Methods最新文献

{"title":"Trans-m5C: A transformer-based model for predicting 5-methylcytosine (m5C) sites","authors":"Haitao Fu ,&nbsp;Zewen Ding ,&nbsp;Wen Wang","doi":"10.1016/j.ymeth.2024.12.010","DOIUrl":"10.1016/j.ymeth.2024.12.010","url":null,"abstract":"<div><div>5-Methylcytosine (m5C) plays a pivotal role in various RNA metabolic processes, including RNA localization, stability, and translation. Current high-throughput sequencing technologies for m5C site identification are resource-intensive in terms of cost, labor, and time. As such, there is a pressing need for efficient computational approaches. Many existing computational methods rely on intricate hand-crafted features, requiring unavailable features, often leading to suboptimal prediction accuracy. Addressing these challenges, we introduce a novel deep-learning method, Trans-m5C. We first categorize m5C sites into NSUN2-dependent and NSUN6-dependent types for independent feature extraction. Subsequently, meticulously crafted transformer neural networks are employed to distill global features. The prediction of m5C sites is then accomplished using a discriminator built from a multi-layer perceptron. A rigorous evaluation for the performance of Trans-m5C on experimentally validated m5C data from human and mouse species reveals that our method offers a competitive edge over both baseline and existing methodologies.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 178-186"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel approaches to biomarker discover","authors":"Brian K. McFarlin PhD (Special Issue Editor)","doi":"10.1016/j.ymeth.2025.01.005","DOIUrl":"10.1016/j.ymeth.2025.01.005","url":null,"abstract":"","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 284-285"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nanotechnological approaches to improve corticosteroids ocular therapy","authors":"María Lina Formica,&nbsp;Juan Matías Pernochi Scerbo,&nbsp;Hamoudi Ghassan Awde Alfonso,&nbsp;Pablo Tomás Palmieri,&nbsp;Julieta Ribotta,&nbsp;Santiago Daniel Palma","doi":"10.1016/j.ymeth.2024.12.008","DOIUrl":"10.1016/j.ymeth.2024.12.008","url":null,"abstract":"<div><div>The administration of corticosteroids is the first-line treatment of the clinical conditions with ocular inflammation. Nonetheless, ocular physiological mechanisms, anatomical barriers and corticosteroid properties prevent it from reaching the target site. Thus, frequent topical administered doses or ocular injections are required, leading to a higher risk of adverse events and poor patient compliance.</div><div>Designing novel drug delivery systems based on nanotechnological tools is a useful approach to overcome disadvantages associated with the ocular delivery of corticosteroids. Nanoparticle-based drug delivery systems represent an alternative to the current dosage forms for the ocular administration of corticosteroids, since due to their particle size and the properties of their materials, they can increase their solubility, improve ocular permeability, control their release and increase bioavailability after their ocular administration. In this way, lipid and polymer-based nanoparticles have been the main strategies developed, giving rise to novel patent applications to protect these innovative drug delivery systems as a product, its preparation or administration method. Additionally, it should be noted that at least 10 clinical trials are being carried out to evaluate the ocular application of different pharmaceutical formulations based on corticosteroid-loaded nanoparticles.</div><div>Through a comprehensive and extensive analysis, this review highlights the impact of nanotechnology applications in ocular inflammation therapy with corticosteroids.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 152-177"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring multi-slice spatially resolved gene expression from H&E-stained histology images with STMCL","authors":"Zhiceng Shi ,&nbsp;Fangfang Zhu ,&nbsp;Wenwen Min","doi":"10.1016/j.ymeth.2024.11.016","DOIUrl":"10.1016/j.ymeth.2024.11.016","url":null,"abstract":"<div><div>Spatial transcriptomics has significantly advanced the measurement of spatial gene expression in the field of biology. However, the high cost of ST limits its application in large-scale studies. Using deep learning to predict spatial gene expression from H&amp;E-stained histology images offers a more cost-effective alternative, but existing methods fail to fully leverage the multimodal information provided by Spatial transcriptomics and pathology images. In response, this paper proposes <strong>STMCL</strong>, a novel multimodal contrastive learning framework. STMCL integrates multimodal information, including histology images, gene expression features of spots, and their locations, to accurately infer spatial gene expression profiles. We tested four different types of multi-slice spatial transcriptomics datasets generated by the 10X Genomics platform. The results indicate that STMCL has advantages over baseline methods in predicting spatial gene expression profiles. Furthermore, STMCL is capable of capturing cancer-specific highly expressed genes and preserving gene expression patterns while maintaining the original spatial structure of gene expression. Our code is available at <span><span>https://github.com/wenwenmin/STMCL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 