Biology Methods and Protocols最新文献

DNA double-strand breaks (DSBs) represent critical events in genome integrity, arising from both endogenous cellular processes and exogenous factors. These breaks are implicated in various genomic aberrations and chromosomal rearrangements, leading to cancers and genetic disorders. Common and rare fragile sites, containing repetitive elements and non-B DNA structures, are particularly prone to breakage under replication stress, which play a pivotal role in cancer development and genetic diseases. Accurate quantification of DNA breaks in the context of repetitive sequences such as microsatellites or non-B DNA structures is technically challenging. We have been comparing four different methods to reliably quantify DSBs in repetitive DNA, namely Southern blot, DSB-PCR, real-time DSB-qPCR, and digital PCR (dPCR). We show here that dPCR offers enhanced sensitivity and specificity compared to other methods. This provides significant applications for future disease diagnosis, understanding molecular mechanisms generating chromosomal breakage and for the development of gene therapies for microsatellite expansion disorders.

Mitochondrial transplantation is a promising but still experimental strategy for treating ischemic and metabolic disorders. A key barrier to its advancement is the lack of scalable, non-invasive methods for tracking transplanted extracellular mitochondria in vivo. Technetium-99m (Tc-99m) radiopharmaceuticals, widely used in SPECT imaging, may offer a clinically compatible solution. Cryopreserved mitochondria derived from HEK-293 cells were incubated with Tc-99m sestamibi, tetrofosmin, pertechnetate, or control solutions. After brief incubation and washing, mitochondrial pellets were analyzed for retained radioactivity. ATP content was measured to assess metabolic function, and electron microscopy was used to evaluate ultrastructural integrity. Tc-99m sestamibi and tetrofosmin showed labeling efficiencies of 2.74% and 2.68%, respectively. Pertechnetate demonstrated minimal uptake (0.34%). Radiolabeled mitochondria retained ATP production comparable to controls. Electron microscopy showed preserved double membranes and cristae. Controls confirmed assay specificity and viability. To our knowledge, this is the first report of radiolabeling isolated mitochondria with clinically approved Tc-99m agents. This platform supports the development of SPECT-compatible protocols for visualizing viable transplanted mitochondria in recipient tissues.

Age is a significant risk factor for mild cognitive impairment (MCI) and Alzheimer's disease (AD) and identifying brain age patterns is critical for comprehending the normal aging and MCI/AD processes. Prior studies have widely established the univariate relationships between brain regions and age, while multivariate associations remain largely unexplored. Herein, various artificial intelligence (AI) models were used to perform brain age prediction using an MRI dataset (n = 825). The optimal AI model was then integrated with the feature importance methods, namely Shapley additive explanations (SHAP), local interpretable model-agnostic explanations, and layer-wise relevance propagation, to identify the significant multivariate brain regions hierarchically involved in this prediction. Our results showed that the deep learning model (referred to as AgeNet) outperformed conventional machine learning models for brain age prediction, and that AgeNet integrated with SHAP (referred to as AgeNet-SHAP) identified all ground-truth perturbed regions as key predictors of brain age in semi-simulation, demonstrating the validity of our methodology. In the experimental dataset, when compared to cognitively normal (CN) participants, MCI exhibited moderate differences in brain regions, whereas AD showed highly robust and widely distributed regional differences. Individualized AgeNet-SHAP regional features further showed associations with clinical severity scores in the AD continuum. These results collectively facilitate data-driven explainable AI approaches for disease progression, diagnostics, prognostics, and personalized medicine efforts.

The dicentric chromosome assay is a well-established biodosimetric method used to assess absorbed ionizing radiation doses by detecting dicentric chromosomal aberrations. Here, we present a detailed, reproducible protocol for applying the dicentric chromosome assay for in vitro evaluation of radioprotective agents, including novel piperazine derivatives compared with amifostine and its active metabolite WR-1065. The protocol covers all key steps-blood sample preparation, in vitro irradiation, lymphocyte culture, metaphase preparation, and scoring of dicentric chromosomes. It highlights critical stages that affect data quality and reproducibility. Integrating manual scoring with automated analysis using the Metafer system ensures accurate and efficient assessment. Thus, this protocol bridges the fields of biological dosimetry and preclinical screening of radioprotective agents, providing a reliable framework for emergency radiation dose estimation and the development of new radiation medical countermeasures.

