Evidence map›Paper›PMID 42239236›Full record

ArticlebioRxiv : the preprint server for biology2026

A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.

Alex Zelter, Michael Riffle, Gennifer E Merrihew, Batool Mutawe, Nicholas Shulman, Justin A Sanders, William S Noble, Danielle P Johnson Erickson, Alec Morimoto, Benjamin A Shaver and 16 more

Abstract readPreprint
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

26 authors.

Alex ZelterDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0002-5331-0577
Michael RiffleDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0003-1633-8607
Gennifer E MerrihewDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0003-4903-0318
Batool MutaweDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Nicholas ShulmanDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Justin A SandersDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
William S NobleDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0001-7283-4715
Danielle P Johnson EricksonDepartment of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA.
Alec MorimotoDepartment of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA.
Benjamin A ShaverDepartment of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA.
Taylor N SteinsDepartment of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA.
Ning CaoDepartment of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA.
Eric C FordDepartment of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA.
Paul A RudnickSpectragen Informatics LLC, Bainbridge Island, WA, USA.
Daniel ChelskySpectragen Informatics LLC, Bainbridge Island, WA, USA.
Kenneth H WanBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, USA.
Jamie L InmanBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, USA.
Hang ChangBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, USA.
Antoine M SnijdersBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, USA.
Jian-Hua MaoBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, USA.ORCID 0000-0001-9320-6021
Susan E CelnikerBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, USA.
Jared De ChantAccelerator Technology & Applied Physics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, USA.
Lieselotte Obst-HueblAccelerator Technology & Applied Physics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, USA.
Kei NakamuraAccelerator Technology & Applied Physics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, USA.
Christine C WuDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID 0009-0000-6518-8487
Michael J MacCossDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0003-1853-0256

Funding

TrainingP41GM103533 · NIGMS · UNIVERSITY OF WASHINGTON · PI DAVIS, TRISHA N. · 2012 to 2021
$20.7M
Fourier transform infrared biodosimetry on skinR21AI194031 · NIAID · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI Jamie L Inman · 2025 to 2026
$396k
NIAID NIH HHS R21 AI194031NIGMS NIH HHS P41 GM103533
6 · The paper itself

Abstract

Ionizing radiation induces molecular responses that may be used to estimate exposure when physical dosimeters are unavailable. Here we present two large-scale proteomics datasets generated from mouse dorsal skin punch samples collected following controlled X-ray exposures spanning multiple doses, dose rates, and post-exposure time points. Experiment 1 comprised 96 samples (including 16 reference samples) collected 6 days after exposure to 0-75 cGy delivered at either 30 or 300 cGy/min. Experiment 2 comprised 936 samples (including 236 reference samples) exposed to 0-100 cGy at either 3 or 28 cGy/min dose rates and harvested between 7 and 150 days post-exposure. All samples were processed using a standardized workflow involving automated bead-based digestion and data-independent acquisition mass spectrometry. The datasets include multiple pooled reference sample types, process controls, and system suitability standards ensuring high quality data. All data presented are available via ProteomeXchange at several levels of processing, from raw files through normalized peptide- and protein-level abundance matrices suitable for biomarker discovery and machine learning applications. This dataset will facilitate generation of new insights into the biological changes and molecular signatures resulting from X-ray exposure in mice and may also help inform future studies in humans.

Identifiers

PMID42239236
PMCPMC13228472

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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.