Evidence map›Paper›PMID 36915410›Full record

ArticleNAR genomics and bioinformatics2023

Characterization of proteome profile data of chemicals based on data-independent acquisition MS with SWATH method.

Hiromu Ishiguro, Tadahaya Mizuno, Yasuo Uchida, Risa Sato, Hayate Sasaki, Shumpei Nemoto, Tetsuya Terasaki, Hiroyuki Kusuhara

Open access · goldAbstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2023. 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
0.2field-weighted citation impact, top 43% of its field
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, 1 citations in OpenAlex.

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

8 authors at 2 institutions in 1 country.

Hiromu IshiguroGraduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan.
Tadahaya MizunoGraduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan.ORCID https://orcid.org/0000-0002-1638-602X
Yasuo UchidaGraduate School of Pharmaceutical Sciences, Tohoku University, Aoba, Aramaki, Aoba-ku, Sendai 980-8578, Japan.
Risa SatoGraduate School of Pharmaceutical Sciences, Tohoku University, Aoba, Aramaki, Aoba-ku, Sendai 980-8578, Japan.
Hayate SasakiGraduate School of Pharmaceutical Sciences, Tohoku University, Aoba, Aramaki, Aoba-ku, Sendai 980-8578, Japan.
Shumpei NemotoGraduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan.
Tetsuya TerasakiGraduate School of Pharmaceutical Sciences, Tohoku University, Aoba, Aramaki, Aoba-ku, Sendai 980-8578, Japan.
Hiroyuki KusuharaGraduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan.
The University of Tokyo · JPTohoku University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transcriptomic data of cultured cells treated with a chemical are widely recognized as useful numeric information that describes the effects of the chemical. This property is due to the high coverage and low arbitrariness of the transcriptomic data as profiles of chemicals. Considering the importance of posttranslational regulation, proteomic profiles could provide insights into the unrecognized aspects of the effects of chemicals. Therefore, this study aimed to address the question of how well the proteomic profiles obtained using data-independent acquisition (DIA) with the sequential window acquisition of all theoretical mass spectra, which can achieve comprehensive and arbitrariness-free protein quantification, can describe chemical effects. We demonstrated that the proteomic data obtained using DIA-MS exhibited favorable properties as profile data, such as being able to discriminate chemicals like the transcriptomic profiles. Furthermore, we revealed a new mode of action of a natural compound, harmine, through profile data analysis using the proteomic profile data. To our knowledge, this is the first study to investigate the properties of proteomic data obtained using DIA-MS as the profiles of chemicals. Our 54 (samples) × 2831 (proteins) data matrix would be an important source for further analyses to understand the effects of chemicals in a data-driven manner.

Identifiers

PMID36915410
PMCPMC10006730
OpenAlexW4323967086

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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.