Methods最新文献

{"title":"Quantitation of F-actin in cytoskeletal reorganization: Context, methodology and implications","authors":"Subhashree Shubhrasmita Sahu ,&nbsp;Parijat Sarkar ,&nbsp;Amitabha Chattopadhyay","doi":"10.1016/j.ymeth.2024.07.009","DOIUrl":"10.1016/j.ymeth.2024.07.009","url":null,"abstract":"<div><p>The actin cytoskeleton is involved in a large number of cellular signaling events in addition to providing structural integrity to the cell. Actin polymerization is a key event during cellular signaling. Although the role of actin cytoskeleton in cellular processes such as trafficking and motility has been extensively studied, the reorganization of the actin cytoskeleton upon signaling has been rarely explored due to lack of suitable assays. Keeping in mind this lacuna, we developed a confocal microscopy based approach that relies on high magnification imaging of cellular F-actin, followed by image reconstruction using commercially available software. In this review, we discuss the context and relevance of actin quantitation, followed by a detailed hands-on approach of the methodology involved with specific points on troubleshooting and useful precautions. In the latter part of the review, we elucidate the method by discussing applications of actin quantitation from our work in several important problems in contemporary membrane biology ranging from pathogen entry into host cells, to GPCR signaling and membrane-cytoskeleton interaction. We envision that future discovery of cell-permeable novel fluorescent probes, in combination with genetically encoded actin-binding reporters, would allow real-time visualization of actin cytoskeleton dynamics to gain deeper insights into active cellular processes in health and disease.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 44-58"},"PeriodicalIF":4.2,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141791580","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":"Hydrogel-based hybrid membrane enhances in vitro ophthalmic drug evaluation in the OphthalMimic device","authors":"Geisa N. Barbalho,&nbsp;Manuel A. Falcão,&nbsp;Venâncio A. Amaral,&nbsp;Jonad L. Contarato,&nbsp;Guilherme M. Gelfuso,&nbsp;Marcilio Cunha-Filho,&nbsp;Tais Gratieri","doi":"10.1016/j.ymeth.2024.07.010","DOIUrl":"10.1016/j.ymeth.2024.07.010","url":null,"abstract":"<div><p>Envisaging to improve the evaluation of ophthalmic drug products while minimizing the need for animal testing, our group developed the OphthalMimic device, a 3D-printed device that incorporates an artificial lacrimal flow, a cul-de-sac area, a moving eyelid, and a surface that interacts effectively with ophthalmic formulations, thereby providing a close representation of human ocular conditions. An important application of such a device would be its use as a platform for dissolution/release tests that closely mimic <em>in vivo</em> conditions. However, the surface that artificially simulates the cornea should have a higher resistance (10 min) than the previously described polymeric films (5 min). For this key assay upgrade, we describe the process of obtaining and thoroughly characterizing a hydrogel-based hybrid membrane to be used as a platform base to simulate the cornea artificially. Also, the OphthalMimic device suffered design improvements to fit the new membrane and incorporate the moving eyelid. The results confirmed the successful synthesis of the hydrogel components. The membrane’s water content (86.25 ± 0.35 %) closely mirrored the human cornea (72 to 85 %). Furthermore, morphological analysis supported the membrane’s comparability to the natural cornea. Finally, the performance of different formulations was analysed, demonstrating that the device could differentiate their drainage profile through the viscosity of PLX 14 (79 ± 5 %), PLX 16 (72 ± 4 %), and PLX 20 (57 ± 14 %), and mucoadhesion of PLXCS0.5 (69 ± 1 %), PLX16CS1.0 (65 ± 3 %), PLX16CS1.25 (67 ± 3 %), and the solution (97 ± 8 %). In conclusion, using the hydrogel-based hybrid membrane in the OphthalMimic device represents a significant advancement in the field of ophthalmic drug evaluation, providing a valuable platform for dissolution/release tests. Such a platform aligns with the ethical mandate to reduce animal testing and promises to accelerate the development of safer and more effective ophthalmic drugs.