Biology Methods and Protocols最新文献

Adaptive, rather than innate, immunity relies mainly on antigen-antibody recognition. This recognition is driven by the binding of specific antibody paratopes to distinct epitopes found on antigens. This interaction is pivotal for immune responses that have been re-purposed for diagnostic and therapeutic purposes. This article focuses on Western blotting, an in vitro technique performed for protein immunodetection. Traditionally, this technique requires separate incubations of both primary and secondary antibodies, for which these antibodies recognize different antigen epitopes (conventional method). We propose a modified protocol combining both antibodies, involving a single incubation step that reduces time and conserves reagents (non-conventional/improved method). This improved protocol will enhance efficiency without compromising detection accuracy. It will support multiplexing, enabling the simultaneous detection of multiple proteins. Despite the positive results found by applying available antibodies, further optimization is required for a more thorough evaluation, to ensure that all antibodies consistently yield successful results in every detection attempt for broader use. Our findings indicate that the tested antibody cocktails remained stable over time, which suggests potential for commercialization of this modified Western blot protocol with a wide scope towards multiplex diagnostic application.

Fluorescence microscopy images of biological samples contain valuable information but require rigorous analysis for accurate and reliable determination of changes in protein localization, fluorescence intensity, and morphology of the studied objects. Traditionally, cells for microscopy are immobilized using chemicals, which can introduce stress. Analysis often focuses only on colocalization and involves manual segmentation and measurement, which are time-consuming and can introduce bias. Our new workflow addresses these issues by gently immobilizing cells using a small agarose block on a microscope coverslip. This approach is suitable for cell-walled cells (yeast, fungi, plants, bacteria), facilitates their live imaging under conditions close to their natural environment and enables the addition of chemicals during time-lapse experiments. The primary focus of the protocol is on the presented analysis workflow, which is applicable to virtually any cell type-we describe cell segmentation using the Cellpose software followed by automated analysis of a multitude of parameters using custom-written Fiji (ImageJ) macros. The results can be easily processed using the provided R markdown scripts or available graphing software. Our method facilitates unbiased batch analysis of large datasets, improving the efficiency and accuracy of fluorescence microscopy research. The reported sample preparation protocol and Fiji macros were used in our recent publications: Microbiol Spectr (2022), DOI: 10.1128/spectrum.01961-22; Microbiol Spectr (2022), DOI: 10.1128/spectrum.02489-22; J Cell Sci (2023), DOI: 10.1242/jcs.260554.

Traumatic brain injury (TBI) can be caused by a sudden blow or jolt to the head, causing irreversible brain damage leading to cellular and functional loss. Mammals cannot repair such damage, which may increase the risk of progressive neurodegeneration. Unlike mammals, lower vertebrates such as zebrafish have the astounding capability to regenerate their brains. A model system would be of great value to study zebrafish brain regeneration. Here, we describe a physical method to induce traumatic injury in the zebrafish brain and outline a pipeline to utilize this model system to explore various aspects of brain regeneration. This will significantly advance the fields of regenerative biology and neuroscience. The method includes inducing TBI and validating this through histological assays, immunohistochemistry, and gene expression analysis. By using this model system, researchers will be able to gain valuable insights into the cellular and molecular mechanisms underlying brain regeneration. Understanding these mechanisms could lead to the identification of potential strategies to address neurodegenerative conditions in higher vertebrates.

Severe acute respiratory syndrome coronavirus infection presents complications known as long COVID, a multisystemic organ disease which allows multidimensional analysis. This study aims to uncover clusters of long COVID cases and establish their correlation with the clinical classification developed at the Clinical Research Unit of Brugmann University Hospital, Brussels. Such an endeavour is instrumental in customizing patient management strategies tailored to the unique needs of each distinct group. A two-stage multidimensional exploratory analysis was performed on a retrospective cohort of 205 long COVID patients, involving a factorial analysis of mixed data, and then hierarchical clustering post component analysis. The study's sample comprised 76% women, with an average age of 44.5 years. Three clinical forms were identified: long, persistent, and post-viral syndrome. Multidimensional analysis using demographic, clinical, and biological variables identified three clusters of patients. Biological data did not provide sufficient differentiation between clusters. This emphasizes the importance of identifying or classifying long COVID patients according to their predominant clinical syndrome. Long COVID phenotypes, as well as clinical forms, appear to be associated with distinct pathophysiological mechanisms or genetic predispositions. This underscores the need for further research.

