Cytodata

The Cell Painting Gallery is a collection of image datasets created using the Cell Painting assay (or similar); it is maintained by the Carpenter--Singh lab at the Broad Institute.

The Broad Bioimage Benchmark Collection (BBBC) is a collection of freely downloadable microscopy image sets. In addition to the images themselves, each set includes a description of the biological application and some type of "ground truth" (expected results).

Public repository of image datasets from published scientific studies.

RxRx1 is a set of 125,514 high-resolution 512x512 6-channel fluorescence microscopy images of human cells under 1,108 genetic perturbations in 51 experimental batches across four cell types. The images were produced by Recursion Pharmaceuticals in their labs in Salt Lake City, Utah. Researchers will use this dataset for studying and benchmarking methods for dealing with biological batch effects, as well as areas in machine learning such as domain adaptation, transfer learning, and k-shot lear…

RxRx19 is the first morphological dataset that demonstrates the rescue of morphological effects of COVID-19.

Among other assays, the HPA performed confocal imaging of displaying the location of more than 2/3 of human proteins in cell lines. Raw images or infered protein subcellular locations can be downloaded.

Cell painting profiles from 1,600 bioactive compounds in U2OS cells (Access from public S3 bucket: s3://cytodata/datasets/Bioactives-BBBC022-Gustafsdottir/profiles/Bioactives-BBBC022-Gustafsdottir/).

Cell painting profiles from 31,770 compounds in U2OS cells (Click to download).

Cell painting profiles from 30,616 compounds in U2OS cells (Center Driven Research Project CDRP) (Download from GigaDB | Access from public S3 bucket: s3://cytodata/datasets/CDRPBIO-BBBC036-Bray/profiles_cp/CDRPBIO-BBBC036-Bray/).

Cell painting matched to L1000 profiles in 4 experiments, including compound and genetic screens (Details on GitHub).

3,072 cell painting profiles from 41 genes knocked down with RNA interference (RNAi) in U2OS cells (Access from GitHub).

Cell painting data from 220 overexpressed genes in U2OS cells (Access from public S3 bucket: s3://cytodata/datasets/TA-ORF-BBBC037-Rohban/profiles_cp/TA-ORF-BBBC037-Rohban/).

A software package for exploration, annotation and classification of cells within large datasets using machine learning.

CellProfiler is a free open-source software for measuring and analyzing cell images.

Interactive data exploration, analysis, and classification of large biological image sets.

Methods for image-based cell profiling in R.

Image processing toolbox for R.

A web app for exploratory data analysis and visualization of arrayed high-throughput screens.

Toolkit for filtering and curation of morphological profiles in Julia.

Methods for image-based cell profiling in Python.

A platform compiling tool for deep-learning based image analyses with a GUI.

Toolbox to extract quantitative phenotypic descriptors and contextual information for histology and multiplex imaging.

2020 review of applications in image-based profiling from a Carpenter lab/pharma perspective.

Introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images.

Describe some recent applications of HCS, ranging from the identification of genes required for specific biological processes to the characterization of genetic interactions.

Describe the state of the art for image-based screening experiments and delineate experimental approaches and image-analysis approaches as well as discussing challenges and future directions, including leveraging CRISPR/Cas9-mediated genome engineering.

Describe applications of image-based profiling including target and MOA identification, lead hopping, library enrichment, gene annotation and identification of disease-specific phenotypes

Overview of image-based profiling, including its applications and challenges.

Discussion on the biology of cell shape changes based on quantitative descriptors.

Introduction to morphological profiling and discussion on what machine learning has to offer.

Review of the different modalities available for genetic screens and which ones are suitable for morphological profiling.

A collection of review and research articles published in Nature Methods related to multiple use cases of deep learning, including noise reduction, segmentation, tracking and representation learning.

A collection of high-content imaging method and application articles published in SLAS Discovery.

Poulsen lab paper from 2020 where antibiotics are tested for their ability to leave mammalian cells as intact as possible, per the Cell Painting assay.

A multivariate method for classifying untreated and treated human cancer cells based on ∼300 single-cell phenotypic measurements.

Profiling disease-gene interaction during adipocyte differentiation.

This study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community.

Use of image-based profiling to screen the bioactivity of environmental chemicals

Using image-based profiles to predict the bioactivity of small molecules in other unrelated assays.

Demonstrating the effects of polypharmacology in MOA prediction while offering solutions for overcoming it in future image-based profiling studies.

Image-based profiling and network analysis of drug combinations.

Image-based profiling of actin organization at the immunological synapse.

Classification of protein subcellular location from confocal microscopy images of the Human Protein Atlas.

Combining confocal imaging and mass spectrometry representations of proteins to predict physical proximity and characterize cellular organization.

Image-based profiles as predictors of apoptosis, proliferation and other cell health descriptors.

Analysis of single-cell profiles to characterize variability, pleiotropy and incomplete penetrance.

Demonstrates feasibility of imaging arrayed CRISPR screens and offers a way to characterize transfection efficacy in individual cells.

Profiling chemical effects on patient-derived organoids.

Profiling the morphological changes induced in 1280 compounds in 12 knockout cell lines.

Changes in genetic interactions across time based on multiple morphological descriptors.

Defining morphological clusters in a large compound screen.

Characterizing genetic interactions by integrating 21 phenotypic descriptors.

Comparing morphological descriptors in a pooled CRISPR screen with in-situ sequencing

Benchmarking profiling modalities, including image-based profiles, for mechanism of action prediction.

Comparing cellular features across stages of the cell cycle.

Decomposing cellular and nuclear shapes in 3D in multiple iPSC and studying association between cellular structures.

Combining metabolic profiling and spatial information to define immune subsets in tumor microenvironments.

Defining cell populations and their interactions in breast cancer based on shape, intensity and contextual information from multiplexed imaging.

Looking for gene networks underlying cellular morphology by matching expression and imaging data.

Model cell morphology with autoencoders to estimate effects of drug combinations.

Concrete description of how morphological information can be extracted from patient material and guide treatment.

Protocol describing the design and execution of experiments using Cell Painting.

Cell Painting assay.

Retrospective method for illumination-correction based on energy minimization.

Method for retrospective shading correction based on entropy minimization.

Adds dispersion and covariances to population averages to capture single-cell heterogeneity.

Minimum redundancy - maximum relevance feature selection framework.

Selfsupervised method to learn feature representations of single cells in microscopy images without labelled training data.

Training CNNs using a weakly supervised approach for feature learning.

End-to-end learning with CNNs to predict bioactivity of small molecules in unrelated assays using image-based profiles.

Comparing several deep learning methods for nuclear segmentation.

Transfer of activation features of generic CNNs to extract features for image-based profiling.

Illumination-correction method accounting for space- and time-dependent biases.

Generalist deep learning model for cell and nucleus segmentation with pre-trained weights.

DeepCell: collection of deep learning segmentation models.

Embedding single-cell and compound profiles using transfer learning, examplified on mechanism of action prediction.

Proposing a score to define significant activity in screens.

Reconstructing images for Cell Painting dyes and ensuring corresponding morphological measurements are preserved.

Method to visualize morphological profiles.

Generative network displaying potential for learning latent representation of biological conditions from cell images.

Direct extraction of phenotypic features from plant images.

Comparison of the information contained in Cell Painting and L1000 assays for the same perturbations.

Unsupervised approach to represent cellular morphology with clusters corresponding to meaningful relations such as mechanism of action. With an overview of deep learning methods for morphological profiling and classification.

Experimental and computational workflow to extract 3D morphological descriptors of organoids using light-sheet microscopy.