Proteomics profiling of research models for studying pancreatic ductal adenocarcinoma.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-02-14 DOI:10.1038/s41597-025-04522-x

Mathilde Resell, Hanne-Line Rabben, Animesh Sharma, Lars Hagen, Linh Hoang, Nan T Skogaker, Anne Aarvik, Eirik Knudsen Bjåstad, Magnus K Svensson, Manoj Amrutkar, Caroline S Verbeke, Surinder K Batra, Gunnar Qvigstad, Timothy C Wang, Anil Rustgi, Duan Chen, Chun-Mei Zhao

{"title":"Proteomics profiling of research models for studying pancreatic ductal adenocarcinoma.","authors":"Mathilde Resell, Hanne-Line Rabben, Animesh Sharma, Lars Hagen, Linh Hoang, Nan T Skogaker, Anne Aarvik, Eirik Knudsen Bjåstad, Magnus K Svensson, Manoj Amrutkar, Caroline S Verbeke, Surinder K Batra, Gunnar Qvigstad, Timothy C Wang, Anil Rustgi, Duan Chen, Chun-Mei Zhao","doi":"10.1038/s41597-025-04522-x","DOIUrl":null,"url":null,"abstract":"<p><p>Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based systems modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. The preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here contribute to the data collection on PDAC, which will be useful for systems modelling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to the generation of research hypotheses, predictions of targets for diagnosis and treatment, and relationships between data variables.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"266"},"PeriodicalIF":5.8000,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11828881/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04522-x","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10-15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based systems modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. The preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here contribute to the data collection on PDAC, which will be useful for systems modelling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to the generation of research hypotheses, predictions of targets for diagnosis and treatment, and relationships between data variables.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.