysis workflow, we mapped about 50 million sequence reads per sample to the Rattus norvegicus genome (Rnor_6.0 Assembly) and identified an average of 27,146 transcripts in Group A, 25,850 transcripts in Group B, and 21,683 transcripts in the control group. All transcripts have a fragment count estimation of at least 10 counts per gene. Differential analysis was performed as pairwise grouping. Comparison of Group A vs MSC identified 3,252 differentially expressed genes. Thirteen significantly expressed genes with the potential roles during neural differentiation were randomly selected for validation with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to neural differentiation of mesenchymal stem cells. Data from the present study analysis revealed a significant overlap yet provided complementary insights in transcriptome profiling of mesenchymal stem cells derived neural progenitor-like cells. Conclusion: Our study represents the first detailed analysis of mesenchymal stem cells derived neural progenitor-like cell transcriptomes, generated by RNA-seq technology. Our data has explored crucial and novel genes involved during the early differentiation of mesenchymal stem cells into neural progenitor-like cells under the influence of different growth factor combinations.

Project description:Dyskeratosis congenita (DC) is an inherited multi-system disorder, characterized by oral leukoplakia, nail dystrophy, and abnormal skin pigmentation, as well as high rates of bone marrow failure, solid tumors, and other medical problems such as osteopenia. DC and telomere biology disorders (collectively referred to as TBD here) are caused by germline mutations in telomere biology genes leading to very short telomeres and limited proliferative potential of hematopoietic stem cells. We found that skeletal stem cells (SSCs) within the bone marrow stromal cell population (BMSCs, also known as bone marrow-derived mesenchymal stem cells), may contribute to the hematological phenotype. TBD-BMSCs exhibited reduced clonogenicity, reduced telomerase activity, spontaneous differentiation into adipocytes and fibrotic cells, and increased senescence in vitro. Upon in vivo transplantation into mice, TBD-BMSCs failed to form bone or support hematopoiesis, unlike normal BMSCs. TERC reduction (a TBD-associated gene) in normal BMSCs by siTERC-RNA recapitulated the TBD-BMSC phenotype by reducing proliferation and secondary colony forming efficiency, and by accelerating senescence in vitro. Microarray profiles of control and siTERC-BMSCs showed decreased hematopoietic factors at the mRNA level, and decreased secretion of factors at the protein level. These findings are consistent with defects in SSCs/BMSCs contributing to bone marrow failure in TBD. RNA (5 mg) isolated from N-BMSCs, siNC-BMSCs and siTERC-BMSCs 72hrs after transfection using an RNeasy Mini kit (Qiagen), was reverse transcribed and hybridized to an Affymetrix GeneChip Human Genome U133 Plus 2.0 array (LMT, NCI-Frederick). Three independent replicates for each experimental condition were carried out to control for intra-sample variation. Genes that were under/over-represented by >2-fold were analyzed using GeneSpring software. Signal intensity values were normalized using RMA (Robust Multi-array Analysis) summarization and baseline transformation to median of all samples was performed. Entities were filtered based on their signal intensity values. Hierarchical clustering was performed on filtered signal intensity (>20.0), non-averaged, fold change >2. A fold change analysis (>10-fold) was performed to generate a list of top genes under/over represented between the groups.

Project description:Summary Purpose: By means of high-throughput data analysis (high-throughput sequencing or next-generation sequencing -NGS-), we addressed the effect of the absence of Ikaros and the Notch pathway activation in mouse fetal liver erythroid cells. Specifically, the goals of this study are (i) to characterize the transcriptome of erythroid cells in the absence of Ikaros, when the Notch pathway is activated or not (by RNA-seq); (ii) to identify patterns in the modification of gene regulation according to the variable conditions; and (iii) to obtain information on mechanisms involved in the variation of gene regulation. Methods: Erythroid cells obtained from Ikaros wild type (WT) or Ikaros knockout (Null) mouse fetal livers at the embryonic stage e14.5. These cells were co-cultured for 48 h on OP9 or OP9-DL1 cells. mRNA profiles were generated by NGS, in biological triplicate, using Illumina cBot 2 System. The quality of the raw reads was assessed with FASTQC. Report show no pool imbalance. After examining the quality of the raw reads, no trimming was deemed necessary. The reads were aligned to the GRCm38/mm10 genome with TopHat. The raw alignment counts were calculated with htseq-count. DESeq2 calculates the differential expression of genes directly from the raw alignment counts calculated with htseq-count. The output from DESeq2 includes the raw counts normalized relative to the total number of reads. The log2 fold change is an estimate of the fold change between the conditions, based on the distribution of the reads. qRT-PCR validation was performed using specific primer sets in real-time PCR with SYBR Green. Results. Based on expression profiles, all samples clustered correctly. The differential expression analysis was perform with all samples. For subsequent analysis, 2452 differentially expressed genes were identified in Ikaros WT vs Ikaros null cells cultured on OP9 cells, using a log2 ≥ 1 and a p value <0.005. Additionally, 2847 differentially expressed genes were identified in Ikaros WT vs Ikaros Null cells cultured on OP9-DL1 cells, using a log2 ≥ 1 and a p value <0.005. Among genes activated by Notch (cells cultured on OP9-DL1), 616 genes were repressed in the absence of Ikaros (Ikaros Null vs WT cells; log2≥ -0.8 and a p value <0.005) and 1558 genes were overexpressed in in the absence of Ikaros (Ikaros Null vs WT cells: log2≥ 0.8 and a p value <0.005), using adjusted p value <0.005. Modified expression or immunoprecipitation of several gene transcripts was confirmed with qRT-PCR. Conclusions (Summary): The results obtained demonstrate that Ikaros can favour repression of Notch target genes but can also be required for proper activation on Notch targets upon Notch pathway activation.

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RNA-seq Analysis of bone marrow mesenchymal stromal cells from DPBS-treated vs FAP inhibitor-treated mice

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