Biology Methods and Protocols最新文献

{"title":"Measurement of oxygen consumption rate in mouse aortic tissue.","authors":"Zhen Zhou, Ripon Sarkar, Jose Emiliano Esparza Pinelo, Alexis Richard, Jay Dunn, Zhao Ren, Callie S Kwartler, Dianna M Milewicz","doi":"10.1093/biomethods/bpaf031","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf031","url":null,"abstract":"<p><p>Thoracic aortic aneurysm and dissection (TAD) is a life-threatening vascular disorder, and smooth muscle cell mitochondrial dysfunction leads to cell death, contributing to TAD. Accurate measurements of metabolic processes are essential for understanding cellular homeostasis in both healthy and diseased states. While assays for evaluating mitochondrial respiration have been well established for cultured cells and isolated mitochondria, no optimized application has been developed for aortic tissue. In this study, we generate an optimized protocol using the Agilent Seahorse XFe24 analyzer to measure mitochondrial respiration in mouse aortic tissue. This method allows for precise measurement of mitochondrial oxygen consumption in mouse aorta, providing a reliable assay for bioenergetic analysis of aortic tissue. The protocol offers a reproducible approach for assessing mitochondrial function in aortic tissues, capturing both baseline OCR and responses to mitochondrial inhibitors, such as oligomycin, FCCP, and rotenone/antimycin A. This method establishes a critical foundation for studying metabolic shifts in aortic tissues and offers valuable insights into the cellular mechanisms of aortic diseases, contributing to a better understanding of TAD progression.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf031"},"PeriodicalIF":2.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12054972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144031880","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":"Navigating the Multiverse: a Hitchhiker's guide to selecting harmonization methods for multimodal biomedical data.","authors":"Murali Aadhitya Magateshvaren Saras, Mithun K Mitra, Sonika Tyagi","doi":"10.1093/biomethods/bpaf028","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf028","url":null,"abstract":"<p><p>The application of machine learning (ML) techniques in predictive modelling has greatly advanced our comprehension of biological systems. There is a notable shift in the trend towards integration methods that specifically target the simultaneous analysis of multiple modes or types of data, showcasing superior results compared to individual analyses. Despite the availability of diverse ML architectures for researchers interested in embracing a multimodal approach, the current literature lacks a comprehensive taxonomy that includes the pros and cons of these methods to guide the entire process. Closing this gap is imperative, necessitating the creation of a robust framework. This framework should not only categorize the diverse ML architectures suitable for multimodal analysis but also offer insights into their respective advantages and limitations. Additionally, such a framework can serve as a valuable guide for selecting an appropriate workflow for multimodal analysis. This comprehensive taxonomy would provide a clear guidance and support informed decision-making within the progressively intricate landscape of biomedical and clinical data analysis. This is an essential step towards advancing personalized medicine. The aims of the work are to comprehensively study and describe the harmonization processes that are performed and reported in the literature and present a working guide that would enable planning and selecting an appropriate integrative model. We present harmonization as a dual process of representation and integration, each with multiple methods and categories. The taxonomy of the various representation and integration methods are classified into six broad categories and detailed with the advantages, disadvantages and examples. A guide flowchart describing the step-by-step processes that are needed to adopt a multimodal approach is also presented along with examples and references. This review provides a thorough taxonomy of methods for harmonizing multimodal data and introduces a foundational 10-step guide for newcomers to implement a multimodal workflow.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf028"},"PeriodicalIF":2.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12043205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143988258","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":"Development of a sperm morphology assessment standardization training tool.","authors":"Katherine R Seymour, Jessica P Rickard, Kelsey R Pool, Taylor Pini, Simon P de Graaf","doi":"10.1093/biomethods/bpaf029","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf029","url":null,"abstract":"<p><p>Training to improve the standardization of subjective assessments in biological science is crucial to improve and maintain accuracy. However, in reproductive science there is no standardized training tool available to assess sperm morphology. Sperm morphology is routinely assessed subjectively across several species and is often used as grounds to reject or retain samples for sale or insemination. As with all subjective tests, sperm morphology assessment is liable to human bias and without appropriate standardization these assessments are unreliable. This proof-of-concept study aimed to develop a standardized sperm morphology assessment training tool that can train and test students on a sperm-by-sperm basis. The following manuscript outlines the methods used to develop a training tool with the capability to account for different microscope optics, morphological classification