Nature Protocols最新文献

Programmable gene integration technologies are an emerging modality with exciting applications in both basic research and therapeutic development. Programmable addition via site-specific targeting elements (PASTE) is a programmable gene integration approach for precise and efficient programmable integration of large DNA sequences into the genome. PASTE offers improved editing efficiency, purity and programmability compared with previous methods for long insertions into the mammalian genome. By combining the specificity and cargo size capabilities of site-specific integrases with the programmability of prime editing, PASTE can precisely insert cargoes of at least 36 kb with efficiencies of up to 60%. Here we outline best practices for design, execution and analysis of PASTE experiments, with protocols for integration of EGFP at the human NOLC1 and ACTB genomic loci and for readout by next generation sequencing and droplet digital PCR. We provide guidelines for designing and optimizing a custom PASTE experiment for integration of desired payloads at alternative genomic loci, as well as example applications for in-frame protein tagging and multiplexed insertions. To facilitate experimental setup, we include the necessary sequences and plasmids for the delivery of PASTE components to cells via plasmid transfection or in vitro transcribed RNA. Most experiments in this protocol can be performed in as little as 2 weeks, allowing for precise and versatile programmable gene insertion.

Neuroimaging has an essential role in studies of brain health and of cerebrovascular and neurodegenerative diseases, requiring the availability of versatile magnetic resonance imaging (MRI) acquisition and processing protocols. We designed and developed a multipurpose high-resolution MRI protocol for large-scale and long-term population neuroimaging studies that includes structural, diffusion-weighted and functional MRI modalities. This modular protocol takes almost 1 h of scan time and is, apart from a concluding abdominal scan, entirely dedicated to the brain. The protocol links the acquisition of an extensive set of MRI contrasts directly to the corresponding fully automated data processing pipelines and to the required quality assurance of the MRI data and of the image-derived phenotypes. Since its successful implementation in the population-based Rhineland Study (ongoing, currently more than 11,000 participants, target participant number of 20,000), the proposed MRI protocol has proved suitable for epidemiological and clinical cross-sectional and longitudinal studies, including multisite studies. The approach requires expertise in magnetic resonance image acquisition, in computer science for the data management and the execution of processing pipelines, and in brain anatomy for the quality assessment of the MRI data. The protocol takes ~1 h of MRI acquisition and ~20 h of data processing to complete for a single dataset, but parallelization over multiple datasets using high-performance computing resources reduces the processing time. By making the protocol, MRI sequences and pipelines available, we aim to contribute to better comparability, interoperability and reusability of large-scale neuroimaging data.

The World Health Organization declared COVID-19 to be a public health emergency of international concern on 30 January 2020 and then a pandemic on 11 March 2020. In early 2020, a group of UK scientists volunteered to provide the public with up-to-date and transparent scientific information. The group formed the Independent Scientific Advisory Group for Emergencies (Independent SAGE) and provided live weekly briefings to the public via YouTube. In this Perspective, we describe how and why this group came together and the challenges it faced. We reflect on 4 years of scientific information broadcasting and discuss the guiding principles followed by Independent SAGE, which may be broadly transferable for strengthening the scientist-public dialogue during public health emergencies in future settings. We discuss the provision of clarity and transparency, engagement with the science-policy interface, the practice of interdisciplinarity, the centrality of addressing inequity, the need for dialogue and partnership with the public, the importance of support for advocacy groups, the diversification of communication channels and modalities, the adoption of regular and organized internal communications, the resourcing and support of the group's communications and the active opposition of misinformation and disinformation campaigns. We reflect on what we might do differently next time and propose research aimed at building the evidence base for optimizing informal scientific advisory groups in crisis situations.

