In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations based on their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells, and predicting assay outcomes using machine learning, among many others. Here we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, golgi apparatus, plasma membrane, endoplasmic reticulum, and mitochondria. The original protocol was updated in 2016 based on several years' experience running it at two sites, after optimizing it by visual stain quality. Here we describe the work of the Joint Undertaking for Morphological Profiling (JUMP) Cell Painting Consortium, aiming to improve upon the assay via quantitative optimization, based on the measured ability of the assay to detect morphological phenotypes and group similar perturbations together. We find that the assay gives very robust outputs despite a variety of changes to the protocol and that two vendors' dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1 to 2 weeks for a typically sized batch of 20 or fewer plates; feature extraction and data analysis take an additional 1 to 2 weeks.

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We present a new, carefully designed and well-annotated dataset of images and image-based profiles of cells that have been treated with chemical compounds and genetic perturbations. Each gene that is perturbed is a known target of at least two compounds in the dataset. The dataset can thus serve as a benchmark to evaluate methods for predicting similarities between compounds and between genes and compounds, measuring the effect size of a perturbation, and more generally, learning effective representations for measuring cellular state from microscopy images. Advancements in these applications can accelerate the development of new medicines.

Autophagy, a lysosomal degradation pathway, plays a crucial role in cellular homeostasis, development, immunity, tumor suppression, metabolism, prevention of neurodegeneration and lifespan extension. Thus, pharmacological stimulation of autophagy may be an effective approach for preventing or treating certain human diseases and/or aging. We sought to establish a method for developing new chemical compounds that specifically induce autophagy. To do this, we developed two assays to identify compounds that target a key regulatory node of autophagy induction – specifically, the binding of Bcl-2 (a negative regulator of autophagy) to Beclin 1 (an allosteric modulator of the Beclin 1/VPS34 lipid kinase complex that functions in autophagy initiation). These assays use either a split-luciferase assay to measure Beclin 1/Bcl-2 binding in cells or an AlphaLISA assay to directly measure direct Beclin 1/Bcl-2 binding in vitro. We screened two different chemical compound libraries, comprising ~300K compounds, to identify small molecules that disrupt Beclin 1/Bcl-2 binding and induce autophagy. Three novel compounds were identified that directly inhibit Beclin 1/Bcl-2 interaction with an IC50 in the micromolar range and increase autophagic flux. These compounds do not demonstrate significant cytotoxicity and they exert selectivity for disruption of Bcl-2 binding to the BH3 domain of Beclin 1 compared to the BH3 domain of the pro-apoptotic Bcl-2 family members, Bax and Bim. Thus, we have identified candidate molecules that serve as lead templates for developing potent and selective Beclin 1/Bcl-2 inhibitors that may be clinically useful as autophagy-inducing agents.

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations based on their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells, and predicting assay outcomes using machine learning, among many others. Here we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, golgi apparatus, plasma membrane, endoplasmic reticulum, and mitochondria. The original protocol was updated in 2016 based on several years' experience running it at two sites, after optimizing it by visual stain quality. Here we describe the work of the Joint Undertaking for Morphological Profiling (JUMP) Cell Painting Consortium, aiming to improve upon the assay via quantitative optimization, based on the measured ability of the assay to detect morphological phenotypes and group similar perturbations together. We find that the assay gives very robust outputs despite a variety of changes to the protocol and that two vendors' dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for a typically sized batch of 20 or fewer plates; feature extraction and data analysis take an additional 1-2 weeks..