# CM4AI Dataset Documentation

Datasheet for Dataset - Human Readable Format

🎯

## Motivation

Why was the dataset created?

Purposes

Description | ID | Name
--- | --- | ---
Deliver machine-readable hierarchical maps of cell architecture as AI-Ready data from multimodal int... | purpose-001 | AI-Ready Cell Architecture Maps for Biomedical AI
Address the grand challenge of interpretable genotype-phenotype learning in genomics and precision m... | purpose-002 | Interpretable Genotype-Phenotype Learning
Establish standards, best practices, and guidelines for ethical AI-readiness in biomedical data. Thi... | purpose-003 | Establishing AI-Readiness Standards for Biomedical Data

Funders

- ID
    funder-001
  Name
    NIH Common Fund Bridge2AI Program
  Description
    Funded through National Institutes of Health grant 1OT2OD032742-01 (Bridge2AI Functional Genomics) and 5U54HG012513-02 (Bridge2AI Bridge Center), administered by NIH Office of the Director. Opportunity Number: OTA-21-008. Project dates: September 1, 2022 to August 31, 2026. FY 2025 funding: $5,289,382 (Direct: $4,632,095, Indirect: $657,287). Additional funding from the Frederick Thomas Fund of the University of Virginia.

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## Composition

What do the instances represent?

Instances

Description | ID | Instance Type | Name
--- | --- | --- | ---
Triple negative breast cancer cell line (RRID:CVCL_0419) established from a metastatic site pleural ... | instance-001 | Cultured cell line from de-identified human tissue, ethically sourced from ATCC | MDA-MB-468 Breast Cancer Cell Line
Human induced pluripotent stem cell (iPSC) line (RRID:CVCL_B5P3) derived from a healthy male Norther... | instance-002 | Cultured cell line from de-identified human tissue, ethically sourced from HipSci | KOLF2.1J Induced Pluripotent Stem Cells
Near-comprehensive set of chromatin regulators encoded by the human genome analyzed via AP-MS, SEC-M... | instance-003 | Protein targets for multi-modal analysis | 100 Chromatin Regulators
Set of metabolic enzymes involved in cancer, neuropsychiatric, and cardiac disorders analyzed via mu... | instance-004 | Protein targets for multi-modal analysis | 100 Metabolic Enzymes

Subpopulations

Description | ID | Name
--- | --- | ---
MDA-MB-468 breast cancer cells in control/untreated condition | subpop-001 | MDA-MB-468 Untreated
MDA-MB-468 breast cancer cells treated with paclitaxel chemotherapy | subpop-002 | MDA-MB-468 Paclitaxel-Treated
MDA-MB-468 breast cancer cells treated with vorinostat chemotherapy | subpop-003 | MDA-MB-468 Vorinostat-Treated
KOLF2.1J induced pluripotent stem cells in undifferentiated/naive state | subpop-004 | KOLF2.1J Undifferentiated iPSCs
KOLF2.1J iPSCs differentiated into neurons | subpop-005 | KOLF2.1J iPSC-Derived Neurons
KOLF2.1J iPSCs differentiated into neural progenitor cells | subpop-006 | KOLF2.1J iPSC-Derived Neural Progenitor Cells (NPCs)
KOLF2.1J iPSCs differentiated into cardiomyocytes | subpop-007 | KOLF2.1J iPSC-Derived Cardiomyocytes

Distribution Formats

Access Urls | Description | ID | Name
--- | --- | --- | ---
https://doi.org/10.18130/V3/DXWOS5, https://doi.org/10.18130/V3/B35XWX, ... (+2 more) | All CM4AI output data packaged as Research Object Crate (RO-Crate) packages containing datasets, met... | format-001 | RO-Crate Packages with Provenance
MassIVE Repository (human iPSCs), MassIVE Repository (human cancer cells) | Mass spectrometry data deposited to MassIVE Repository (Proteomics community-supported repository). ... | format-002 | Mass Spectrometry Data in MassIVE
NCBI BioProject, Sequence Read Archive (SRA) | Raw sequence data from CRISPR perturbation screens deposited to NCBI BioProject/Sequence Read Archiv... | format-003 | Sequence Data in NCBI SRA
https://www.ndexbio.org | Cell maps shared via Network Data Exchange (NDEx) for visualization and access. Maps can be visualiz... | format-004 | Hierarchical Cell Maps in NDEx
https://doi.org/10.18130/V3/DXWOS5, LibraData University of Virginia | Archived RO-Crates available in University of Virginia's LibraData data archive (instance of Harvard... | format-005 | University of Virginia Dataverse

🔍

## Collection Process

How was the data acquired?