Methods to create electronic cigarette (e-cigarette) vapor condensate are needed for use in e-cigarette vapor exposure studies. There are currently several methods to produce condensate described in the literature, but they are often cost-prohibitive, complex, or potentially hazardous, thus limiting the true availability of these methods to many researchers in the field. Here, we developed a method to make e-cigarette vapor condensate utilizing a button-activated vaping device and inexpensive supplies such as a syringe, vinyl tubing of varying diameters, an assortment of fittings, a conical tube, and ordinary, hard-sided, ice packs. The method of condensate production described here produced a yield of 35 µL of condensate per 15 puffs of e-cigarette vapor. This method is cost-effective, easy to perform, and can be readily used by researchers at a wide variety of institutions.

Taphonomic research aims at reconstructing processes affecting the preservation and modification of paleobiological entities. Recent critiques of the reliability of deep learning (DL) for taphonomic analysis of bone surface modifications (BSMs), such as that presented by Courtenay et al. based on a selection of earlier published studies, have raised concerns about the efficacy of the method. Their critique, however, overlooked fundamental principles regarding the use of small and unbalanced datasets in DL. By reducing the size of the training and validation sets-resulting in a training set only 20% larger than the testing set, and some class validation sets that were under 10 images-these authors may inadvertently have generated underfit models in their attempt to replicate and test the original studies. Moreover, errors in coding during the preprocessing of images have resulted in the development of fundamentally biased models, which fail to effectively evaluate and replicate the reliability of the original studies. In this study, we do not aim to directly refute their critique, but instead use it as an opportunity to reassess the efficiency and resolution of DL in taphonomic research. We revisited the original DL models applied to three targeted datasets, by replicating them as new baseline models for comparison against optimized models designed to address potential biases. Specifically, we accounted for issues stemming from poor-quality image datasets and possible overfitting on validation sets. To ensure the robustness of our findings, we implemented additional methods, including enhanced image data augmentation, k-fold cross-validation of the original training-validation sets, and a few-shot learning approach using both supervised learning and model-agnostic meta-learning. The latter methods facilitated the unbiased use of separate training, validation, and testing sets. The results across all approaches were consistent, with comparable-if not almost identical-outcomes to the original baseline models. As a final validation step, we used images of recently generated BSM to act as testing sets with the baseline models. The results also remained virtually invariant. This reinforces the conclusion that the original models were not subject to methodological overfitting and highlights their nuanced efficacy in differentiating BSM. However, it is important to recognize that these models represent pilot studies, constrained by the limitations of the original datasets in terms of image quality and sample size. Future work leveraging larger datasets with higher-quality images has the potential to enhance model generalization, thereby improving the applicability and reliability of DL approaches in taphonomic research.

CRISPR-based genome editing relies on guide RNA sequences to target specific regions of interest. A large number of methods have been developed to predict how efficient different guides are at inducing indels. As more experimental data becomes available, methods based on machine learning have become more prominent. Here, we explore whether quantifying the uncertainty around these predictions can be used to design better guide selection strategies. We demonstrate how using a deep ensemble approach achieves better performance than utilizing a single model. This approach can also provide uncertainty quantification. This allows to design, for the first time, strategies that consider uncertainty in guide RNA selection. These strategies achieve precision over 90% and can identify suitable guides for >93% of genes in the mouse genome.