The aim of this study was to make unstructured neuropathological data, located in the NeuroBioBank (NBB), follow Findability, Accessibility, Interoperability, and Reusability principles and investigate the potential of large language models (LLMs) in wrangling unstructured neuropathological reports. By making the currently inconsistent and disparate data findable, our overarching goal was to enhance research output and speed. The NBB catalog currently includes information from medical records, interview results, and neuropathological reports. These reports contain crucial information necessary for conducting an in-depth analysis of NBB data but have multiple formats that vary across different NBB biorepositories and change over time. In this study, we focused on a subset of 822 donors with Parkinson's disease (PD) from seven NBB biorepositories. We developed a data model with combined Brain Region and Pathological Findings data at its core. This approach made it easier to build an extraction pipeline and was flexible enough to convert resulting data to Common Data Elements, a standardized data collection tool used by the neuroscience community to improve consistency and facilitate data sharing across studies. This pilot study demonstrated the potential of LLMs in structuring unstructured neuropathological reports of PD patients available in the NBB. The pipeline enabled successful extraction of detailed tissue-level (microscopic) and gross anatomical (macroscopic) observations, along with staging information from pathology reports, with extraction quality comparable to manual curation results. To our knowledge, this is the first attempt to automatically standardize neuropathological information at this scale. The collected data have the potential to serve as a valuable resource for PD researchers, facilitating integration with clinical information and genetic data (such as genome-wide genotyping and whole-genome sequencing) available through the NBB, thereby enabling a more comprehensive understanding of the disease.

In patients with monoclonal gammopathies, tumor B cells or plasma cells secrete a monoclonal antibody (M protein) that not only serves as a biomarker for tumor tracking but can also cause potentially life-threatening organ damage. This damage is driven by the patient-specific sequence of the M protein. Methods for sequencing M proteins have been limited by their high cost and time-consuming nature, and while recent approaches using next-generation sequencing or mass spectrometry offer advancements, they may require tumor cell sorting, are limited to subsets of immunoglobulin genes or patients, and/or lack extensive validation. To address these limitations, we have recently developed a novel method, termed Single Molecule Real-Time Sequencing of the M protein (SMaRT M-Seq), which combines the unbiased amplification of expressed immunoglobulin genes via inverse PCR from circularized cDNA with long-read DNA sequencing, followed by bioinformatic and immunogenetic analyses. This approach enables the unambiguous identification of full-length variable sequences of M protein genes across diverse patient cohorts, including those with low tumor burden. Our protocol has undergone technical validation and has been successfully applied to large patient cohorts with monoclonal gammopathies. Here we present the step-by-step protocol for SMaRT M-Seq. By enabling the parallel sequencing of M proteins from a large number of samples in a cost-effective and time-efficient manner, SMaRT M-Seq facilitates access to patient-specific M protein sequences, advancing personalized medicine approaches and enabling deeper mechanistic studies in monoclonal gammopathies.

Post-COVID conditions (PCC) emerged during the pandemic, prompting a rise in the use of Digital Health Technologies (DHTs) to manage lockdowns and hospital overcrowding. Real-time tracking and information analyses were crucial to strengthening the global research response. This study aims to map the use of modern digital approaches in estimating the prevalence, predicting, diagnosing, treating, monitoring, and prognosis of PCC. This review was conducted by searching PubMed and Scopus databases for keywords and synonyms related to DHTs, Smart Healthcare Systems, and PCC based on the World Health Organization definition. Articles published from 1 January 2020 to 21 May 2024 were screened for eligibility based on predefined inclusion criteria, and the PRISMA framework was used to report the findings from the retained studies. Our search identified 377 studies, but we retained 23 studies that used DHTs, artificial intelligence (AI), and infodemiology to diagnose, estimate prevalence, predict, treat, and monitor PCC. Notably, a few interventions used infodemics to identify the clinical presentations of the disease, while most utilized Electronic Health Records and AI tools to estimate diagnosis and prevalence. However, we found that AI tools were scarcely used for monitoring symptoms, and studies involving SHS were non-existent in low- and middle-income countries (LMICs). These findings show several DHTs used in healthcare, but there is an urgent need for further research in SHS for complex health conditions, particularly in LMICs. Enhancing DHTs and integrating AI and infodemiology provide promising avenues for managing epidemics and related complications, such as PCC.

