Benhammou, J. N. | Search bioRxiv

bioRxiv2024

Hepatic lipopolysaccharide binding protein partially uncouples inflammation from fibrosis in MAFLD

Non-alcoholic fatty liver disease (NAFLD), recently renamed metabolic-associated fatty liver disease (MAFLD), is the most common liver disease worldwide. The progression to fibrosis, occurring against a backdrop of hepatic steatosis and inflammation, critically determines liver-related morbidity and mortality. Inflammatory processes contribute to various stages of MAFLD and thought to instigate hepatic fibrosis. For this reason, targeting inflammation has been heavily nominated as a strategy to mitigate liver fibrosis. Lipopolysaccharide binding protein (LBP) is a secreted protein that plays an established role in innate immune responses. Here, using adoptive transfer studies and tissue-specific deletion models we show that hepatocytes are the dominant contributors to circulating LBP. In a murine model of MAFLD, hepatocyte-specific deletion of LBP restrained hepatic inflammation and improved liver function abnormalities, but not measures of fibrosis. Human studies, including genetic evidence, corroborate an important role for LBP in hepatic inflammation with minimal impact on fibrosis. Collectively, our data argues against the idea that targeting hepatic inflammation is a viable approach to reducing fibrosis.

physiology↗

bioRxiv2019

Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

bioinformatics↗