ID *
https://doi.org/10.18130/V3/DXWOS5
Name
CM4AI
Title
Cell Maps for Artificial Intelligence (CM4AI)
Description
CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institutes of Health's (NIH) Bridge to Artificial Intelligence (Bridge2AI) program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research. The project delivers 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. Data streams include immunofluorescence (IF) subcellular microscopy for spatial proteomics, affinity purification mass spectroscopy (AP-MS) and size exclusion mass spectroscopy (SEC-MS) 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 algorithms, and output cell maps are packaged with provenance graphs and rich metadata as AI-Ready datasets in RO-Crate format using the FAIRSCAPE framework.
Page
https://www.cm4ai.org
Language
en
Keywords

- Cell Maps
- Artificial Intelligence
- AI-Ready Data
- Bridge2AI
- Functional Genomics
- Protein-Protein Interactions
- Spatial Proteomics
- CRISPR Perturbation
- Hierarchical Cell Maps
- MDA-MB-468
- KOLF2.1J
- iPSC
- Breast Cancer
- Chromatin Modifiers
- Metabolic Enzymes
- Immunofluorescence
- Mass Spectrometry
- AP-MS
- SEC-MS
- Perturb-Seq
- FAIR Principles
- RO-Crate
- FAIRSCAPE
- Visible Neural Networks
- Deep Learning

Addressing Gaps

Description | ID | Name
--- | --- | ---
Address the limitation that machine learning models in genomics and precision medicine are typically... | gap-001 | Black Box AI Models in Genomic Medicine
Provide fully provenanced, ethically validated, and FAIR-compliant AI-ready datasets with machine-re... | gap-002 | Lack of AI-Ready Biomedical Datasets with Provenance
Create integrated datasets combining protein localization (spatial proteomics), protein-protein inte... | gap-003 | Integration of Multimodal Cellular Data

Creators

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Contributor | Trey Ideker | creator-001 | -
Contributor | Jean-Christophe Bélisle-Pipon | creator-002 | -
Contributor | Timothy Clark | creator-003 | -
Contributor | Jake Yue Chen | creator-004 | -
Contributor | Nevan J Krogan | creator-005 | -
Contributor | Emma Lundberg | creator-006 | -
Contributor | Prashant Mali | creator-007 | -
Contributor | Sarah J Ratcliffe | creator-008 | -
Contributor | Vardit Ravitsky | creator-009 | -
Contributor | Andrej Sali | creator-010 | -
Contributor | Wade Loren Schulz | creator-011 | -
Contributor | Ying Ding | creator-012 | -
Contributor | Samah Fodeh | creator-013 | -
Contributor | Cynthia Brandt | creator-014 | -
Contributor | Pamela Payne-Foster | creator-015 | -

Subsets

Description | ID | Name
--- | --- | ---
Immunofluorescence-based staining (ICC-IF) and confocal microscopy images displaying spatial localiz... | subset-001 | Spatial Proteomics IF Images - MDA-MB-468
Affinity purification mass spectrometry (AP-MS) data on endogenously tagged cell lines mapping prote... | subset-002 | Protein-Protein Interaction AP-MS Data
Size exclusion chromatography coupled to mass spectrometry (SEC-MS) for proteome-wide complex/ inter... | subset-003 | Protein-Protein Interaction SEC-MS Data
Genome-scale CRISPRi perturbation cell atlas in undifferentiated KOLF2.1J human induced pluripotent ... | subset-004 | CRISPR Perturbation Cell Atlas
Integrated hierarchical cell maps produced by Multi-Scale Integrated Cell (MuSIC) pipeline from fusi... | subset-005 | Hierarchical Cell Maps via MuSIC

Sampling Strategies

1. ID
     sampling-001
   Name
     Disease-Relevant Cell Line Selection
   Description
     Purposive selection of two disease-relevant cell lines: MDA-MB-468 triple negative breast cancer cell line for cancer research, and KOLF2.1J iPSCs for neuropsychiatric and cardiac disorder research. Both cell lines ethically sourced and well-characterized in the literature.
   Is Sample
     - False
   Is Random
     - False
   Is Representative
     - False
   Strategies
     - Selection of commercially available, ethically sourced cell lines
     - MDA-MB-468 chosen for triple-negative breast cancer research applications
     - KOLF2.1J chosen as reference iPSC line for large-scale collaborative studies
     - Both cell lines have extensive existing characterization data

