Biology Methods and Protocols最新文献

{"title":"Development of a large-scale rapid LAMP diagnostic testing platform for pandemic preparedness and outbreak response.","authors":"Rinie van Beuningen, Kin Ki Jim, Maikel Boot, Michel Ossendrijver, Bart J F Keijser, Jeroen H B van de Bovenkamp, Willem J G Melchers, Tim Kievits","doi":"10.1093/biomethods/bpae090","DOIUrl":"10.1093/biomethods/bpae090","url":null,"abstract":"<p><p>The coronavirus disease 2019 (COVID-19) pandemic underscored the necessity for rapid and efficient diagnostic testing to mitigate outbreaks and control disease transmission. While real-time reverse transcriptase quantitative PCR (RT-qPCR) has been the gold standard due to its high sensitivity and specificity, its logistical complexities and extended turnaround times highlighted the need for alternative molecular methods and non-standard equipment and consumables not subject to supply chain pressure. Loop-mediated isothermal amplification (LAMP) offers several advantages over RT-qPCR, including faster processing time, assay flexibility and cost-effectiveness. During the pandemic, LAMP was successfully demonstrated as a viable alternative to RT-qPCR for SARS-Related Coronavirus 2 detection. However, due to a 100 to 1,000-fold increase in testing volumes, there was an imminent need for automating and scaling up existing LAMP testing workflows leveraging a robotic infrastructure, while retaining analytical performance and cost-effectiveness. In 2020, the Foundation TOMi started the \"TOMi corona initiative\" to develop and validate a high-throughput, end-to-end, automated, scalable single-step RNA purification, and LAMP-based COVID-19 testing system called SMART-LAMP (Scalable Molecular Automation for Rapid Testing using LAMP) that can process up to 40,000 samples per day using existing laboratory equipment infrastructure with sensitivity comparable to RT-qPCR. This system provides a rapid and scalable diagnostic solution for future pandemics, capable of processing over 40,000 samples per day. In addition, the system is designed to minimize consumable costs and reduces the overall use of plastics to align with increasingly strict sustainability goals that will be imposed over the coming years. Importantly, this system and public-private partnerships in the TOMi corona initiative has the potential to serve as a baseline to enhance pandemic preparedness and response capabilities.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae090"},"PeriodicalIF":2.5,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11634539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814397","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":"iHDSel software: The price equation and the population stability index to detect genomic patterns compatible with selective sweeps. An example with SARS-CoV-2.","authors":"Antonio Carvajal-Rodríguez","doi":"10.1093/biomethods/bpae089","DOIUrl":"10.1093/biomethods/bpae089","url":null,"abstract":"<p><p>A large number of methods have been developed and continue to evolve for detecting the signatures of selective sweeps in genomes. Significant advances have been made, including the combination of different statistical strategies and the incorporation of artificial intelligence (machine learning) methods. Despite these advances, several common problems persist, such as the unknown null distribution of the statistics used, necessitating simulations and resampling to assign significance to the statistics. Additionally, it is not always clear how deviations from the specific assumptions of each method might affect the results. In this work, allelic classes of haplotypes are used along with the informational interpretation of the Price equation to design a statistic with a known distribution that can detect genomic patterns caused by selective sweeps. The statistic consists of Jeffreys divergence, also known as the population stability index, applied to the distribution of allelic classes of haplotypes in two samples. Results with simulated data show optimal performance of the statistic in detecting divergent selection. Analysis of real severe acute respiratory syndrome coronavirus 2 genome data also shows that some of the sites playing key roles in the virus's fitness and immune escape capability are detected by the method. The new statistic, called <i>J_HAC</i> , is incorporated into the iHDSel (informed HacDivSel) software available at https://acraaj.webs.uvigo.es/iHDSel.html.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae089"},"PeriodicalIF":2.5,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11646571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830126","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":"Correction to: Heterozygous <i>KCNH2</i> variant phenotyping using Flp-In HEK293 and high-throughput automated patch clamp electrophysiology.","authors":"","doi":"10.1093/biomethods/bpae085","DOIUrl":"https://doi.org/10.1093/biomethods/bpae085","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/biomethods/bpab003.].