Biology Methods and Protocols最新文献

Tissue specimens taken from primary tumors or metastases contain important information for diagnosis and treatment of cancer patients. Multiplex imaging allows in situ visualization of heterogeneous cell populations, such as immune cells, in tissue samples. Most image processing pipelines first segment cell boundaries and then measure marker expression to assign cell phenotypes. In dense tissue environments, this segmentation-first approach can be inaccurate due to segmentation errors or overlapping cells. Here, we introduce the machine-learning pipeline "ImmuNet", which identifies positions and phenotypes of cells without segmenting them. ImmuNet is easy to train: human annotators only need to click on an immune cell and score its expression of each marker-drawing a full cell outline is not required. We trained and evaluated ImmuNet on multiplex images from human tonsil, lung cancer, prostate cancer, melanoma, and bladder cancer tissue samples and found it to consistently achieve error rates below 5%-10% across tissue types, cell types, and tissue densities, outperforming a segmentation-based baseline method. Furthermore, we externally validate ImmuNet results by comparing them to flow cytometric cell count measurements from the same tissue. In summary, ImmuNet is an effective, simpler alternative to segmentation-based approaches when only cell positions and phenotypes, but not their shapes, are required for downstream analyses. Thus, ImmuNet helps researchers to analyze cell positions in multiplex tissue images more easily and accurately.

Peroxidase DNAzymes are single-stranded, stable G-quadruplexes structures that exhibit catalytic activity with cofactor hemin. This class of DNAzymes offers several advantages over traditional protein and RNA catalysts, including thermal stability, resistance to hydrolysis, and easy of synthesis in the laboratory. However, their use in medicine, biology, and chemistry is limited due to their low catalytic rates. Selecting and evolving for higher catalytic rates has been challenging due to limitations in selection methodology which generally use affinity as the selection pressure instead of kinetics. We previously evolved a new peroxidase DNAzyme (mSBDZ-X-3) through a directed evolution method, which was subsequently used for proximity labelling in a proteomic experiment in cell culture. Herein, we present a detailed protocol for this function-based laboratory evolution method to evolve peroxidase DNAzymes for future laboratory implementation. This approach is based on capturing self-biotinylated DNA, which is catalyzed by intrinsic peroxidase activity to select for DNAzyme molecules. The selection method uses fluorescence-based real-time monitoring of the DNA pools, allowing for the enrichment of catalytic activity and capture of catalytic DNA across evolutionary selection rounds. The evolved mSBDZ-X-3 DNAzyme attributes parallel G-quadruplex structure and demonstrates better catalytic properties than DNAzyme variants evolved previously. The influence of critical reaction parameters is outlined. This protocol enables discovery of improved peroxidase DNAzyme/RNAzyme variants from natural or chemical-modified nucleotide libraries. The approach could be applicable for the selection of catalytic activities in a variety of directed molecular evolution contexts.

Intravenous injection provides a direct, rapid, and efficient route for delivering drugs or other substances, particularly for compounds with poor intestinal absorption or molecules (e.g. proteins) that are prone to structural changes and degradation within the digestive system. While Drosophila larvae represent a well-established genetic model for studying developmental and physiological pathways, as well as human diseases, their use in analyzing the molecular effects of substance exposure remains limited. In this study, we present a highly efficient injection method for Drosophila first- and second-instar larvae. Despite causing a slight developmental delay, this method achieves a high survival rate and offers a quick, easily adjustable protocol. The process requires 3-5 h to inject 150-300 larvae, depending on the microcapillary needle, microinjection system, and the compound being administered. As proof of concept, we compared the effects of injecting ferritin protein into Fer1HCH⁰⁰⁴⁵¹ mutant first instar larvae with those of dietary ferritin administration. Our results show that ferritin injection rescues Fer1HCH mutants, a result that cannot be achieved through dietary delivery. This approach is particularly valuable for the delivery of complex compounds in cases where oral administration is impaired or limited by the digestive system.

