Abstract
BackgroundGenome-wide transcriptional profiling of patient blood samples offers a powerful tool to investigate underlying disease mechanisms and personalized treatment decisions. Most studies are based on analysis of total peripheral blood mononuclear cells (PBMCs), a mixed population. In this case, accuracy is inherently limited since cell subset-specific differential expression of gene signatures will be diluted by RNA from other cells. While using specific PBMC subsets for transcriptional profiling would improve our ability to extract knowledge from these data, it is rarely obvious which cell subset(s) will be the most informative.ResultsWe have developed a computational method (Subset Prediction from Enrichment Correlation, SPEC) to predict the cellular source for a pre-defined list of genes (i.e. a gene signature) using only data from total PBMCs. SPEC does not rely on the occurrence of cell subset-specific genes in the signature, but rather takes advantage of correlations with subset-specific genes across a set of samples. Validation using multiple experimental datasets demonstrates that SPEC can accurately identify the source of a gene signature as myeloid or lymphoid, as well as differentiate between B cells, T cells, NK cells and monocytes. Using SPEC, we predict that myeloid cells are the source of the interferon-therapy response gene signature associated with HCV patients who are non-responsive to standard therapy.ConclusionsSPEC is a powerful technique for blood genomic studies. It can help identify specific cell subsets that are important for understanding disease and therapy response. SPEC is widely applicable since only gene expression profiles from total PBMCs are required, and thus it can easily be used to mine the massive amount of existing microarray or RNA-seq data.
Highlights
Genome-wide transcriptional profiling of patient blood samples offers a powerful tool to investigate underlying disease mechanisms and personalized treatment decisions
We have developed a computational method to predict the most likely cellular source for a pre-defined gene expression signature using transcriptional profiling data from total peripheral blood mononuclear cells (PBMCs)
Let’s consider a study where we find that Interferon Stimulated Genes (ISGs) are up-regulated in a subset of patients that fail to respond to a particular therapy
Summary
Genome-wide transcriptional profiling of patient blood samples offers a powerful tool to investigate underlying disease mechanisms and personalized treatment decisions. Most studies are based on analysis of total peripheral blood mononuclear cells (PBMCs), a mixed population. In this case, accuracy is inherently limited since cell subset-specific differential expression of gene signatures will be diluted by RNA from other cells. Most genome-wide expression studies are based on analysis of total peripheral blood mononuclear cells (PBMCs). PBMCs are composed of over a dozen cell subsets that are derived from a common progenitor in the bone marrow (Figure 1). These cells are commonly divided into myeloid and lymphoid cells. Neutrophils, which normally compose the majority of cells in a blood sample (40-80%), are normally excluded by the methods used to isolate PBMCs, but may account for up to 20% of a PBMC sample due to contamination [9]
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