Biology subjects

Raphael Gottardo

Publications and source records attributed to Raphael Gottardo.

3 recordsLinked to original sources

bioRxiv2015

MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA-seq data.

Single-cell transcriptomic profiling enables the unprecedented interrogation of gene expression heterogeneity in rare cell populations that would otherwise be obscured in bulk RNA sequencing experiments. The stochastic nature of transcription is revealed in the bimodality of single-cell transcriptomic data, a feature shared across single-cell expression platforms. There is, however, a paucity of computational tools that take advantage of this unique characteristic. We present a new methodology to analyze single-cell transcriptomic data that models this bimodality within a coherent generalized linear modeling framework. We propose a two-part, generalized linear model that allows one to characterize biological changes in the proportions of cells that are expressing each gene, and in the positive mean expression level of that gene. We introduce the cellular detection rate, the fraction of genes turned on in a cell, and show how it can be used to simultaneously adjust for technical variation and so-called \"extrinsic noise\" at the single-cell level without the use of control genes. Our model permits direct inference on statistics formed by collections of genes, facilitating gene set enrichment analysis. The residuals defined by such models can be manipulated to interrogate cellular heterogeneity and gene-gene correlation across cells and conditions, providing insights into the temporal evolution of networks of co-expressed genes at the single-cell level. Using two single-cell RNA-seq datasets, including newly generated data from Mucosal Associated Invariant T (MAIT) cells, we show how model residuals can be used to identify significant changes across biologically relevant gene sets that are missed by other methods and characterize cellular heterogeneity in response to stimulation.

Bioinformatics↗

bioRxiv2014

Mucosal effects of tenofovir 1% gel

BACKGROUNDTenofovir gel is being evaluated for vaginal and rectal pre-exposure prophylaxis against sexual HIV transmission. Because this is a new prevention strategy targeting large numbers of healthy people, we broadly assessed its effects on the mucosa.\n\nMETHODS AND FINDINGSIn MTN-007, a phase 1, randomized, double-blinded rectal microbicide trial, we used systems genomics/proteomics to determine the effect of tenofovir 1% gel, nonoxynol-9 2% gel, placebo gel or no treatment on rectal biopsies taken at baseline, after one application or after seven daily applications (15 subjects/arm). Experiments were repeated using primary vaginal epithelial cells from four healthy women. After seven days of administration, tenofovir 1% gel had broad-ranging biological effects on the rectal mucosa, which were much more pronounced than--but different from--those caused by the detergent nonoxynol-9. Tenofovir profoundly suppressed anti-inflammatory mediators such as interleukin 10; increased T cell densities; caused mitochondrial dysfunction, possibly by blocking PNPT1 expression; and altered regulatory pathways of cell differentiation and survival. Except for leukocyte-derived factors, all these effects were replicated in primary vaginal epithelial cells, which also proliferated significantly faster in tenofovir's presence.\n\nCONCLUSIONSTenofovir's suppression of anti-inflammatory activity could diminish its prophylactic efficacy over time. The breadth of mucosal changes, including mitochondrial dysfunction and epithelial proliferation, raises questions about its safety for long-term topical use. These findings suggest that a systems biology evaluation of mucosal effects may be beneficial before advancing to large-scale efficacy trials with topical HIV prevention agents that achieve high, long-lasting local drug concentrations.

Microbiology↗

bioRxiv2014

Modeling bi-modality improves characterization of cell cycle on gene expression in single cells

Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cells phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%-17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.

Bioinformatics↗