The main objective of the ZebraCool programme was to create a positive attitude and curiosity towards science by bringing experimental activities within schools using an introductory cognitive and sensory approach. This innovative programme was offered at all levels of primary and secondary education including vocational high schools. Thematic workshops can be carried out on various themes such as comparative anatomy and embryology, molecular biology and evolution, or toxicology and endocrine disruptors. They were on an ad hoc basis or as part of an annual school project using zebrafish as a model. This animal was a very attractive entry point for the educator to motivate students to appreciate biology, in particular in the field of molecular biology and evolution. For each practical workshop, the student was an actor in his/her learning, which was intended to arouse the curiosity and desire to understand and learn. The programme was based on close collaboration between class teachers and programme educators to adapt workshops' content to the school curriculum. Students conducted their own experiments, formulated and tested hypotheses, learned laboratory techniques, collected, and analysed data. ZebraCool scientific activities fell within a conceptual framework of evolutionary biology through which participants perceived their own inner fish through the comparison of biological processes between humans and zebrafish.

In this protocol for obtaining doubled haploids plants (DH), we propose a new method for microspore isolation. This method is useful for genotypes of the Brassicaceae family with low responsiveness to DH technology. For such crops, it allows increasing the embryo yield several times and sometimes obtaining embryos for the first time. This method of microspore isolation reduces the mechanical impact on the bud tissue, which minimizes somatic cell destruction and reduces to get it into the preparation through the filter, thus increasing its purity. The new isolation method also increases the relative concentration of embryogenic microspores in the preparation. This is possible because the anther tissues are not destroyed during the isolation process. Therefore, the anther retains its structure and microspores of early and late stages are trapped by the anther tissue, thus the anther acts as a sieve. Late stages are trapped because of their larger size, while early stages are trapped because they are even more tightly bound to the anther tissue. Together, these factors increase the efficiency of the technology for DH production in vitro microspore culture. This protocol article provides a detailed experimental protocol to the method presented in the experimental article (E.V. Kozar, E.G. Kozar, E.A. Domblides. Effect of the Method of Microspore Isolation on the Efficiency of Isolated Microspore Culture In Vitro for Brassicaceae Family. Horticulturae. 2022. Vol. 8, No. 10. P. 864. DOI 10.3390/horticulturae8100864) but does not repeat all the results documenting the efficacy of the actual method.

The coronavirus disease 2019 (COVID-19) pandemic underscored the necessity for rapid and efficient diagnostic testing to mitigate outbreaks and control disease transmission. While real-time reverse transcriptase quantitative PCR (RT-qPCR) has been the gold standard due to its high sensitivity and specificity, its logistical complexities and extended turnaround times highlighted the need for alternative molecular methods and non-standard equipment and consumables not subject to supply chain pressure. Loop-mediated isothermal amplification (LAMP) offers several advantages over RT-qPCR, including faster processing time, assay flexibility and cost-effectiveness. During the pandemic, LAMP was successfully demonstrated as a viable alternative to RT-qPCR for SARS-Related Coronavirus 2 detection. However, due to a 100 to 1,000-fold increase in testing volumes, there was an imminent need for automating and scaling up existing LAMP testing workflows leveraging a robotic infrastructure, while retaining analytical performance and cost-effectiveness. In 2020, the Foundation TOMi started the "TOMi corona initiative" to develop and validate a high-throughput, end-to-end, automated, scalable single-step RNA purification, and LAMP-based COVID-19 testing system called SMART-LAMP (Scalable Molecular Automation for Rapid Testing using LAMP) that can process up to 40,000 samples per day using existing laboratory equipment infrastructure with sensitivity comparable to RT-qPCR. This system provides a rapid and scalable diagnostic solution for future pandemics, capable of processing over 40,000 samples per day. In addition, the system is designed to minimize consumable costs and reduces the overall use of plastics to align with increasingly strict sustainability goals that will be imposed over the coming years. Importantly, this system and public-private partnerships in the TOMi corona initiative has the potential to serve as a baseline to enhance pandemic preparedness and response capabilities.

