Gene Expression
Nebulas
Gene Expression NebulasSummary: In vitro differentiation of human stem cells can produce pancreatic beta cells, the insulin-secreting cell type whose loss underlies Type 1 Diabetes. As a step towards mastery of this process, we report on transcriptional profiling of >100,000 individual cells sampled during in vitro beta cell differentiation and describe the cells that emerge. We resolve populations corresponding to beta cells, alpha-like poly-hormonal cells, non-endocrine cells that resemble pancreatic exocrine cells and a previously unreported population resembling enterochromaffin cells. We show that the beta and alpha-like cells are stable for weeks in culture without exogenous growth factors and that gene expression changes associated with in vivo beta cell maturation are recapitulated in vitro. We demonstrate that stem-cell derived enterochromaffin cells can synthesize and secrete serotonin in vitro. To remove exocrine cells, we characterize a scalable re-aggregation technique that efficiently selects endocrine cells. Finally, we use a high-resolution sequencing time course to characterize gene expression dynamics during human pancreatic endocrine induction from which we develop a lineage model of in vitro beta cell differentiation. This study provides a deeper perspective on the current state of human stem cell differentiation and is a jumping-off point for future endeavors in in vitro differentiation of pancreatic islet cells and their application in regenerative medicine.
Overall Design: Single-cell mRNA sequencing of pluripotent stem cells differentiating in vitro towards pancreatic beta cells.The data & metadata match the initial submission of the manuscript, not the final version.
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| Growth Protocol: | Human embryonic and pluripotent stem cells were maintained and differentiated as described in the methods of (Veres et al. 2018). Differentiation was carried out using step-wise protocols aimed at gradually producing beta cells in vitro. |
| Treatment Protocol: | - |
| Extract Protocol: | Differentiated clusters were prepared for single cell RNA sequencing as follows: 1-2 mL suspension culture was sampled from the spinner flask, dissociated with TrypLE Express (5-15 minutes at 37 °C), quenched with cold PBS + 1% BSA and gently dispersed with a P1000 pipette. Cells were then centrifuged (300 rpm, 3 min), resuspended in cold PBS+1% BSA and filtered through a 70 µm mesh filter. Centrifugation, resuspension and filtering was repeated a total of 3 times. Cells were then counted and resuspended to the working dilution for inDrops (100,000 cells/mL) in 1X PBS with 13% Optiprep (Sigma; D1556).Single cell RNA sequencing was carried out using the inDrops platform, as previously described (Klein 2015, Zilionis 2016). Most samples were run using ‘inDrops v2’ barcoded hydrogel beads (1 Cell Bio, Harvard Single Cell Core), and one experiment used ‘inDrops v3’ beads (Harvard Single Cell Core). Following the inDrops protocol, each biological sample was split into several aliquots of 1000-3000 cells after encapsulation. At least two library aliquots were prepared separately from each sample, indexing using recommended index sequences, pooled and sequencing on a NextSeq 500 (Illumina). |
| Library Construction Protocol: | - |
| Molecule Type: | poly(A)+ RNA |
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| Library Layout: | PAIRED |
| Library Strand: | Forward |
| Platform: | ILLUMINA |
| Instrument Model: | Illumina NextSeq 500; Illumina HiSeq 2500 |
| Strand-Specific: | Specific |
| Data Resource | GEN Sample ID | GEN Dataset ID | Project ID | BioProject ID | Sample ID | Sample Name | BioSample ID | Sample Accession | Experiment Accession | Release Date | Submission Date | Update Date | Species | Race | Ethnicity | Age | Age Unit | Gender | Source Name | Tissue | Cell Type | Cell Subtype | Cell Line | Disease | Disease State | Development Stage | Mutation | Phenotype | Case Detail | Control Detail | Growth Protocol | Treatment Protocol | Extract Protocol | Library Construction Protocol | Molecule Type | Library Layout | Strand-Specific | Library Strand | Spike-In | Strategy | Platform | Instrument Model | Cell Number | Reads Number | Gbases | AvgSpotLen1 | AvgSpotLen2 | Uniq Mapping Rate | Multiple Mapping Rate | Coverage Rate |
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