</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae085"},"PeriodicalIF":2.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808177","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":"MRI-based whole-brain elastography and volumetric measurements to predict brain age.","authors":"Claudio Cesar Claros-Olivares, Rebecca G Clements, Grace McIlvain, Curtis L Johnson, Austin J Brockmeier","doi":"10.1093/biomethods/bpae086","DOIUrl":"10.1093/biomethods/bpae086","url":null,"abstract":"<p><p>Brain age, as a correlate of an individual's chronological age obtained from structural and functional neuroimaging data, enables assessing developmental or neurodegenerative pathology relative to the overall population. Accurately inferring brain age from brain magnetic resonance imaging (MRI) data requires imaging methods sensitive to tissue health and sophisticated statistical models to identify the underlying age-related brain changes. Magnetic resonance elastography (MRE) is a specialized MRI technique which has emerged as a reliable, non-invasive method to measure the brain's mechanical properties, such as the viscoelastic shear stiffness and damping ratio. These mechanical properties have been shown to change across the life span, reflect neurodegenerative diseases, and are associated with individual differences in cognitive function. Here, we aim to develop a machine learning framework to accurately predict a healthy individual's chronological age from maps of brain mechanical properties. This framework can later be applied to understand neurostructural deviations from normal in individuals with neurodevelopmental or neurodegenerative conditions. Using 3D convolutional networks as deep learning models and more traditional statistical models, we relate chronological age as a function of multiple modalities of whole-brain measurements: stiffness, damping ratio, and volume. Evaluations on held-out subjects show that combining stiffness and volume in a multimodal approach achieves the most accurate predictions. Interpretation of the different models highlights important regions that are distinct between the modalities. The results demonstrate the complementary value of MRE measurements in brain age models, which, in future studies, could improve model sensitivity to brain integrity differences in individuals with neuropathology.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpae086"},"PeriodicalIF":2.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11790219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143123758","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":"Deep learning and transfer learning for brain tumor detection and classification.","authors":"Faris Rustom, Ezekiel Moroze, Pedram Parva, Haluk Ogmen, Arash Yazdanbakhsh","doi":"10.1093/biomethods/bpae080","DOIUrl":"10.1093/biomethods/bpae080","url":null,"abstract":"<p><p>Convolutional neural networks (CNNs) are powerful tools that can be trained on image classification tasks and share many structural and functional similarities with biological visual systems and mechanisms of learning. In addition to serving as a model of biological systems, CNNs possess the convenient feature of transfer learning where a network trained on one task may be repurposed for training on another, potentially unrelated, task. In this retrospective study of public domain MRI data, we investigate the ability of neural network models to be trained on brain cancer imaging data while introducing a unique camouflage animal detection transfer learning step as a means of enhancing the networks' tumor detection ability. Training on glioma and normal brain MRI data, post-contrast T1-weighted and T2-weighted, we demonstrate the potential success of this training strategy for improving neural network classification accuracy. Qualitative metrics such as feature space and DeepDreamImage analysis of the internal states of trained models were also employed, which showed improved generalization ability by the models following camouflage animal transfer learning. Image saliency maps further this investigation by allowing us to visualize the most important image regions from a network's perspective while learning. Such methods demonstrate that the networks not only 'look' at the tumor itself when deciding, but also at the impact on the surrounding tissue in terms of compressions and midline shifts. These results suggest an approach to brain tumor MRIs that is comparable to that of trained radiologists while also exhibiting a high sensitivity to subtle structural changes resulting from the presence of a tumor.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae080"},"PeriodicalIF":2.