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}
Pub Date : 2025-12-12DOI: 10.1016/j.slast.2025.100386
XingYu Yang , Qin Qiu , Yu Tang , WeiDi Sun , XiFang Wu , XiaoJiang Li , YanXin Ren
Purpose
The PBRM1 (PB1) gene, which encodes BAF180, a specific subunit of the PBAF SWI / SNF complex, is extensively studied in some other cancers, yet its effects and related mechanisms in NPC remains inadequately understood. Aerobic glycolysis is one of the hallmarks of cancer, and whether PBRM1 is involved in this metabolic metastasis in NPC remains unclear.
Methods
We established NPC cell lines with knockdown of PBRM1 and performed functional analysis to understand the impact of their production. Based on the RNA-seq data, we mainly analyzed the activity of the AKT-mTOR signaling pathway and examined the expression levels of some key glycolytic genes including HIF 1α, PFKP, ENO 1, PKM and LDHA. Using in vivo experiments, we verified the effect of PBRM1 on the proliferation of NPC.
Results
Our findings indicate that PBRM1 deficiency enhances proliferation, migration, and invasion in both CNE1 and CNE2 cells. Notably, PBRM1 downregulate activates the AKT-mTOR pathway, upregulating glycolytic enzymes and lactate production. Subcutaneous tumor formation assay in nude mice also showed that knockdown of PBRM1 promoted NPC cells growth.
Conclusion
This study illuminates PBRM1′s tumor suppressor role, highlighting the AKT-mTOR pathway and aerobic glycolysis as potential therapeutic targets in NPC.
{"title":"Elucidating the role of PBRM1 in NPC via RNA-seq transcriptomic sequencing","authors":"XingYu Yang , Qin Qiu , Yu Tang , WeiDi Sun , XiFang Wu , XiaoJiang Li , YanXin Ren","doi":"10.1016/j.slast.2025.100386","DOIUrl":"10.1016/j.slast.2025.100386","url":null,"abstract":"<div><h3>Purpose</h3><div>The PBRM1 (PB1) gene, which encodes BAF180, a specific subunit of the PBAF SWI / SNF complex, is extensively studied in some other cancers, yet its effects and related mechanisms in NPC remains inadequately understood. Aerobic glycolysis is one of the hallmarks of cancer, and whether PBRM1 is involved in this metabolic metastasis in NPC remains unclear.</div></div><div><h3>Methods</h3><div>We established NPC cell lines with knockdown of PBRM1 and performed functional analysis to understand the impact of their production. Based on the RNA-seq data, we mainly analyzed the activity of the AKT-mTOR signaling pathway and examined the expression levels of some key glycolytic genes including HIF 1α, PFKP, ENO 1, PKM and LDHA. Using in vivo experiments, we verified the effect of PBRM1 on the proliferation of NPC.</div></div><div><h3>Results</h3><div>Our findings indicate that PBRM1 deficiency enhances proliferation, migration, and invasion in both CNE1 and CNE2 cells. Notably, PBRM1 downregulate activates the AKT-mTOR pathway, upregulating glycolytic enzymes and lactate production. Subcutaneous tumor formation assay in nude mice also showed that knockdown of PBRM1 promoted NPC cells growth.</div></div><div><h3>Conclusion</h3><div>This study illuminates PBRM1′s tumor suppressor role, highlighting the AKT-mTOR pathway and aerobic glycolysis as potential therapeutic targets in NPC.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"36 ","pages":"Article 100386"},"PeriodicalIF":3.7,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145757713","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 : 2025-12-12DOI: 10.1016/j.slast.2025.100385
Yixian Liu , Xin Lan , Xiaoyi Zhang , Xinyi Ren , Hongmin Duan , Yanrong Dan , Dong Duan , Ganghua Lu
<div><h3>Background</h3><div>Ovarian cancer (OC) is the deadliest malignant tumor among gynecological tumors. The current treatment measures for OC remain not optimistic, so it is important to determine reliable prognostic biomarkers to prolong OC patients' survival.