Biology subjects

Seirup, M.

Publications and source records attributed to Seirup, M..

3 recordsLinked to original sources

bioRxiv2020

Rapid changes in chromatin structure during dedifferentiation of primary hepatocytes in vitro

Primary hepatocytes are widely used in the pharmaceutical industry to screen drug candidates for hepatotoxicity, but isolated hepatocytes quickly dedifferentiate and lose their mature metabolic function in culture. Attempts have been made to better recapitulate the in vivo liver environment in culture, but the full spectrum of signals required to maintain hepatocyte function in vitro remains elusive. Here we studied the dedifferentiation process in detail through RNA-sequencing of hepatocytes cultured over eight days. We identified three distinct phases of dedifferentiation. An early phase, where mature hepatocyte genes are rapidly downregulated in a matter of hours. A middle phase, where fetal genes are activated, leading to hepatocytes with a fetal phenotype. A late phase, where initially rare contaminating non-parenchymal cells over-grow the culture as the hepatocytes gradually die. Using genetically tagged hepatocytes, we demonstrate that the cells reactivating fetal marker alpha-fetoprotein arise from cells previously expressing the mature hepatocyte marker albumin, and not from albumin negative precursor cells, proving that hepatocytes undergo true dedifferentiation. To better understand the signaling events that result in the rapid down-regulation of mature hepatocyte genes, we examined changes in chromatin accessibility of hepatocytes during the first 24h of culture using ATAC-seq. We find that drastic and rapid changes in chromatin accessibility occurs immediately upon start of culture. Using binding motif analysis of the areas of open chromatin sharing similar temporal profiles, we identify several candidate transcription factors potentially involved in the dedifferentiation of primary hepatocytes in culture.

cell biology↗

bioRxiv2020

Dynamic regulatory module networks for inference of cell type specific transcriptional networks

Multi-omic datasets with parallel transcriptomic and epigenomic measurements across time or cell types are becoming increasingly common. However, integrating these data to infer regulatory network dynamics is a major challenge. We present Dynamic Regulatory Module Networks (DRMNs), a novel approach that uses multi-task learning to infer cell type-specific cis-regulatory networks dynamics. Compared to existing approaches, DRMN integrates expression, chromatin state and accessibility, accurately predicts cis-regulators of context-specific expression and models network dynamics across linearly and hierarchically related contexts. We apply DRMN to three dynamic processes of different experimental designs and predict known and novel regulators driving cell type-specific expression patterns.

bioinformatics↗

bioRxiv2019

Tradeoff between more cells and higher read depth for single-cell RNA-seq spatial ordering analysis of the liver lobule

As newer single-cell protocols generate increasingly more cells at reduced sequencing depths, the value of a higher read depth may be overlooked. Using data from three different single-cell RNA-seq protocols that lend themselves to having either higher read depth (Smart-seq) or many cells (MARS-seq and 10X), we evaluate their ability to recapitulate biological signals in the context of pseudo-spatial reconstruction. Overall, we find gene expression profiles after spatial-reconstruction analysis are highly reproducible between datasets despite being generated by different protocols and using different computational algorithms. While UMI based protocols such as 10X and MARS-seq allow for capturing more cells, Smart-seqs higher sensitivity and read-depth allows for analysis of lower expressed genes and isoforms. Additionally, we evaluate trade-offs for each protocol by performing subsampling analyses, and find that optimizing the balance between sequencing depth and number of cells within a protocol is important for efficient use of resources. Our analysis emphasizes the importance of selecting a protocol based on the biological questions and features of interest.

genomics↗