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808179","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":"Practical application of PMA-qPCR assay for determination of viable cells of inter-species biofilm of <i>Candida albicans-Staphylococcus aureus</i>.","authors":"Samuel Kendra, Jarmila Czucz Varga, Barbora Gaálová-Radochová, Helena Bujdáková","doi":"10.1093/biomethods/bpae081","DOIUrl":"10.1093/biomethods/bpae081","url":null,"abstract":"<p><p>Determining the number of viable cells by calculating colony-forming units is time-consuming. The evaluation of mixed biofilms consisting of different species is particularly problematic. Therefore, the aim of this study was to optimize a molecular method-propidium monoazide quantitative polymerase chain reaction (PMA-qPCR)-for accurate and consistent differentiation between living and dead cells. In the practical experimental example, the number of genome copies representing living cells was determined in a mixed biofilm of <i>Candida albicans</i>-<i>Staphylococcus aureus</i> inhibited by photodynamic inactivation. Optimal conditions such as PMA concentration and the duration of light exposure, the optimization of DNA isolation from the mixed biofilm and standardization of PMA-qPCR parameters were tested prior to the main experiment. The genome copy number was calculated based on the known amount of genomic DNA in the qPCR and the genome size of the respective microorganism. The results showed that photodynamic inactivation in the presence of 1 mM methylene blue decreased the total genome copy number from 1.65 × 10⁸ to 3.19 × 10⁷, and from 4.39 × 10⁷ to 1.91 × 10⁷ for <i>S. aureus</i> and <i>C. albicans</i> (<i>P </i><<i> </i>0.01), respectively. The main disadvantage is the overestimation of the number of living cells represented by genome copy numbers. Such cells are unable to reproduce and grow (no vitality) and are continuously dying. On the other hand, PMA-qPCR determines the copy numbers of all microbial species, including a mix of eukaryotic yeasts and prokaryotic bacteria in a biofilm in one step, which is a great advantage.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae081"},"PeriodicalIF":2.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808244","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 new breath collection method for analyzing volatile organic compounds from intubated mouse models.","authors":"Alastair Taylor, Sylvia Blum, Madeleine Ball, Owen Birch, Hsuan Chou, Julia Greenwood, Shane Swann, Lara Pocock, Max Allsworth, Billy Boyle, Kerstin Geillinger-Kaestle","doi":"10.1093/biomethods/bpae087","DOIUrl":"10.1093/biomethods/bpae087","url":null,"abstract":"<p><p>A new pre-clinical method for capturing breath samples from intubated mice is presented. This method significantly reduces background levels, allowing more accurate measurements of VOCs originating from the breath (\"on-breath\") as opposed to background contamination. The method was developed by integrating industry-standard volatile-capturing sorbent tubes with respiratory mechanics measurement equipment (flexiVent^®), resulting in a mouse breath sample that can be transported and analyzed by TD-GC-MS and other central lab technologies. Using the methodology, the discrimination between on-breath VOCs from background compounds provides a cleaner dataset, which can accelerate the validation of VOCs identified from mouse models and their translation to clinical trials. Three metrics were developed to identify on-breath VOCs, with 22 identified using Type 1 (50% of the breath samples exceeding three standard deviations above the mean signal of the system blanks), 34 with Type 2 (<i>P</i>-value ≤ .05 between paired breath and blank samples), and 61 with Type 3 (ROC-AUC value ≥ 0.8 to differentiate between breath and blank samples). The number of compounds seen at elevated levels on mouse breath was quantified and compared to the levels seen on human breath samples to compare methodologies.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae087"},"PeriodicalIF":2.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808122","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":"Spontaneous breaking of symmetry in overlapping cell instance segmentation using diffusion models.","authors":"Julius B Kirkegaard","doi":"10.1093/biomethods/bpae084","DOIUrl":"10.1093/biomethods/bpae084","url":null,"abstract":"<p><p>Instance segmentation is the task of assigning unique identifiers to individual objects in images. Solving this task requires breaking the inherent symmetry that semantically similar objects must result in distinct outputs. Deep learning algorithms bypass this break-of-symmetry by training specialized predictors or by utilizing intermediate label representations. However, many of these approaches break down when faced with overlapping labels that are ubiquitous in biomedical imaging, for instance for segmenting cell layers. Here, we discuss the reason for this failure and offer a novel approach for instance segmentation based on diffusion models that breaks this symmetry spontaneously. Our method outputs pixel-level instance segmentations matching the performance of models such as cellpose on the cellpose fluorescent cell dataset, while also permitting overlapping labels.