Background & Aims: The progression of non-alcoholic steatohepatitis (NASH) to fibrosis and hepatocellular carcinoma (HCC) is aggravated by auto-aggressive T cells. The gut-liver axis contributes to NASH, but the mechanisms involved and the consequences for NASH-induced fibrosis and liver cancer remain unknown. We investigated the role of gastrointestinal B cells in the development of NASH, fibrosis and NASH-induced HCC.
View Article and Find Full Text PDFWe report Cytopath, a method for trajectory inference that takes advantage of transcriptional activity information from the RNA velocity of single cells to perform trajectory inference. Cytopath performs this task by defining a Markov chain model, simulating an ensemble of possible differentiation trajectories, and constructing a consensus trajectory. We show that Cytopath can recapitulate the topological and molecular characteristics of the differentiation process under study.
View Article and Find Full Text PDFMotivation: Improvements in single-cell RNA-seq technologies mean that studies measuring multiple experimental conditions, such as time series, have become more common. At present, few computational methods exist to infer time series-specific transcriptome changes, and such studies have therefore typically used unsupervised pseudotime methods. While these methods identify cell subpopulations and the transitions between them, they are not appropriate for identifying the genes that vary coherently along the time series.
View Article and Find Full Text PDFSingle-cell RNA sequencing (scRNA-seq) approaches have transformed our ability to resolve cellular properties across systems, but are currently tailored toward large cell inputs (>1,000 cells). This renders them inefficient and costly when processing small, individual tissue samples, a problem that tends to be resolved by loading bulk samples, yielding confounded mosaic cell population read-outs. Here, we developed a deterministic, mRNA-capture bead and cell co-encapsulation dropleting system, DisCo, aimed at processing low-input samples (<500 cells).
View Article and Find Full Text PDFObjective: To determine association of socioeconomic status, defined by educational status (ES), with awareness, treatment and control of cardiovascular risk factors.
Methods: We performed an epidemiological study at 11 cities in India using cluster sampling. 6198 subjects (3426 men, 2772 women, response 62%, age 48±10 years) were evaluated for sociodemographic, lifestyle, anthropometric and biochemical factors.