Collection Mechanisms

Description | ID | Name
--- | --- | ---
Automated fixation and permeabilization protocols using pipetting robot for MDA-MB-468 and KOLF2.1J ... | collection-001 | Immunofluorescence Spatial Proteomics Imaging
Endogenous tagging of genes in cell lines followed by affinity purification mass spectrometry (AP-MS... | collection-002 | Affinity Purification Mass Spectrometry
Size exclusion chromatography coupled to mass spectrometry (SEC-MS) for proteome-wide complex/ inter... | collection-003 | Size Exclusion Chromatography Mass Spectrometry
Single-cell CRISPR screens using CRISPR lentiviral library targeting 100 chromatin factors with 6 gu... | collection-004 | CRISPR Perturbation Screens

Acquisition Methods

Description | ID | Name
--- | --- | ---
High-resolution confocal microscopy of immunofluorescence-stained cells capturing four channels: DAP... | acquisition-001 | Confocal Microscopy for Subcellular Imaging
State-of-the-art mass spectrometry-based proteomics including AP-MS on endogenously tagged cell line... | acquisition-002 | Mass Spectrometry for Protein Interactions
Single-cell RNA sequencing using 10x Genomics 3'HT kit to capture transcriptional states following C... | acquisition-003 | Single-Cell RNA Sequencing for Perturbation Mapping
Multi-Scale Integrated Cell (MuSIC) pipeline integrates PPI embeddings and image embeddings using co... | acquisition-004 | MuSIC Pipeline Integration

Preprocessing Strategies

Description | ID | Name | Preprocessing Details
--- | --- | --- | ---
PPI networks processed using node2vec deep learning model to reduce dimensionality and produce PPI e... | preproc-001 | Deep Learning Embedding Generation | node2vec deep learning for PPI network dimensionality reduction, Human Protein Atlas deep learning model for image embedding, ... (+2 more)
Community detection performed on co-embedding space using multiscale community detection algorithms ... | preproc-002 | Hierarchical Community Detection | All-by-all similarity computation in co-embedding space, Multiscale community detection using Cytoscape algorithms, ... (+2 more)
Two-pronged annotation approach: (1) Alignment to known protein function and pathway resources inclu... | preproc-003 | Cell Map Annotation | Alignment to Gene Ontology for functional annotation, Alignment to Reactome pathways for pathway annotation, ... (+2 more)
Standardized automated fixation and permeabilization protocols using pipetting robot. Consistent sta... | preproc-004 | Quality Control for Imaging Data | Automated protocols for consistency, Standardized staining across all conditions, ... (+2 more)
Quality control and validation of AP-MS and SEC-MS data. Data currently undergoing quality checks be... | preproc-005 | Quality Control for Mass Spectrometry Data | QC procedures for AP-MS data, QC procedures for SEC-MS data, ... (+2 more)

Cleaning Strategies

Cleaning Details | Description | ID | Name
--- | --- | --- | ---
RO-Crate packaging with metadata and provenance, JSON-Schema validation of all datasets, ... (+3 more) | All datasets packaged using FAIRSCAPE framework which creates RO-Crate packages with datasets, metad... | cleaning-001 | FAIRSCAPE AI-Readiness Packaging
GO mapping for protein functions, Reactome mapping for pathways, ... (+3 more) | Data mapped to applicable standards and ontologies including Gene Ontology (GO), Reactome, Protein D... | cleaning-002 | Data Standard Mapping
PDB structural information integration, AlphaFoldDB structure integration, ... (+3 more) | Bioinformatics pipeline developed for annotating MuSIC communities with available structural informa... | cleaning-003 | Integrative Structure Modeling Annotation

Maintainers

1. ID
     maintainer-001
   Name
     CM4AI Consortium
   Description
     Multidisciplinary consortium managing dataset maintenance including University of California San Diego (lead), University of California San Francisco, Stanford University, University of Virginia, Yale University, University of Alabama at Birmingham, Simon Fraser University, and The Hastings Center. Data Governance Committee led by Jillian Parker. Ethical Review by Vardit Ravitsky and Jean-Christophe Belisle-Pipon.
   Maintainer Details
     - University of California San Diego (lead institution, Ideker Lab)
     - UCSF (Krogan Lab - protein interactions, Sali Lab - structure modeling)
     - Stanford University (Lundberg Lab - spatial proteomics)
     - University of Virginia (Clark Lab - standards and FAIRSCAPE)
     - Yale University (Schulz Lab - workforce development)
     - University of Alabama at Birmingham (Chen Lab - teaming, U-BRITE platform)
     - Simon Fraser University (Bélisle-Pipon - ethics)
     - The Hastings Center (Ravitsky - ethics)
     - Data Governance Committee (Jillian Parker)

