Virtual screening for small-molecule pathway regulators by image-profile matching

Rohban, Mohammad Hossein; Fuller, Ashley M.; Tan, Ceryl; Goldstein, Jonathan T.; Syangtan, Deepsing; Gutnick, Amos; DeVine, Ann; Nijsure, Madhura P.; Rigby, Megan; Sacher, Joshua R.; Corsello, Steven M.; Peppler, Grace B.; Bogaczynska, Marta; Boghossian, Andrew S.; Ciotti, Gabrielle E.; Hands, Allison; Mekareeya, Aroonroj; Doan, Minh; Gale, Jennifer; Derynck, Rik; Turbyville, Thomas J.; Boerckel, Joel D.; Singh, Shantanu; Kiessling, Laura L.; Schwarz, Thomas L.; Varelas, Xaralabos; Wagner, Florence F.; Kafri, Ran; Eisinger-Mathason, T.S. Karin; Carpenter, Anne E.

doi:10.1016/j.cels.2022.08.003

Cited by 23 publications

(22 citation statements)

References 74 publications

(96 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In creating such a large public data set and making it easier to reproduce our exact technique, our goal is to help researchers better match the public set to their own samples of interest. However, major work still remains in being able to match samples across different experiments 12 ; a major goal of the JUMP Consortium is to develop such methods. We hope future technical advancements will make it possible to match samples across widely variant assays, but until then, matching our protocol to the degree possible represents the best chance of being able to match to the public set.…”

Section: Limitationsmentioning

confidence: 99%

“…We recently became interested in creating a very large public Cell Painting data set due to the success of multiple approaches where image data can be used to predict not just particular phenotypes about the parts of the cell that were stained, but entirely orthogonal data such as gene expression data or the outcome of a biochemical assay 10,11 . Such a database could also allow queries for compounds that match genes 12 and vice versa. It stands to reason that a large, well constructed set of image-based phenotypic screen data could not only inspire new computational tools to mine such data, but also serve as a resource for researchers to compare their own data against, accelerating discovery for thousands of scientists globally.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing the Cell Painting assay for image-based profiling

Cimini

Chandrasekaran

Kost‐Alimova

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing the Cell Painting assay for image-based profiling

Cimini

Chandrasekaran

Kost‐Alimova

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…If so, profile data can be used in other ways, e.g., by simply using the profile as a signature of the sample and trying to use drugs to revert this disease phenotype to a healthy‐associated phenotype. If one has access to computational experts, one can also try to query their data against publicly available datasets (Rohban et al., 2022), though these approaches are currently still experimental. The interpretation of complex profiles is a challenge, but when successful can propel research in new directions to uncover exciting new mechanisms.…”

Section: Commentarymentioning

confidence: 99%

Interpreting Image‐based Profiles using Similarity Clustering and Single‐Cell Visualization

et al. 2023

Self Cite

View full text Add to dashboard Cite

Image-based profiling quantitatively assesses the effects of perturbations on cells by capturing a breadth of changes via microscopy. Here, we provide two complementary protocols to help explore and interpret data from image-based profiling experiments. In the first protocol, we examine the similarity among perturbed cell samples using data from compounds that cluster by their mechanisms of action. The protocol includes steps to examine feature-driving differences between samples and to visualize correlations between features and treatments to create interpretable heatmaps using the open-source web tool Morpheus. In the second protocol, we show how to interactively explore images together with the numerical data, and we provide scripts to create visualizations of representative single cells and image sites to understand how changes in features are reflected in the images. Together, these two tutorials help researchers interpret image-based data to speed up research.

show abstract

“…Alternative methods are, for instance, based on screening approaches with a single-cell transcriptomic readout . Segregation of different cell types/states in microscopy images can be conducted by staining, by in situ sequencing, or by segregation based on morphological features which is often enabled by AI/ML. , …”

Section: Molecular Glue Degrader Discovery Via Advanced Phenotypic Sc...mentioning

confidence: 99%

“…170 Segregation of different cell types/states in microscopy images can be conducted by staining, 171 by in situ sequencing, 172 or by segregation based on morphological features which is often enabled by AI/ML. 173,174 One strategy to introduce diversity is to conduct phenotypic screens not in homogeneous populations of cancer cell lines, but instead in a cellular background with more physiological relevance. To that end, several examples have documented the power of chemical screens in organoids 175,176 or directly in patient material.…”

Section: Introducing Diversity In Phenotypicmentioning

confidence: 99%

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

2023

View full text Add to dashboard Cite

Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.

show abstract

Virtual screening for small-molecule pathway regulators by image-profile matching

Cited by 23 publications

References 74 publications

Optimizing the Cell Painting assay for image-based profiling

Optimizing the Cell Painting assay for image-based profiling

Interpreting Image‐based Profiles using Similarity Clustering and Single‐Cell Visualization

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Contact Info

Product

Resources

About