# CM4AI Dataset Documentation

Datasheet for Dataset - Human Readable Format

🎯

## Motivation

Why was the dataset created?

Purposes

Description | ID | Name | Response
--- | --- | --- | ---
Create AI-ready maps of human cell architecture from disease-relevant cell lines as part of the NIH ... | cm4ai:purpose:ai-ready-maps | Create AI-ready cell architecture maps | CM4AI's overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred ...
Enable interpretable genotype-phenotype learning by providing visible machine learning systems infor... | cm4ai:purpose:interpretable-genotype-phenotype | Enable interpretable genotype-phenotype learning | The CM4AI project seeks to map the spatiotemporal architecture of human cells and use these maps tow...

Funders

- ID
    cm4ai:funder:nih-bridge2ai
  Name
    NIH Bridge2AI Functional Genomics
  Description
    National Institutes of Health Bridge to Artificial Intelligence program, Functional Genomics Data Generation Project. This work was funded by the National Institutes of Health under awards 1OT2OD032742-01 (Bridge2AI Functional Genomics) and 5U54HG012513-02 (Bridge2AI Bridge Center), and by the Frederick Thomas Fund of the University of Virginia. Grant number: 1OT2OD032742-01. Project period: September 1, 2022 to August 31, 2026.

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## Composition

What do the instances represent?

Instances

Description | ID | Instance Type | Name
--- | --- | --- | ---
Expressed genome-scale CRISPRi Perturbation Cell Atlas in undifferentiated KOLF2.1J human induced pl... | cm4ai:instance:crispr-perturbation | Perturb-seq single-cell RNA sequencing data | CRISPR Perturbation Cell Atlas
Size exclusion chromatography-mass spectroscopy (SEC-MS) data from undifferentiated KOLF2.1J human i... | cm4ai:instance:sec-ms-ipsc | Protein-protein interaction mass spectrometry data | SEC-MS protein-protein interactions in iPSCs
SEC-MS protein-protein interaction data from iPSC-derived NPCs (neural progenitor cells), neurons, a... | cm4ai:instance:sec-ms-differentiated | Protein-protein interaction mass spectrometry data | SEC-MS in differentiated iPSC derivatives
Spatial localization of 563 proteins of interest in untreated cells of the breast cancer cell line M... | cm4ai:instance:if-images-untreated | Immunofluorescence confocal microscopy images | Immunofluorescence images - untreated MDA-MB-468
Spatial localization of 563 proteins in MDA-MB-468 breast cancer cells treated with paclitaxel chemo... | cm4ai:instance:if-images-paclitaxel | Immunofluorescence confocal microscopy images | Immunofluorescence images - paclitaxel-treated MDA-MB-468
Spatial localization of 563 proteins in MDA-MB-468 breast cancer cells treated with vorinostat chemo... | cm4ai:instance:if-images-vorinostat | Immunofluorescence confocal microscopy images | Immunofluorescence images - vorinostat-treated MDA-MB-468
AP-MS on endogenously tagged cell lines for protein-protein interaction mapping. 17 genes endogenous... | cm4ai:instance:ap-ms | Protein-protein interaction mass spectrometry data | Affinity purification mass spectrometry
Hierarchical directed acyclic graph (DAG) maps of cell architecture produced by integrating protein ... | cm4ai:instance:cell-maps | Network data structure (hierarchical graph) | Hierarchical cell maps

Distribution Formats

Access Urls | Description | ID | Name
--- | --- | --- | ---
https://dataverse.lib.virginia.edu/dataset.xhtml?persistentId=doi:10.18130/V3/B35XWX | Datasets packaged in RO-Crate format with rich metadata and provenance graphs. | cm4ai:format:ro-crate | RO-Crate packages
https://dataverse.lib.virginia.edu | University of Virginia's LibraData (Dataverse) NIH-approved generalist repository. | cm4ai:format:dataverse | LibraData Dataverse repository
https://cm4ai.org, http://www.cm4ai.org | CM4AI project portal using U-BRITE platform for open dissemination. | cm4ai:format:portal | CM4AI web portal
https://www.ndexbio.org | Cell maps shared via NDEx for visualization in web browsers or Cytoscape. | cm4ai:format:ndex | Network Data Exchange

🔍

## Collection Process

How was the data acquired?

