Biology subjects

Patrick Danaher

Publications and source records attributed to Patrick Danaher.

3 recordsLinked to original sources

bioRxiv2016

Gene expression markers of Tumor Infiltrating Leukocytes

BackgroundAssays of the abundance of immune cell populations in the tumor microenvironment promise to inform immune oncology research and the choice of immunotherapy for individual patients. We propose to measure the intratumoral abundance of various immune cells populations with gene expression. In contrast to IHC and flow cytometry, gene expression assays yield high information content from a clinically practical workflow. Previous studies of gene expression in purified immune cells have reported hundreds of genes showing enrichment in a single cell type, but the utility of these genes in tumor samples is unknown. We describe a novel statistical method for using co-expression patterns in large tumor gene expression datasets to validate previously reported candidate cell type marker genes, and we use this method to winnow previously published gene lists down to a subset of high confidence marker genes.\n\nMethodsWe use co-expression patterns in 9986 samples from The Cancer Genome Atlas (TCGA) to validate previously reported cell type marker genes. We compare immune cell scores derived from these genes to measurements from flow cytometry and immunohistochemistry. We characterize the reproducibility of our cell scores in replicate runs of RNA extracted from FFPE tumor tissue.\n\nResultsWe identify a list of 60 marker genes whose expression levels quantify 14 immune cell populations. Cell type scores calculated from these genes are concordant with flow cytometry and IHC readings, show high reproducibility in replicate RNA samples from FFPE tissue, and reveal an intricate picture of the immune infiltrate in TCGA. Most genes previously reported to be enriched in a single cell type have co-expression patterns inconsistent with cell type specificity.\n\nConclusionsDue to their concise gene set, computational simplicity and utility in tumor samples, these cell type gene signatures may be useful in future discovery research and clinical trials to understand how tumors and therapeutic intervention shape the immune response.

Bioinformatics↗

bioRxiv2014

Facile semi-automated forensic body fluid identification by multiplex solution hybridization of NanoString® barcode probes to specific mRNA targets

A DNA profile from the perpetrator does not reveal, per se, the circumstances by which it was transferred. Body fluid identification by mRNA profiling may allow extraction of contextual 'activity level' information from forensic samples. Here we describe the development of a prototype multiplex digital gene expression (DGE) method for forensic body fluid/tissue identification based upon solution hybridization of color-coded NanoString(R) probes to 23 tissue/body fluid specific mRNA targets. The body fluids/tissues targeted were peripheral blood, semen, saliva, vaginal secretions, menstrual blood and skin. We tested and compared a simple 5 minute room temperature cellular lysis protocol against standard RNA isolation from same source material as a means to facilitate ease-of-use in forensic sample processing. We first describe a model for gene expression in a sample from a single body fluid and then extend that model to mixtures of body fluids. We then describe calculation of maximum likelihood estimates (MLEs) of body fluid quantities in a sample, and we describe the use of likelihood ratios to test for the presence of each body fluid in a sample. Known single source blood, semen, vaginal secretions, menstrual blood and skin samples all demonstrated the expected tissue specific gene expression for at least two of the chosen biomarkers. Saliva samples were more problematic, with their previously identified characteristic genes exhibiting poor specificity. Nonetheless the most specific saliva biomarker, HTN3, was expressed at a higher level in saliva than in any of the other tissues. As a preliminary indication of the ability of the method to discern admixtures of body fluids, five mixtures were prepared. Two of the five mixtures were called perfectly using the assay algorithm, and one of the component fluids was identified in the each of the 'false negative' mixtures. Crucially, our algorithm produced zero false positive fluid identifications across this study's 98 samples. Further optimization of the biomarker 'Codeset' will be required before it can be used in casework, particularly with respect to increasing the signal to noise ratio of the saliva biomarkers. With suitable modifications, this simplified protocol with minimal hands on requirement should facilitate routine use of mRNA profiling in casework laboratories.

Genomics↗

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↗