Biology Methods and Protocols最新文献

The skin is the largest organ in the body and is the site for a diverse set of diseases. Yet, given the complexity of the cutaneous tissue, there is a limited availability of data in the literature on skin proteomics. Here, we proposed an adapted and optimized protocol for the extraction of proteins from human skin, using a combination of chemical and mechanical lysis approaches. For this, we used of a lysis buffer containing 2% SDS, 50 mM TEAB, and a 1% protease and phosphatase inhibitor cocktail, in addition to Matrix A beads and a FastPrep-24 5G homogenizer. For the characterization of the samples, the obtained proteins were purified and digested using the SP3 method (Single-pot, solid phase, sample preparation), and analyzed by nano liquid chromatography coupled with tandem mass spectrometry. In this way, we were able to identify around 6000 proteins in the skin samples from healthy individuals and patients with the fungal infection sporotrichosis. Our improved methodology could significantly enrich our understanding of skin biology and provide new perspectives for the discovery of biomarkers and therapeutic targets for cutaneous diseases.

The remarkable capacity of the liver to regenerate its lost mass after resection makes living donor liver transplantation a successful treatment option. However, donor heterogeneity significantly influences recovery trajectories, highlighting the need for individualized monitoring. With the rising incidence of liver diseases, safer transplant procedures and improved donor care are urgently needed. Current clinical markers provide only limited snapshots of recovery, making it challenging to predict long-term outcomes. Following partial hepatectomy, precise liver mass recovery requires tightly regulated hepatocyte proliferation. We identified distinct gene expression patterns associated with liver regeneration by analyzing blood-derived gene expression measurements from twelve donors followed over a year. Using a deep learning-based framework, we integrated these patterns with a mathematical model of hepatocyte transitions to develop a personalized, progressive mechanistic digital twin-a virtual liver model that predicts donor-specific recovery trajectories. Central to our approach is a mechanistically identifiable latent space, defined by variables derived from a physiologically grounded differential equation model of liver regeneration, which enables biologically interpretable, bidirectional mapping between gene expression data and model dynamics. This approach integrates clinical genomics and computational modeling to enhance post-surgical care, ensuring safer transplants and improved donor recovery.

Efficient interrogation of neurobiology remains bottlenecked by obtaining mature neurons. Immortalized cell lines still require lengthy differentiation periods to obtain neuron-like cells, which may not efficiently differentiate and are challenging to transfect with plasmids relative to other cell lines such as HEK-293's. To overcome challenges with limited access to cells that express mature neuronal proteins, we knocked out the RE1-silencing transcription factor (REST) from HEK-293's to create a novel neuron-like cell, which we name Neuro293. RNA-sequencing and bioinformatics analyses revealed a significant upregulation of genes associated with neurobiology and membrane excitability including pre-/post-synaptic proteins, voltage gated ion channels, neuron-cytoskeleton, as well as neurotransmitter synthesis, packaging, and release. Western blot validated the upregulation of Synapsin-1 (Syn1) and Snap-25 as two neuron-restricted proteins, as well as the potassium channel Kv1.2. Immunocytochemistry against Neurofilament 200 kd revealed a significant upregulation and accumulation in singular processes extending from Neuro293's cell body. Similarly, while Syn1 increased in the cell body, Syn1 protein accumulated at the ends of processes extruding from Neuro293's. Neuro293's express reporter-genes through the Syn1 promoter after infection with adeno-associated viruses (AAV). However, transient transfection with AAV2 plasmids led to leaky expression through promoter-independent mechanisms. Despite an upregulation of many voltage-gated ion channels, Neuro293's do not possess excitable membranes. Collectively, REST-knockout in HEK-293's induces a quickly dividing and easily transfectable cell line that expresses neuron-restricted and mature neuronal proteins which can be used for high-throughput biochemical interrogation, however, without further modifications neither HEK-293's or Neuro293's exhibit properties of excitable membranes.

