Correcting positional correlations in Affymetrix® genome chips

A statistical framework for high-content phenotypic profiling using cellular feature distributions

High-content screening (HCS) uses microscopy images to generate phenotypic profiles of cell morphological data in high-dimensional feature space. While HCS provides detailed cytological information at single-cell resolution, these complex datasets are usually aggregated into summary statistics that do not leverage patterns of biological variability within cell populations. Here we present a broad-spectrum HCS analysis system that measures image-based cell features from 10 cellular compartments across multiple assay panels. We introduce quality control measures and statistical strategies to streamline and harmonize the data analysis workflow, including positional and plate effect detection, biological replicates analysis and feature reduction. We also demonstrate that the Wasserstein distance metric is superior over other measures to detect differences between cell feature distributions. With this workflow, we define per-dose phenotypic fingerprints for 65 mechanistically diverse compounds, provide phenotypic path visualizations for each compound and classify compounds into different activity groups.

Coevolution of Residues Provides Evidence of a Functional Heterodimer of 5-HT2AR and 5-HT2CR Involving Both Intracellular and Extracellular Domains

Serotonin is a neurotransmitter that plays a role in regulating activities such as sleep, appetite, mood and substance abuse disorders; serotonin receptors 5-HT2AR and 5-HT2CR are active within pathways associated with substance abuse. It has been suggested that 5-HT2AR and 5-HT2CR may form a dimer that affects behavioral processes. Here we study the coevolution of residues in 5-HT2AR and 5-HT2CR to identify potential interactions between residues in both proteins. Coevolution studies can detect protein interactions, and since the thus uncovered interactions are subject to evolutionary pressure, they are likely functional. We assessed the significance of the 5-HT2AR/5-HT2CR interactions using randomized phylogenetic trees and found the coevolution significant (p-value = 0.01). We also discuss how co-expression of the receptors suggests the predicted interaction is functional. Finally, we analyze how several single nucleotide polymorphisms for the 5-HT2AR and 5-HT2CR genes affect their interaction. Our findings are the first to characterize the binding interface of 5-HT2AR/5-HT2CR and indicate a correlation between this interface and location of SNPs in both proteins.

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.