</div></div><div><h3>Methods</h3><div>The OC-related transcriptome data were downloaded from the University of California Santa Cruz (UCSC) and we obtained the Differentially Expressed Genes (DEGs) between OC and normal samples. The Nectins and Nectin-like (Necl) scores of OC patients were calculated by single-sample Gene Set Enrichment Analysis (ssGSEA), the correlation between these scores and the prognosis of OC patients was explored using the Kaplan-Meier survival curve. DEGs were overlapped with Nectins and Necls-related genes selected by Weighted Gene Co-expression Network Analysis (WGCNA) to obtain the differentially expressed Nectins and Necls-related genes (DENNGs). Next, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were carried out on DENNGs. The Protein-Protein Interaction (PPI) network of DENNGs was constructed and hub genes were screened. In addition, univariate and multivariate Cox regression analysis were used to obtain the prognosis-related genes of OC patients and construct a prognostic model. Gene Set Variation Analysis (GSVA) was performed on the genes of high and low risk groups. SsGSEA algorithm was used to calculate the immune cell scores and the correlation between different immune cells and prognosis-related genes was explored.</div></div><div><h3>Results</h3><div>We obtained 583 DENNGs by crossing 6778 DEGs in OC and 584 Nectin- and Necl-related genes. The scores of Nectins and Necls in the OC group increased significantly, together with the poor prognosis. KEGG and GO analyses showed that DENNGs were mainly associated with cell proliferation, aging, canceration, and virus infection. Univariate and multivariate Cox analyses screened six prognosis-related genes (PTTG1, MELK, CENPF, PLK1, KIF20A, TOP2A) and modeled prognosis risks. Furthermore, a nomogram that integrated the risk model and patient age accurately predicted OC prognosis. The results of GSVA showed that TGF-β-mediated epithelial-mesenchymal transition, tumor cell invasion and metastasis were activated in the high-risk group, while pathways related to tumor cell invasiveness, such as hedgehog (Hh) mediated by hypoxia, were activated in the low-risk group. Finally, we found five immune cells (activated CD4+ <em>T</em> cells, central memory CD4+ <em>T</em> cells, central memory CD8+ <em>T</em> cells, T follicular helper cells (tTFH), and type II helper T cells(Th2)) different between OC and normal samples, and prognosis-related genes were positively correlated with activated CD4+ cells and Th2 of immune cells but negatively correlated with tTFH.</div></div><div><h3>Conclusion</h3><div>We identified six prognosis-related genes and constructed a prognostic mo
{"title":"A novel prognostic model in ovarian cancer based on the Nectin family and Necl-like molecules related transcriptomics","authors":"Yixian Liu , Xin Lan , Xiaoyi Zhang , Xinyi Ren , Hongmin Duan , Yanrong Dan , Dong Duan , Ganghua Lu","doi":"10.1016/j.slast.2025.100385","DOIUrl":"10.1016/j.slast.2025.100385","url":null,"abstract":"<div><h3>Background</h3><div>Ovarian cancer (OC) is the deadliest malignant tumor among gynecological tumors. The current treatment measures for OC remain not optimistic, so it is important to determine reliable prognostic biomarkers to prolong OC patients' survival.</div></div><div><h3>Methods</h3><div>The OC-related transcriptome data were downloaded from the University of California Santa Cruz (UCSC) and we obtained the Differentially Expressed Genes (DEGs) between OC and normal samples. The Nectins and Nectin-like (Necl) scores of OC patients were calculated by single-sample Gene Set Enrichment Analysis (ssGSEA), the correlation between these scores and the prognosis of OC patients was explored using the Kaplan-Meier survival curve. DEGs were overlapped with Nectins and Necls-related genes selected by Weighted Gene Co-expression Network Analysis (WGCNA) to obtain the differentially expressed Nectins and Necls-related genes (DENNGs). Next, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were carried out on DENNGs. The Protein-Protein Interaction (PPI) network of DENNGs was constructed and hub genes were screened. In addition, univariate and multivariate Cox regression analysis were used to obtain the prognosis-related genes of OC patients and construct a prognostic model. Gene Set Variation Analysis (GSVA) was performed on the genes of high and low risk groups. SsGSEA algorithm was used to calculate the immune cell scores and the correlation between different immune cells and prognosis-related genes was explored.