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 21-31"},"PeriodicalIF":4.2,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141791579","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":"Magic-angle spinning NMR spectral editing of polysaccharides in whole cells using the DREAM scheme","authors":"Loic Delcourte ,&nbsp;Mélanie Berbon ,&nbsp;Marion Rodriguez ,&nbsp;Kamalraj Subban ,&nbsp;Alons Lends ,&nbsp;Axelle Grélard ,&nbsp;Estelle Morvan ,&nbsp;Birgit Habenstein ,&nbsp;Sven J. Saupe ,&nbsp;Laurence Delhaes ,&nbsp;Vishukumar Aimanianda ,&nbsp;Asen Daskalov ,&nbsp;Antoine Loquet","doi":"10.1016/j.ymeth.2024.07.003","DOIUrl":"10.1016/j.ymeth.2024.07.003","url":null,"abstract":"<div><p>Most bacterial, plant and fungal cells possess at their surface a protective layer called the cell wall, conferring strength, plasticity and rigidity to withstand the osmotic pressure. This molecular barrier is crucial for pathogenic microorganisms, as it protects the cell from the local environment and often constitutes the first structural component encountered in the host-pathogen interaction. In pathogenic molds and yeasts, the cell wall constitutes the main target for the development of clinically-relevant antifungal drugs. In the past decade, solid-state NMR has emerged as a powerful analytical technique to investigate the molecular organization of microbial cell walls in the context of intact cells. ¹³C NMR chemical shift is an exquisite source of information to identify the polysaccharides present in the cell wall, and two-dimensional ¹³C–¹³C correlation experiments provide an efficient tool to rapidly access the polysaccharide composition in whole cells. Here we investigate the use of the adiabatic DREAM (for dipolar recoupling enhancement through amplitude modulation) recoupling scheme to improve solid-state NMR analysis of polysaccharides in intact cells. We demonstrate the advantages of two-dimensional ¹³C–¹³C experiments using the DREAM recoupling scheme. We report the spectral editing of polysaccharide signals by varying the radio-frequency carrier position. We provide practical considerations for the implementation of DREAM experiments to characterize polysaccharides in whole cells. We demonstrate the approach on intact fungal cells of <em>Neurospora crassa</em> and <em>Aspergillus fumigatus</em>, a model and a pathogenic filamentous fungus, respectively. The approach could be envisioned to efficiently reduce the spectral crowding of more complex cell surfaces, such as cell wall and peptidoglycan in bacteria.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 59-67"},"PeriodicalIF":4.2,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324001658/pdfft?md5=4cb6701b30ce29fb81d3fadffdddede7&pid=1-s2.0-S1046202324001658-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141756380","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":"Carbon dots fluorescence can be used to distinguish isobaric peptide and to monitor protein oligomerization dynamics","authors":"Gabriele Antonio Zingale ,&nbsp;Irene Pandino ,&nbsp;Nunzio Tuccitto ,&nbsp;Alessia Distefano ,&nbsp;Federico Calì ,&nbsp;Damiano Calcagno ,&nbsp;Giuseppe Grasso","doi":"10.1016/j.ymeth.2024.07.005","DOIUrl":"10.1016/j.ymeth.2024.07.005","url":null,"abstract":"<div><p>Carbon dots (CD) are widely investigated particles with interesting fluorescent properties which are reported to be used for various purposes, as they are biocompatible, resistant to photobleaching and with tuneable properties depending on the specific CD surface chemistry. In this work, we report on the possibility to use opportunely designed CD to distinguish among isobaric peptides almost undistinguishable by mass spectrometry, as well as to monitor protein aggregation phenomena. Particularly, cell-penetrating peptides containing the carnosine moiety at different positions in the peptide chain produce sequence specific fluorescent signals. Analogously, different insulin oligomerization states can also be distinguished by the newly proposed experimental approach. The latter is here described in details and can be potentially applied to any kind of peptide or protein.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 1-8"},"PeriodicalIF":4.2,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324001671/pdfft?md5=176318c356dc91f1394d09cbbe7cd148&pid=1-s2.0-S1046202324001671-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141747138","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":"Strategy for modeling higher-order G-quadruplex structures recalcitrant to NMR determination","authors":"T. Michael Sabo,&nbsp;John O. Trent,&nbsp;Jonathan B. Chaires,&nbsp;Robert C. Monsen","doi":"10.1016/j.ymeth.2024.07.004","DOIUrl":"10.1016/j.ymeth.2024.07.004","url":null,"abstract":"<div><p>Guanine-rich nucleic acids can form intramolecularly folded four-stranded structures known as G-quadruplexes (G4s). Traditionally, G4 research has focused on short, highly modified DNA or RNA sequences that form well-defined homogeneous compact structures. However, the existence of longer sequences with multiple G4 repeats, from proto-oncogene promoters to telomeres, suggests the potential for more complex higher-order structures with multiple G4 units that might offer selective drug-targeting sites for therapeutic development. These larger structures present significant challenges for structural characterization by traditional high-resolution methods like multi-dimensional NMR and X-ray crystallography due to their molecular complexity. To address this current challenge, we have developed an integrated structural biology (ISB) platform, combining experimental and computational methods to determine self-consistent molecular models of higher-order G4s (xG4s). Here we outline our ISB method using two recent examples from our lab, an extended c-Myc promoter and long human telomere G4 repeats, that highlights the utility and generality of our approach to characterizing biologically relevant xG4s.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 9-20"},"PeriodicalIF":4.2,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S104620232400166X/pdfft?md5=d9df89c01de0c554c5bd908f0250814a&pid=1-s2.0-S104620232400166X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141733061","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":"Machine learning-based prediction of diabetic patients using blood routine data","authors":"Honghao Li ,&nbsp;Dongqing Su ,&nbsp;Xinpeng Zhang ,&nbsp;Yuanyuan He ,&nbsp;Xu Luo ,&nbsp;Yuqiang Xiong ,&nbsp;Min Zou ,&nbsp;Huiyan Wei ,&nbsp;Shaoran Wen ,&nbsp;Qilemuge Xi ,&nbsp;Yongchun Zuo ,&nbsp;Lei Yang","doi":"10.1016/j.ymeth.2024.07.001","DOIUrl":"10.1016/j.ymeth.2024.07.001","url":null,"abstract":"<div><p>Diabetes stands as one of the most prevalent chronic diseases globally. The conventional methods for diagnosing diabetes are frequently overlooked until individuals manifest noticeable symptoms of the condition. This study aimed to address this gap by collecting comprehensive datasets, including 1000 instances of blood routine data from diabetes patients and an equivalent dataset from healthy individuals. To differentiate diabetes patients from their healthy counterparts, a computational framework was established, encompassing eXtreme Gradient Boosting (XGBoost), random forest, support vector machine, and elastic net algorithms. Notably, the XGBoost model emerged as the most effective, exhibiting superior predictive results with an area under the receiver operating characteristic curve (AUC) of 99.90% in the training set and 98.51% in the testing set. Moreover, the model showcased commendable performance during external validation, achieving an overall accuracy of 81.54%. The probability generated by the model serves as a risk score for diabetes susceptibility. Further interpretability was achieved through the utilization of the Shapley additive explanations (SHAP) algorithm, identifying pivotal indicators such as mean corpuscular hemoglobin concentration (MCHC), lymphocyte ratio (LY%), standard deviation of red blood cell distribution width (RDW-SD), and mean corpuscular hemoglobin (MCH). This enhances our understanding of the predictive mechanisms underlying diabetes. To facilitate the application in clinical and real-life settings, a nomogram was created based on the logistic regression algorithm, which can provide a preliminary assessment of the likelihood of an individual having diabetes. Overall, this research contributes valuable insights into the predictive modeling of diabetes, offering potential applications in clinical practice for more effective and timely diagnoses.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 156-162"},"PeriodicalIF":4.2,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141632223","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":"Methodological approaches to studying phase separation and HIV-1 replication: Current and future perspectives","authors":"Andrew J. Mouland ,&nbsp;Bao-An Chau ,&nbsp;Vladimir N. Uversky","doi":"10.1016/j.ymeth.2024.07.002","DOIUrl":"10.1016/j.ymeth.2024.07.002","url":null,"abstract":"<div><p>This article reviews tried-and-tested methodologies that have been employed in the first studies on phase separating properties of structural, RNA-binding and catalytic proteins of HIV-1. These are described here to stimulate interest for any who may want to initiate similar studies on virus-mediated liquid–liquid phase separation. Such studies serve to better understand the life cycle and pathogenesis of viruses and open the door to new therapeutics.