Cell Maps for Artificial Intelligence - Data Release

Version 1.2

Clark, Timothy; Schaffer, Leah; Obernier, K; Al Manir, Sadnan; Churas, Christopher; Dailamy, A; Doctor, Y; Forget, A; Hansen, JN; Hu, M; Lenkiewicz, J; Levinson, MA; Marquez, C; Mohan, J; Nourreddine, S; Niestroy, J; Pratt, D; Qian, G; Thaker, S; Belisle-Pipon, J-C; Brandt, C; Chen, J; Ding, Y; Fodeh, S; Krogan, N; Lundberg, E; Mali, P; Payne-Foster, P; Ratcliffe, S; Ravitsky, V; Sali, A; Schulz, W; Ideker, T, 2024, "Cell Maps for Artificial Intelligence - Data Release", https://doi.org/10.18130/V3/DXWOS5, University of Virginia Dataverse, V1

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Dataset Description	This collection is the 0.5 alpha data release of the the Cell Maps for Artificial Intelligence (CM4AI) Functional Genomics Data Generation Project, a component of the U.S. National Institute of Health’s (NIH) Bridge2AI program. CM4AI’s objective is to deliver machine-readable hierarchical maps of cell architecture as AI-Ready data produced from multimodal interrogation of 100 chromatin modifiers and 100 metabolic enzymes involved in cancer, neuropsychiatric, and cardiac disorders in disease-relevant cell lines under perturbed and unperturbed conditions, utilizing state-of-the-art mass spectrometry based proteomics, spatial proteomics / cell imaging, and genetic perturbations using CRISPR. CM4AI input data streams are generated using immunofluorescence (IF) subcellular microscopy for spatial proteomics data; affinity purification mass spectroscopy (AP-MS) and size exclusion mass spectroscopy (SEC-MS) methods for protein-protein interaction (PPI) data; and single-cell CRISPR-Cas perturbation screens by cell type. Input data streams are integrated via the multi-scale integrated cell (MuSIC) software pipeline employing deep learning models and community detection algorithms2, and output cell maps are packaged with provenance graphs and rich metadata as AI-Ready datasets in RO-Crate format using an extended, client-server version of the FAIRSCAPE framework. This data is Copyright (c) 2024 The Regents of the University of California except where otherwise noted. Spatial proteomics raw image data is copyright (c) 2024 The Board of Trustees of the Leland Stanford Junior University. It is licensed for reuse under Creative Commons Attribution ShareAlike NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/). Attribution is required to the copyright holders and the authors. Any publications referencing this data or derived products. should cite the related article as well as directly citing this data collection.
Subject	Medicine, Health and Life Sciences
Related Publication	Clark T, Schaffer L, Obernier K, Al Manir S, Churas CP, Dailamy A, Doctor Y, Forget A, Hansen JN, Hu M, Lenkiewicz J, Levinson MA, Marquez C, Mohan J, Nourreddine S, Niestroy J, Pratt D, Qian G, Thaker S, Belisle-Pipon J-C, Brandt C, Chen J, Ding Y, Fodeh S, Krogan N, Lundberg E, Mali P, Payne-Foster P, Ratcliffe S, Ravitsky V, Sali A, Schulz W, Ideker T. Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines. BioRXiv 2024.
License/Data Use Agreement	CC BY-NC-SA 4.0

