Pub Date : 2026-01-21DOI: 10.1016/j.slast.2026.100393
Yue Feng , Guoyan Liu
Background
Ovarian cancer (OC) remains the most lethal gynecologic malignancy, primarily due to late-stage diagnosis resulting from nonspecific early symptoms. This study aims to identify novel genetic targets and elucidate the underlying mechanisms driving OC progression by integrating multi-omics datasets.
Methods
We comprehensively analyzed OC datasets from the Gene Expression Omnibus (GEO) database and applied Mendelian randomization (MR) integrating genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) data to identify OC-associated genes. Cross-analysis revealed genes co-expressed with both disease-relevant and differentially expressed genes (DEGs), followed by pathway and functional enrichment investigations.
Results
Sixteen significant genes were identified, including XPR1, SPINT1, NFE2L3, FGFRL1, SLC24A4, CDC42EP3, PAPLN, GRAMD1B, TMEM71, MAP1A, CD36, ADRA2A, MYL9, PPBP, SIGLEC11 and CMTM5. These genes predominantly regulate tumor immune cell activity, with CIBERSORT analysis revealing distinct immune cell distribution patterns in OC.
Conclusions
Our findings provide novel insights into OC molecular mechanisms and highlight promising therapeutic targets, establishing a foundation for future research and clinical applications.
{"title":"Transcriptomics reveals new therapeutic targets for ovarian cancer","authors":"Yue Feng , Guoyan Liu","doi":"10.1016/j.slast.2026.100393","DOIUrl":"10.1016/j.slast.2026.100393","url":null,"abstract":"<div><h3>Background</h3><div>Ovarian cancer (OC) remains the most lethal gynecologic malignancy, primarily due to late-stage diagnosis resulting from nonspecific early symptoms. This study aims to identify novel genetic targets and elucidate the underlying mechanisms driving OC progression by integrating multi-omics datasets.</div></div><div><h3>Methods</h3><div>We comprehensively analyzed OC datasets from the Gene Expression Omnibus (GEO) database and applied Mendelian randomization (MR) integrating genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) data to identify OC-associated genes. Cross-analysis revealed genes co-expressed with both disease-relevant and differentially expressed genes (DEGs), followed by pathway and functional enrichment investigations.</div></div><div><h3>Results</h3><div>Sixteen significant genes were identified, including XPR1, SPINT1, NFE2L3, FGFRL1, SLC24A4, CDC42EP3, PAPLN, GRAMD1B, TMEM71, MAP1A, CD36, ADRA2A, MYL9, PPBP, SIGLEC11 and CMTM5. These genes predominantly regulate tumor immune cell activity, with CIBERSORT analysis revealing distinct immune cell distribution patterns in OC.</div></div><div><h3>Conclusions</h3><div>Our findings provide novel insights into OC molecular mechanisms and highlight promising therapeutic targets, establishing a foundation for future research and clinical applications.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"37 ","pages":"Article 100393"},"PeriodicalIF":3.7,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.slast.2026.100389
Lei An
Postoperative nausea and vomiting (PONV) are still significant issues in the perioperative care that impact patient recovery and satisfaction, but the underlying molecular pathways that lead to personal predisposition are not comprehensively understood. In order to fill this gap we performed a multi-omics study incorporating bulk RNA sequencing, single cell transcriptomics, circRNA profiling, and alternative splicing evaluation and genotype expression colocalization analyses to detail regulatory networks of PONV. Analysis of differential expression showed inflammatory pathways and neurotransmission pathways as key driving factors in the development of symptoms, and HIF1A and STAT3 were found to be prominent central nodes in a variety of data layers. Cell type-specific transcriptional signatures indicative of neuroimmune interaction as a driving force were identified at the single-cell level with regulatory noncoding elements including differentiation of back-splice junction support of circPTGS2 and circGABRA3 and alternative splicing of GABRA3 indicating further post-transcriptional regulation. Convergent molecular signals were observed between matched datasets of patients with Integration of bulk and single-cell expression with BisqueRNA deconvolution and Harmony batch correction. These results present the initial transcriptomics-wide multi-dimensional model to integrate genetic variation, RNA organization and cellular heterogeneity to describe PONV susceptibility. The paper is supporting the sale of potential biomarker(s) that promise to inform any future clinical prediction framework and tailored antiemetic alternatives, which forms the basis of translating to diagnostic and therapeutic uses. Clinical implementation will be provided with further validation, such as protein-level validation and splice variants PCR confirmation and increased multicentric cohorts.