A large number of methods have been developed and continue to evolve for detecting the signatures of selective sweeps in genomes. Significant advances have been made, including the combination of different statistical strategies and the incorporation of artificial intelligence (machine learning) methods. Despite these advances, several common problems persist, such as the unknown null distribution of the statistics used, necessitating simulations and resampling to assign significance to the statistics. Additionally, it is not always clear how deviations from the specific assumptions of each method might affect the results. In this work, allelic classes of haplotypes are used along with the informational interpretation of the Price equation to design a statistic with a known distribution that can detect genomic patterns caused by selective sweeps. The statistic consists of Jeffreys divergence, also known as the population stability index, applied to the distribution of allelic classes of haplotypes in two samples. Results with simulated data show optimal performance of the statistic in detecting divergent selection. Analysis of real severe acute respiratory syndrome coronavirus 2 genome data also shows that some of the sites playing key roles in the virus's fitness and immune escape capability are detected by the method. The new statistic, called J_HAC , is incorporated into the iHDSel (informed HacDivSel) software available at https://acraaj.webs.uvigo.es/iHDSel.html.

[This corrects the article DOI: 10.1093/biomethods/bpab003.].

Brain age, as a correlate of an individual's chronological age obtained from structural and functional neuroimaging data, enables assessing developmental or neurodegenerative pathology relative to the overall population. Accurately inferring brain age from brain magnetic resonance imaging (MRI) data requires imaging methods sensitive to tissue health and sophisticated statistical models to identify the underlying age-related brain changes. Magnetic resonance elastography (MRE) is a specialized MRI technique which has emerged as a reliable, non-invasive method to measure the brain's mechanical properties, such as the viscoelastic shear stiffness and damping ratio. These mechanical properties have been shown to change across the life span, reflect neurodegenerative diseases, and are associated with individual differences in cognitive function. Here, we aim to develop a machine learning framework to accurately predict a healthy individual's chronological age from maps of brain mechanical properties. This framework can later be applied to understand neurostructural deviations from normal in individuals with neurodevelopmental or neurodegenerative conditions. Using 3D convolutional networks as deep learning models and more traditional statistical models, we relate chronological age as a function of multiple modalities of whole-brain measurements: stiffness, damping ratio, and volume. Evaluations on held-out subjects show that combining stiffness and volume in a multimodal approach achieves the most accurate predictions. Interpretation of the different models highlights important regions that are distinct between the modalities. The results demonstrate the complementary value of MRE measurements in brain age models, which, in future studies, could improve model sensitivity to brain integrity differences in individuals with neuropathology.

Convolutional neural networks (CNNs) are powerful tools that can be trained on image classification tasks and share many structural and functional similarities with biological visual systems and mechanisms of learning. In addition to serving as a model of biological systems, CNNs possess the convenient feature of transfer learning where a network trained on one task may be repurposed for training on another, potentially unrelated, task. In this retrospective study of public domain MRI data, we investigate the ability of neural network models to be trained on brain cancer imaging data while introducing a unique camouflage animal detection transfer learning step as a means of enhancing the networks' tumor detection ability. Training on glioma and normal brain MRI data, post-contrast T1-weighted and T2-weighted, we demonstrate the potential success of this training strategy for improving neural network classification accuracy. Qualitative metrics such as feature space and DeepDreamImage analysis of the internal states of trained models were also employed, which showed improved generalization ability by the models following camouflage animal transfer learning. Image saliency maps further this investigation by allowing us to visualize the most important image regions from a network's perspective while learning. Such methods demonstrate that the networks not only 'look' at the tumor itself when deciding, but also at the impact on the surrounding tissue in terms of compressions and midline shifts. These results suggest an approach to brain tumor MRIs that is comparable to that of trained radiologists while also exhibiting a high sensitivity to subtle structural changes resulting from the presence of a tumor.