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Citation Metadata

Persistent Identifier	doi:10.18130/V3/DXWOS5
Publication Date	2024-05-11
Title	Cell Maps for Artificial Intelligence - Data Release
Author	Clark, Timothy (University of Virginia) - ORCID: 0000-0003-4060-7360 Schaffer, Leah (University of California, San Diego) - ORCID: 0000-0001-6339-9141 Obernier, K (University of California, San Francisco) - ORCID: 0000-0002-4025-1299 Al Manir, Sadnan (University of Virginia) - ORCID: 0000-0003-4647-3877 Churas, Christopher (University of California, San Diego) - ORCID: 0000-0001-9998-705X Dailamy, A (University of California, San Diego) - ORCID: 0000-0002-6711-8260 Doctor, Y (University of California, San Diego) - ORCID: 0009-0009-0483-7506 Forget, A (University of California, San Francisco) - ORCID: 0000-0003-0223-0312 Hansen, JN (Stanford University) - ORCID: 0000-0002-4650-9094 Hu, M (University of California, San Diego) - ORCID: 0000-0002-1571-8029 Lenkiewicz, J (University of California, San Diego) - ORCID: 0000-0001-7252-8638 Levinson, MA (University of Virginia) - ORCID: 0000-0003-0384-8499 Marquez, C (University of California, San Diego) - ORCID: 0000-0003-3960-420X Mohan, J (University of California, San Diego) - ORCID: 0000-0003-4535-3486 Nourreddine, S (University of California, San Diego) - ORCID: 0000-0003-3881-7588 Niestroy, J (University of Virginia) - ORCID: 0000-0002-1103-3882 Pratt, D (University of California, San Deigo) - ORCID: 0000-0002-1471-9513 Qian, G (University of California, San Deigo) - ORCID: 0009-0005-4217-2745 Thaker, S (University of Alabama at Birmingham) - ORCID: 0000-0001-6730-2773 Belisle-Pipon, J-C (Simon Fraser University) - ORCID: 0000-0002-8965-8153 Brandt, C (Yale University) - ORCID: 0000-0001-8179-1796 Chen, J (University of Alabama at Birmingham) - ORCID: 0000-0002-6112-415X Ding, Y (University of Texas at Austin) - ORCID: 0000-0003-2567-2009 Fodeh, S (Yale University) - ORCID: 0000-0003-4664-3143 Krogan, N (University of California, San Francisco) - ORCID: 0000-0003-4902-337X Lundberg, E (Stanford University) - ORCID: 0000-0001-7034-0850 Mali, P (University of California, San Deigo) - ORCID: 0000-0002-3383-1287 Payne-Foster, P (University of Alabama) - ORCID: 0000-0002-3508-3577 Ratcliffe, S (University of Virginia) - ORCID: 0000-0002-6644-8284 Ravitsky, V (University of Montreal) - ORCID: 0000-0002-7080-8801 Sali, A (University of California, San Deigo) - ORCID: 0000-0003-0435-6197 Schulz, W (Yale University) - ORCID: 0000-0002-2048-4028 Ideker, T (University of California, San Deigo) - ORCID: 0000-0002-1708-8454
Point of Contact	Use email button above to contact. Clark, Timothy (University of Virginia)
Dataset Description	This collection is the 0.5 alpha data release of the the Cell Maps for Artificial Intelligence (CM4AI) Functional Genomics Data Generation Project, a component of the U.S. National Institute of Health’s (NIH) Bridge2AI program. CM4AI’s objective is to deliver machine-readable hierarchical maps of cell architecture as AI-Ready data produced from multimodal interrogation of 100 chromatin modifiers and 100 metabolic enzymes involved in cancer, neuropsychiatric, and cardiac disorders in disease-relevant cell lines under perturbed and unperturbed conditions, utilizing state-of-the-art mass spectrometry based proteomics, spatial proteomics / cell imaging, and genetic perturbations using CRISPR. CM4AI input data streams are generated using immunofluorescence (IF) subcellular microscopy for spatial proteomics data; affinity purification mass spectroscopy (AP-MS) and size exclusion mass spectroscopy (SEC-MS) methods for protein-protein interaction (PPI) data; and single-cell CRISPR-Cas perturbation screens by cell type. Input data streams are integrated via the multi-scale integrated cell (MuSIC) software pipeline employing deep learning models and community detection algorithms2, and output cell maps are packaged with provenance graphs and rich metadata as AI-Ready datasets in RO-Crate format using an extended, client-server version of the FAIRSCAPE framework. This data is Copyright (c) 2024 The Regents of the University of California except where otherwise noted. Spatial proteomics raw image data is copyright (c) 2024 The Board of Trustees of the Leland Stanford Junior University. It is licensed for reuse under Creative Commons Attribution ShareAlike NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/). Attribution is required to the copyright holders and the authors. Any publications referencing this data or derived products. should cite the related article as well as directly citing this data collection.
Subject	Medicine, Health and Life Sciences
Related Publication	Clark T, Schaffer L, Obernier K, Al Manir S, Churas CP, Dailamy A, Doctor Y, Forget A, Hansen JN, Hu M, Lenkiewicz J, Levinson MA, Marquez C, Mohan J, Nourreddine S, Niestroy J, Pratt D, Qian G, Thaker S, Belisle-Pipon J-C, Brandt C, Chen J, Ding Y, Fodeh S, Krogan N, Lundberg E, Mali P, Payne-Foster P, Ratcliffe S, Ravitsky V, Sali A, Schulz W, Ideker T. Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines. BioRXiv 2024.
Language	English
Data Creation Date	2024-05-10
Funding Information	NIH: https://reporter.nih.gov/project-details/10473403
Depositor	Levinson, Maxwell
Deposit Date	2024-05-10

Life Sciences Metadata

Factor Type	Organism
Organism	Homo sapiens
Cell Type	MDA-MB-468 breast cancer cell lines; NIH-iPCS-1 KOLF2.1J induced pluripotent stem cells

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