</div></div><div><h3>Results</h3><div>We obtained 583 DENNGs by crossing 6778 DEGs in OC and 584 Nectin- and Necl-related genes. The scores of Nectins and Necls in the OC group increased significantly, together with the poor prognosis. KEGG and GO analyses showed that DENNGs were mainly associated with cell proliferation, aging, canceration, and virus infection. Univariate and multivariate Cox analyses screened six prognosis-related genes (PTTG1, MELK, CENPF, PLK1, KIF20A, TOP2A) and modeled prognosis risks. Furthermore, a nomogram that integrated the risk model and patient age accurately predicted OC prognosis. The results of GSVA showed that TGF-β-mediated epithelial-mesenchymal transition, tumor cell invasion and metastasis were activated in the high-risk group, while pathways related to tumor cell invasiveness, such as hedgehog (Hh) mediated by hypoxia, were activated in the low-risk group. Finally, we found five immune cells (activated CD4+ <em>T</em> cells, central memory CD4+ <em>T</em> cells, central memory CD8+ <em>T</em> cells, T follicular helper cells (tTFH), and type II helper T cells(Th2)) different between OC and normal samples, and prognosis-related genes were positively correlated with activated CD4+ cells and Th2 of immune cells but negatively correlated with tTFH.</div></div><div><h3>Conclusion</h3><div>We identified six prognosis-related genes and constructed a prognostic mo","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"36 ","pages":"Article 100385"},"PeriodicalIF":3.7,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145758442","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 : 2025-12-11DOI: 10.1016/j.slast.2025.100376
Rana Alabdan , Hechmi Shili , Ghada Moh. Samir Elhessewi , Mukhtar Ghaleb , Eman M Alanazi , Nouf Helal Alharbi , Rowida Mohammed Alharbi , Asma A. Alhashmi
The existing multi-omic analyses are frequently confined to individual tissues, and the regulatory picture of the systemic regulator of complex physiology and disease is hidden. To fill this gap, we have created a unified systems biology model of the high-throughput dissection of inter-organ genetic networks. Our model incorporates transcriptomic, epigenomic and proteomic analysis of five major organs (liver, kidney, heart, lung, brain) using the Multi-Omics Factor Analysis (MOFA+) tool, specifically, cross-tissue coordination. We characterized 27 evidence-heavy cross-tissue modules (FDR < 0.05) that are major hubs such as *HNF4Aenda NRF2cheng8loadmasterregulatingconstitutionembryonicstemcellularinfoncogenes recognize them. One notable observation was liver-kidney metabolic axis, significant cross-talks in hepatocyte organoids are confirmed with CRISPR knockdown, which suppresses the expression of transporters expressed by the kidney. Our work offers a scalable validated framework that goes beyond organ-centric perspectives, which can be used as a potent tool of systemic disease modelling and precision medicine.
{"title":"High-throughput dissection of inter-organ genetic networks: A multi-omic systems biology approach","authors":"Rana Alabdan , Hechmi Shili , Ghada Moh. Samir Elhessewi , Mukhtar Ghaleb , Eman M Alanazi , Nouf Helal Alharbi , Rowida Mohammed Alharbi , Asma A. Alhashmi","doi":"10.1016/j.slast.2025.100376","DOIUrl":"10.1016/j.slast.2025.100376","url":null,"abstract":"<div><div>The existing multi-omic analyses are frequently confined to individual tissues, and the regulatory picture of the systemic regulator of complex physiology and disease is hidden. To fill this gap, we have created a unified systems biology model of the high-throughput dissection of inter-organ genetic networks. Our model incorporates transcriptomic, epigenomic and proteomic analysis of five major organs (liver, kidney, heart, lung, brain) using the Multi-Omics Factor Analysis (MOFA+) tool, specifically, cross-tissue coordination. We characterized 27 evidence-heavy cross-tissue modules (FDR < 0.05) that are major hubs such as *HNF4Aenda NRF2cheng8loadmasterregulatingconstitutionembryonicstemcellularinfoncogenes recognize them. One notable observation was liver-kidney metabolic axis, significant cross-talks in hepatocyte organoids are confirmed with CRISPR knockdown, which suppresses the expression of transporters expressed by the kidney. Our work offers a scalable validated framework that goes beyond organ-centric perspectives, which can be used as a potent tool of systemic disease modelling and precision medicine.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"36 ","pages":"Article 100376"},"PeriodicalIF":3.7,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145752326","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号