Evidence map›Paper›PMID 42486102›Full record

ArticleCell reports methods2026

HIT-MAP: A scalable approach to multimodal mapping of subcellular organization.

Gege Qian, Joonwon Kim, Richa Tiwari, Benjamin J Polacco, Antoine Forget, Leah V Schaffer, Hark Kyun Kim, Jiahao Gao, Yuan Zhou, Gwendolyn M Jang and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Gege QianBioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA; Department of Medicine, University of California San Diego, La Jolla, CA, USA.
Joonwon KimDepartment of Pediatrics, University of California San Diego, La Jolla, CA, USA; Department of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea.
Richa TiwariQuantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA.
Benjamin J PolaccoQuantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA.
Antoine ForgetQuantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA.
Leah V SchafferDepartment of Medicine, University of California San Diego, La Jolla, CA, USA.
Hark Kyun KimDepartment of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Jiahao GaoDepartment of Medicine, University of California San Diego, La Jolla, CA, USA.
Yuan ZhouQuantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA.
Gwendolyn M JangQuantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA.
Marcus R KellyDepartment of Medicine, University of California San Diego, La Jolla, CA, USA; Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
Xiaoyu ZhaoDepartment of Medicine, University of California San Diego, La Jolla, CA, USA; Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
Helene FoussardQuantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA.
Liudeng ZhangDepartment of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Nevan KroganQuantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA; Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA; Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.
Trey IdekerDepartment of Medicine, University of California San Diego, La Jolla, CA, USA; Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA, USA. Electronic address: tideker@health.ucsd.edu.
Alejandro ChavezBioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA. Electronic address: chavez2@health.ucsd.edu.

Funding

Bridge2AI: Cell Maps for AI (CM4AI) Data Generation ProjectOT2OD032742 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Jean-Christophe Bélisle-Pipon, TIMOTHY W CLARK · 2022 to 2026
$21.5M
The Cancer Cell Map Initiative v2.0U54CA274502 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Nevan J Krogan · 2022 to 2026
$14.2M
Methods to Rapidly Explore Combinatorial Diversity and Their Application to CRISPR-Cas9 SystemsDP2NS131566 · NINDS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Alejandro Chavez · 2022 to 2026
$2.4M
Protein tagging at scale to enable functional genomic studiesR21HG011855 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CHAVEZ, ALEJANDRO · 2021 to 2021
$633k
NCI NIH HHS U54 CA274502NHGRI NIH HHS R21 HG011855NIH HHS OT2 OD032742NINDS NIH HHS DP2 NS131566
6 · The paper itself

Abstract

Multimodal mapping of subcellular protein organization through imaging and interaction proteomics has been mostly confined to large consortia, owing to high per-target costs, reliance on target-specific antibodies or libraries of epitope-tagged cDNAs, and the lack of unified pipelines for coordinated data acquisition. Here, we present HIT-MAP (high-throughput integrated tagging for cell mapping), an end-to-end framework that couples endogenous epitope tagging with optimized wide-field immunofluorescence imaging and affinity purification-mass spectrometry (AP-MS). Applied to a pilot set of 16 representative proteins in HEK293T cells, HIT-MAP recovers 576 high-confidence protein-protein interactions, identifies canonical complexes, and resolves cross-modality protein communities. Integrative analysis identifies CCDC12 as a previously uncharacterized component of the Bact spliceosomal complex, supported by AP-MS interaction data, tag-free size-exclusion chromatography-mass spectrometry (SEC-MS) co-fractionation, and Perturb-seq transcriptional signatures. HIT-MAP lowers technical and economic barriers to generating coordinated multimodal protein maps.

Indexed as

Protein Interaction MappingProteomicsHEK293 CellsHumansMass SpectrometryProtein Interaction Mapsaffinity purification-mass spectrometrycell mapCP: cell biologyCP: systems biologyendogenous taggingimmunofluorescencemultimodal data integrationmultiscale protein organizationspatial proteomicsspliceosomevirtual cellwide-field microscopy

Identifiers

PMID42486102
PMCPMC13615500

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.