ID *
https://doi.org/10.18130/V3/B35XWX
Name
CM4AI
Title
Cell Maps for Artificial Intelligence - March 2025 Data Release (Beta)
Description
This dataset is the March 2025 Data Release of Cell Maps for Artificial Intelligence (CM4AI; CM4AI.org), the Functional Genomics Grand Challenge in the NIH Bridge2AI program. This Beta release includes perturb-seq data in undifferentiated KOLF2.1J iPSCs; SEC-MS data in undifferentiated KOLF2.1J iPSCs and iPSC-derived NPCs, neurons, and cardiomyocytes; and IF images in MDA-MB-468 breast cancer cells in the presence and absence of chemotherapy (vorinostat and paclitaxel). 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. CM4AI output data are packaged with provenance graphs and rich metadata as AI-ready datasets in RO-Crate format using the FAIRSCAPE framework. CM4AI is a collaboration of UCSD, UCSF, Stanford, UVA, Yale, UA Birmingham, Simon Fraser University, and the Hastings Center.
Page
https://dataverse.lib.virginia.edu/dataset.xhtml?persistentId=doi:10.18130/V3/B35XWX
Language
en
Keywords

- AI
- affinity purification
- AP-MS
- artificial intelligence
- breast cancer
- Bridge2AI
- cardiomyocyte
- CM4AI
- CRISPR/Cas9
- CRISPR/Cas perturbation screens
- functional genomics
- induced pluripotent stem cell
- iPSC
- KOLF2.1J
- machine learning
- mass spectroscopy
- MDA-MB-468
- neural progenitor cell
- NPC
- neuron
- paclitaxel
- perturb-seq
- perturbation sequencing
- protein-protein interaction
- protein localization
- single-cell RNA sequencing
- scRNAseq
- SEC-MS
- size exclusion chromatography
- subcellular imaging
- vorinostat
- RO-Crate
- FAIRSCAPE
- cell maps
- hierarchical maps
- visible machine learning
- VNN
- visible neural networks
- chromatin modifiers
- metabolic enzymes
- immunofluorescence
- IF imaging
- spatial proteomics

Addressing Gaps

Description | ID | Name | Response
--- | --- | --- | ---
Address the limitation of black-box machine learning models in genomics by creating visible neural n... | cm4ai:gap:black-box-ml | Address black-box machine learning | Machine learning models show great promise in analyzing the human genome but their inner workings ar...
Fill the gap in availability of high-quality, FAIR, AI-ready functional genomics datasets with rich ... | cm4ai:gap:fair-ai-datasets | Provide FAIR AI-ready functional genomics datasets | CM4AI addresses the need for ethical, FAIR, AI-Ready biomedical data that are fully characterized wi...

Creators

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Contributor | Trey Ideker | cm4ai:creator:ideker | -
Contributor | Timothy W Clark | cm4ai:creator:clark | -
Contributor | Nevan J Krogan | cm4ai:creator:krogan | -
Contributor | Emma Lundberg | cm4ai:creator:lundberg | -
Contributor | Prashant Mali | cm4ai:creator:mali | -
Contributor | Andrej Sali | cm4ai:creator:sali | -
Contributor | Jean-Christophe Bélisle-Pipon | cm4ai:creator:belisle-pipon | -
Contributor | Cynthia Brandt | cm4ai:creator:brandt | -
Contributor | Jake Yue Chen | cm4ai:creator:chen | -
Contributor | Ying Ding | cm4ai:creator:ding | -
Contributor | Samah Fodeh | cm4ai:creator:fodeh | -
Contributor | Pamela Payne-Foster | cm4ai:creator:payne-foster | -
Contributor | Sarah J Ratcliffe | cm4ai:creator:ratcliffe | -
Contributor | Vardit Ravitsky | cm4ai:creator:ravitsky | -
Contributor | Wade Loren Schulz | cm4ai:creator:schulz | -

