A Multivariate Computational Method to Analyze High-Content RNAi Screening Data.

J Biomol Screen
Authors
Keywords
Abstract

High-content screening (HCS) using RNA interference (RNAi) in combination with automated microscopy is a powerful investigative tool to explore complex biological processes. However, despite the plethora of data generated from these screens, little progress has been made in analyzing HC data using multivariate methods that exploit the full richness of multidimensional data. We developed a novel multivariate method for HCS, multivariate robust analysis method (M-RAM), integrating image feature selection with ranking of perturbations for hit identification, and applied this method to an HC RNAi screen to discover novel components of the DNA damage response in an osteosarcoma cell line. M-RAM automatically selects the most informative phenotypic readouts and time points to facilitate the more efficient design of follow-up experiments and enhance biological understanding. Our method outperforms univariate hit identification and identifies relevant genes that these approaches would have missed. We found that statistical cell-to-cell variation in phenotypic responses is an important predictor of hits in RNAi-directed image-based screens. Genes that we identified as modulators of DNA damage signaling in U2OS cells include B-Raf, a cancer driver gene in multiple tumor types, whose role in DNA damage signaling we confirm experimentally, and multiple subunits of protein kinase A.

Year of Publication
2015
Journal
J Biomol Screen
Volume
20
Issue
8
Pages
985-97
Date Published
2015 Sep
ISSN
1552-454X
URL
DOI
10.1177/1087057115583037
PubMed ID
25918037
PubMed Central ID
PMC5377593
Links
Grant list
R21 NS063917 / NS / NINDS NIH HHS / United States
R01-ES015339 / ES / NIEHS NIH HHS / United States
R21-NS063917 / NS / NINDS NIH HHS / United States
R01 ES015339 / ES / NIEHS NIH HHS / United States
U54 CA112967 / CA / NCI NIH HHS / United States
R01 GM104047 / GM / NIGMS NIH HHS / United States
P30 ES002109 / ES / NIEHS NIH HHS / United States
U54-CA112967 / CA / NCI NIH HHS / United States
Howard Hughes Medical Institute / United States
P30-ES002109 / ES / NIEHS NIH HHS / United States