Multiparametric Analysis of Screening Data: Growing Beyond the Single Dimension to Infinity and Beyond

Abstract

Advances in instrumentation now allow the development of screening assays that are capable of monitoring multiple readouts such as transcript or protein levels, or even multiple parameters derived from images. Such advances in assay technologies highlight the complex nature of biology and disease. Harnessing this complexity requires integration of all the different parameters that can be measured rather than just monitoring a single dimension as is commonly used. Although some of the methods used to combine multiple measurements, such as principal component analysis, are commonly used for microarray analysis, biologists are not yet using many of the tools that have been developed in other fields to address such issues. Visualization of multiparametric data sets is one of the major challenges in this field, and a depiction of the results in a manner that can be readily interpreted is essential. This article describes a number of assay systems being used to generate such data sets en masse, and the methods being applied to their visualization and analysis. We also discuss some of the challenges of applying methods developed in other fields to biology.

Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis

“…[ 2 ] High content screening (HCS) in particular carries great potential for drug discovery and development, combining the historic breadth and versatility of visual microscopy with the power, speed, and efficiency of automated screening. [ 3 , 4 ] Yet the apparent potential of HCS has remained frustratingly unrealized: in their 2014 review of multidimensional small molecule profiling, Wolpaw and Stockwell devoted only one page of thirty-five to quantitative imaging, concluding that “only a minority of compounds displayed an appreciable phenotype”. [ 5 ] Despite the lack of progress in the field, we feel that many potential insights remain untapped in the analysis and representation of HCS data, and such findings can be unlocked with improved analytical methods.…”

Gene-signature-derived IC50s/EC50s reflect the potency of causative upstream targets and downstream phenotypes

“…Using the same readout will in many cases contribute to increased biological relevancy at all stages of the drug discovery process. Similar multiplexed readouts like the data from cell painting or metabolomics 29,30 might also benefit from our multiplexed potency methods.…”

Arrayed functional genetic screenings in pluripotency reprogramming and differentiation

“…Two main approaches can be used to classify or group cells with similar phenotypes, supervised, and unsupervised. Unsupervised approaches are based on clustering algorithms, which group unlabeled cells based on the similarity of their multiparametric phenotypic profiles [132–134]. In turn, supervised approaches are mainly based on machine learning algorithms, where a human specialist assigns a reduced set of cells with the desired phenotypes to specific classes, in order to train the algorithm, which can then automatically classify all cells in the screening.…”