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

TitleA Multivariate Computational Method to Analyze High-Content RNAi Screening Data.
Publication TypeJournal Article
Year of Publication2015
AuthorsRameseder, J, Krismer, K, Dayma, Y, Ehrenberger, T, Hwang, MKyung, Airoldi, EM, Floyd, SR, Yaffe, MB
JournalJ Biomol Screen
Date Published2015 Sep
KeywordsAlgorithms, Animals, Cell Line, Computer Simulation, DNA Damage, Gene Knockdown Techniques, High-Throughput Screening Assays, Humans, Models, Biological, Phenotype, Proto-Oncogene Proteins B-raf, RNA Interference, RNA, Messenger, RNA, Small Interfering

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.

Alternate JournalJ Biomol Screen
PubMed ID25918037
Grant ListP30-ES002109 / ES / NIEHS NIH HHS / United States
R01-ES015339 / ES / NIEHS NIH HHS / United States
R21-NS063917 / NS / NINDS NIH HHS / United States
U54-CA112967 / CA / NCI NIH HHS / United States
/ / Howard Hughes Medical Institute / United States