Retention Limit

ID
  retention-001
Name
  Long-Term Preservation Plan
Description
  Digital data maintained according to NIH data sharing policies with long-term preservation in University of Virginia's LibraData repository supported by committed institutional funds. No planned sunset for data availability. Archived RO-Crates with persistent identifiers (ARK, future DOIs) ensure long-term accessibility and citability.
Retention Details
  - NIH data sharing policy compliance
  - UVA Dataverse institutional commitment
  - Persistent identifiers (ARK, future DOIs)
  - No planned data sunset
  - Machine-readable metadata for long-term discoverability

Sensitive Elements

1. ID
     sensitive-001
   Name
     Cell Line Origin Metadata
   Description
     While cell lines are de-identified and cannot be matched to specific individuals, metadata about cell line origins (age, sex, race of original donor) is retained for scientific context. This metadata does not constitute identifiable human subjects data under current knowledge.
   Sensitive Elements Present
     False
   Sensitivity Details
     - De-identified commercial cell lines
     - Donor demographic metadata for scientific context only
     - Cannot be matched to individuals with current knowledge
     - Ethically sourced from ATCC and HipSci

External Resources

Description | External Resources | ID | Name
--- | --- | --- | ---
Official project website and data portal using U-BRITE platform | https://www.cm4ai.org | resource-001 | CM4AI Project Website
Federal grant information and project details for Bridge2AI Functional Genomics | https://reporter.nih.gov/project-details/11211616 | resource-002 | NIH RePORTER Project Details
LibraData repository with archived RO-Crates and data releases | https://doi.org/10.18130/V3/DXWOS5, https://doi.org/10.18130/V3/B35XWX, https://doi.org/10.18130/V3/F3TD5R | resource-003 | University of Virginia Dataverse
Clark T, et al. Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from... | https://doi.org/10.1101/2024.05.21.589311 | resource-004 | bioRxiv Publication
AI-readiness framework documentation, tutorial, and installation instructions | https://fairscape.github.io | resource-005 | FAIRSCAPE Framework Documentation
Open source IMP package for integrative structure modeling | http://integrativemodeling.org | resource-006 | Integrative Modeling Platform
Repository and visualization platform for cell maps and networks | https://www.ndexbio.org | resource-007 | Network Data Exchange (NDEx)
Parent NIH Common Fund program supporting AI-ready biomedical datasets | https://commonfund.nih.gov/bridge2ai | resource-008 | Bridge2AI Program
Collaboration partner for data curation and integration | https://www.nih-cfde.org | resource-009 | NIH Common Fund Data Ecosystem (CFDE)
Mass spectrometry data repository for iPSC and cancer cell data | MassIVE Repository | resource-010 | MassIVE Proteomics Repository
Repository for CRISPR perturbation screen raw sequence data | NCBI BioProject, Sequence Read Archive (SRA) | resource-011 | NCBI Sequence Read Archive
Nourreddine S, et al. A PERTURBATION CELL ATLAS OF HUMAN INDUCED PLURIPOTENT STEM CELLS. bioRxiv. 20... | https://doi.org/10.1101/2024.11.03.621734 | resource-012 | Perturbation Cell Atlas Publication

🚀

## Uses

What (other) tasks could the dataset be used for?

Tasks

Description | ID | Name
--- | --- | ---
Integrate multimodal data streams (spatial proteomics via IF imaging, protein-protein interactions v... | task-001 | Multi-Scale Cell Mapping via MuSIC Pipeline
Enable development of visible neural networks (VNNs) and visible machine learning tools that use hie... | task-002 | Visible Neural Network Development
Characterize cell architecture and protein interactions in disease-relevant cell lines including tre... | task-003 | Disease-Relevant Cell Line Characterization
Develop ethical AI frameworks and governance structures for biomedical data, including Value-Sensiti... | task-004 | Ethical AI Framework Development

Intended Uses

Description | ID | Name
--- | --- | ---
Primary intended use is training and development of artificial intelligence and machine learning mod... | use-001 | AI Model Training for Functional Genomics
Development of visible neural networks (VNNs) that use hierarchical cell maps as interpretable model... | use-002 | Visible Neural Network Development
Research into interpretable genotype-phenotype learning using multi-scale cell maps. Enables underst... | use-003 | Genotype-Phenotype Mapping Research
Analysis of cellular responses to drug treatments (paclitaxel, vorinostat) to predict drug response ... | use-004 | Drug Response and Synergy Prediction
Study of disease mechanisms in cancer, neuropsychiatric disorders, and cardiac disorders through ana... | use-005 | Disease Mechanism Research
Use as exemplar for future AI-ready biomedical dataset development, demonstrating best practices in ... | use-006 | Model for AI-Ready Biomedical Dataset Development

