1.0.0 Cell Painting
- Phillip Clarke (Unlicensed)
- Philipp Kainz
- Bendeguz Zovathi (Unlicensed)
Application Name | Cell Painting |
|---|---|
Version | 1.0.0 |
Documentation Version | 16.10.2023 - 1 |
Input Image(s) | Multichannel Images; 5-8 channels; 8 Bit or 16 Bit |
Input Parameter(s) | Regions of interest (optional) |
Keywords | cell painting, cells, nuclei, cytoplasm, fluorescent, multichannel, in-vitro, microscopy, morphological profiling, high-throughput analysis |
Short Description | This application automatically evaluates multi-well plate multichannel images that utilize five fluorescent dyes. The cells, nuclei and cytoplasm are segmented and quantified in each image. |
References / Literature | For more information regarding Cell Painting please see: |
Table of contents
IKOSA Prisma Cell Painting Assay
You can use this image analysis application or any of our other applications in your account on the IKOSA platform. If it is not in the list of available applications, please contact your organization's administrator or our team at support@ikosa.ai.
Application description
Cell Painting is the morphological profiling of cell populations to study the phenotypic or functional impacts of perturbations. This application automatically evaluates multi-well plate multichannel images that utilize five fluorescent dyes. The cells, nuclei and cytoplasms are segmented and quantified in each image.
This analysis can be performed on multichannel images containing five- to eight channels. The first five channels must be in a specific order (see Input data requirements section).
In the following sections, we provide the input data requirements that are necessary to obtain accurate image analysis results and a description of the output files.
Input data requirements
Input image(s)
Input for this application is the following image data:
Image type | Color channels | Color depth (per channel) | Size (px) | Resolution (μm/px) |
|---|---|---|---|---|
Multichannel
Check image formats | Minimum of 5 and up to 8. The first five channels must be in the following order:
Further channels (up to 3) are supported and may be in any order.
| 8 Bit or 16 Bit | Standard images: max: 3,072 x 3,072 | typically: 0.3-1.16 |
Image content The captured high-content microscopy images provide information about various subcellular structures and compartments such as:
Optionally, up to 3 further channels can be analyzed for the same field of view.
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Important:
For all images, the following requirements apply:
The illumination must be constant throughout the image(s).
The sample must be in focus, i.e. no blurry regions in image(s).
The images must have a minimum of five channels in the following order:
Mito
AGP
RNA
ER
DNA
Input parameter(s)
No additional input parameters are required for this application.
As an optional parameter, a single or multiple regions of interest (ROIs) can be defined in which the analysis should be performed (‘inclusion ROIs’).
Description of output files and their content
Files
File format | Description |
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|---|---|---|---|
| 1 | csv | results_01_cells.csv: A csv file containing the analysis results for the segmented objects in each channel for the input image or all inclusion ROIs. Overall 1917 object features are extracted for each cellular compartment. The extracted feature groups are:
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| 2 | csv | results.csv: A csv file containing the analysis results for the input image or all inclusion ROIs. |
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| 3 | jpg | results_vis/<compartment_id>_<compartment_name>_vis.jpg (for multichannel image, no ROI), or results_vis/<compartment_id>_<compartment_name>_<roi-id>.jpg (for multichannel image, ROI <roi-id>): A visualization of the analysis result for a specific compartment (of a multichannel image) for either the whole image (if no inclusion ROIs selected for analysis) or each individual inclusion ROI. Description for visualizations:
Please note: If the objects' borders are within a 5-pixel distance from the image border, they will be excluded from segmentation and feature extraction, to prevent non-complete cell analysis. Please note: The model operates under the assumption that the cells are mononuclear.  Cell segmentation output  Nucleus segmentation output For this application the dataset “CPG0000-JUMP-pilot” was used, available on https://registry.opendata.aws/cellpainting-gallery/. |
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| 4 | json | annotation_results.json: JSON file containing detected compartment objects (covered regions). The position is measured from the left upper corner (1,1) of the image. |
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| 5 | json | roiMeta.json: A json file containing all information regarding the ROIs defined for the analysis job to ensure reproducibility. The file is empty if no ROIs were defined for analysis. |
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| 6 | jpg | rois_visualization.jpg: An overview visualization to show locations of all analyzed ROIs for the 2D image of a multichannel image. |
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| 7 | json | jobResultBundleMeta.json: A json file containing all information regarding the analysis job (application name and version, project, etc.) to ensure reproducibility. |
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Content
results_01_cells.csv
Single csv-file
Column NO. | Column name | Description |
|---|---|---|
1 | roi_id | <roi-id> starting from “ROI-01”. Empty, if no inclusion ROI is specified and the whole image was analyzed. |
2 | roi_name | Custom text to identify the ROI. Empty, if no inclusion ROI is specified and the whole image was analyzed. |
3 | roi_size [Px^2] | Size of the ROI that was analyzed in pixels^2. The size of the whole image is given if no inclusion ROI is specified and the whole image was analyzed. |
4 | object_id | Identifier number of the individual compartments. |
4 - 156 | Cells_Intensity_<feature> | Intensity feature measurements for identified objects. |
157 - 380 | Cells_Correlation_<feature> | Correlation between intensities in different channels within identified objects. |
381 - 394 | Cells_Neighbors_<feature> | Properties about the relationships of the neighboring objects. |
395 - 419 | Cells_AreaShape_<feature> | Area and shape features of the identified objects. |
420 - 449 | Cells_AreaShape_Zernike_<feature> | Distribution of intensities across the identified objects. |
450 - 577 | Cells_Granularity_<feature> | Spectra of size measurements of the textures within the identified objects. |
578 - 673 | Cells_RadialDistribution_<feature> | Spatial distribution of intensities within each identified object. |
674 - 1921 | Cells_Texture_<feature> | Degree and nature of textures within identified objects to quantify their roughness and smoothness. |
results.csv
Single csv-file
Column NO. | Column name | Examples | Value range | Description |
|---|---|---|---|---|
1 | roi_id | ROI-03 | ROI-01 - | <roi-id> starting from “ROI-01”. Empty, if no inclusion ROI is specified and the whole image was analyzed. |
2 | roi_name | “central” | text | Custom text to identify the ROI. Empty, if no inclusion ROI is specified and the whole image was analyzed. |
3 | roi_size [Px^2] | 1212212 | 1 - | Size of the ROI that was analyzed in pixels^2. The size of the whole image is given if no inclusion ROI is specified and the whole image was analyzed. |
4 | bit_depth [bit] | 8 | 8 or 16 | Number of bits used to represent the value of each pixel of the image. |
5 | number_of_channels | 6 | 5 - 8 | The count of the distinct data layers to represent the uploaded image. |
6 | Cells_total_area [Px^2] | 1011878 | 0 - #of pixels in image | Total area with adherent cells in Pixel^2, includes cell-covered area and free/floating cells). |
7 | Cells_total_area [%] | 68.6 | 0 - 100 | Ratio of total area with adherent cells to the total area of the image. |
8 | Cells_total_num_of_objects | 5 | 0 - | The total number of the complete cells on the analyzed image. |
9 | Cells_Overall_mean_intensity | 37.9136 | 0 - 65535 | The overall mean intensity for the compartments. |
10 - 17 | Cells_<channel>_mean_intensity | 10.0729816 | 0 - 65535 | The mean channel intensity for each compartment. |
Error information
More information about errors can be found in the Application Error Documentation.
Contact
If you have any questions about this app, as well as suggestions or ideas for new ones, email us at support@ikosa.ai.
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