187-195"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142926191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microplate fluorescence quenching for high throughput screening of affinity constants – Serum albumins and zearalenones case study","authors":"Heiðar Már Aðalsteinsson ,&nbsp;Lavínia Quartin Pinto ,&nbsp;Tatiana Q. Aguiar ,&nbsp;José A. Teixeira ,&nbsp;Luís Abrunhosa","doi":"10.1016/j.ymeth.2024.12.011","DOIUrl":"10.1016/j.ymeth.2024.12.011","url":null,"abstract":"<div><div>Measurements of changes in fluorescence signal is one of the most commonly applied methods for studying protein-ligand affinities. These measurements are generally carried out using cuvettes in spectrofluorometers, which can only measure one sample at a time. This makes screening procedures for multiple ligands and proteins extremely laborious, as each protein must be measured with multiple ligand concentrations, and usually in triplicate. Moreover, multiple equations exist to extract the affinity constants and other information from the data, and their underlying assumptions are often disregarded. In this study, the affinities of human, bovine and rat serum albumins for the mycotoxin zearalenone and five of its common derivatives were measured in 96-well microplates, allowing quick measurements of multiple samples using less reagent amounts. In comparison to measurements using a cuvette in a spectrofluorometer, the microplate method was shown to reproduce the affinity constants accurately. The results were discussed in terms of common pitfalls regarding experimental setup and available equations to analyze protein-ligand binding in fluorescence quenching assays. The commonly used Stern-Volmer equation was discussed in detail and the results used to show how inaccurate it is when a fluorescent protein-ligand complex is formed, and when other underlying approximations are ignored.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 253-263"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retraction notice to “A methodological framework for rigorous systematic reviews: Tailoring comprehensive analyses to clinicians and healthcare professionals” [Methods 225 (2024) 38–43]","authors":"Stefano Mancin ,&nbsp;Marco Sguanci ,&nbsp;Giuliano Anastasi ,&nbsp;Lea Godino ,&nbsp;Alessio Lo Cascio ,&nbsp;Emanuela Morenghi ,&nbsp;Michela Piredda ,&nbsp;Maria Grazia De Marinis","doi":"10.1016/j.ymeth.2025.01.007","DOIUrl":"10.1016/j.ymeth.2025.01.007","url":null,"abstract":"","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Page 314"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BCDB: A dual-branch network based on transformer for predicting transcription factor binding sites","authors":"Jia He ,&nbsp;Yupeng Zhang ,&nbsp;Yuhang Liu ,&nbsp;Zhigan Zhou ,&nbsp;Tianhao Li ,&nbsp;Yongqing Zhang ,&nbsp;Boqia Xie","doi":"10.1016/j.ymeth.2024.12.006","DOIUrl":"10.1016/j.ymeth.2024.12.006","url":null,"abstract":"<div><div>Transcription factor binding sites (TFBSs) are critical in regulating gene expression. Precisely locating TFBSs can reveal the mechanisms of action of different transcription factors in gene transcription. Various deep learning methods have been proposed to predict TFBS; however, these models often need help demonstrating ideal performance under limited data conditions. Furthermore, these models typically have complex structures, which makes their decision-making processes difficult to transparentize. Addressing these issues, we have developed a framework named BCDB. This framework integrates multi-scale DNA information and employs a dual-branch output strategy. Integrating DNABERT, convolutional neural networks (CNN), and multi-head attention mechanisms enhances the feature extraction capabilities, significantly improving the accuracy of predictions. This innovative method aims to balance the extraction of global and local information, enhancing predictive performance while utilizing attention mechanisms to provide an intuitive way to explain the model's predictions, thus strengthening the overall interpretability of the model. Prediction results on 165 ChIP-seq datasets show that BCDB significantly outperforms other existing deep learning methods in terms of performance. Additionally, since the BCDB model utilizes transfer learning methods, it can transfer knowledge learned from many unlabeled data to specific cell line prediction tasks, allowing our model to achieve cross-cell line TFBS prediction. The source code for BCDB is available on <span><span>https://github.com/ZhangLab312/BCDB</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 141-151"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized biochemical method for human Polyphosphate quantification","authors":"Blanca Lázaro ,&nbsp;Ana Sarrias ,&nbsp;Francisco J. Tadeo ,&nbsp;Joan Marc Martínez-Láinez ,&nbsp;Ainhoa Fernández ,&nbsp;Eva Quandt ,&nbsp;Blanca Depares ,&nbsp;Tobias Dürr-Mayer ,&nbsp;Henning Jessen ,&nbsp;Javier Jiménez ,&nbsp;Josep Clotet ,&nbsp;Samuel Bru","doi":"10.1016/j.ymeth.2025.01.001","DOIUrl":"10.1016/j.ymeth.2025.01.001","url":null,"abstract":"<div><div>Polyphosphate (polyP), a biopolymer composed of phosphates, impacts a wide range of biological functions and pathological conditions in all organisms. However, polyP’s intricate physiology and structure in human cells have remained elusive, largely due to the lack of a reliable quantification method including its extraction. In this study, we assess critical points in the whole process: extraction, purification, and quantification polyP from human cell lines. We developed a highly efficient method that extracts between 3 and 100 times more polyP than previously achieved. Supported by Nuclear Magnetic Resonance (NMR), our approach confirms that mammalian polyP is primarily a linear unbranched polymer. We applied the optimized method to commonly used human cell lines, uncovering important variations of intracellular polyP that correlate with the expression levels of specific polyP converting enzymes. This study underscores the importance of employing several techniques for polyP characterization in parallel and provides a valuable and standardized tool for further exploration in this field.