systems, and species of spermatozoa assessed. The generation of images, their classification, organization, and integration into a web interface, along with its design and outputs, are described. Briefly, images of spermatozoa were generated by taking field of view (FOV) images at 40× magnification on DIC optics, amounting to a total of 3,600 FOV images from 72 rams (50 FOV/ram). These FOV images were cropped to only show one sperm per image using a novel machine-learning algorithm. The resulting 9,365 images were labelled by three experienced assessors, and those with 100% consensus on all labels (4821/9365) were integrated into a web interface able to provide both (i) instant feedback to users on correct/incorrect labels for training purposes, and (ii) an assessment of user proficiency. Future studies will test the effectiveness of the training tool to educate students on the application of a variety of morphology classification systems. If proven effective, it will be the first standardized method to train individuals in sperm morphology assessment and help to improve understanding of how training should be conducted.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf029"},"PeriodicalIF":2.5,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12036963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053557","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":"A deep learning pipeline for morphological and viability assessment of 3D cancer cell spheroids.","authors":"Ajay K Mali, Sivasubramanian Murugappan, Jayashree Rajesh Prasad, Syed A M Tofail, Nanasaheb D Thorat","doi":"10.1093/biomethods/bpaf030","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf030","url":null,"abstract":"<p><p>Three-dimensional (3D) spheroid models have advanced cancer research by better mimicking the tumour microenvironment compared to traditional <b>two-</b>dimensional cell cultures. However, challenges persist in high-throughput analysis of morphological characteristics and cell viability, as traditional methods like manual fluorescence analysis are labour-intensive and inconsistent. Existing AI-based approaches often address segmentation or classification in isolation, lacking an integrated workflow. We propose a scalable, two-stage deep learning pipeline to address these gaps: (i) a U-Net model for precise detection and segmentation of 3D spheroids from microscopic images, achieving 95% prediction accuracy, and (ii) a CNN Regression Hybrid method for estimating live/dead cell percentages and classifying spheroids, with an <math> <mrow> <msup><mrow><mi>R</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </math> value of 98%. This end-to-end pipeline automates cell viability quantification and generates key morphological parameters for spheroid growth kinetics. By integrating segmentation and analysis, our method addresses environmental variability and morphological characterization challenges, offering a robust tool for drug discovery, toxicity screening, and clinical research. This approach significantly improves efficiency and scalability of 3D spheroid evaluations, paving the way for advancements in cancer therapeutics.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf030"},"PeriodicalIF":2.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015474","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":"Assessing nutritional pigment content of green and red leafy vegetables by image analysis: Catching the \"red herring\" of plant digital color processing via machine learning.","authors":"Avinash Agarwal, Filipe de Jesus Colwell, Viviana Andrea Correa Galvis, Tom R Hill, Neil Boonham, Ankush Prashar","doi":"10.1093/biomethods/bpaf027","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf027","url":null,"abstract":"<p><p>Estimating pigment content of leafy vegetables via digital image analysis is a reliable method for high-throughput assessment of their nutritional value. However, the current leaf color analysis models developed using green-leaved plants fail to perform reliably while analyzing images of anthocyanin (Anth)-rich red-leaved varieties due to misleading or \"red herring\" trends. Hence, the present study explores the potential for machine learning (ML)-based estimation of nutritional pigment content for green and red leafy vegetables simultaneously using digital color features. For this, images of <i>n </i>=<i> </i>320 samples from six types of leafy vegetables with varying pigment profiles were acquired using a smartphone camera, followed by extract-based estimation of chlorophyll (Chl), carotenoid (Car), and Anth. Subsequently, three ML methods, namely, Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR), were tested for predicting pigment contents using RGB (Red, Green, Blue), HSV (Hue, Saturation, Value), and <i>L*a*b*</i> (Lightness, Redness-greenness, Yellowness-blueness) datasets individually and in combination. Chl and Car contents were predicted most accurately using the combined colorimetric dataset via SVR (<i>R²</i> = 0.738) and RFR (<i>R²</i> = 0.573), respectively. Conversely, Anth content was predicted most accurately using SVR with HSV data (<i>R²</i> = 0.818). While Chl and Car could be predicted reliably for green-leaved and Anth-rich samples, Anth could be estimated accurately only for Anth-rich samples due to Anth masking by Chl in green-leaved samples. Thus, the present findings demonstrate the scope of implementing ML-based leaf color analysis for assessing the nutritional pigment content of red and green leafy vegetables in tandem.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf027"},"PeriodicalIF":2.