{"id":682,"date":"2023-08-02T12:00:47","date_gmt":"2023-08-02T12:00:47","guid":{"rendered":"https:\/\/terrabioappdev.wpenginepowered.com\/microscopy-in-the-cloud-cellprofiler-and-cell-painting-on-terra\/"},"modified":"2023-12-27T04:55:57","modified_gmt":"2023-12-27T04:55:57","slug":"microscopy-in-the-cloud-cellprofiler-and-cell-painting-on-terra","status":"publish","type":"post","link":"https:\/\/terra.bio\/microscopy-in-the-cloud-cellprofiler-and-cell-painting-on-terra\/","title":{"rendered":"Microscopy in the cloud: CellProfiler and Cell Painting on Terra"},"content":{"rendered":"

Posted by: Carmen Diaz Verdugo<\/b><\/p>\n

Authors: Carmen Diaz Verdugo, Stephen Fleming, Nicole Deflaux and Amy Unruh<\/b><\/p>\n

Carmen Diaz Verdugo is a Computational Scientist in the Precision Cardiology Lab, and a member of Patrick Ellinor\u2019s group. Stephen Fleming is a Senior Machine Learning Scientist in the Methods group of the Data Sciences Platform at the Broad Institute, and he is also a member of the Precision Cardiology Lab. Nicole Deflaux and Amy Unruh are software engineers at Verily Life Sciences and members of the Terra team.\u00a0<\/span><\/i><\/p>\n

In this guest blog post, Carmen, Stephen, Nicole, and Amy introduce several cloud-optimized workflows for running CellProfiler and Cell Painting pipelines at scale on Terra, the secure biomedical research platform co-developed by Broad Institute of MIT and Harvard, Microsoft, and Verily. The workflows live in a publicly-accessible <\/span><\/i>Broad GitHub repository<\/span><\/i><\/a> and are <\/span><\/i>hosted on Dockstore<\/span><\/i><\/a>.<\/span><\/i><\/p>\n

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Image-based cell profiling<\/span><\/h3>\n

Image-based \u2013 or \u201cmorphological\u201d \u2013 profiling combines high-content image-based assays,\u00a0such as the <\/span>Cell Painting<\/span><\/a> assay, with computational methods to analyze the resulting data. The Cell Painting assay uses a collection of fluorescent dyes that are multiplexed in different channels to reveal the most relevant cellular compartments or organelles such as the nuclei, cytoskeleton or mitochondria. Cells are placed in multiwell plates, stained, perturbed either genetically or with compounds, and imaged with a high-throughput microscopy system. Next, using image analysis software (for which we use the open-source software <\/span>CellProfiler<\/span><\/a>), cells are identified, segmented, and thousands of morphological features are measured. The collection of features is known as a profile, and it describes the morphological properties of the cells treated with the specific perturbations.<\/span><\/p>\n

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From: <\/span>Bray, MA., Singh, S., Han, H. <\/span>et al.<\/span><\/i> Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. <\/span>Nat Protoc<\/span><\/i> 11, 1757\u20131774 (2016) [<\/span>https:\/\/doi.org\/10.1038\/nprot.2016.105<\/span><\/a>]<\/span><\/p>\n

Cell Painting is an emerging valuable tool, and several labs in academic settings and pharma companies use this assay, in large part because of its several applications in the drug discovery pipeline. The Broad Institute of MIT and Harvard, where the Cell Painting assay protocol was developed, launched a collaboration with industry and non-profit partners called Joint Undertaking in Morphological Profiling with Cell Painting (<\/span>JUMP-CP<\/span><\/a>). This consortium recently released a large collection of Cell Painting datasets covering 136,000 chemical and genetic perturbations open to the scientific community for data exploration. The CellProfiler and Cell Painting workflows in Terra that will be described in this blog are tools to facilitate the analysis and exploration of these large datasets using cloud services.\u00a0<\/span><\/p>\n

Image analysis with CellProfiler<\/span><\/h3>\n

CellProfiler is open-source software for measuring and analyzing images, designed with an intuitive user interface that allows scientists to analyze large amounts of microscopy images automatically. The CellProfiler project was started by <\/span>Anne E. Carpenter<\/span><\/a> and <\/span>Thouis (Ray) Jones<\/span><\/a> and it is currently based in the <\/span>Cimini Lab<\/span><\/a> at the Broad Institute.\u00a0<\/span><\/p>\n

CellProfiler can read and analyze most common microscopy image formats, and the software offers a wide range of modules for image preprocessing, object\/cell segmentation, and measuring quantities such as area shape, intensity, and texture. The measurements are exported as CSV files or a SQLite database.<\/span><\/p>\n

The need for scale<\/span><\/h3>\n

Currently, with the advances in automated high-content microscopy systems and robotic equipment in laboratories, lab scientists are able to generate large amounts of data that are challenging not just to analyze, but also to store. Research labs and pharma companies alike are running up against a potential bottleneck: running the software pipelines to extract CellProfiler features can take longer than collecting the actual data, not to mention all the downstream analyses that follow CellProfiler feature extraction. Running CellProfiler pipelines sequentially on a single computer is not scalable. Bespoke solutions using multiple compute nodes on local computer clusters can work, but require a great deal of skilled development and maintenance, and involve a large learning curve. That\u2019s where parallel compute in the cloud can offer a truly scalable alternative for those with lots of data to process.<\/span><\/p>\n

A cloud-based solution<\/span><\/h3>\n

Terra<\/span><\/a> is a scalable platform that allows researchers to access data, run analyses, and collaborate using secure cloud services. We have created a series of WDL <\/span>workflows<\/span><\/a> that allows you to run CellProfiler, for either Cell Painting pipelines or any custom pipeline, and <\/span>PyCytominer<\/span><\/a> tools, which are a collection of functions to process high-dimensional readouts from high-content experiments. These pipelines can be run with very little setup at the click of a button. Compute capacity will automatically scale with the size of the dataset, performing the analysis in parallel, and Terra will email you when the job is complete. <\/span>The WDL workflows include three pipelines \u2013 \u201cCellProfiler\u201d, \u201cCell Painting\u201d, and \u201cCytomining\u201d:<\/span><\/p>\n