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 211-222"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Massively parallel flow-cytometry-based screening of hematopoietic lineage cell populations from up to 25 donors simultaneously","authors":"Jan Devan ,&nbsp;Michaela Sandalova ,&nbsp;Pamela Bitterli ,&nbsp;Nick Herger ,&nbsp;Tamara Mengis ,&nbsp;Kenta Brender ,&nbsp;Irina Heggli ,&nbsp;Oliver Distler ,&nbsp;Stefan Dudli","doi":"10.1016/j.ymeth.2024.11.014","DOIUrl":"10.1016/j.ymeth.2024.11.014","url":null,"abstract":"<div><div>This study aimed to develop a method allowing high-dimensional and technically uniform screening of surface markers on cells of hematopoietic origin. High-dimensional screening of cell phenotypes is primarily the domain of single-cell RNA sequencing (RNAseq), which allows simultaneous analysis of the expression of thousands of genes in several thousands of cells. However, rare cell populations can often substantially impact tissue homeostasis or disease pathogenesis, and dysregulation of rare populations can easily be missed when only a few thousand cells are analyzed. With the presented methodological approach, it is possible to screen hundreds of markers on millions of cells in a technically uniform manner and thus identify and characterize changes in rare populations.</div><div>We utilize the highly expressed markers CD45 on immune cells and CD71 on erythroid progenitors to create unique fluorescent barcodes on each of the 25 samples. Double-barcoded samples are co-stained with a broad immunophenotyping panel. The panel is designed in such a way that allows the addition of PE-labelled antibody, which was used for screening purposes. Multiplexed samples are divided into hundreds of aliquots and co-stained, each aliquot with a different PE-labelled antibody. Utilizing a broad immunophenotyping panel and machine-learning algorithms, we can predict the co-expression of hundreds of screened markers with a high degree of precision. This technique is suitable for screening immune cells in bone marrow from different locations, blood specimens, or any tissue with a substantial presence of immune cells, such as tumors or inflamed tissue areas in autoimmune conditions. It represents an approach that can significantly improve our ability to recognize dysregulated immune cell populations and, if needed, precisely target subsequent experiments covering lower cell counts such as RNAseq.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 45-53"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142749496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FedKD-CPI: Combining the federated knowledge distillation technique to accomplish synergistic compound-protein interaction prediction","authors":"Xuetao Wang ,&nbsp;Qichang Zhao ,&nbsp;Jianxin Wang","doi":"10.1016/j.ymeth.2024.12.014","DOIUrl":"10.1016/j.ymeth.2024.12.014","url":null,"abstract":"<div><div>Compound-protein interaction (CPI) prediction is critical in the early stages of drug discovery, narrowing the search space for CPIs and reducing the cost and time required for traditional high-throughput screening. However, CPI-related data are usually distributed across different institutions and their sharing is restricted because of data privacy and intellectual property rights. Constructing a scheme that enhances multi-institutional collaboration to improve prediction accuracy while protecting data privacy is essential. To this end, we propose FedKD-CPI, the first framework based on federated knowledge distillation, to effectively facilitate multi-party CPI collaborative prediction and ensure data privacy and security. FedKD-CPI uses knowledge distillation technology to extract the updated knowledge of all client models and train the model on the server to achieve knowledge aggregation, which can effectively utilize the knowledge contained in public and private data. We evaluate FedKD-CPI on three benchmark datasets and compare it with four baselines. The results show that FedKD-CPI is very close to centralized learning and significantly better than localized learning. Furthermore, FedKD-CPI outperforms federated learning-based baselines on independent and identically distributed data and non-independent and identically distributed data. Overall, FedKD-CPI improves the CPI prediction while ensuring data security and promoting institutions' collaboration to accelerate drug discovery.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"234 ","pages":"Pages 275-283"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142997925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}