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12057810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062736","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":"Quantitative tools for analyzing rhizosphere pH dynamics: localized and integrated approaches.","authors":"Poonam Kanwar, Stan Altmeisch, Petra Bauer","doi":"10.1093/biomethods/bpaf026","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf026","url":null,"abstract":"<p><p>The rhizosphere, the region surrounding plant roots, plays a critical role in nutrient acquisition, root development, and plant-soil interactions. Spatial variations in rhizosphere pH along the root axis are shaped by environmental cues, nutrient availability, microbial activity, and root growth patterns. Precise detection and quantification of these pH changes are essential for understanding plant plasticity and nutrient efficiency. Here, we present a refined methodology integrating pH indicator bromocresol purple with a rapid, non-destructive electrode-based system to visualize and quantify pH variations along the root axis, enabling high-resolution and scalable monitoring of root-induced pH changes in the rhizosphere. Using this approach, we investigated the impact of iron (Fe) availability on rhizosphere pH dynamics in wild-type (WT) and bHLH39-overexpressing (39Ox) seedlings. bHLH39, a key basic helix-loop-helix transcription factor in Fe uptake, enhances Fe acquisition when overexpressed, often leading to Fe toxicity and reduced root growth under Fe-sufficient conditions. However, its role in root-mediated acidification remains unclear. Our findings reveal that 39Ox plants exhibit enhanced rhizosphere acidification, whereas WT roots display zone-specific pH responses depending on Fe availability. To refine pH measurements, we developed two complementary electrode-based methodologies: localized rhizosphere pH change for region-specific assessment and integrated rhizosphere pH change for net root system variation. These techniques improve resolution, accuracy, and efficiency in large-scale experiments, providing robust tools for investigating natural and genetic variations in rhizosphere pH regulation and their role in nutrient mobilization and ecological adaptation.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf026"},"PeriodicalIF":2.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12036966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015478","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":"Digital pathology assessment of kidney glomerular filtration barrier ultrastructure in an animal model of podocytopathy.","authors":"Aksel Laudon, Zhaoze Wang, Anqi Zou, Richa Sharma, Jiayi Ji, Winston Tan, Connor Kim, Yingzhe Qian, Qin Ye, Hui Chen, Joel M Henderson, Chao Zhang, Vijaya B Kolachalama, Weining Lu","doi":"10.1093/biomethods/bpaf024","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf024","url":null,"abstract":"<p><p>Transmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and podocyte foot processes (PFP). Podocytopathy is associated with glomerular filtration barrier morphological changes observed experimentally and clinically by measuring GBM or PFP width. However, these measurements are currently performed manually. This limits research on podocytopathy disease mechanisms and therapeutics due to labor intensiveness and inter-operator variability. We developed a deep learning-based digital pathology computational method to measure GBM and PFP width in TEM images from the kidneys of Integrin-Linked Kinase (ILK) podocyte-specific conditional knockout (cKO) mouse, an animal model of podocytopathy, compared to wild-type (WT) control mouse. We obtained TEM images from WT and ILK cKO littermate mice at 4 weeks old. Our automated method was composed of two stages: a U-Net model for GBM segmentation, followed by an image processing algorithm for GBM and PFP width measurement. We evaluated its performance with a 4-fold cross-validation study on WT and ILK cKO mouse kidney pairs. Mean [95% confidence interval (CI)] GBM segmentation accuracy, calculated as Jaccard index, was 0.73 (0.70-0.76) for WT and 0.85 (0.83-0.87) for ILK cKO TEM images. Automated and manual GBM width measurements were similar for both WT (<i>P</i> = .49) and ILK cKO (<i>P</i> = .06) specimens. While automated and manual PFP width measurements were similar for WT (<i>P</i> = .89), they differed for ILK cKO (<i>P</i> < .05) specimens. WT and ILK cKO specimens were morphologically distinguishable by manual GBM (<i>P</i> < .05) and PFP (<i>P</i> < .05) width measurements. This phenotypic difference was reflected in the automated GBM (<i>P</i> < .05) more than PFP (<i>P</i> = .06) widths. Our deep learning-based digital pathology tool automated measurements in a mouse model of podocytopathy. This proposed method provides high-throughput, objective morphological analysis and could facilitate podocytopathy research.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf024"},"PeriodicalIF":2.