This study introduces an approach to classifying histopathological images for detecting dysplasia in oral cancer through the fusion of support vector machine (SVM) classifiers trained on deep learning features extracted from InceptionResNet-v2 and vision transformer (ViT) models. The classification of dysplasia, a critical indicator of oral cancer progression, is often complicated by class imbalance, with a higher prevalence of dysplastic lesions compared to non-dysplastic cases. This research addresses this challenge by leveraging the complementary strengths of the two models. The InceptionResNet-v2 model, paired with an SVM classifier, excels in identifying the presence of dysplasia, capturing fine-grained morphological features indicative of the condition. In contrast, the ViT-based SVM demonstrates superior performance in detecting the absence of dysplasia, effectively capturing global contextual information from the images. A fusion strategy was employed to combine these classifiers through class selection: the majority class (presence of dysplasia) was predicted using the InceptionResNet-v2-SVM, while the minority class (absence of dysplasia) was predicted using the ViT-SVM. The fusion approach significantly outperformed individual models and other state-of-the-art methods, achieving superior balanced accuracy, sensitivity, precision, and area under the curve. This demonstrates its ability to handle class imbalance effectively while maintaining high diagnostic accuracy. The results highlight the potential of integrating deep learning feature extraction with SVM classifiers to improve classification performance in complex medical imaging tasks. This study underscores the value of combining complementary classification strategies to address the challenges of class imbalance and improve diagnostic workflows.

Cancer treatments often lose effectiveness as tumors develop resistance to single-agent therapies. Combination treatments can overcome this limitation, but the overwhelming combinatorial space of drug-dose interactions makes exhaustive experimental testing impractical. Data-driven methods, such as machine and deep learning, have emerged as promising tools to predict synergistic drug combinations. In this work, we systematically investigate the use of categorical embeddings within Deep Neural Networks to enhance drug synergy predictions. These learned and transferable encodings capture similarities between the elements of each category, demonstrating particular utility in scarce data scenarios.

Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and validated on a dataset of 1001 patients (510 ECE-positive, 491 ECE-negative cases). ProSliceFinder achieved an area under the ROC curve (AUC) of 0.92 (95% confidence interval [CI]: 0.89-0.94) for slice classification, while ExCapNet demonstrated robust performance with an AUC of 0.88 (95% CI: 0.83-0.92) for patient-level ECE detection. Additionally, AutoRadAI's modular design ensures scalability and adaptability for applications beyond ECE detection. Validated through a user-friendly web-based interface for seamless clinical integration, AutoRadAI highlights the potential of AI-driven solutions in precision oncology. This framework improves diagnostic accuracy and streamlines preoperative staging, offering transformative applications in PCa management and beyond.

Thoracic aortic aneurysm and dissection (TAD) is a life-threatening vascular disorder, and smooth muscle cell mitochondrial dysfunction leads to cell death, contributing to TAD. Accurate measurements of metabolic processes are essential for understanding cellular homeostasis in both healthy and diseased states. While assays for evaluating mitochondrial respiration have been well established for cultured cells and isolated mitochondria, no optimized application has been developed for aortic tissue. In this study, we generate an optimized protocol using the Agilent Seahorse XFe24 analyzer to measure mitochondrial respiration in mouse aortic tissue. This method allows for precise measurement of mitochondrial oxygen consumption in mouse aorta, providing a reliable assay for bioenergetic analysis of aortic tissue. The protocol offers a reproducible approach for assessing mitochondrial function in aortic tissues, capturing both baseline OCR and responses to mitochondrial inhibitors, such as oligomycin, FCCP, and rotenone/antimycin A. This method establishes a critical foundation for studying metabolic shifts in aortic tissues and offers valuable insights into the cellular mechanisms of aortic diseases, contributing to a better understanding of TAD progression.

