A novel induced pluripotent stem cell model of schwann cell differentiation reveals NF2 -related gene regulatory networks of the extracellular matrix.

Schwann cells are vital to development and maintenance of the peripheral nervous system and their dysfunction has been implicated in a range of neurological and neoplastic disorders, including NF2 -related schwannomatosis. We have developed a novel human induced pluripotent stem cell (hiPSC) model for the study of Schwann cell differentiation in health and disease. We performed transcriptomic, immunofluorescence, and morphological analysis of hiPSC derived Schwann cell precursors (SPCs) and terminally differentiated Schwann-like cells (SLCs) representing distinct stages of development. To further validate our findings, we performed integrated, cross-species analyses across multiple external datasets at bulk and single cell resolution. Our hiPSC model of Schwann cell development shared overlapping gene expression signatures with human amniotic mesenchymal stem cell (hAMSCs) derived SLCs and in vivo mouse models, but also revealed unique features that may reflect species-specific aspects of Schwann cell biology. Moreover, we have identified gene co-expression modules that are dynamically regulated during hiPSC to SLC differentiation associated with ear and neural development, cell fate determination, the NF2 gene, and extracellular matrix (ECM) organization. By cross-referencing results between multiple datasets and analyses, we have identified potential new genes that are related to NF2 for further study including: ANXA1, CDH6, COL1A1, COL8A1, MFAP5, IGFBP5, FGF1, AHNAK, CDKN2B, LOX, CAV1 , and CAV2 . Our hiPSC model further provides a tractable platform for studying Schwann cell development in the context of human disease.

Identification of type 2 diabetes- and obesity-associated human ß-cells using deep transfer learning.

Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet ß-cells are the sole source of endogenous insulin and our understanding of ß-cell dysfunction and death in type 2 diabetes (T2D) is incomplete. Single-cell RNA-seq data supports heterogeneity as an important factor in ß-cell function and survival. However, it is difficult to identify which ß-cell phenotypes are critical for T2D etiology and progression. Our goal was to prioritize specific disease-related ß-cell subpopulations to better understand T2D pathogenesis and identify relevant genes for targeted therapeutics. To address this, we applied a deep transfer learning tool, DEGAS, which maps disease associations onto single-cell RNA-seq data from bulk expression data. Independent runs of DEGAS using T2D or obesity status identified distinct ß-cell subpopulations. A singular cluster of T2D-associated ß-cells was identified; however, ß-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic or T2D donors. The obesity-associated non-diabetic cells were enriched for translation and unfolded protein response genes compared to T2D cells. We selected DLK1 for validation by immunostaining in human pancreas sections from healthy and T2D donors. DLK1 was heterogeneously expressed among ß-cells and appeared depleted from T2D islets. In conclusion, DEGAS has the potential to advance our holistic understanding of the ß-cell transcriptomic phenotypes, including features that distinguish ß-cells in obese non-diabetic or lean T2D states. Future work will expand this approach to additional human islet omics datasets to reveal the complex multicellular interactions driving T2D.