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae084"},"PeriodicalIF":2.5,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808261","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":"Enhancing negative control selection: A comparative analysis of random and targeted sampling techniques for obtaining High-Quality RNA from normal breast tissue.","authors":"Komal Mehta, Archana Sharma, Anurag Mehta, Juhi Tayal","doi":"10.1093/biomethods/bpae083","DOIUrl":"10.1093/biomethods/bpae083","url":null,"abstract":"<p><p>Molecular profiling is a crucial aspect of cancer therapy selection, underscoring the necessity for representative sampling of both tumor and normal tissues. While much attention has been given to representative tumor sampling, there has been a notable lack of exploration into the issue of poor RNA quality in normal breast tissue processing. Normal breast tissue from the same patient is often used as a negative control for most \"-omics\" experiments. RNA extracted from normal breast tissues frequently contains nucleic acids from surrounding adipocytes, endothelial cells, and immune cells, leading to a low representation of ductal elements and skewed results. Therefore, ensuring a complete representation of breast glandular tissue is imperative. The study aimed to investigate the variations in RNA enrichment between a random sampling technique and a targeted sampling approach when visually selecting normal breast tissue sections as negative controls for \"-omics\" experiments. Fifteen female breast cancer subjects who underwent Modified Radical Mastectomy were selected for the study. Normal Breast tissue was visually examined, and samples were collected from random fat pockets (random sampling) and fibromuscular grey-white streak areas (targeted sampling). RNA was isolated, followed by spectrophotometric analysis, agarose gel electrophoresis and Agilent Tape station analysis. Histopathological assessments and a gene expression study for housekeeping genes were performed on both subsets. Tissues collected through targeted sampling exhibited significantly higher RNA quality than those obtained via random sampling. Histopathological analysis revealed cellular areas abundant in terminal ductular units within the targeted samples, and a final validation qPCR showed that the targeted samples were the most representative of normal breast glandular tissue. The comparative analysis of the two sampling methods clearly indicates that the targeted approach, with its superior accuracy and reliability, is the more practical choice for obtaining representative normal breast glandular tissue for \"-omics\" experiments.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae083"},"PeriodicalIF":2.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808215","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":"Efficient protocol for isolating human fibroblast from primary skin cell cultures: application to keloid, hypertrophic scar, and normal skin biopsies.","authors":"Sri Suciati Ningsih, Sri Widia A Jusman, Rahimi Syaidah, Raisa Nauli, Fadilah Fadilah","doi":"10.1093/biomethods/bpae082","DOIUrl":"10.1093/biomethods/bpae082","url":null,"abstract":"<p><p>This protocol introduces a streamlined and efficient method for isolating human fibroblast from skin primary cell culture with a specific focus on its application to keloid, hypertrophic scar, and normal skin biopsies. Additionally, the absence of suitable animal models for keloid and hypertrophic scar has led preclinical research to rely on in vitro studies using primary cell cultures. This approach addresses the challenges of existing protocols in terms of time, cost, equipment, and technical expertise required. The method involves derivation, culture, and characterization analysis including cell proliferation, migration, and fibroblastic marker (Vimentin, CD90, CD73, and CD105) expression. Our study yielded high amounts of fibroblast from tested skin explants while maintaining their in vivo-like characteristics and behaviour. Immunostaining assay confirmed that the cultivated fibroblast was positively expressed Vimentin. Flowcytometry results showed high expression of CD90 and CD73 while relatively showing lower expression of CD105. Fibroblast derived from keloid tissue showed the highest rate of proliferation and migration ability compared to the other samples. These findings suggest an efficient and reproducible technique to cultivate high qualified fibroblast from human skin in normal or pathological condition, particularly for keloid and hypertrophic scar. The application of this protocol provides a foundation for further studies to investigate the progression and potential intervention of aberrant fibrotic dermatological disorder, in vitro.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"9 1","pages":"bpae082"},"PeriodicalIF":2.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11565194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648945","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}