Biology Methods and Protocols最新文献

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.

Glyoxalase II (Glo II) is a crucial enzyme in the glyoxalase system, and plays a vital role in detoxifying harmful metabolites and maintaining cellular redox balance. Dysregulation of Glo II has been linked to various health conditions, including cancer and diabetes. This study introduces a novel method using 2,4-dinitrophenylhydrazine (2,4-DNPH) to measure Glo II activity. The principle behind this approach is the formation of a colored hydrazone complex between 2,4-DNPH and pyruvate produced by the Glo II-catalyzed reaction. Glo II catalyzes the hydrolysis of S-D-lactoylglutathione (SLG), generating D-lactate and reduced glutathione (GSH). The D-lactate is then converted to pyruvate by lactate dehydrogenase, then reacting with 2,4-DNPH to form a brown-colored hydrazone product. The absorbance of this complex, measured at 430 nm, allows for the quantification of Glo II activity. The study rigorously validates the 2,4-DNPH method, demonstrating its stability, sensitivity, linearity, and resistance to interference from various biochemical substances. Compared to the existing UV method, this 2,4-DNPH-Glo II assay shows a strong correlation. The new protocol for measuring Glo II activity using 2,4-DNPH is simple, cost-effective, and accurate, making it a valuable tool for researchers and medical professionals. Its potential for widespread use in various laboratory settings, from academic research to clinical diagnostics, offers significant opportunities for future research and medical applications.

A central theme in cancer research is to increase our understanding of the cancer tissue microenvironment, which is comprised of a complex and spatially heterogeneous ecosystem of malignant and non-malignant cells, both of which actively contribute to an intervening extracellular matrix. Laser microdissection (LMD) enables histology selective harvest of cellular subpopulations from the tissue microenvironment for their independent molecular investigation, such as by high-throughput DNA and RNA sequencing. Although enabling, LMD often requires a labor-intensive investment to harvest enough cells to achieve the necessary DNA and/or RNA input requirements for conventional next-generation sequencing workflows. To increase efficiencies, we sought to use a commonplace dual preparatory (DP) procedure to isolate DNA and RNA from the same LMD harvested tissue samples. While the yield of DNA from the DP protocol was satisfactory, the RNA yield from the LMD harvested tissue samples was significantly poorer compared to a dedicated RNA preparation procedure. We determined that this low yield of RNA was due to incomplete partitioning of RNA in this widely used DP protocol. Here, we describe a modified DP protocol that more equally partitions nucleic acids and results in significantly improved RNA yields from LMD-harvested cells.

Characterization of T-cell receptors (TCRs) repertoire was revolutionized by next-generation sequencing technologies; however, standardization using biological controls to facilitate precision of current alignment and assembly tools remains a challenge. Additionally, availability of TCR libraries for off-the-shelf cloning and engineering TCR-specific T cells is a valuable resource for TCR-based immunotherapies. We established nine human TCR α and β clones that were evaluated using the 5'-rapid amplification of cDNA ends-like RNA-based TCR sequencing on the Illumina platform. TCR sequences were extracted and aligned using MiXCR, TRUST4, and CATT to validate their sensitivity and specificity and to validate library preparation methods. The correlation between actual and expected TCR ratios within libraries confirmed accuracy of the approach. Our findings established the development of biological standards and library of TCR clones to be leveraged in TCR sequencing and engineering. The remaining human TCR clones' libraries for a more diverse biological control will be generated.

Artificial intelligence is increasingly driving early drug design, offering novel approaches to virtual screening. Phenotypic virtual screening (PVS) aims to predict how cancer cell lines respond to different compounds by focusing on observable characteristics rather than specific molecular targets. Some studies have suggested that deep learning may not be the best approach for PVS. However, these studies are limited by the small number of tested molecules as well as not employing suitable performance metrics and dissimilar-molecules splits better mimicking the challenging chemical diversity of real-world screening libraries. Here we prepared 60 datasets, each containing approximately 30 000-50 000 molecules tested for their growth inhibitory activities on one of the NCI-60 cancer cell lines. We conducted multiple performance evaluations of each of the five machine learning algorithms for PVS on these 60 problem instances. To provide even a more comprehensive evaluation, we used two model validation types: the random split and the dissimilar-molecules split. Overall, about 14 440 training runs aczross datasets were carried out per algorithm. The models were primarily evaluated using hit rate, a more suitable metric in VS contexts. The results show that all models are more challenged by test molecules that are substantially different from those in the training data. In both validation types, the D-MPNN algorithm, a graph-based deep neural network, was found to be the most suitable for building predictive models for this PVS problem.