Subsets

Description | ID | Name
--- | --- | ---
Expressed genome-scale CRISPRi Perturbation Cell Atlas in undifferentiated KOLF2.1J human induced pl... | cm4ai:subset:crispr-atlas | CRISPR Perturbation Cell Atlas
Size exclusion chromatography-mass spectroscopy data on undifferentiated KOLF2.1J human induced plur... | cm4ai:subset:sec-ms-ipsc | Protein-protein Interaction SEC-MS iPSCs
Spatial localization of 563 proteins in untreated MDA-MB-468 breast cancer cells imaged by ICC-IF an... | cm4ai:subset:if-untreated | Protein Localization Images - Untreated
Spatial localization of 563 proteins in MDA-MB-468 breast cancer cells treated with paclitaxel, imag... | cm4ai:subset:if-paclitaxel | Protein Localization Images - Paclitaxel
Spatial localization of 563 proteins in MDA-MB-468 breast cancer cells treated with vorinostat, imag... | cm4ai:subset:if-vorinostat | Protein Localization Images - Vorinostat

Sampling Strategies

Description | ID | Name
--- | --- | ---
Disease-relevant cell lines selected to enable AI-ready functional genomics mapping for cancer, neur... | cm4ai:sampling:cell-line-selection | Disease-relevant cell line selection
Selection of 100 chromatin regulators and 100 metabolic enzymes involved in cancer, neuropsychiatric... | cm4ai:sampling:chromatin-modifiers | Chromatin modifier selection
Selection of chemotherapy treatments (paclitaxel and vorinostat) and cell differentiation states to ... | cm4ai:sampling:perturbation-conditions | Perturbation and treatment conditions

External Resources

Description | External Resources | ID | Name
--- | --- | --- | ---
Official CM4AI project website and portal | https://cm4ai.org, https://www.cm4ai.org | cm4ai:resource:project-website | CM4AI Project Website
Key publications describing CM4AI methods and datasets | https://doi.org/10.1101/2024.05.21.589311, https://doi.org/10.1101/2024.11.03.621734 | cm4ai:resource:publications | CM4AI Publications
Platform for sharing and visualizing cell maps | https://www.ndexbio.org | cm4ai:resource:ndex | Network Data Exchange (NDEx)
AI-readiness framework for metadata and provenance | https://fairscape.github.io | cm4ai:resource:fairscape | FAIRSCAPE Framework
Software tools for cell map creation and analysis | https://github.com/idekerlab, http://integrativemodeling.org | cm4ai:resource:software-tools | CM4AI Software Tools

Acquisition Methods

Acquisition Details | Description | ID | Name | Was Directly Observed
--- | --- | --- | --- | ---
Endogenous tagging of genes in MDA-MB-468 cells, AP-MS data acquisition under three conditions (untreated, paclitaxel, vorinostat), 17 genes tagged in Year 1, 34 additional genes in process | AP-MS on endogenously tagged cell lines to map protein-protein interactions of 100 chromatin regulat... | cm4ai:acquisition:ap-ms | Affinity purification mass spectrometry | True
SEC-MS performed on MDA-MB-468 cells under three conditions, Identified 72/100 chromatin modifiers with 52 in protein complexes, ... (+3 more) | SEC-MS for proteome-wide complex and interaction mapping as orthogonal approach to AP-MS. | cm4ai:acquisition:sec-ms | Size exclusion chromatography mass spectrometry | True
Automated fixation and permeabilization protocols using pipetting robot, Four-channel staining (DAPI, calreticulin, tubulin, target protein), ... (+3 more) | IF staining and high-resolution confocal microscopy for spatial proteomics mapping of protein subcel... | cm4ai:acquisition:immunofluorescence | Immunofluorescence confocal microscopy | True
CRISPR lentiviral library targeting 100 chromatin factors with 6 guide RNAs per gene, MDA-MB-468 and KOLF2 CRISPR lines with inducible dCas9, ... (+4 more) | CRISPR-Cas9 perturbation screens for genetic perturbation mapping with single-cell RNA sequencing re... | cm4ai:acquisition:crispr-perturbation | Single-cell CRISPR perturbation screens | True

