dataesr/scientific-paragraphs-categorization huggingface dataset:
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. The Joint Undertaking for Morphological Profiling (JUMP) Cell Painting Consortium was formed in 2019 to create such a dataset, with a goal to optimize and select a single set of staining conditions prior to creating more than 150,000 perturbation profiles across a dozen sites around the world. With these goals in mind, in this work we describe the optimizations done over more than a year to select optimal conditions for the JUMP Consortium's production data set.
In A549, any if not all of the lines could have been suitable based on efficiency and percent matching; we therefore chose based on the line with the fewest restrictions on sharing between partners, which for the particular four clones in question was the polyclonal line. Additional experiments quantified by cell count only helped decide final cell densities, viral amounts, and other conditions (Supplementary Methods) We next carried the parental and polyclonal lines forward to a single large experiment that we called CPJUMP1  13  , using the JUMP-Target plates to address questions 1-7 above; question 8 was assessed in a second, later experiment. While neither line performed poorly in any of our experiments, on the whole U2OS displayed a slightly higher cross-modality matching (Figure  3B ), and so U2OS was selected to be used for producing the JUMP-CP Consortium's full dataset (questions 1-3).
The next thing a researcher setting up a Cell Painting assay must decide is exactly which stains to use and in which concentrations. Although variations on Cell Painting can be powerful for investigating particular areas of biology  8, 9  , for optimization of the canonical assay for this public dataset, the consortium only considered the original six dyes used in earlier versions of the assay (with the exception of DAPI for Hoechst, see Supplementary Figure  4B ). We did briefly qualitatively assess if there were any benefits of moving WGA to an ultraviolet dye or MitoTracker to an orange dye, but quickly dismissed them: no immediate large improvement was shown (data not shown) and it would require reoptimization of the entire stain panel. A large number of stain concentration adjustments were tested; they are broken out comprehensively in Supplementary Figure  4.
Optimization of image analysis conditions In our experience, creating accurate segmentations is crucial but difficult to optimize in a rule-driven way without substantial effort to create 'ground truth' for evaluating changes in the pipeline's parameters  16. We have described elsewhere  17  a number of resources for learning to do so effectively, and the workflow described here includes steps for iterating on segmentation to ensure it is optimal before feature extraction. As deep learning segmentation tools such as StarDist  18  and Cellpose  19  become more popular, they may help solve many difficult segmentation issues; these tools may be used independently and then object labels matrices can be brought into CellProfiler, used via their ImageJ implementations using the RunImageJMacro  20  or with their CellProfiler plugins (https://github.
com/CellProfiler/CellProfiler-plugins) if CellProfiler is installed from source. 16 nce objects are segmented, in general we recommend measuring as many image-based features as is practical in your image analysis software, where "practical'' has a few considerations, such as a) the amount of time needed to generate these measurements and b) limitation on your output size based on file constraints -SQLite unless manually compiled only allows 2000 columns in a given table  , for example. An open question about Cell Painting is how much each stain contributes to the information content of the assay. Likewise, the contributions of different measurement categories are unknown. Therefore we analyzed the relative contributions towards percent replicating of different measurement types (Figure  6A ), cell compartments (Figure  6B ), and channels (Supplementary Figure  6 ) in the context of the 90 compounds present in the JUMP-MOA plate, but note that these breakdowns describe only the phenotypes present in these plates; any specific phenotype(s) of interest may crucially depend on compartments, stains, or features that are less critical for the majority of these 47 mechanisms of action.
To create a sufficiently compact data representation, the 7 categories present were split 3 onto the X (Correlation, Granularity, and Intensity) and 4 onto the Y (AreaShape, Neighbors, RadialDistribution, and Texture) axes; this allows visualization of the 127 possible unique combinations. A channel-by-channel breakdown of the importance of the feature categories is provided as Supplementary Figure  8. B) A parallel analysis of the same experiment as in part A, but with the 3 compartments present, 2 on the X axis (Cytoplasm and Nuclei) and Cells on the Y axis. C) Assessment of the effect of varying the measurement scales in CellProfiler (ie measuring Texture at 3 and 5 pixel spacings vs 5 and 10 pixel spacings) on percent replicating for two plates of the CPJUMP1 experiment. Using larger measurement scales in the MeasureGranularity, MeasureTexture, and MeasureObjectNeighbors modules seemed to produce a very small decrease in percent replicating but no major effect.
