Authors:
Wenjun Ju, Casey S Greene, Felix Eichinger, Viji Nair, Jeffery B Hodgin, Markus Bitzer, Young-suk Lee, Qian Zhu, Masami Kehata, Min Li, Song Jiang, Maria Pia Rastaldi, Clemens D Cohen, Olga G Troyanskaya, and Matthias Kretzler
Summary:
Cell-lineage-specific transcripts, i.e. those with expression restricted to a limited set of cell types, are essential for differentiated tissue function, mediate acquired chronic diseases, and are implicated in hereditary organ failure. However, experimental identification of cell-lineage specific genes in a genome-scale manner is infeasible for most solid human tissues. To address this challenge, we developed the first genome-scale computational method to identify genes with cell-lineage-specific expression, even in lineages not separable by experimental micro-dissection. Our machine learning-based approach leverages high-throughput functional-genomics data from tissue homogenates in a novel iterative statistical framework. We applied this method to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary and most acquired glomerular kidney disease. We systematically evaluated our predictions by immunohistochemistry, verifying selective tissue-distribution of podocyte-specific genes within the kidney. Our in silico approach was significantly more accurate (65% accuracy in human) than predictions based on direct measurement of in vivo fluorescence-tagged murine podocytes (23% accuracy in human). In agreement with the hypothesis that cell-lineage specific transcripts are likely to be associated with disease, our method identified genes implicated as causal in hereditary glomerular disease and involved in molecular pathways of acquired and chronic renal diseases. Furthermore, based on expression analysis of human kidney disease biopsy samples, we demonstrated that expression of the podocyte genes identified by our approach is significantly related to the degree of renal impairment in patients. Our approach is general and broadly applicable to define lineage specificity in both cell physiology and human disease contexts. We provide a user-friendly website that enables researchers to easily apply this method to any cell-lineage or tissue of interest (nano.princeton.edu). Identified cell-lineage specific transcripts are expected to play essential tissue-specific roles in organogenesis and disease and can provide starting points for the development of organ-specific diagnostics and therapies.
Source:
Genome Research; (08/15/13)