Image-based cell phenotyping with deep learning

Pratapa, Aditya; Doron, Michael; Caicedo, Juan C.

doi:10.1016/j.cbpa.2021.04.001

Cited by 70 publications

(54 citation statements)

References 88 publications

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“…Chemical structure information might also be useful 53 , though this would require significant adaptation to incorporate because it is not a property one can obtain for the genes used as queries in our matching approach, and the goal is not to identify compounds of similar structure (diversity is usually preferred). More advanced computational methods are also on the horizon, from feature extraction 58 to machine learning on new benchmark datasets of gene-compound pairs 59 ; we would expect supervised machine learning to work better than our unsupervised correlation-based approach 9 . We anticipate that image profile-based virtual screening provides a new accelerant toward meeting the pressing need for novel therapeutics.…”

Section: Discussionmentioning

confidence: 99%

Virtual screening for small molecule pathway regulators by image profile matching

Rohban

Fuller

Tan

et al. 2021

Preprint

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Identifying chemical regulators of biological pathways is currently a time-consuming bottleneck in developing therapeutics and small-molecule research tools. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to a disease of interest. Here, we instead use a broad, virtual screening approach that matches compounds to pathways based on phenotypic information in public data. Our computational strategy efficiently uncovered small molecule regulators of three pathways, containing p38ɑ (MAPK14), YAP1, or PPARGC1A (PGC-1α). We first selected genes whose overexpression yielded distinct image-based profiles in the Cell Painting assay, a microscopy assay involving six stains that label eight cellular organelles/components. To identify small molecule regulators of pathways involving those genes, we used publicly available Cell Painting profiles of 30,616 small molecules to identify compounds that yield morphological effects either positively or negatively correlated with image-based profiles for specific genes. Subsequent assays validated compounds that impacted the predicted pathway activities. This image profile-based drug discovery approach could transform both basic research and drug discovery by identifying useful compounds that modify pathways of biological and therapeutic interest, thus using a computational query to replace certain customized labor- and resource-intensive screens.

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Section: Discussionmentioning

confidence: 99%

Virtual screening for small molecule pathway regulators by image profile matching

Rohban

Fuller

Tan

et al. 2021

Preprint

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“…Bioimages are typically collected across multiple conditions spanning, for example, different replicates, cell types and time scales, as well as various perturbations, such as mechanical, genetic or biochemical. Layering in additional molecular information, such as gene expression, cell lineage or chromatin accessibility (Dries et al, 2021) from high-throughput sequencing experiments, brings the challenge of integrating data from multiple modalities, and the challenge of quantifying how predictive of one another the different modalities can be (Pratapa et al, 2021). Common tasks in multi-modal transfer learning particularly relevant to bioimage analysis include integrating and visualizing data from different sources (data fusion), translating between different modes (transfer) and aligning data collected across multiple modes (alignment).…”

Section: Multimodal Learningmentioning

confidence: 99%

Deep learning for bioimage analysis in developmental biology

et al. 2021

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Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.

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“…Biomedical research is an exceptionally satisfying domain on which to apply advances in machine learning, and yet its application has been relatively limited to supervised learning for medical imaging from patients, including classification and segmentation of X rays and MRI, PET, and CT scans. Similarly, deep-learning based image analysis for cell biology has generally focused on supervised tasks, such as segmentation (Caicedo et al, 2019; Moen et al, 2019), while representation learning and other image modeling applications have lagged behind (Pratapa, Doron, & Caicedo, 2021).…”

Section: Introductionmentioning

confidence: 99%

Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations

Chandrasekaran

Cimini

Goodale

et al. 2022

Preprint

Self Cite

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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.

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Image-based cell phenotyping with deep learning

Cited by 70 publications

References 88 publications

Virtual screening for small molecule pathway regulators by image profile matching

Virtual screening for small molecule pathway regulators by image profile matching

Deep learning for bioimage analysis in developmental biology

Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations

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