Collection Mechanisms

Description | ID | Mechanism Details | Name
--- | --- | --- | ---
Mass spectrometry instruments for AP-MS and SEC-MS protein interaction mapping. | cm4ai:mechanism:mass-spectrometry | Affinity purification coupled with mass spectrometry, Size exclusion chromatography coupled with mass spectrometry, Proteome-wide interaction profiling | Mass spectrometry instruments
High-resolution confocal microscopy for immunofluorescence imaging of protein subcellular localizati... | cm4ai:mechanism:confocal-microscopy | Confocal microscopy with four-channel imaging, Immunofluorescence-based staining (ICC-IF), Automated pipetting robot for sample preparation | Confocal microscopy
CRISPR-Cas9 gene perturbation with single-cell RNA sequencing for transcriptional and fitness phenot... | cm4ai:mechanism:crispr-cas9 | CRISPRi gene knockdown with inducible dCas9, Lentiviral library delivery, ... (+2 more) | CRISPR-Cas9 perturbation system
Multi-Scale Integrated Cell (MuSIC) pipeline for integrating multimodal data streams into hierarchic... | cm4ai:mechanism:music-pipeline | node2vec deep learning for PPI network embedding, Human Protein Atlas deep learning for image embedding, ... (+4 more) | MuSIC data integration pipeline

Data Collectors

Collector Details | Description | ID | Name
--- | --- | --- | ---
Ideker Lab - project coordination and cell map integration, Mali Lab - genetic perturbation mapping, Sali Lab - integrative structure modeling | UCSD teams led by Trey Ideker (PI), Prashant Mali, and Andrej Sali. | cm4ai:collector:ucsd | University of California San Diego
Krogan Laboratory - AP-MS and SEC-MS data generation, Protein complex mapping across conditions | UCSF teams led by Nevan Krogan for protein-protein interaction data. | cm4ai:collector:ucsf | University of California San Francisco
Lundberg Laboratory - immunofluorescence imaging, Spatial proteomics mapping with confocal microscopy, Copyright holder for raw image data | Stanford team led by Emma Lundberg for spatial proteomics. | cm4ai:collector:stanford | Stanford University
Standards Module - FAIRSCAPE framework development, AI-readiness packaging and metadata, Data repository (LibraData/Dataverse) | UVA team led by Timothy Clark for AI-readiness standards. | cm4ai:collector:uva | University of Virginia
Data curation and standards, Internship program hosting | Yale team led by Cynthia Brandt, Samah Fodeh, and Wade Schulz. | cm4ai:collector:yale | Yale University
Teaming Module - U-BRITE platform for portal, Open dissemination of data, maps, and tools, Internship program hosting | UAB team led by Jake Chen for teaming and dissemination. | cm4ai:collector:uab | University of Alabama at Birmingham

Collection Timeframes

Description | ID | Name | Timeframe Details
--- | --- | --- | ---
September 1, 2022 to August 31, 2026 with ongoing data augmentation. | cm4ai:timeframe:project-period | Overall project period | Project start September 1 2022, Project end August 31 2026, ... (+4 more)
Year 1 data generation including chromatin modifiers, spatial proteomics, and CRISPR screens. | cm4ai:timeframe:year1 | Year 1 accomplishments | 100 chromatin regulators mapped in MDA-MB-468 cells, Single-cell CRISPR screens completed, ... (+2 more)

Preprocessing Strategies

Description | ID | Name | Preprocessing Details
--- | --- | --- | ---
Generation of embeddings from PPI networks and IF images using deep learning models for dimensionali... | cm4ai:preprocessing:embedding | Deep learning embeddings | node2vec deep learning for PPI network embedding, Human Protein Atlas deep learning for image embedding, Contrastive learning for co-embedding integration
Integration of PPI and image embeddings through MuSIC pipeline to create co-embeddings and hierarchi... | cm4ai:preprocessing:integration | Multimodal data integration | Co-embedding integration with minimal information loss, Community detection based on co-embedding similarities, Hierarchy creation from multi-resolution community detection
Annotation of protein assemblies in cell maps using ontology alignment and large language models. | cm4ai:preprocessing:annotation | Cell map annotation | Alignment to Gene Ontology and Reactome pathways, LLM-based naming of protein assemblies with confidence scores, Evaluation of overlap with known cell biology
Packaging of datasets with rich metadata, provenance graphs, and schemas in RO-Crate format for AI-r... | cm4ai:preprocessing:ro-crate-packaging | RO-Crate packaging with FAIRSCAPE | FAIRSCAPE-CLI client for RO-Crate creation, JSON-Schema data dictionaries for all datasets, ... (+4 more)