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 147-155"},"PeriodicalIF":4.2,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141603067","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":"RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints","authors":"","doi":"10.1016/j.ymeth.2024.06.011","DOIUrl":"10.1016/j.ymeth.2024.06.011","url":null,"abstract":"<div><p>Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementation, with trajectory data play a pivotal role in drug discovery. The advancement of high-performance computing and deep learning technology becomes popular and critical to predict protein properties from vast trajectory data, posing challenges regarding data features extraction from the complicated simulation data and dimensionality reduction. Simultaneously, it is essential to provide a meaningful explanation of the biological mechanism behind dimensionality. To tackle this challenge, we propose a new unsupervised model named RevGraphVAMP to intelligently analyze the simulation trajectory. This model is based on the variational approach for Markov processes (VAMP) and integrates graph convolutional neural networks and physical constraint optimization to enhance the learning performance. Additionally, we introduce attention mechanism to assess the importance of key interaction region, facilitating the interpretation of molecular mechanism. In comparison to other VAMPNets models, our model showcases competitive performance, improved accuracy in state transition prediction, as demonstrated through its application to two public datasets and the Shank3-Rap1 complex, which is associated with autism spectrum disorder. Moreover, it enhanced dimensionality reduction discrimination across different substates and provides interpretable results for protein structural characterization.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 163-174"},"PeriodicalIF":4.2,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553850","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":"Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors","authors":"Yan-Ting Jin ,&nbsp;Yang Tan ,&nbsp;Zhong-Hua Gan ,&nbsp;Yu-Duo Hao ,&nbsp;Tian-Yu Wang ,&nbsp;Hao Lin ,&nbsp;Bo Tang","doi":"10.1016/j.ymeth.2024.06.012","DOIUrl":"10.1016/j.ymeth.2024.06.012","url":null,"abstract":"<div><p>DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing <em>cis</em>-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The <em>F</em>-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 125-132"},"PeriodicalIF":4.2,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141533187","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":"scGAAC: A graph attention autoencoder for clustering single-cell RNA-sequencing data","authors":"Lin Zhang ,&nbsp;Haiping Xiang ,&nbsp;Feng Wang ,&nbsp;Zepeng Chen ,&nbsp;Mo Shen ,&nbsp;Jiani Ma ,&nbsp;Hui Liu ,&nbsp;Hongdang Zheng","doi":"10.1016/j.ymeth.2024.06.010","DOIUrl":"10.1016/j.ymeth.2024.06.010","url":null,"abstract":"<div><p>Single-cell RNA-sequencing (scRNA-seq) enables the investigation of intricate mechanisms governing cell heterogeneity and diversity. Clustering analysis remains a pivotal tool in scRNA-seq for discerning cell types. However, persistent challenges arise from noise, high dimensionality, and dropout in single-cell data. Despite the proliferation of scRNA-seq clustering methods, these often focus on extracting representations from individual cell expression data, neglecting potential intercellular relationships. To overcome this limitation, we introduce scGAAC, a novel clustering method based on an attention-based graph convolutional autoencoder. By leveraging structural information between cells through a graph attention autoencoder, scGAAC uncovers latent relationships while extracting representation information from single-cell gene expression patterns. An attention fusion module amalgamates the learned features of the graph attention autoencoder and the autoencoder through attention weights. Ultimately, a self-supervised learning policy guides model optimization. scGAAC, a hypothesis-free framework, performs better on four real scRNA-seq datasets than most state-of-the-art methods. The scGAAC implementation is publicly available on Github at: <span>https://github.com/labiip/scGAAC</span><svg><path></path></svg>.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 115-124"},"PeriodicalIF":4.2,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141475598","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}