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Cai, J. J.

Publications and source records attributed to Cai, J. J..

22 records · Page 2Linked to original sources

Single cell expression variability in highly homogeneous populations of human cells is widespread, heritable, and implies cell function

Because of recent technological developments, single-cell assays such as single-cell RNA sequencing (scRNA-seq) have become much more widely available and have achieved unprecedented resolution in revealing cell heterogeneity. The extent of intrinsic cell-to-cell variability in gene expression, or single cell expression variability (scEV), has thus been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what its implications are for multi-cellular organisms. We therefore analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs). For each of the three cell types, we estimated scEV in homogeneous populations of cells; we identified 465, 466, and 291 highly variable genes (HVGs), respectively. These HVGs were enriched with specific functions precisely relevant to the cell types, from which the scRNA-seq data used to identify HVGs were generated--e.g., HVGs identified in lymphoblastoid cells were enriched in cytokine signaling pathways, LAECs collagen formation, and DFs keratinization. HVGs were deeply embedded in gene regulatory networks specific to corresponding cell types. We also found that scEV is a heritable trait, partially determined by cell donors genetic makeups. Furthermore, across genes, especially immune genes, levels of scEV and between-individual variability in gene expression were positively correlated, suggesting a potential link between the two variabilities measured at different organizational levels. Taken together, our results support the \"variation is function\" hypothesis, which postulates that scEV is required for higher-level system function. Thus, we argue that quantifying and characterizing scEV in relevant cell types may deepen our understating of normal as well as pathological cellular processes.

genetics↗

Single-cell RNA sequencing of a European and an African lymphoblastoid cell line

In biomedical research, lymphoblastoid cell lines (LCLs), often established by in vitro infection of resting B cells with Epstein Barr Virus, are commonly used as surrogates for peripheral blood lymphocytes. Genomic and transcriptomic information on LCLs has been used to study the impact of genetic variation on gene expression in humans. Here we present single-cell RNA sequencing (scRNA-seq) data on GM12878 and GM18502--two LCLs derived from the blood of female donors of European and African ancestry, respectively. Cells from three samples (the two LCLs and a 1:1 mixture of the two) were prepared separately using a 10X Genomics Chromium Controller and deeply sequenced. The final dataset contained 7,045 cells from GM12878, 5,189 from GM18502, and 5,820 from the mixture, offering valuable information on single-cell gene expression in highly homogenous cell populations. This dataset is a suitable reference of population differentiation in gene expression at the single-cell level. Data from the mixture provides additional valuable information facilitating the development of statistical methods for data normalization and batch effect correction.

genetics↗

scGEApp: a Matlab app for feature selection on single-cell RNA sequencing data

MotivationThe recent development of single-cell technologies, especially single-cell RNA sequencing (scRNA-seq), provides an unprecedented level of resolution to the cell type heterogeneity. It also enables the study of gene expression variability across individual cells within a homogenous cell population. Feature selection algorithms have been used to select biologically meaningful genes while controlling for sampling noise. An easy-to-use application for feature selection on scRNA-seq data requires integration of functions for data filtering, normalization, visualization, and enrichment analyses. Graphic user interfaces (GUIs) are desired for such an application. ResultsWe used native Matlab and App Designer to develop scGEApp for feature selection on singlecell gene expression data. We specifically designed a new feature selection algorithm based on the 3D spline fitting of expression mean (), coefficient of variance (CV), and dropout rate (rdrop), making scGEApp a unique tool for feature selection on scRNA-seq data. Our method can be applied to single-sample or two-sample scRNA-seq data, identify feature genes, e.g., those with unexpectedly high CV for given and rdrop of those genes, or genes with the most feature changes. Users can operate scGEApp through GUIs to use the full spectrum of functions including normalization, batch effect correction, imputation, visualization, feature selection, and downstream analyses with GSEA and GOrilla. Availabilityhttps://github.com/jamesjcai/scGEApp Contact: jcai@tamu.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

Osmoregulation in freshwaters: Gene expression in the gills of a Neotropical cichlid in contrasting pH and ionic environments

Freshwater habitats of the Neotropics exhibit a gradient from relatively neutral, ion-rich whitewater to acidic, ion-poor blackwater. Closely related species often show complementary distributions among ionic habitats, suggesting that adaptation to divergent osmoregulatory environments may be an important driver of Neotropical fish diversity. However, little is known about the evolutionary tradeoffs involved in ionoregulation across distinct freshwater environments. Here, we surveyed gill mRNA expression of Cichla ocellaris var. monoculus, a Neotropical cichlid, to examine cellular and physiological responses to experimental conditions mimicking whitewater and blackwater.\n\nGene ontology enrichment of expressed genes indicated that the gills were remodeled during both forms of environmental challenge, with changes biased towards the cellular membrane. We observed expression of signaling pathways from both the acute and extended response phases, including evidence that growth hormone (GH) may mediate osmoregulation in whitewater through paracrine expression of insulin-like growth factor I (IGF-I), but not through the GH receptor, which instead showed correlated up-regulation with the prolactin receptor and insulin-like growth factor II in blackwater.\n\nDifferential expression of genes related to paracellular tight junctions and transcellular ion transport showed responses similar to euryhaline fishes in fresh versus seawater, with some exceptions, suggesting that relaxed ion retention via the gills, possibly mediated by the GH/IGF-I axis, is a strong candidate for evolutionary modification in whitewater and blackwater endemic populations. In each osmoregulatory domain, we saw examples of contrasting differential expression of paralogs of genes that are single copy in most terrestrial vertebrates, indicating that adaption by fishes to diverse physicochemcial environments has capitalized on diversification of osmoregulatory gene families.

ecology↗