[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124856-en":3,"doc-seo-124856-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124856,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing","Intact (whole) cell MALDI TOF mass spectrometry is a low-cost, robust approach used to generate cellular fingerprints for distinguishing cell types, isogenous lines, and even metabolic states. The work addresses whether a single-gene perturbation can be detected without prior lysis or extraction by using SKOV3 ovarian cancer cells with TUSC3 silencing. Spectral data are analyzed with five machine-learning algorithms, all achieving accuracy above 90% and revealing subtle changes. The results support intact-cell MALDI TOF MS as a practical quality-control tool for routine cell and tissue cultures.","Heliyon 10 (2024) e29936  \nContents lists available at ScienceDirect  \nHeliyon  \njournal [homepage:](homepage: www.cell.com/heliyon)[ www.cell.com/heliyon](homepage: www.cell.com/heliyon)  \n| Research article\u003Cbr>Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing\u003Cbr>Luk´ˇas Peˇcinka a, b, Luk´ˇas Mor´ˇan c, d, Petra Kovaˇcovicov´a b, c, Francesca Melonie, Josef Havela, b, Tiziana Pivettae, Petr Vaˇnhara b, c, *\u003Cbr>a Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic\u003Cbr>b International Clinical Research Center, St. Anne’s University Hospital Brno, Czech Republic c Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic\u003Cbr>d Research Centre for Applied Molecular Oncology (RECAMO), Masaryk Memorial Cancer Institute, Brno, Czech Republic e Chemical and Geological Sciences Department, University of Cagliari, Cittadella Universitaria, Monserrato, Italy |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Intact cell MALDI TOF MS Machine learning Biotyping\u003Cbr>TUSC3\u003Cbr>R programming language Bioinformatics\u003Cbr>Quality control\u003Cbr>Cell culture |  | Intact (whole) cell MALDI TOF mass spectrometry is a commonly used tool in clinical microbiology for several decades. Recently it was introduced to analysis of eukaryotic cells, including cancer and stem cells. Besides targeted metabolomic and proteomic applications, the intact cell MALDI TOF mass spectrometry provides a sufficient sensitivity and specificity to discriminate cell types, isogenous cell lines or even the metabolic states. This makes the intact cell MALDI TOF mass spectrometry a promising tool for quality control in advanced cell cultures with a potential to reveal batch-to-batch variation, aberrant clones, or unwanted shifts in cell phenotype. However, cellular alterations induced by change in expression of a single gene has not been addressed by intact cell mass spectrometry yet. In this work we used a well-characterized human ovarian cancer cell line SKOV3 with silenced expression of a tumor suppressor candidate 3 gene (TUSC3). TUSC3 is involved in co-translational N-glycosylation of proteins with well-known global impact on cell phenotype. Altogether, this experimental design represents a highly suitable model for optimization of intact cell mass spectrometry and analysis of spectral data. Here we investigated five machine learning algorithms (k-nearest neighbors, decision tree, random forest, partial least squares discrimination, and artificial neural network) and optimized their performance either in pure populations or in two-component mixtures composed of cells with normal or silenced expression ofTUSC3. All five algorithms reached accuracy over 90 % and were able to reveal even subtle changes in mass spectra corresponding to alterations ofTUSC3 expression. In summary, we demonstrate that spectral fingerprints generated by intact cell MALDI-TOF mass spectrometry coupled to a machine learning classifier can reveal minute changes induced by alteration of a single gene, and therefore contribute to the portfolio of quality control applications in routine cell and tissue cultures. |\n\n1. Introduction  \nMatrix-assisted laser desorption/ionization time offlight mass spectrometry (MALDI-TOF MS) is a widely used analytical technique  \n* Corresponding author. Masaryk University, Faculty of Medicine, Kamenice 5, Brno, Czech Republic.  \nE-mail address: [PVanhara@med.muni.cz](PVanhara@med.muni.cz) (P. Vaˇnhara).  \n[https://doi.org/10.1016/j.heliyon.2024.e29936](https://doi.org/10.1016/j.heliyon.2024.e29936)  \nReceived 12 April 2024; Accepted 17 April 2024  \nAvailable online 22 April 2024  \n2405-8440/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0","cbCaiuPJfvz78JG2","https://ap.wps.com/l/cbCaiuPJfvz78JG2","pdf",5481180,1,10,"English","en",105,"# Introduction\n## Intact cell MALDI-TOF MS and applications\n## Challenges in eukaryotic cell biotyping\n## Spectral preprocessing and machine learning approaches","[{\"question\":\"What is intact cell MALDI TOF mass spectrometry, and how does it support cell biotyping?\",\"answer\":\"It uses whole, undisturbed cells as input without lysis, fractionation, or protein extraction. The generated spectra act as cellular fingerprints that can discriminate cell types, lines, and metabolic states.\"},{\"question\":\"How was the single-gene effect tested in the study?\",\"answer\":\"SKOV3 human ovarian cancer cells were used with silenced TUSC3 expression, a tumor suppressor involved in co-translational N-glycosylation affecting cell phenotype. The design enables assessment of whether spectral changes reflect this single-gene alteration.\"},{\"question\":\"Which machine learning algorithms were evaluated, and what performance was achieved?\",\"answer\":\"Five algorithms were tested: k-nearest neighbors, decision tree, random forest, partial least squares discrimination, and artificial neural network. All reached accuracy over 90% and detected even subtle spectral changes linked to altered TUSC3 expression.\"}]","Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing | PDF",1785895055,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"intact-cell-mass-spectrometry-coupled-with-machine-learning-reveals-minute-changes-induced-by-single-gene-silencing","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/intact-cell-mass-spectrometry-coupled-with-machine-learning-reveals-minute-changes-induced-by-single-gene-silencing/124856/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is intact cell MALDI TOF mass spectrometry, and how does it support cell biotyping?","Question",{"text":75,"@type":76},"It uses whole, undisturbed cells as input without lysis, fractionation, or protein extraction. The generated spectra act as cellular fingerprints that can discriminate cell types, lines, and metabolic states.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the single-gene effect tested in the study?",{"text":80,"@type":76},"SKOV3 human ovarian cancer cells were used with silenced TUSC3 expression, a tumor suppressor involved in co-translational N-glycosylation affecting cell phenotype. The design enables assessment of whether spectral changes reflect this single-gene alteration.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms were evaluated, and what performance was achieved?",{"text":84,"@type":76},"Five algorithms were tested: k-nearest neighbors, decision tree, random forest, partial least squares discrimination, and artificial neural network. 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