Pancreatic cancer is an aggressive malignancy with a poor prognosis. Single-nucleotide mutations in the KRAS gene are detected in the majority of patients with pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer. Identifying KRAS mutations by liquid biopsy could be effective for detecting de novo and recurrent PDAC; however, sensitive and accurate detection remains challenging. We examined the utility of oligoribonucleotide interference-PCR (ORNi-PCR) followed by real-time PCR or droplet digital PCR (ddPCR) for detecting KRAS single-nucleotide mutations by liquid biopsy. A model of cell-free DNA was used to demonstrate that the ORNi-PCR-based methods are more sensitive and accurate for detecting KRAS mutant DNA than conventional real-time PCR or ddPCR. ORNi-PCR-based methods could be useful for early detection of de novo and recurrent PDAC by liquid biopsy for cancer diagnosis.

The coronavirus disease-19 pandemic has resulted in a significant global health crisis, causing hundreds of millions of cases and millions of deaths. Despite being declared endemic, SARS-CoV-2 infection continues to pose a significant risk, particularly for immunocompromised individuals, highlighting the need for a more sensitive and specific detection. Reverse transcription digital droplet polymerase chain reaction (RT-ddPCR) possesses a sensitive and absolute quantification compared to the gold standard. This study is the first to optimize RT-ddPCR for detecting SARS-CoV-2 in saliva specimens using a commercially available RT-qPCR kit. Optimization involved the assessment of the RT-ddPCR reaction mixture, annealing temperature adjustments, and validation using 40 stored saliva specimens. RT-qPCR was used as a reference method in this study. Compatibility assessment revealed that ddPCR Supermix for Probes (no dUTP) was preferable with an optimal annealing temperature of 57.6°C. Although a 25% higher primer/probe concentration provides a higher amplitude in droplet separation of positive control, the number of copy numbers decreased. An inverse correlation between Ct value and copy number concentration was displayed, presenting that the lower the Ct value, the higher the concentration, for the N and E genes with r² values of 0.98 and 0.85, respectively. However, ORF1ab was poorly correlated (r² of 0.34). The sensitivity of targeted and E genes was 100% and 93.3%, respectively; as for the specificity, the percentage ranged from 80.8% to 91.3%. This study implicates the applicability of a modified method in the ddPCR platform for similar types of pathogens using saliva specimens.

The elimination of brownish pigments from plant protein extracts has been a challenge in plant biochemistry studies. Although numerous approaches have been developed to reduce pigments for enzyme assays, none has been able to completely remove pigments from plant protein extracts for biochemical studies. A simple and effective protocol was developed to completely remove pigments from plant protein extracts. Proteins were extracted from red anthocyanin-rich transgenic and greenish wild-type tobacco cells cultured on agar-solidified Murashige and Skoog medium. Protein extracts from these cells were brownish or dark due to the pigments. Four approaches were comparatively tested to show that the diethylaminoethyl (DEAE)-Sephadex anion exchange gel column was effective in completely removing pigments to obtain transparent pigment-free protein extracts. A Millipore Amicon^® Ultra 10K cut-off filter unit was used to effectively desalt proteins. Moreover, the removal of pigments significantly improved the measurement accuracy of total soluble proteins. Furthermore, enzymatic assays using catechol as a substrate coupled with high-performance liquid chromatography analysis demonstrated that the pigment-free proteins not only showed polyphenol oxidase (PPO) activity but also enhanced the catalytic activity of PPO. Taken together, this protocol is effective for extracting pigment-free plant proteins for plant biochemistry studies. A simple and effective protocol was successfully developed to not only completely and effectively remove anthocyanin and polyphenolics-derived quinone pigments from plant protein extracts but also to decrease the effects of pigments on the measurement accuracy of total soluble proteins. This robust protocol will enhance plant biochemical studies using pigment-free native proteins, which in turn increase their reliability and sensitivity.