Biology subjects

Madsen, J. G. S.

Publications and source records attributed to Madsen, J. G. S..

2 recordsLinked to original sources

bioRxiv2024

Limitations in mitochondrial programming restrain the differentiation and maturation of human stem cell-derived β cells

Pluripotent stem cell (SC)-derived islets offer hope as a renewable source for {beta} cell replacement for type 1 diabetes (T1D), yet functional and metabolic immaturity may limit their long-term therapeutic potential. Here, we show that limitations in mitochondrial transcriptional programming impede the formation of SC-derived {beta} (SC-{beta}) cells. Utilizing transcriptomic profiling, assessments of chromatin accessibility, mitochondrial phenotyping, and lipidomics analyses, we observed that SC-{beta} cells exhibit reduced oxidative and mitochondrial fatty acid metabolism compared to primary human islets that are related to limitations in key mitochondrial transcriptional networks. Surprisingly, we found that reductions in glucose-stimulated mitochondrial respiration in SC-islets were not associated with alterations in mitochondrial mass, structure, or genome integrity. In contrast, SC-islets show limited expression of targets of PPAR, which regulate mitochondrial programming, yet whose functions in {beta} cell differentiation are unknown. Importantly, treatment with WY14643, a potent PPAR agonist, induced expression of mitochondrial targets, improved insulin secretion, and increased the formation of SC-{beta} cells both in vitro and following transplantation. Thus, PPAR-dependent mitochondrial programming promotes the differentiation of SC-{beta} cells and may be a promising target to improve {beta} cell replacement efforts for T1D.

developmental biology↗

bioRxiv2023

Automatic quality control of single-cell and single-nucleus RNA-seq using valiDrops

Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) measures gene expression in individual cells or nuclei, which enables unbiased characterization of cell types and states in tissues. However, the isolation of cells or nuclei for sxRNA-seq can introduce artifacts, such as cell damage and transcript leakage. This can distort biological signals and introduce contamination from debris. Thus, the identification of barcodes con-taining high-quality cells or nuclei is a critical analytical step in the processing of sxRNA-seq data. Here, we present valiDrops, which is a novel data-adaptive method to identify high-quality barcodes and flag dead cells. In valiDrops, barcodes are initially filtered using data-adaptive thresholding on community-standard quality metrics and subsequently, valiDrops uses a novel clustering-based approach to identify barcodes with biological distinct signals. We benchmark valiDrops and existing methods and find that the biological signals from cell types and states are more distinct, easier to separate and more consistent after filtering by valiDrops. Finally, we show that valiDrops can be used to predict and flag dead cells with high accuracy. This novel classifier can further improve data quality or be used to identify dead cells to interrogate the biology of cell death. Thus, valiDrops is an effective and easy-to-use method to remove barcodes associated with low quality cells or nuclei from sxRNA-seq datasets, thereby improving data quality and biological interpretation. Our method is openly available as an R package at www.github.com/madsen-lab/valiDrops.

bioinformatics↗