BMC methods最新文献

{"title":"Mosquito Tissue Ultrastructure-Expansion Microscopy (MoTissU-ExM) enables ultrastructural and anatomical analysis of malaria parasites and their mosquito.","authors":"Benjamin Liffner, Thiago Luiz Alves E Silva, Joel Vega-Rodriguez, Sabrina Absalon","doi":"10.1186/s44330-024-00013-4","DOIUrl":"10.1186/s44330-024-00013-4","url":null,"abstract":"<p><strong>Background: </strong>Study of malaria parasite cell biology is challenged by their small size, which can make visualisation of individual organelles difficult or impossible using conventional light microscopy. In recent years, the field has attempted to overcome this challenge through the application of ultrastructure expansion microscopy (U-ExM), which physically expands a biological sample approximately 4.5-fold. To date, U-ExM has mostly been used to visualise blood-stage parasites and used exclusively on parasites in vitro.</p><p><strong>Methods: </strong>Here we develop Mosquito Tissue U-ExM (MoTissU-ExM), a method for preparing dissected mosquito salivary glands and midguts by U-ExM. MoTissU-ExM preserves both host and parasite ultrastructure, enabling visualisation of oocysts and sporozoites in situ. We also provide a point-by-point protocol for how to perform MoTissU-ExM.</p><p><strong>Results: </strong>We validate that MoTissU-ExM samples expand as expected, provide a direct comparison of the same dissected tissues before and after MoTissU-ExM, and highlight some of the key host and parasite structures that can be visualised following MoTissU-ExM.</p><p><strong>Discussion: </strong>We discuss potential use cases for MoTissU-ExM for study of malaria parasite biology, and more broadly. We detail drawbacks or challenges MoTissU-ExM and imaging these expanded tissues, along with information troubleshooting this technique. Finally, we discuss how MoTissU-ExM could be applied and adapted in future to increase its utility.</p>","PeriodicalId":519945,"journal":{"name":"BMC methods","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12714001/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145806966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confirming size-exclusion chromatography as a clinically relevant extracellular vesicles separation method from 1mL plasma through a comprehensive comparison of methods","authors":"S. Robinson, Mark Samuels, William Jones, Nicolas A. Stewart, Murat Eravci, N. Mazarakis, D.C. Gilbert, Giles Critchley, G. Giamas","doi":"10.1186/s44330-024-00007-2","DOIUrl":"https://doi.org/10.1186/s44330-024-00007-2","url":null,"abstract":"","PeriodicalId":519945,"journal":{"name":"BMC methods","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mini-MEndR: a miniaturized 96-well predictive assay to evaluate muscle stem cell-mediated repair.","authors":"Nitya Gulati, Sadegh Davoudi, Bin Xu, Saifedine T Rjaibi, Erik Jacques, Justin Pham, Amir Fard, Alison P McGuigan, Penney M Gilbert","doi":"10.1186/s44330-024-00005-4","DOIUrl":"10.1186/s44330-024-00005-4","url":null,"abstract":"<p><strong>Background: </strong>Functional evaluation of molecules that are predicted to promote stem cell mediated endogenous repair often requires in vivo transplant studies that are low throughput and hinder the rate of discovery. To offer greater throughput for functional validation studies, we miniaturized, simplified and expanded the functionality of a previously developed muscle endogenous repair (MEndR) in vitro assay that was shown to capture significant events of in vivo muscle endogenous repair.</p><p><strong>Methods: </strong>The mini-MEndR assay consists of miniaturized cellulose scaffolds designed to fit in 96-well plates, the pores of which are infiltrated with human myoblasts encapsulated in a fibrin-based hydrogel to form engineered skeletal muscle tissues. Pre-adsorbing thrombin to the cellulose scaffolds facilitates in situ tissue polymerization, a critical modification that enables new users to rapidly acquire assay expertise. Following the generation of the 3D myotube template, muscle stem cells (MuSCs), enriched from digested mouse skeletal muscle tissue using an improved magnetic-activated cell sorting protocol, are engrafted within the engineered template. Murine MuSCs are fluorescently labeled, enabling co-evaluation of human and mouse Pax7⁺ cell responses to drug treatments. A regenerative milieu is introduced by injuring the muscle tissue with a myotoxin to initiate endogenous repair \"in a dish\". Phenotypic data is collected at endpoints with a high-content imaging system and is analyzed using ImageJ-based image analysis pipelines.</p><p><strong>Results: </strong>The miniaturized format and modified manufacturing protocol cuts reagent costs in half and hands-on seeding time ~ threefold, while the image analysis pipelines save 40 h of labour. By evaluating multiple commercially available human primary myoblast lines in 2D and 3D culture, we establish quality assurance metrics for cell line selection that standardizes myotube template quality. In vivo outcomes (enhanced muscle production and Pax7⁺ cell expansion) to a known modulator of MuSC mediated repair (p38/β MAPK inhibition) are recapitulated in the miniaturized culture assay, but only in the presence of stem cells and the regenerative milieu.</p><p><strong>Discussion: </strong>The miniaturized predictive assay offers a simple, scaled platform to co-investigate human and mouse skeletal muscle endogenous repair molecular modulators, and thus is a promising strategy to accelerate the muscle endogenous repair discovery pipeline.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44330-024-00005-4.</p>","PeriodicalId":519945,"journal":{"name":"BMC methods","volume":"1 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11173370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery of optimal cell type classification marker genes from single cell RNA sequencing data.","authors":"Angela Liu, Beverly Peng, Ajith V Pankajam, Thu Elizabeth Duong, Gloria Pryhuber, Richard H Scheuermann, Yun Zhang","doi":"10.1186/s44330-024-00015-2","DOIUrl":"10.1186/s44330-024-00015-2","url":null,"abstract":"<p><strong>Background: </strong>The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data.</p><p><strong>Methods: </strong>NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells.</p><p><strong>Results: </strong>By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions in simulation studies and on its ability to identify markers with higher On-Target Fraction values for closely related cell types in real data, and outperforms other marker gene selection approaches for cell type classification with significantly higher F-beta scores when applied to datasets from three human organs-brain, kidney, and lung.</p><p><strong>Discussion: </strong>Finally, we discuss potential use cases of the NS-Forest marker genes, including for designing spatial transcriptomics gene panels and semantic representation of cell types in biomedical ontologies, for the broad user community.</p>","PeriodicalId":519945,"journal":{"name":"BMC methods","volume":"1 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}