{"title":"Multi-omics and transcriptomic profiling of anesthetic response reveals RNA regulatory networks in postoperative nausea and vomiting","authors":"Lei An","doi":"10.1016/j.slast.2026.100389","DOIUrl":"10.1016/j.slast.2026.100389","url":null,"abstract":"<div><div>Postoperative nausea and vomiting (PONV) are still significant issues in the perioperative care that impact patient recovery and satisfaction, but the underlying molecular pathways that lead to personal predisposition are not comprehensively understood. In order to fill this gap we performed a multi-omics study incorporating bulk RNA sequencing, single cell transcriptomics, circRNA profiling, and alternative splicing evaluation and genotype expression colocalization analyses to detail regulatory networks of PONV. Analysis of differential expression showed inflammatory pathways and neurotransmission pathways as key driving factors in the development of symptoms, and HIF1A and STAT3 were found to be prominent central nodes in a variety of data layers. Cell type-specific transcriptional signatures indicative of neuroimmune interaction as a driving force were identified at the single-cell level with regulatory noncoding elements including differentiation of back-splice junction support of circPTGS2 and circGABRA3 and alternative splicing of GABRA3 indicating further post-transcriptional regulation. Convergent molecular signals were observed between matched datasets of patients with Integration of bulk and single-cell expression with BisqueRNA deconvolution and Harmony batch correction. These results present the initial transcriptomics-wide multi-dimensional model to integrate genetic variation, RNA organization and cellular heterogeneity to describe PONV susceptibility. The paper is supporting the sale of potential biomarker(s) that promise to inform any future clinical prediction framework and tailored antiemetic alternatives, which forms the basis of translating to diagnostic and therapeutic uses. Clinical implementation will be provided with further validation, such as protein-level validation and splice variants PCR confirmation and increased multicentric cohorts.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"37 ","pages":"Article 100389"},"PeriodicalIF":3.7,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145991671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.slast.2025.100387
Chang Liu , Hui Zhang
{"title":"Mass spectrometry applications for high-throughput experimentation in supporting drug discovery","authors":"Chang Liu , Hui Zhang","doi":"10.1016/j.slast.2025.100387","DOIUrl":"10.1016/j.slast.2025.100387","url":null,"abstract":"","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"36 ","pages":"Article 100387"},"PeriodicalIF":3.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145757690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.slast.2025.100375
Hongxia Liu
{"title":"Retraction notice to “Clinical Observation and Evaluation of Health Management Intervention in Controlling Senile Chronic Diseases such as Hyperlipidemia” [SLAS Technology 33 (2025) 100318]","authors":"Hongxia Liu","doi":"10.1016/j.slast.2025.100375","DOIUrl":"10.1016/j.slast.2025.100375","url":null,"abstract":"","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"36 ","pages":"Article 100375"},"PeriodicalIF":3.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145752377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.slast.2025.100378
Mirco Plante , Antoine Champie , François Michaud , Sébastien Rodrigue
Synthetic biology is a rapidly evolving discipline that seeks to understand, modify, design, and build biological systems by applying modular and systemic principles inspired by engineering. Automation in synthetic biology offers significant gains in efficiency, reproducibility, and standardization, enabling more reliable and scalable experiments while reducing human fatigue and health risks. This shift allows researchers to focus on experimental design, data analysis, and innovation rather than repetitive tasks. More recently, artificial intelligence has begun to reshape laboratory work at a cognitive level, enabling machines to analyze data, make decisions, and learn from experience. Artificial intelligence in biology has the potential to accelerate discovery, optimize experimental design, and enhance data analysis by identifying patterns beyond human capabilities. The convergence of robotics and artificial intelligence offers a promising future for synthetic biology but also raises ethical concerns. As the creation of engineered life becomes increasingly automated and shaped by intelligent agents, questions about governance, responsibility, and transparency become more pressing. In this article, we examine the progress and prospects of both physical (robotic) and cognitive (intelligent agent) automation in synthetic biology. We begin with an overview of automation technologies in industrial and laboratory settings, then discuss the objectives and challenges of synthetic biology from an automation perspective. Finally, we propose a dual conceptual framework: one for total automation of the Design–Build–Test–Learn (DBTL) cycle, and another for progressive automation adaptable to diverse laboratory contexts. Our aim is to support the development and responsible implementation of automation systems in synthetic biology laboratories.
