Link cellular objects to functions through associations between proteins, cell structure, proximity, and transcriptomics
ID: B2AI_USECASE:16
Name: Link cellular objects to functions through associations between proteins, cell structure proximity, and transcriptomics.
Description: As per Qin et al. (2021) Nature (https://doi.org/10.1038/s41586-021-04115-9), imaging data and biophysical association data may be combined to develop measurements of protein distance within subcellular systems. This use case builds on that strategy by adding a third component: measurement of transcript changes under perturbation conditions for each protein. For the CM4AI GC, this process involves evidence graphs. The result here is not a full subcellular map, but rather the integrated data necessary to assemble such a map.
Category: integration
Involved in: Experimental Design, Metadata Management, Quality Control
Data Topics:
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B2AI_TOPIC:19 (Microscale Imaging)
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B2AI_TOPIC:28 (Proteome)
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B2AI_TOPIC:27 (Protein Structure Model)
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B2AI_TOPIC:34 (Transcriptome)
Enables:
- B2AI_USECASE:24 (Develop multi-scale maps of human cell architecture.)
Enabled by:
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B2AI_USECASE:10 (Obtain molecular proximity observations from microscopy images of human cells.)
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B2AI_USECASE:11 (Obtain proteome data from human cell samples.)
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B2AI_USECASE:12 (Obtain transcriptome data from human cell populations perturbed through CRISPR-driven mutagenesis.)
Relevant to GCs:
- B2AI_ORG:116
Standards and Tools:
Contributor: Harry Caufield (ORCID:0000-0001-5705-7831)