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Next Generation Sequencing Facilitates Quantitative Analysis of Human Pluripotent Stem Cell-Derived Hematopoietic Cell Transcriptomes

ABSTRACT: Next-generation sequencing (NGS) has significantly advanced the elucidation of developmental signaling mechanisms that are important for different cell lineage formation from human pluripotent stem cells (hPSCs). We report here the application of RNA-sequencing technology for transcriptome profile of hPSC-derived hematopoietic cells, and compare to those of other cell lineages including hPSCs, mesoderm, primary aorta-gonad-mesonephros (AGM) cells and cord blood hematopoietic stem cells (HSCs). IIIumina HiSeq 2X150 bp sequencing was performed on three hematopoietic cell samples from three hPSC lines by GENEWIZ. The resulting sequence reads (about 34 million reads per sample) were mapped to human genome (hg19) using HISAT, and the RefSeq transcript levels (RPKMs) were quantified using the python script rpkmforgenes.py. Our RNA-seq data confirmed the stable expression of key hematopoietic cell markers including CD45, RUNX1, CD34 and HOXA clusters, and the gene set enrichment analysis (GSEA) showed enrichment in “aorta development”, “cell migration”, “hematopoietic stem cell proliferation”, “Notch signaling regulation”, et al. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to hematopoietic function. This study presented a detailed analysis of hematopoietic transcriptomes generated by RNA-seq technology, providing insight into the mechanisms underlying the differentiation of hPSCs into hematopoietic cells.

ORGANISM(S): Homo sapiens

PROVIDER: GSE155196 | GEO | 2022/05/13

REPOSITORIES: GEO

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Project description:Next-generation sequencing (NGS) has significantly facilitated the analysis of the gene profile and elucidated the molecular mechanisms important for specific cell lineage differentiated from human pluripotent stem cells (hPSCs). Here we report the application of RNA-sequencing technology for transcriptome profile of primary human brain microvascular endothelial cells (BMECs) and hPSC-derived BMECs, and comparison of transcriptomes among cells, including hPSCs, mesodermal progenitors, ectodermal progenitors, endodermal progenitors, hBMECs and hPSC-derived BMECs from hPSCs. Four different BMEC samples differentiated from hPSCs by two different methods and hBMECs were performed in IIIumina HiSeq2500. The resulting sequence reads (about 20 million reads per sample) were mapped to human genome (hg19) using HISAT, and the RefSeq transcript levels (FPKMs) were quantified using the python script rpkmforgenes.py. We next analyzed the expression of a subset of genes that regulate key BBB attributes, including tight junctions and molecular transporters. The gene set comprises 20 tight junction-related genes and an unbiased list of all 25 CLDN genes, all 407 solute carrier (SLC) transporters, and all 53 ATP-binding cassette (ABC) transporters regardless of prior knowledge of BBB association. Primary human BMECs expressed 234 of these genes. BMECs differentiated from hPSCs via the defined method expressed many of these same genes (206 of 234 (88%)) as did BMECs differentiated via the UM method (208 of 234 (89%), indicating a close similarity between hPSC-derived BMECs and primary human BMECs with respect to transcripts having likely relevance to BBB function. RNA sequencing was used to compare global gene expression profiles in the hPSC-derived BMECs with BMECs generated from our previously reported co-differentiation system and primary human BMECs. As expected, hPSC-derived BMECs from three independent differentiations clustered closely and were similar to those generated from the undefined UM platform. Moreover, the hPSC-derived BMECs clustered with primary human BMECs and were distinct from undifferentiated hPSCs and hPSC-derived ectoderm, endoderm and mesodermThis study provides detailed transcriptome comparison between hBMECs and hPSC-derived BMECs and unveils important genes related BMEC phenotypes.

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Next Generation Sequencing Facilitates Quantitative Analysis of Human Pluripotent Stem Cell-Derived Hematopoietic Cell Transcriptomes

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