Discouraged Uses

Description | ID | Name
--- | --- | ---
Laboratory data from cell lines are not to be used in clinical decision-making or any context involv... | discouraged-001 | Clinical Decision-Making Without Validation
This is an interim/beta release with data not yet in completed final form. Some datasets are under t... | discouraged-002 | Use During Incomplete Data Release
Datasets require domain expertise for meaningful analysis and interpretation. Not suitable for use w... | discouraged-003 | Analysis Without Domain Expertise

License And Use Terms

ID
  license-001
Name
  Creative Commons Attribution Non-Commercial Share-Alike
Description
  Data licensed for reuse under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (https://creativecommons.org/licenses/by-nc-sa/4.0/). Attribution is required to the copyright holders and the Cell Maps for Artificial Intelligence project. Any publications referencing this data or derived products should cite the bioRxiv article (Clark T, et al. Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines. BioRXiv, May 2024. doi:10.1101/2024.05.21.589311) and directly cite the data collection. Commercial use requires separate license negotiation with copyright holder (UCSD, Stanford, and/or UCSF depending upon specific data package). A Data Access Committee will supervise ethical matters related to dataset distribution and potential dual licensing for commercial use. Copyright (c) 2025 The Regents of the University of California except where otherwise noted. Spatial proteomics raw image data is copyright (c) 2025 The Board of Trustees of the Leland Stanford Junior University.
License Terms
  - Attribution required to copyright holders and authors
  - Non-commercial use only (commercial requires separate license)
  - Share-alike - derivative works must use same license
  - Must cite bioRxiv publication and data collection DOI
  - Data Access Committee oversight for ethical distribution

📤

## Distribution

How will the dataset be distributed?

License
CC BY-NC-SA 4.0
🔄

## Maintenance

How will the dataset be maintained?

Updates

ID
  updates-001
Name
  Quarterly Data Releases and Maintenance Plan
Description
  Dataset regularly updated and augmented through end of project in November 2026. Beta releases on quarterly basis with periodic data augmentation. Initial alpha release (v0.5) provided as supplemental data. March 2025 Beta (V1.4) includes perturb-seq in KOLF2.1J iPSCs, SEC-MS in iPSCs and derivatives, and IF images in MDA-MB-468 under three conditions. June 2025 Beta (V2.1) revision adds RGB IF images, ro-crate metadata corrections, and naming convention changes. Future releases will include computed cell maps and complete integration of all data streams. Long-term preservation in University of Virginia Dataverse with committed institutional support.
Frequency
  Quarterly updates through November 2026; long-term preservation thereafter
Update Details
  - Alpha release v0.5 (supplemental data)
  - March 2025 Beta release V1.4 (doi:10.18130/V3/B35XWX)
  - June 2025 Beta release V2.1 (doi:10.18130/V3/F3TD5R)
  - Quarterly augmentation through November 2026
  - Future releases to include computed cell maps
  - Final release expected November 2026
  - Long-term preservation in UVA Dataverse

👥

## Human Subjects

Does the dataset relate to people?

Human Subject Research

ID
  hsr-001
Name
  CM4AI Non-Human Subjects Research
Description
  CM4AI data are distinctive within Bridge2AI in that they are non-clinical data from tissue cultures and are considered to be de-identified as they cannot be matched, with current knowledge, to a human subject. Both cell lines (MDA-MB-468 and KOLF2.1J) are commercially available, ethically sourced, de-identified cell lines. MDA-MB-468 available from ATCC. KOLF2.1J available from HipSci resource for non-profit organizations via simple MTA. Ethics team developed comprehensive plan for ethical preparation, licensing, dissemination, and data access supervision balancing openness with IP protection and commercialization monitoring.
Involves Human Subjects
  False
IRB Approval
  - Not applicable - de-identified cell lines from commercial sources
Ethics Review Board
  - CM4AI Ethics Module (Vardit Ravitsky, Jean-Christophe Bélisle-Pipon)
  - Data Access Committee (Jillian Parker)
  - Bridge2AI Ethics Working Group participation
Special Populations
  - MDA-MB-468 derived from 51-year-old black female (de-identified)
  - KOLF2.1J derived from healthy male Northern European donor (de-identified)

Generated on 2025-12-20 19:23:28 using Bridge2AI Data Sheets Schema