Evidence map›Paper›PMID 41993479›Full record

ArticlebioRxiv : the preprint server for biology2026

Spatially Anchored Regulatory State Inference in Melanoma.

Jagan Mohan Reddy Dwarampudi, Veena Kochat, Suresh Satpati, Md Ishtyaq Mahmud, Humaira Anzum, Khalida Wani, Alexander Lazar, Ajay Kumar Saw, Jared Malke, Hien V Nguyen and 2 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

12 authors.

Jagan Mohan Reddy DwarampudiDepartment of Electrical and Computer Engineering, University of Houston, Houston, USA.ORCID 0009-0006-2947-4164
Veena KochatDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0003-2614-3790
Suresh SatpatiDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0002-8988-6113
Md Ishtyaq MahmudDepartment of Electrical and Computer Engineering, University of Houston, Houston, USA.ORCID 0000-0001-6280-7035
Humaira AnzumDepartment of Electrical and Computer Engineering, University of Houston, Houston, USA.
Khalida WaniDepartment of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0002-2383-3908
Alexander LazarDepartment of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ajay Kumar SawDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Jared MalkeDepartment of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0001-9884-8111
Hien V NguyenDepartment of Electrical and Computer Engineering, University of Houston, Houston, USA.
Kunal RaiDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID 0000-0003-2321-6894
Tania BanerjeeDepartment of Electrical and Computer Engineering, University of Houston, Houston, USA.ORCID 0000-0003-4737-0001

Funding

Consortium for Translational and Precision HealthUM1TR004539 · NCATS · BAYLOR COLLEGE OF MEDICINE · PI Bettina M. Beech, FASIHA KANWAL · 2024 to 2026
$17.1M
NCATS NIH HHS UM1 TR004539
6 · The paper itself

Abstract

Spatial transcriptomics (ST) captures gene expression within tissue architecture but lacks direct regulatory information, while single-cell multiome assays profile transcriptional and chromatin states without spatial context. We present a framework for spatially anchored regulatory inference that integrates Visium ST with single-cell multiome data to infer spatially resolved regulatory programs. Building upon GraphST, we introduce spatially regularized cell-to-spot mapping and propagate chromatin accessibility and transcription factor motif activity into tissue space. Regulatory analysis is performed at the spatial domain level via joint differential expression and accessibility testing, along with quantitative concordance assessment. Applied to melanoma tissue sections, the framework reveals spatially localized regulatory programs and shows that assignment strategy substantially affects downstream regulatory stability. This modular approach enables interpretable gene-, peak-, and transcription factor-level outputs for multimodal spatial analysis.

Indexed as

Multimodal integrationRegulatory inferenceSingle-cell multiomeSpatial transcriptomicsVisium

Identifiers

PMID41993479
PMCPMC13082042

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.