Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen

Abstract

Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/ inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data sources, such as those generated from transcriptional and proteomic studies, but comparable methods have not been developed to use high-content morphological data, which are emerging principally from large-scale RNAi screens, to these ends. Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy. Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling relationships between these proteins. We find that our method makes mediocre predictions using only RhoGAP single-knockdown morphological data, yet achieves vastly improved accuracy by including original data from a double-knockdown RhoGAP genetic screen, which likely reflects the redundant network structure of RhoGAP/GTPase signaling. We consider other possible methods for inference and show that our primary model outperforms the alternatives. This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations.

Identification of potential drug targets for tuberous sclerosis complex by synthetic screens combining CRISPR-based knockouts with RNAi

“…Previous combinatorial screens in Drosophila cells have been performed by treating cells with multiple RNAi reagents simultaneously (9, 10). However, whereas this approach has been used successfully, limitations of RNAi including incomplete transfection, partial knockdown, and off-target effects are compounded, leading to high false-negative and false-positive rates.…”

“…However, when multiple RNAi reagents are used in combination, the consequences of off-target effects and variable knockdown efficiencies are compounded, leading to high false-positive and false-negative rates (9, 10). Deconvolving biologically meaningful candidates from such screens requires extensive secondary screening and validation, making this approach time-consuming and expensive.…”

Models of signalling networks – what cell biologists can gain from them and give to them

“…; Nir et al, 2010). An ambitious but worthwhile long-term goal should be to build faithful models of cell signalling in vivo, and work has already begun in this direction (Lau et al, 2012) (Box 3).…”

Image informatics for studying signal transduction in cells interacting with 3D matrices

“…High-level statistical analysis combines the "differences" between the responses of different TC with a-priori knowledge on the biology of the signal transduction pathway of interest in order to infer conclusions about the role of pathway components. This study implements only low-level statistical processing and follows the procedure proposed by [12]: the feature matrix of each TC is initially normalized and its dimension is reduced by principal component analysis. The "difference" between the responses of two TC is calculated using the Mahalanobis distance metric.…”

“…via genetic manipulation), large scale imaging, image processing and statistics to study complex signal transduction pathways. So far, image informatics has been applied in studies of cells cultured on standard culture dishes [8][9][10][11][12]. Unfortunately, the 2D surface of a culture dish presents to cells an environment that is very different compared to the microenvironments felt inside tissues.…”