For example, because the pixel intensities will range from 0 to 65,535 for a 16-bit image, a rule of thumb is for the typical sample to yield a maximum intensity of ~32,000. This guideline will prevent saturation (i. e. , reaching the value 65,535) from samples that are brighter because of a perturbation. Do not use shading correction if you are using the recommended CellProfiler workflow for image analysis, as the background illumination heterogeneities will be corrected post acquisition using the CellProfiler software. Before beginning the complete imaging run, it is useful to capture images from three to five wells at a few different locations across the plate, in order to confirm that the microscope is operating as expected and the acquisition settings are optimal for the experiment and cell line at hand.
• Image processing software CellProfiler biological image analysis software is used to extract per-cell morphology feature data from the Cell Painting images, and can optionally be used to extract per-image QC metrics (see Box 2). The software and associated pipelines are designed to handle both low-and high-throughput analysis, but we routinely run this software as part of this protocol on thousands, even millions, of imaged fields of view. To download and install the open-source CellProfiler software, go to http://www. cellprofiler. org, follow the download links for CellProfiler, and follow the installation instructions. The current version at the time of writing is 4. 2. 1. This protocol assumes basic knowledge of the CellProfiler image analysis software package. Extensive online documentation and tutorials can be found at http://www.
cellprofiler. org/. In addition, the '?' buttons within CellProfiler's interface provide detailed help. The pipelines used here are compatible with CellProfiler version 4. 1. 3 and above. This protocol uses three CellProfiler pipelines to perform the following tasks: illumination correction, segmentation, and morphological feature extraction. Additionally, you can use an optional QC pipeline to quantify image quality. See the Computational Equipment section for a link to the CellProfiler pipelines used in this protocol. Each module of the pipelines is annotated with details about the purpose of the module and considerations in making adjustments to the settings. The annotations may be found at the top of the settings, in the panel labeled 'Module notes'. The pipelines are configured on the assumption that the image files follow the nomenclature of the Perkin Elmer Opera Phenix system, in which the plate/well/site metadata are encoded as part of the filename.
The plate and well metadata in particular are essential because CellProfiler uses the plate metadata in order to process the images on a per-plate basis, and the plate and well metadata are needed for linking the plate layout information with the images for the downstream profiling analysis. Therefore, images coming from a different acquisition system may require adjustments to the Metadata module to capture this information; please refer to the help function for this module for more details or our video tutorial walkthrough of the Input modules https://youtu. be/Z_pUWuOV06Q. The QC and morphological feature extraction pipelines are set to write out cellular features to a. csv for each well and site, respectively, using the ExportToSpreadsheet module. We provide Python scripts to generate per-well profiles from the extracted features.
• Computing system Small batches of images can be processed on most modern desktop computers. If the number of images to analyze is sufficiently large (e. g. , >∼1,000 images), processing time on a single computer becomes resource limiting. For large batches of images, we recommend using a computing cluster if available, such as a high-performance server farm, or a cloud-computing platform such as Amazon Web Services (AWS). Substantial setup effort is required for both cluster computing and cloud-distributed computing, and will probably require enlisting the help of your IT department. Please refer to our GitHub webpage (https://github. com/CellProfiler/CellProfiler/wiki/Adapting-CellProfiler-to-a-LIMS-environment) for more details on cluster computing, and our Distributed CellProfiler GitHub repository wiki (https://github.