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11992336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143986524","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":"Aptamers as innovative tools for malaria diagnosis and treatment: advances and future perspectives.","authors":"Wendy Yulieth Royero-Bermeo, Miryan Margot Sánchez-Jiménez, Juan David Ospina-Villa","doi":"10.1093/biomethods/bpaf025","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf025","url":null,"abstract":"<p><p>Malaria, caused by <i>Plasmodium</i> spp. parasites (<i>P. vivax</i>, P<i>. falciparum</i>, <i>P. ovale</i>, <i>P. malariae</i>, and <i>P. knowlesi</i>), remains a significant global health challenge, with 263 million cases and 567 000 deaths reported in 2023. Diagnosis in endemic regions relies on clinical symptoms, microscopy, and rapid diagnostic tests. Although widely used, microscopy suffers from variability in sensitivity due to operator expertise and low parasitemia. Rapid diagnostic tests, which are favored for their simplicity and speed, show high sensitivity for <i>P. vivax</i> but reduced accuracy (80%) for <i>P. falciparum</i>, which is attributed to deletions in histidine-rich protein 2/3 proteins caused by <i>Pfhrp2/3</i> gene mutations. Innovative diagnostic and therapeutic technologies, such as aptamers, are gaining attention. Aptamers are single-stranded oligonucleotides that bind specifically to target molecules with high affinity. They have shown promise in disease diagnosis, therapeutics, and environmental monitoring. In malaria, aptamers are being explored as highly sensitive and specific diagnostic tools capable of detecting <i>Plasmodium</i> proteins across all infection stages. Additionally, they offer potential for novel therapeutic strategies, enhancing disease control and treatment options. These advancements highlight the use of aptamers as versatile and innovative approaches for addressing malaria and other infectious diseases. A comprehensive literature search was conducted in the PubMed, ScienceDirect, and SCOPUS databases via the keywords \"Aptamers\" AND \"Malaria\" AND \"Aptamers\" AND \"Plasmodium.\" Additionally, patent searches were carried out in the LENS, WIPO, and LATIPAT databases via the same search terms. In total, 88 relevant articles were selected for this review, providing a comprehensive and evidence-based foundation to discuss emerging aptamer technologies for malaria diagnosis and treatment. The proteins commonly employed in rapid malaria diagnostic tests, such as histidine-rich protein 2, <i>P.</i> lactate dehydrogenase, and prostaglandin dehydrogenase, are highlighted. However, the identification of new targets, such as HMIGB1 and DRX1 (1-deoxy-d-xylulose-5-phosphate reductoisomerase), and the detection of whole cells have also been emphasized.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf025"},"PeriodicalIF":2.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11992340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053532","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":"Optimized immunofluorescence for liver structure analysis: Enhancing 3D resolution and minimizing tissue autofluorescence.","authors":"Elena Zoppolato, Hasse Mol, Carlos Estrella-García, Nicole Vizcaino-Rodríguez, Diana Sanchez, Nicole Procel, Isabel Baroja, Leticia Sansores-Garcia, Iván M Moya","doi":"10.1093/biomethods/bpaf023","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf023","url":null,"abstract":"<p><p>The study of liver biology and pathology through marker expression analysis and tissue structure visualization is constrained by the high autofluorescence caused by the presence of lipofuscins, vitamin A, and lipid droplets, which traditional staining methods do not effectively quench. This leads to low signal-to-noise ratios, obscured expression levels, and reduced structural resolution. We mitigated liver tissue autofluorescence using Sudan Black B staining, which effectively quenches background signals from lipid and lipofuscin accumulation. Additionally, these protocols typically use thin paraffin sections (5-7 µm), which limit the analysis of larger and more complex liver structures. Liver tissue is highly organized in three dimensions, with large hepatocytes (20-30 µm in diameter) arranged around sinusoids and bile canaliculi, which form intricate branching networks. Thin sections cannot capture this 3D organization, providing only a \"snapshot\" of the tissue at one plane. Here, we present an optimized immunofluorescence protocol using 100-200 µm vibratome-cut liver sections to enable a more comprehensive 3D-like analysis of liver architecture. Finally, our protocol includes antigen retrieval steps tailored to each antibody, maximizing epitope accessibility and signal clarity. Together, these improvements provide a robust method for detailed liver studies with enhanced specificity and structural resolution in immunofluorescent staining. This protocol is particularly suited for researchers focused on liver regeneration, cancer, chronic disease pathology, and structural analysis. However, other researchers interested in exploring complex tissue structures in other autofluorescent tissues, such as the kidney, brain, pancreas, spleen, and adipose tissue, will also find this method beneficial.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf023"},"PeriodicalIF":2.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040401","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":"Mass photometry as a fast, facile characterization tool for direct measurement of mRNA length.","authors":"Andrew Schmudlach, Saralynn Spear, Yimin Hua, Stephanie Fertier-Prizzon, Jianmei Kochling","doi":"10.1093/biomethods/bpaf021","DOIUrl":"10.1093/biomethods/bpaf021","url":null,"abstract":"<p><p>Oligonucleotide integrity is a critical quality attribute for many new therapeutic modalities. Current assays often measure attributes such as length using capillary electrophoresis or liquid chromatography. The length is then corroborated with sequencing data to ensure oligonucleotide quality. An orthogonal measure to these classical separations is to measure intact mass, which traditional mass spectrometry cannot. Herein, we report the use of mass photometry to directly measure RNA length using RNA ladders as a calibrant.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf021"},"PeriodicalIF":2.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11954547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754947","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}