Biology Methods and Protocols最新文献

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.

Obesity is defined as a disease in which abnormal excessive body fat accumulation causes adverse effects on health. One proposed contributing factor to the rise in obesity is the exposure to endocrine disruptors acting as obesogens. Semitransparent zebrafish larvae, with their well-developed white adipose tissue (WAT), offer a unique opportunity for studying the effects of toxicant chemicals and pharmaceuticals on adipocyte dynamics and whole-organism adiposity in a vertebrate model. The work presented here is a detailed optimized zebrafish obesogenic test (ZOT) protocol. The method allows to assess the effects of diet composition, drugs and environmental contaminants, acting as obesogens or anti-obesogens, alone or in combination, on WAT levels in zebrafish larvae. Zootechnical parameter guidelines, including larvae rearing conditions, feeding, and selection of larvae to be enrolled are provided. An optimized procedure for in vivo staining of adipocyte lipid droplets with Nile Red before and after exposure to compounds is provided to enhance reproducibility. Using suitable subcutaneous WAT locations, a rationally defined guide for wide-field fluorescence microscopy signal acquisition is proposed. The ZOT analysis software was developed to enable automated and efficient image data processing by using custom-trained supervised deep-learning models. The present ZOT protocol distinguishes intrinsic variability of the test method from the biological effect measured. It is the basis of a specific, sensitive, and robust quantitative in vivo assay for high-throughput screening of compounds and food content that influence adipocyte hyper/hypotrophy. As such, it provides relevant information for environmental as well as human risk and benefit assessments.

γ-aminobutyrate (GABA) is a non-proteinogenic amino acid produced by glutamate decarboxylase (GAD) that functions as a vital neurotransmitter in animals, and as an important metabolite and signaling molecule in plants and microbes. "GABase" consists of a mixture of recombinant GABA transaminase (GABA-T) and succinic semialdehyde dehydrogenase (SSDH) that is widely used for spectrophotometric quantification of glutamate decarboxylase (GAD) activity or GABA levels in tissue extracts. Both can be conveniently monitored at 340 nm owing to the sequential conversion of GABA into succinate by GABA-T and SSDH, and concomitant reduction of NADP⁺ into NADPH by SSDH. Currently, these assays rely on commercially available GABase from Pseudomonas fluorescens. However, the excessive cost of commercial GABase prompted us to develop an inexpensive and rapid "DIY" method for producing GABase by cloning, expressing and purifying His₆-tagged GABA-T and SSDH from Escherichia coli. We validated our in-house GABase preparation by comparing GAD activities and GABA levels of the model plant Arabidopsis thaliana with those obtained using commercial GABase. Both pET30a plasmids for expressing E. coli His₆-GABA-T and His₆-SSDH have been deposited into AddGene (www.addgene.com). Our protocols for producing and using recombinant E. coli GABase should be of interest to any researcher who studies eukaryotic or prokaryotic GABA and/or GAD activity.

The precise determination of viral titers in virological studies is a critical step to assess the infectious viral concentration of a sample. Although conventional titration methods, such as endpoint dilution or plaque forming units are the gold standards, their widespread use for screening experiments remains limited due to the time-consuming aspect and resource-intensive requirements. This study introduces a rapid and user-friendly high-throughput screening assay for evaluating viral titers. The colorimetric method used relies upon assessing virus-induced cytopathic effects by measuring the reduction of a tetrazolium reagent to formazan through cellular dehydrogenation within mitochondria. The resulting formazan quantity is correlated with the viral titer and can be easily quantified by a colorimetric measurement. In this perspective, this manuscript describes two case studies for the titration of the porcine respiratory coronavirus virus and bovine alpha herpesvirus 1, highlighting, respectively, a linear regime between 100 and 2000 TCID₅₀/ml and 500- ${10}^6$ PFU/ml for rapid titration within these ranges. The proposed technique's advantages and drawbacks are discussed, along with potential applications such as drug screening and the assessment of viral survival on inert surfaces.

Cells respond to environmental stresses such as cold and osmotic stresses. These stresses induce signal transduction pathways in cells. However, the molecular mechanisms activated by cold and osmotic stresses in higher eukaryotes remain elusive. Previously, we described a reporter system utilizing inducible translocation trap that detects nuclear translocation of 2-amino-3-ketobutyrate coenzyme A ligase (KBL) in response to cold and osmotic stresses. In the present study, we developed additional reporter systems to detect intracellular events induced by these stresses. These reporter systems will be instrumental to elucidate the intracellular signaling mechanisms activated by these stresses.