Start CellProfiler, if you are not already running it. 32. Load the Segmentation pipeline into CellProfiler by selecting File > Import > Pipeline from File from the CellProfiler main menu and selecting segmentation. cppipe or dragging and dropping the pipeline from your file browser into the pipeline panel on the left of the interface. CRITICAL STEP Because capturing subtle phenotypes is important for profiling, accurate nuclei and cell body identification is essential for success. This pipeline enables troubleshooting of nuclei and cell segmentation across an entire batch by outputting an image per well with nuclei and cell objects overlaid for visual inspection. The optimal parameters determined in this pipeline must be manually transferred to the analysis pipeline. 33. Select the Images input module in the 'Input modules' panel to the top-left of the interface.
If not satisfied with the segmentation, repeat the segmentation pipeline workflow with different segmentation parameters. To simplify image visualization, particularly in larger datasets, you may wish to create whole-plate montages of your images using methods described in Dobson et al  17. The ANTICIPATED RESULTS section outlines the expected nuclei and cell identification quality. Run quality control to extract image quality measurements: See Box 2 for this optional step. Run the final analysis pipeline to extract morphological features: 37. Start CellProfiler, if you are not already running it. 38. Load the analysis pipeline into CellProfiler by selecting File > Import > Pipeline from File from the CellProfiler main menu and selecting analysis. cppipe or dragging and dropping the pipeline from your file browser into the pipeline panel on the left of the interface.
39. Select the Images input module in the 'Input modules' panel to the top-left of the interface. From your file browser, drag and drop the folder(s) containing your raw images into the 'File list' panel. 40. If you needed to change segmentation parameters in your Segmentation pipeline for accurate object identification, make those same changes to the IdentifyPrimaryObjects and IdentifySecondaryObjects modules for the identification of NucleiIncludingEdges and CellsIncludingEdges, respectively. 41. If you did not run a separate QC pipeline and would like to measure image-level quality metrics, check the two MeasureImageQuality modules at the beginning of the pipeline to include those measurements in the analysis pipeline. 42. Click the 'Output settings' button at the bottom-left of the interface.
Creation, normalization, and feature reduction of per-well profiles All steps in this section are covered in more detail and are continuously updated in the profiling-handbook 25 TIMING 8-16 h per 384-well plate 45. Set up a terminal for bash scripting as well as a language you are comfortable writing scientific code in; most of our tooling is in Python  21  since it has a large number of useful packages you can use to write  [26] [27] [28] [29] [30] [31] [32]  or execute  33  code per our templates but it is not mandatory. 46. Create per-well mean aggregated profiles using the pycytominer 34 command `collate. py` If you have run your CellProfiler pipeline in a cluster computing environment, you can also use this same script to aggregate multiple CSV files into a single SQLite database before aggregation using the cytominer-database  35  package.
This step can take 8-16 hours per plate per plate to run; if running multiple plates, it is strongly recommended to use parallel  36  or a similar tool to parallelize profile creation. 47. Create a metadata file describing the treatments on each plate of your experiment. Instructions for creating this file are available as part of the profiling-recipe  37  and profiling-handbook. 48. Follow the instructions in the profiling-template  38  and profiling-handbook to create a new template analysis repository and weld a copy of the profiling-recipe into it. 49. Follow the instructions in the profiling-recipe to set up your profiling configuration file for your current batch of data. 50. Execute the profiling-recipe script to create annotated, normalized, and/or feature reduced profiles. This step will take a few minutes per plate.
We have found that the SYTO 14 image is the most amenable for finding cell edges, as it has fairly distinct boundaries between touching cells. For help in optimizing the output, if needed, see the TROUBLESHOOTING table  (Table 4 ). The image analysis pipeline produces the raw numerical features extracted from nuclei, cell, and cytoplasm objects identified in the images. If run locally, these are saved as SQLite database files with one table per object. If run distributed, these are saved into per-object. csvs (i. e. Cells. csv, Cytoplasm. csv, and Nuclei. csv) in a per site folder. The. csv's contain one row for each object in each image, and ∼2,000 columns containing the values for the different morphological features that have been measured for that object. The pipeline also outputs Experiment.