{"title":"Toward full automation in synthetic biology: A progressive conceptual framework integrating robotics and intelligent agents","authors":"Mirco Plante , Antoine Champie , François Michaud , Sébastien Rodrigue","doi":"10.1016/j.slast.2025.100378","DOIUrl":"10.1016/j.slast.2025.100378","url":null,"abstract":"<div><div>Synthetic biology is a rapidly evolving discipline that seeks to understand, modify, design, and build biological systems by applying modular and systemic principles inspired by engineering. Automation in synthetic biology offers significant gains in efficiency, reproducibility, and standardization, enabling more reliable and scalable experiments while reducing human fatigue and health risks. This shift allows researchers to focus on experimental design, data analysis, and innovation rather than repetitive tasks. More recently, artificial intelligence has begun to reshape laboratory work at a cognitive level, enabling machines to analyze data, make decisions, and learn from experience. Artificial intelligence in biology has the potential to accelerate discovery, optimize experimental design, and enhance data analysis by identifying patterns beyond human capabilities. The convergence of robotics and artificial intelligence offers a promising future for synthetic biology but also raises ethical concerns. As the creation of engineered life becomes increasingly automated and shaped by intelligent agents, questions about governance, responsibility, and transparency become more pressing. In this article, we examine the progress and prospects of both physical (robotic) and cognitive (intelligent agent) automation in synthetic biology. We begin with an overview of automation technologies in industrial and laboratory settings, then discuss the objectives and challenges of synthetic biology from an automation perspective. Finally, we propose a dual conceptual framework: one for total automation of the Design–Build–Test–Learn (DBTL) cycle, and another for progressive automation adaptable to diverse laboratory contexts. Our aim is to support the development and responsible implementation of automation systems in synthetic biology laboratories.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"36 ","pages":"Article 100378"},"PeriodicalIF":3.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145710303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.slast.2025.100373
Kerstin Thurow , Oliver Peter , Patrick Courtney , Károly Széll , Ádám Wolf
The increasing complexity of modern life science laboratories presents unique challenges for automation and robotics that extend beyond traditional industrial applications. As laboratory workflows become increasingly intricate, the integration of robotic systems has become essential to improve efficiency, reproducibility, and scalability. This special issue highlights recent advances in laboratory automation, focusing on innovative robotic solutions that enhance experimental precision and operational throughput. We explore key technological developments, standardization efforts, and emerging trends that are shaping the future of automation. By addressing both the opportunities and current limitations of robotic systems in laboratory environments, this editorial provides insights into the evolution of intelligent automation in life sciences.
{"title":"Editorial: Robotics in laboratory automation","authors":"Kerstin Thurow , Oliver Peter , Patrick Courtney , Károly Széll , Ádám Wolf","doi":"10.1016/j.slast.2025.100373","DOIUrl":"10.1016/j.slast.2025.100373","url":null,"abstract":"<div><div>The increasing complexity of modern life science laboratories presents unique challenges for automation and robotics that extend beyond traditional industrial applications. As laboratory workflows become increasingly intricate, the integration of robotic systems has become essential to improve efficiency, reproducibility, and scalability. This special issue highlights recent advances in laboratory automation, focusing on innovative robotic solutions that enhance experimental precision and operational throughput. We explore key technological developments, standardization efforts, and emerging trends that are shaping the future of automation. By addressing both the opportunities and current limitations of robotic systems in laboratory environments, this editorial provides insights into the evolution of intelligent automation in life sciences.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"36 ","pages":"Article 100373"},"PeriodicalIF":3.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145757735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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