We aimed to develop and validate a standardized, qualitative-quantitative protocol for digital IHC analysis to assess neurodevelopmental biomarkers in placental tissue. Placental tissues from 60 births were obtained from the Western Region Birth Cohort (ROC), and IHC staining was performed using Novolink^TM Polymer System. The primary antibody against 11βHSD2 protein was used for protocol development, and ANXA1 was employed for validation. Slides were digitized using the Aperio ScanScope XT, and image analysis was conducted using the Positive Pixel Count V9 algorithm. Protein expression levels were calculated using the IHC Index formula. Protocol steps included combined optical and digital evaluation, representative fields per slide, intra- and interobserver validation, and assessment of reproducibility. Digital analysis of three random fields (scale bar: 300 µm) showed strong concordance with optical microscopy assessments for 11βHSD2 placental expression. Intraobserver validation showed a strong correlation (τ: 0.70, P < .001) and a substantial concordance (k_w: 0.67; P-value < .001), while interobserver comparisons also yielded substantial agreement (k_w: 0.61, P < .001), confirming the protocol's reliability. Validation using ANXA1 expression revealed moderate intra- and interobserver concordance (k_w: 0.50 and k_w: 0.48, respectively; both P < .001), reinforcing the protocol's applicability across different proteins. In conclusion, we established a reproducible digital IHC analysis protocol that enhances reliability in exploratory research. This approach optimizes image quantification, minimizes observer bias, and contributes to advances in developmental biology research and digital pathology focused on placental neurodevelopment biomarkers.

Allergens are a major concern in determining protein safety, especially with the growing use of recombinant proteins in new medical products. These proteins require a careful allergenicity assessment to guarantee their safety. However, traditional laboratory tests for allergenicity are expensive and time-consuming. To address this challenge, bioinformatics offers efficient and cost-effective alternatives for predicting protein allergenicity. Deep learning models offer a promising solution for this purpose. Recently, with the emergence of protein language models(pLMs), high-quality and impactful feature vectors can be extracted from protein sequences using these specialized language models. Although different computational methods can be effective individually, combining them could improve the prediction results. Given this hypothesis, can we develop a more powerful approach than existing methods to predict protein allergenicity? In this study, we developed an enhanced deep learning model to predict the potential allergenicity of proteins based on their primary structure represented as protein sequences. In simple terms, this model classifies protein sequences into allergenic or non-allergenic classes. Our approach utilizes two pLMs to extract distinct feature vectors for each sequence, which are then fed into a deep neural network (DNN) model for classification. Combining these feature vectors improves the results. Finally, we integrated our top-performing models using ensemble modeling techniques. This approach could balance the model's sensitivity and specificity. Our proposed model demonstrates an improvement compared to existing models, achieving a sensitivity of 97.91%, a specificity of 97.69%, an accuracy of 97.80%, and an area under the receiver operating characteristic curve of 99% using the standard 2-fold cross-validation. The AllerTrans model has been deployed as a web-based prediction tool and is publicly accessible at: https://huggingface.co/spaces/sfaezella/AllerTrans.

Age-associated disease management depends significantly on chronological age and macro-level clinical data sets. However, the biological age captures bio-physiological deterioration more precisely than the chronological age. Biological ageing is the accumulation of successive damage to various cells, tissues, and individual organs over the ageing period. It is the explicit reflection of functional decline. Therefore, quantifying biological age can be highly valuable for improving clinical management of age-related changes. Various epigenetic clocks have been used to quantify biological age. However, epigenetics alone cannot fully account for the complex ageing process, which involves ageing hallmarks, signalling pathways, clinical phenotypes, physiological functions, environmental exposures, and lifestyle habits. Therefore, the primary purpose of this pilot study is the feasibility testing and trajectory mapping of the ageing biomarkers across diverse age-based subgroups. This study will help to find reliable, reproducible, robust, and integrative ageing biomarkers to quantify biological age. This community-based prospective cohort study will be conducted at the National Centre of Ageing, All India Institute of Medical Sciences, New Delhi. This study will include 250 participants from six cohorts, i.e. newborns, adolescents (10-19 years), young adults (20-39 years), middle-aged individuals (40-59 years), young olds (60-79 years), and the oldest old (above 80 years). Forty individuals from each cohort will be recruited to study blood and stool biomarkers along with a comprehensive assessment of cognitive behaviour, psychological well-being, functional capacity, gut health, nutritional behaviour, and physiological measures. Participants will also be monitored in real time through wearable devices. After five years, participants will be followed up with the same biomarkers to gain insights about the speed of ageing, predicting disease and mortality. Multi-domain data will be integrated to develop a deep learning-based multi-model algorithm for biological age estimation. This first-of-its-kind study would provide an exhaustive understanding of the ageing process throughout life, 0-100 years. Integrative biomarkers would make a precise determination of biological age. Additionally, studying change in these parameters after five years would elucidate the pace of biological ageing and predict life expectancy and disability.