Evidence map›Paper›PMID 41663435›Full record

ArticleNature communications2026

High-throughput multi-organ proteomics workflow for drug efficacy and toxicity analysis.

Yun Xiong, Lin Tan, Wai-Kin Chan, Dandan Zhu, Huimin Zhang, Eric S Yin, Sri Ramya Donepudi, Jibin Ding, Bo Wei, Bao Tran and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

19 authors.

Yun Xiong *Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.
Lin Tan *Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.
Wai-Kin Chan *Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.
Dandan ZhuDepartment of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0003-4609-1238
Huimin ZhangDepartment of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Eric S YinDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.
Sri Ramya DonepudiDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.ORCID http://orcid.org/0000-0002-2845-1212
Jibin DingDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.
Bo WeiDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.ORCID http://orcid.org/0000-0001-5573-5715
Bao TranDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.
Sara MartinezDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.
Iqbal MahmudDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.ORCID http://orcid.org/0000-0001-7729-8122
Faiza Hanif WaghuDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.
Hamish I StewartThermo Fisher Scientific GmbH, Bremen, Germany.
Daniel J HermansonThermo Fisher Scientific GmbH, Bremen, Germany.
Rehan AkbaniDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.ORCID http://orcid.org/0000-0003-2686-7721
John N WeinsteinDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA.ORCID http://orcid.org/0000-0001-9401-6908
Junjie ChenDepartment of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0002-1493-2189
Philip L LorenziDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC), Houston, TX, USA. plorenzi@coh.org.ORCID http://orcid.org/0000-0003-0385-7774

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid and comprehensive analysis of complex proteomes across large sample sets is vital for unlocking the potential of systems biology. We present a high-throughput mass spectrometry (MS) proteomics method that integrates narrow-window data-independent acquisition (nDIA) with short-gradient micro-flow chromatography, enabling profiling of >240 samples per day. This optimized MS approach identifies 6,201 and 7,466 human proteins with 1- and 2-min gradients, respectively. As a practical application, we analyzed 507 samples composed of 13 different tissues from mice treated with the enzyme-drug L-asparaginase (ASNase) or its glutaminase-free Q59L mutant, generating a quantitative profile of 11,472 proteins following drug treatment. The MS results confirmed the impact of ASNase on amino acid metabolism in solid tissues. Further analysis revealed broad suppression of anticoagulants and cholesterol metabolism and uncovered numerous tissue-specific dysregulated pathways. In summary, the optimized high-throughput proteomics method accelerates systems-level analysis of a preclinical model to generate biological insights and clinically actionable hypotheses.

Indexed as

High-Throughput Screening AssaysProteomeProteomicsAmino AcidsAnimalsAsparaginaseCholesterolHumansMass SpectrometryMiceWorkflowAmino AcidsAsparaginaseCholesterolProteome

Identifiers

PMID41663435
PMCPMC13083947

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.