BioProfiling.jl: profiling biological perturbations with high-content imaging in single cells and heterogeneous populations

Abstract

Motivation High-content imaging screens provide a cost-effective and scalable way to assess cell states across diverse experimental conditions. The analysis of the acquired microscopy images involves assembling and curating raw cellular measurements into morphological profiles suitable for testing biological hypotheses. Despite being a critical step, general-purpose and adaptable tools for morphological profiling are lacking and no solution is available for the high-performance Julia programming language. Results Here, we introduce BioProfiling.jl, an efficient end-to-end solution for compiling and filtering informative morphological profiles in Julia. The package contains all the necessary data structures to curate morphological measurements and helper functions to transform, normalize and visualize profiles. Robust statistical distances and permutation tests enable quantification of the significance of the observed changes despite the high fraction of outliers inherent to high-content screens. This package also simplifies visual artifact diagnostics, thus streamlining a bottleneck of morphological analyses. We showcase the features of the package by analyzing a chemical imaging screen, in which the morphological profiles prove to be informative about the compounds' mechanisms of action and can be conveniently integrated with the network localization of molecular targets. Availability The Julia package is available on GitHub: https://github.com/menchelab/BioProfiling.jl We also provide Jupyter notebooks reproducing our analyses: https://github.com/menchelab/BioProfilingNotebooks Supplementary information Supplementary data are available at Bioinformatics online.

Discovery of Molecular Glue Degraders via Isogenic Morphological Profiling

“…Thereafter, feature selection was applied, and predictions/interpretations on compound bioactivity were made from the profiles (Figure B). We first explored the morphological profiles within the RKO WT context following a typical computational analysis for the CPA. − We refer to this strategy as “global feature selection”, reflecting the way that the strategy is applied to the profiles of all compound treatments globally. For morphological profiles within the RKO WT context, a set of 470 features is selected by global feature selection.…”