The application of machine learning (ML) techniques in predictive modelling has greatly advanced our comprehension of biological systems. There is a notable shift in the trend towards integration methods that specifically target the simultaneous analysis of multiple modes or types of data, showcasing superior results compared to individual analyses. Despite the availability of diverse ML architectures for researchers interested in embracing a multimodal approach, the current literature lacks a comprehensive taxonomy that includes the pros and cons of these methods to guide the entire process. Closing this gap is imperative, necessitating the creation of a robust framework. This framework should not only categorize the diverse ML architectures suitable for multimodal analysis but also offer insights into their respective advantages and limitations. Additionally, such a framework can serve as a valuable guide for selecting an appropriate workflow for multimodal analysis. This comprehensive taxonomy would provide a clear guidance and support informed decision-making within the progressively intricate landscape of biomedical and clinical data analysis. This is an essential step towards advancing personalized medicine. The aims of the work are to comprehensively study and describe the harmonization processes that are performed and reported in the literature and present a working guide that would enable planning and selecting an appropriate integrative model. We present harmonization as a dual process of representation and integration, each with multiple methods and categories. The taxonomy of the various representation and integration methods are classified into six broad categories and detailed with the advantages, disadvantages and examples. A guide flowchart describing the step-by-step processes that are needed to adopt a multimodal approach is also presented along with examples and references. This review provides a thorough taxonomy of methods for harmonizing multimodal data and introduces a foundational 10-step guide for newcomers to implement a multimodal workflow.

Training to improve the standardization of subjective assessments in biological science is crucial to improve and maintain accuracy. However, in reproductive science there is no standardized training tool available to assess sperm morphology. Sperm morphology is routinely assessed subjectively across several species and is often used as grounds to reject or retain samples for sale or insemination. As with all subjective tests, sperm morphology assessment is liable to human bias and without appropriate standardization these assessments are unreliable. This proof-of-concept study aimed to develop a standardized sperm morphology assessment training tool that can train and test students on a sperm-by-sperm basis. The following manuscript outlines the methods used to develop a training tool with the capability to account for different microscope optics, morphological classification systems, and species of spermatozoa assessed. The generation of images, their classification, organization, and integration into a web interface, along with its design and outputs, are described. Briefly, images of spermatozoa were generated by taking field of view (FOV) images at 40× magnification on DIC optics, amounting to a total of 3,600 FOV images from 72 rams (50 FOV/ram). These FOV images were cropped to only show one sperm per image using a novel machine-learning algorithm. The resulting 9,365 images were labelled by three experienced assessors, and those with 100% consensus on all labels (4821/9365) were integrated into a web interface able to provide both (i) instant feedback to users on correct/incorrect labels for training purposes, and (ii) an assessment of user proficiency. Future studies will test the effectiveness of the training tool to educate students on the application of a variety of morphology classification systems. If proven effective, it will be the first standardized method to train individuals in sperm morphology assessment and help to improve understanding of how training should be conducted.

Three-dimensional (3D) spheroid models have advanced cancer research by better mimicking the tumour microenvironment compared to traditional two-dimensional cell cultures. However, challenges persist in high-throughput analysis of morphological characteristics and cell viability, as traditional methods like manual fluorescence analysis are labour-intensive and inconsistent. Existing AI-based approaches often address segmentation or classification in isolation, lacking an integrated workflow. We propose a scalable, two-stage deep learning pipeline to address these gaps: (i) a U-Net model for precise detection and segmentation of 3D spheroids from microscopic images, achieving 95% prediction accuracy, and (ii) a CNN Regression Hybrid method for estimating live/dead cell percentages and classifying spheroids, with an $R^2$ value of 98%. This end-to-end pipeline automates cell viability quantification and generates key morphological parameters for spheroid growth kinetics. By integrating segmentation and analysis, our method addresses environmental variability and morphological characterization challenges, offering a robust tool for drug discovery, toxicity screening, and clinical research. This approach significantly improves efficiency and scalability of 3D spheroid evaluations, paving the way for advancements in cancer therapeutics.