Maintainers

1. ID
     cm4ai:maintainer:cm4ai-team
   Name
     CM4AI Project Team
   Description
     Multi-institutional team responsible for dataset maintenance.
   Maintainer Details
     - Point of contact Trey Ideker (University of California San Diego)
     - Data depositor Justin Niestroy (University of Virginia)
     - Data Access Committee for ethical supervision

Ethical Reviews

Description | ID | Name | Review Details
--- | --- | --- | ---
Ethics team employs Value-Sensitive Design (VSD) methodology for ethical AI development. | cm4ai:ethics:value-sensitive-design | Value-Sensitive Design framework | Conceptual work to craft axiological repository of values, Design standards and expectations for responsible AI, ... (+2 more)
Mixed empirical research combining qualitative and quantitative methods to capture community insight... | cm4ai:ethics:stakeholder-engagement | Stakeholder engagement research | Diverse perspective integration in ethical guidelines, Community insights on values and concerns, Tools for ethical, legal, and social ramifications awareness
Bridge2AI Data Sharing and Dissemination Working Group Code of Conduct for data access. | cm4ai:ethics:code-of-conduct | Bridge2AI Code of Conduct | Basic requirements for Bridge2AI Open House participants, Attestation required prior to accessing CM4AI datasets, Ethical commitments from data users

Data Protection Impacts

Description | ID | Impact Details | Name
--- | --- | --- | ---
Ethical preparation, licensing, dissemination, and data access supervision balancing openness with i... | cm4ai:protection:licensing | CC BY-NC-SA 4.0 for open non-commercial use, Commercial use requires separate licensing, ... (+2 more) | Licensing and data governance
Guidelines and best practices for ethical development of AI systems leveraging CM4AI data. | cm4ai:protection:responsible-ai | Anticipation of potential AI/ML applications, Integration into data governance frameworks, ... (+2 more) | Responsible AI development framework

🚀

## Uses

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

Tasks

Description | ID | Name | Response
--- | --- | --- | ---
Create hierarchical directed acyclic graph (DAG) maps where each node represents an assembly of prot... | cm4ai:task:cell-mapping | Hierarchical cell mapping | Cell maps are hierarchical directed acyclic graphs (DAG), where each node represents an assembly of ...
Integrate three complementary mapping approaches - proteomic mass spectrometry, cellular imaging, an... | cm4ai:task:multimodal-integration | Multimodal data integration | The project launches a coordinated effort involving three complementary mapping approaches: affinity...
Package datasets with FAIR compliance, provenance graphs, complete schemas, validation procedures, d... | cm4ai:task:ai-readiness | AI-readiness packaging | AI-Ready biomedical data are fully characterized FAIR data of known provenance, which can be ethical...

Existing Uses

Description | Examples | ID | Name
--- | --- | --- | ---
Scientific publications describing CM4AI methods and initial findings. | Clark T, et al. Cell Maps for Artificial Intelligence. bioRxiv 2024.05.21.589311, Nourreddine S, et al. A PERTURBATION CELL ATLAS OF HUMAN iPSCs. bioRxiv 2024.11.03.621734 | cm4ai:use:publications | Publications using CM4AI data
March 2024 CodeFest event for training researchers in CM4AI data and tools. | 38 registrants with 40% female and 30% from underrepresented communities, Detailed tutorials on data derivation and integration, Production-ready software demonstration | cm4ai:use:codefest | CM4AI CodeFest training event

