Dataset Information


In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (glomeruli)

ABSTRACT: To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.

ORGANISM(S): Homo sapiens  

SUBMITTER: Jeffery B Hodgin  Olga G Troyanskaya   Wenjun Ju   Casey S Greene   Clemens D Cohen   Matthias Kretzler   Masami Kehata   Viji Nair   Young-suk Lee   Felix Eichinger   Maria P Rastaldi   Min Li   Markus Bitzer   Qian Zhu    

PROVIDER: E-GEOD-47183 | ArrayExpress | 2013-08-12



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