Search bioRxivSearch

Biology subjects

Kaestner, K. H.

Publications and source records attributed to Kaestner, K. H..

2 recordsLinked to original sources

A network of microRNAs acts to promote cell cycle exit and differentiation of human pancreatic endocrine cells

Pancreatic endocrine cell differentiation is orchestrated by transcription factors that operate in a gene regulatory network to activate endocrine lineage genes and repress lineage-inappropriate genes. MicroRNAs (miRNAs) are important modulators of gene expression, yet their role in endocrine cell differentiation has not been explored system-wide. Here we characterize miRNA-regulatory networks active in human endocrine cell differentiation by combining small RNA sequencing, miRNA overexpression experiments, and network modeling approaches. This analysis identifies Let-7g, Let-7a, miR-200a, and miR-375 as endocrine-enriched miRNAs with high impact on driving endocrine differentiation-associated gene expression changes. These miRNAs target different sets of transcription factors, which converge on a network of genes involved in cell cycle regulation. When expressed in human embryonic stem cell-derived pancreatic progenitors these miRNAs induce cell cycle exit and promote endocrine cell differentiation. Our study delineates the role of miRNAs in human endocrine cell differentiation and identifies miRNAs that could facilitate endocrine cell reprogramming.

developmental biology

Comparative analysis of commercially available single-cell RNA sequencing platforms for their performance in complex human tissues

The past five years have witnessed a tremendous growth of single-cell RNA-seq methodologies. Currently, there are three major commercial platforms for single-cell RNA-seq: Fluidigm C1, Clontech iCell8 (formerly Wafergen) and 10x Genomics Chromium. Here, we provide a systematic comparison of the throughput, sensitivity, cost and other performance statistics for these three platforms using single cells from primary human islets. The primary human islets represent a complex biological system where multiple cell types coexist, with varying cellular abundance, diverse transcriptomic profiles and differing total RNA contents. We apply standard pipelines optimized for each system to derive gene expression matrices. We further evaluate the performance of each system by benchmarking single-cell data with bulk RNA-seq data from sorted cell fractions. Our analyses can be generalized to a variety of complex biological systems and serve as a guide to newcomers to the field of single-cell RNA-seq when selecting platforms.

genomics