Intended Uses

Description | Examples | ID | Name
--- | --- | --- | ---
Development of visible neural networks (VNNs) that provide interpretable AI/ML predictions based on ... | Visible machine learning systems informed by cell maps, Interpretable genotype-phenotype predictions, ... (+2 more) | cm4ai:intended:visible-ml | Visible machine learning development
AI/ML research on functional genomics data including transcriptional phenotypes, protein interaction... | Machine learning on multimodal functional genomics data, Disease mechanism understanding, ... (+2 more) | cm4ai:intended:biomedical-ai | Biomedical AI research
Research on protein assemblies, cellular compartments, and protein complex organization. | Protein complex characterization, Subcellular localization studies, ... (+2 more) | cm4ai:intended:cell-biology | Cell biology research

Future Use Impacts

Description | ID | Impact Details | Name
--- | --- | --- | ---
Dataset characteristics specific to MDA-MB-468 and KOLF2.1J cell lines may not generalize to all cel... | cm4ai:impact:cell-line-specific | MDA-MB-468 specific to triple-negative breast cancer context, KOLF2.1J from healthy male Northern European donor, ... (+2 more) | Cell line-specific characteristics
Limitations inherent to mass spectrometry, microscopy, and CRISPR perturbation technologies. | cm4ai:impact:technological-limitations | Mass spectrometry detection limits, Antibody availability and specificity constraints, ... (+3 more) | Technological and methodological limitations

Discouraged Uses

Description | Discouragement Details | ID | Name
--- | --- | --- | ---
Commercial use is restricted by CC BY-NC-SA 4.0 license and requires separate licensing negotiation.... | Commercial use not permitted under CC BY-NC-SA 4.0, Separate license required from copyright holders (UCSD, Stanford, UCSF), Data Access Committee supervision for commercial licensing | cm4ai:discouraged:commercial | Commercial use without license
Data are from cell lines, not clinical samples, and should not be directly applied to clinical decis... | Non-clinical data from tissue cultures, De-identified and cannot be matched to human subjects, Requires clinical validation before medical application | cm4ai:discouraged:clinical-direct | Direct clinical application without validation

License And Use Terms

ID
  cm4ai:license:cc-by-nc-sa
Name
  Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Description
  Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license. Data are Copyright 2025 The Regents of the University of California except where otherwise noted. Spatial proteomics raw image data copyright 2025 The Board of Trustees of the Leland Stanford Junior University.
License Terms
  - Attribution required to copyright holders and authors
  - Citation of bioRxiv article required
  - Non-commercial use only without separate license
  - ShareAlike - derivatives must use same license
  - Software under BSD-3 and MIT open source licenses
  - FAIRSCAPE under MIT license

📤

## Distribution

How will the dataset be distributed?

License
CC BY-NC-SA 4.0
Version Access

ID
  cm4ai:version-access
Name
  Dataset versioning
Description
  Dataset versioned through University of Virginia Dataverse with DOI persistence.
Version Details
  - DOI doi:10.18130/V3/B35XWX for persistent access
  - Dataverse versioning system tracks updates
  - Software packages versioned for reproducibility
  - Version referenced in dataset metadata

🔄

## Maintenance

How will the dataset be maintained?

Updates

ID
  cm4ai:updates
Name
  Regular data augmentation
Description
  Data will be augmented regularly through the end of the project in August 2026.
Update Details
  - Ongoing data generation and release
  - Regular updates to LibraData repository
  - Additional cell lines and conditions planned
  - Year 2 expansion to iPSC differentiation states

👥

## Human Subjects

Does the dataset relate to people?

Human Subject Research

ID
  cm4ai:ethics:non-human-subjects
Name
  Non-human subjects research
Description
  CM4AI data are distinctive in being non-clinical data from tissue cultures that are considered de-identified as they cannot be matched to human subjects with current knowledge. Data from ethically sourced cell lines: MDA-MB-468 from ATCC with established provenance, and KOLF2.1J from HipSci with donor consent for research use. No direct human subjects involvement; cell lines cannot be matched to individuals.
Involves Human Subjects
  False

Generated on 2025-12-09 18:07:08 using Bridge2AI Data Sheets Schema