Please refer to our Distributed CellProfiler GitHub repository wiki (https://github. com/CellProfiler/Distributed-CellProfiler/wiki) for more details on cloud computing using AWS and our command line startup guide https://github. com/CellProfiler/CellProfiler/wiki/Getting-started-using-CellProfiler-from-the-command-line for more details on cluster computing. Additional information on batch processing on a cluster or in the cloud is available on our video Headless CellProfiler/DistributedCellProfiler Tutorial (https://youtu. be/LuJxIGGhRek). When running batch processes, we recommend exporting data to spreadsheets as it is easier to aggregate a large number of spreadsheets than databases. Uncheck or remove any ExportToDatabase modules in your pipelines and add instead ExportToSpreadsheet modules.
There are two ways to create batch information for CellProfiler: LoadData and CreateBatchFiles. LoadData: LoadData is our preferred method, especially for use with Distributed CellProfiler and cloud computing. Detailed instructions can be found at https://cytomining. github. io/profiling-handbook/ 1. Insert the LoadData module into the pipeline by pressing the '+' button and selecting the module from the 'File Processing' category. Move this module to the beginning of the pipeline and configure it to find your file. 2. Create LoadData. csvs. If you are using a Perkin Elmer microscope you can use our pe2loaddata script, available at https://github. com/broadinstitute/pe2loaddata, to create the LoadData. csv file. Alternatively, if you have loaded your images into CellProfiler using the Input modules, you can export a file list from CellProfiler using File > Export > Image Set Listing.
3. The end result of this step will be a LoadData. csv file. This file, when used in conjunction with a CellProfiler pipeline (. cppipe), contains the information needed to run in 'headless' mode on the cluster or in the cloud CreateBatchFiles: If you have already created a CellProfiler project (. cpproj) with fully populated Input modules, you can 1. Insert the CreateBatchFiles module into the pipeline by pressing the '+' button and selecting the module from the 'File Processing' category. Move this module to the end of the pipeline. 2. Configure the CreateBatchFiles module by setting the 'Local root path' and 'Cluster root path' settings. If your computer mounts the file system differently than the cluster computers, CreateBatchFiles can replace the necessary parts of the paths to the image and output files.
There are a number of command-line arguments to CellProfiler that allow customization of the input and output folder locations, as well as which images are to be processed on a given computing node. Enlist an IT specialist to specify the mechanism for sending out the individual CellProfiler processes to the computing cluster nodes. Please refer to our GitHub webpage https://github. com/CellProfiler/CellProfiler/wiki/Adapting-CellProfiler-to-a-LIMS-environment for more details.
Press the 'Analyze Images' button at the bottom-left of the interface. A progress bar in the bottom-right will indicate the estimated time of completion. 7. Visualize the output from the CellProfiler QC pipeline; our recommendation is to use KNIME, an open-source data analytics tool. a. Download and install the current version of KNIME (https://www. knime. com/downloads) b. Within the KNIME client, install the HCS Tools extension (https://www. knime. com/community/hcs-tools) c. Download the KNIME workflow JUMP_QC_Plate-CV_v1. knwf from https://github. com/broadinstitute/imaging-platform-pipelines/tree/master/JUMP_production 8. Run KNIME and load KNIME workflow a. Right-click the CSV Reader Node "Top Line Per-Image", select "Configure…", and set the File path to the TopLineImage. csv output from the CellProfiler workflow b.
no. 50128822) or Liconic Standard desktop computer. Access to a remote-host computing cluster or cloud-computing platform (optional; recommended if planning to acquire >1,000 fields of view) • CellProfiler biological image analysis software 20. Available at http://www. cellprofiler. org • CellProfiler pipelines: we describe four pipelines in this protocol for illumination correction, segmentation, QC (optional), and feature extraction. The pipelines are available at https://github. com/broadinstitute/imaging-platform-pipelines/tree/master/JUMP_production and were created using CellProfiler 4. 1. 3. Please see the module notes within the pipelines for Cell Painting-specific documentation. Our Cell Painting wiki (broad. io/CellPaintingWiki) contains a static copy of all files used in the protocol, as well as updates to these files (e.
