| Literature DB >> 34782629 |
Aaron M Earley1, Lena F Burbulla1,2,3,4, Dimitri Krainc1, Rajeshwar Awatramani5.
Abstract
During cellular specification, transcription factors orchestrate cellular decisions through gene regulation. By hijacking these transcriptional networks, human pluripotent stem cells (hPSCs) can be specialized into neurons with different molecular identities for the purposes of regenerative medicine and disease modeling. However, molecular fine tuning cell types to match their in vivo counterparts remains a challenge. Directing cell fates often result in blended or incomplete neuron identities. A better understanding of hPSC to neuron gene regulation is needed. Here, we used single cell RNA sequencing to resolve some of these graded molecular identities during human neurogenesis from hPSCs. Differentiation platforms were established to model neural induction from stem cells, and we characterized these differentiated cell types by 10x single cell RNA sequencing. Using single cell trajectory and co-expression analyses, we identified a co-regulated transcription factor module expressing achaete-scute family basic helix-loop-helix transcription factor 1 (ASCL1) and neuronal differentiation 1 (NEUROD1). We then tested the function of these transcription factors in neuron subtype differentiation by gene knockout in a novel human system that reports the expression of tyrosine hydroxylase (TH), the rate limiting enzyme in dopamine synthesis. ASCL1 was identified as a necessary transcription factor for regulating dopaminergic neurotransmitter selection.Entities:
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Year: 2021 PMID: 34782629 PMCID: PMC8593045 DOI: 10.1038/s41598-021-01366-4
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Figure 110x single cell RNA sequencing of a human iPSC-derived neural mixed culture. (a) Neural mixed culture differentiation timeline (complete method of chemical induction described in “Materials and methods” section). *Time of harvest for 10x single cell RNA sequencing. (b) UMAP of integrated cell type clustering (n = 3 independent differentiations) with cell classes defined by cell surface markers. (c) Merged UMAP of cell type clustering from (b). (d) Dot plot of scaled normalized RNA expression for radial glial and neuronal markers across cell classes. (e) Frequency of cell classes (left) and total numbers of cells sequenced (right) (n = 3 independent differentiations, mean ± SEM). (f) Feature maps of normalized RNA expression plotted as UMAP for radial glial and neuronal markers.
Figure 2Single cell trajectory analysis of a human iPSC-derived neural mixed culture. (a) UMAP of learned trajectory graph from integrated cell type clustering with cells ordered in pseudotime. (b) Co-regulated gene modules of predicted lineage subset in (a) and visualized as scaled expression to percent of maximum expression. (c) Gene modules from (b) defined as progenitor (module 3), neuronal (module 7), and transitional state (module 11). (d) Table of example genes expressed in modules from (c). (e) Expression of radial glial and neuronal markers varying as a function of pseudotime on lineage subset from (b). (f) Expression of Notch, Shh, and Wnt related pathway genes as a function of pseudotime on lineage subset from (b). (g) Expression of gene module 11 transitional state bHLH transcription factors as a function of pseudotime on lineage subset from (b).
Figure 310x single cell RNA sequencing of FACS purified human TH expressing neurons programmed from iPSCs. (a) FACS plot of live TH expressing cells from a Th-P2A-tdTomato iPSC line. (b) UMAP of integrated cell type clustering from FACS purification with cell types numbered (n = 3 independent differentiations). (c) Merged UMAP of cell type clustering from (b). (d) Stacked violin plots of normalized RNA expression for neuronal markers comparing neural mixed culture cell classes to purified neuron cell type clusters. (e) Feature maps of normalized RNA expression plotted as UMAP for neuronal, glutamatergic, dopaminergic, and bHLH transcription factor genes.
Figure 4Distinct loss of function effects of ASCL1 and NEUROD1 on neurotransmitter selection in human iPSC-derived neurons. (a) RT-qPCR of culture temporal gene expression (n = 3 independent differentiations, relative gene expression normalized GAPDH). (b) Gene structure of iPSC KO lines; exons-boxes and introns-lines. ASCL1 coding sequence, yellow. NEUROD1 coding sequence, purple. (c) Gel electrophoresis of genomic DNA PCR for ASCL1 (left) and NEUROD1 (right); lane 1-WT, lane 2-ASCL1 KO, lane 3-NEUROD1 KO lines. (d) RT-qPCR of culture gene expression for ASCL1 and NEUROD1 for iPSC KO lines (n = 3 independent differentiations, relative gene expression normalized GAPDH, **Undetectable). (e) Trypan blue cell count of total cell numbers from dissociated cultures (n = 4 independent differentiations). (f) FACS plot of live TH expressing cells for WT, ASCL1 KO, and NEUROD1 KO lines. (g) Mean fluorescent intensity (left) and percentage TH+ expressing cell (right) quantification of FACS analysis from (f) (n = 4 independent differentiations). (h) Intracellular TH staining quantified by flow cytometry for WT, ASCL1 KO, and NEUROD1 KO lines (n = 4 independent differentiations). (i) RT-qPCR culture gene expression of dopaminergic, glutamatergic, and pan-neural genes for WT, ASCL1 KO, and NEUROD1 KO lines (n = 4 independent differentiations, relative gene expression normalized GAPDH). Error bars for (a, d, e, g, h, i), mean ± SEM. Kruskal Wallis test with Dunn’s post-hoc test for (g, h, i), *p < 0.05 significance compared to WT.