Biology Methods and Protocols最新文献

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.

Protein maldigestion and malabsorption lead to malnutrition and are a feature of exocrine pancreatic insufficiency (EPI). Although it is the current standard, measurement of nitrogen in stool to assess protease activity is indirect. Up to 80% of hydrolysed proteins appear in blood in the form of peptides, so we developed a method to measure peptide-derived amino acids in plasma as a relevant measure of proteolysis, verified its accuracy, precision, and linearity, and validated it in a porcine model. We modified a ninhydrin method. Large proteins were eliminated from plasma with 10 kDa-cut-off centrifugal filters. Free and total amino acids were measured in permeate before and after its hydrolysis. Peptide-derived amino acids were quantified by subtracting free amino acids from total amino acids. We verified the method in vitro and by comparing results in healthy and EPI pigs. The accuracy of the analysis was close to 100%, with excellent precision (mean relative standard deviation for low, medium, and high amino acid levels = 0.88%) and with stringent linearity (r²  = 0.986, %RE = 5.23). The high-throughput ninhydrin method detected levels of peptide-derived amino acids in vivo with maximal changes seen approximately 2 hours postprandially in young pigs. The AUC and Cmax were significantly higher in healthy compared to EPI pigs (P = .0026 and P = .0037, respectively). The high-throughput ninhydrin method is a sensitive, reliable, and practical method for the estimation of dietary peptide-derived amino acids. This assay endpoint could serve as a direct biomarker of protein digestion and absorption.

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.