g. , to accommodate updated software versions or updated versions of the protocol). • Raw image data stored in the Cell Painting Gallery on the Registry of Open Data on AWS (https://registry. opendata. aws/cellpainting-gallery/) from chemical perturbations applied to U2OS cells. See download A. csv of per-well profiles generated by running the profiling script, created from the provided sample images. See download information at https://github. com/carpenterlab/2022_Cimini_NatureProtocols/blob/main/README. md. • A Python 21 or Conda installation. • (Optional) KNIME data analytics software 22 with HCS Tools extension 23 and sample workflow (See Box 2 for details). • (Optional) A GUI based SQLite reader, such as DB Browser for SQLite ( https://sqlitebrowser. org/) • (Optional) Plate reader software, such as PerkinElmer EnVision Manager • (Optional) Data analysis software, such as PRISM from GraphPad Software, LLC product from PerkinElmer (cat.
Acknowledgements The authors appreciate the more than 100 scientists who have contributed to the organization and scientific direction of the  JUMP Cell Painting Consortium. The authors thank the original developers of earlier versions of the protocol, who contributed to prior papers describing the assay 3,4 ; those who are not also authors on this paper are:  M. A Bray ,  H. Han ,  C. T. Davis ,  B. Borgeson ,  C. Hartland ,  S. M. Gustafsdottir ,  C. C. Gibson ,  V. Ljosa ,  K. L. Sokolnicki ,  J. A. Wilson ,  D. Walpita ,  M. M. Kemp ,  K. Petri Seiler ,  H. A. Carrel ,  T. R. Golub ,  S. L. Schreiber ,  P. A. Clemons , and  A. F. Shamji.
Funding Sources The authors gratefully acknowledge a grant from the Massachusetts Life Sciences Center Bits to Bytes Capital Call program for funding the data production. We appreciate funding to support data analysis and interpretation from members of the JUMP Cell Painting Consortium and from the National Institutes of Health (NIH MIRA R35 GM122547 to AEC). We would like to acknowledge the Consortium's Supporting Partner PerkinElmer for providing an in-kind contribution of the PhenoVue TM Cell Painting JUMP kit. The authors also gratefully acknowledge the use of the PerkinElmer Opera Phenix High-Content/High-Throughput imaging system at the Broad Institute, funded by the S10 Grant NIH OD-026839-01. HSA was supported by a postdoctoral scholarship from the Knut and Alice Wallenberg Foundation.
6. 6 Press the 'Analyze Images' button at the bottom-left of the interface. A progress bar in the bottom-right will indicate the estimated time of completion. 7. Visualize the output from the CellProfiler QC pipeline; our recommendation is to use KNIME, an open-source data analytics tool. a. Download and install the current version of KNIME (https://www. knime. com/downloads) b. Within the KNIME client, install the HCS Tools extension (https://www. knime. com/community/hcs-tools) c. Download the KNIME workflow JUMP_QC_Plate-CV_v1. knwf from https://github. com/broadinstitute/imaging-platform-pipelines/tree/master/JUMP_production 8. Run KNIME and load KNIME workflow a. Right-click the CSV Reader Node "Top Line Per-Image", select "Configure…", and set the File path to the TopLineImage. csv output from the CellProfiler workflow b.
no. T9284) Instruments: Liconic incubator, Model: STX-220 HR (with stacker and rotating plate carousel) or Thermo Heracell VIOS 160i CO2 Incubator at 37 °C, 5% CO2 (ThermoFisher Scientific, Model Vios 160i) • Automated liquid handler: Multidrop Combi Reagent Dispenser (Thermo Fisher Scientific, cat. no. 5840300) or Freedom EVO with 384-channel arm (Tecan, cat no. MCA384) or Janus (PerkinElmer, Model JANUSMPD). Pressurized valve dispensing systems (GNF WDII & OTD) are more consistent than peristaltic (Combi/EL406). information at https://github. com/carpenterlab/2022_Cimini_NatureProtocols/blob/main/README. md. Note that this plate of images is much larger than we recommend running on a standard desktop computer. To use this data on a local computer we recommend filtering down the images in CellProfiler to a single row, for example.