Deep learning approaches have been increasingly applied to the discovery of novel chemical compounds. These predictive approaches can accurately model compounds and increase true discovery rates, but they are typically black box in nature and do not generate specific chemical insights. Explainable deep learning aims to 'open up' the black box by providing generalizable and human-understandable reasoning for model predictions. These explanations can augment molecular discovery by identifying structural classes of compounds with desired activity in lieu of lone compounds. Additionally, these explanations can guide hypothesis generation and make searching large chemical spaces more efficient. Here we present an explainable deep learning platform that enables vast chemical spaces to be mined and the chemical substructures underlying predicted activity to be identified. The platform relies on Chemprop, a software package implementing graph neural networks as a deep learning model architecture. In contrast to similar approaches, graph neural networks have been shown to be state of the art for molecular property prediction. Focusing on discovering structural classes of antibiotics, this protocol provides guidelines for experimental data generation, model implementation and model explainability and evaluation. This protocol does not require coding proficiency or specialized hardware, and it can be executed in as little as 1-2 weeks, starting from data generation and ending in the testing of model predictions. The platform can be broadly applied to discover structural classes of other small molecules, including anticancer, antiviral and senolytic drugs, as well as to discover structural classes of inorganic molecules with desired physical and chemical properties.

Tissue clearing, coupled with immunostaining, enables the transition from two-dimensional to three-dimensional pathology and has the potential to substantially improve data quality for biomedical diagnostics. Nevertheless, the workflows are limited by the complex sample processing protocols. Approaches for the parallel processing of samples, to include tissue clearing, immunostaining, imaging and analysis can increase three-dimensional pathology throughput. Here we detail a step-by-step approach that combines a tissue clearing device with a six-well multiwell plate to increase the throughput compared with methods using conventional clearing protocols. The six-well multiplate allows for parallel tissue clearing of multiple samples and is compatible with passive tissue clearing methods including Clear, Unobstructed Brain/Body Imaging Cocktails and Computational (CUBIC) analysis. In addition, gel embedding is performed without moving the samples from the wells, and a series of steps such as imaging with a high-speed light-sheet microscope and analysis in the cloud can be performed. Although this procedure slightly extends the overall time required for preparing and analyzing a single sample, it reduces the effort required at each step, such as reagent exchange and gel embedding, which results in an overall reduction in hands-on time due to the parallel sample processing. We describe a series of whole-organ analyses, including high-throughput tissue clearing, staining, gel embedding, imaging and data analysis in the cloud, as a useful platform for cellular biology and pathology. The total process varies depending on the presence or absence of immunostaining, but for some six-well plates, the tissue clearing process, imaging and data analysis can be completed within 10 d.

The advancement of research on human cerebellar development and diseases has been hindered by the lack of a cell-based system that mirrors the cellular diversity and functional characteristics of the human cerebellum. Here, we describe our protocol for a human pluripotent stem cell-derived human cerebellar organoid (hCerO) model, which successfully replicates the cellular diversity of the fetal cerebellum along with some of its distinct cytoarchitectural features. Our approach involves the patterning of human pluripotent stem cells, resulting in the generation of both cerebellar excitatory and inhibitory progenitor populations-specifically, the rhombic lip and ventricular zone progenitors, respectively. This patterning strategy leads to the reproducible differentiation of the major neurons of the cerebellum such as granule cells and Purkinje cells within just one month of culture. hCerOs serve as platforms for molecular, cellular and functional assays, including single-cell transcriptomics, immunohistochemistry and investigations into calcium dynamics and electrophysiological properties. Remarkably, the cultivation of hCerOs for up to 8 months enables the healthy survival and maturation of Purkinje cells, which exhibit molecular and electrophysiological features akin to their in vivo counterparts. Overall, our protocol generates and allows for the long-term culture of all major cell types within the cerebellum. Consequently, this significant advancement provides the developmental neurobiology field with a robust platform for exploring both cerebellar development and diseases within an all-human system. This protocol can be easily implemented by a technician with cell culture experience and takes 1-2 months to complete with an option for extended maturation over the course of several months.

Structure determines function. The discovery of the DNA double-helix structure revealed how genetic information is stored and copied. In the mammalian cell nucleus, up to two meters of DNA is compacted by histones to form nucleosome/DNA particle chains that form euchromatin and heterochromatin domains, chromosome territories and mitotic chromosomes upon cell division. A critical question is what are the structures, interactions and 3D organization of DNA as chromatin in the nucleus and how do they determine DNA replication timing, gene expression and ultimately cell fate. To visualize genomic DNA across these different length scales in the nucleus, we developed ChromEMT, a method that selectively enhances the electron density and contrast of DNA and interacting nucleosome particles, which enables nucleosome chains, chromatin domains, chromatin ultrastructure and 3D organization to be imaged and reconstructed by using multi-tilt electron microscopy tomography (EMT). ChromEMT exploits a membrane-permeable, fluorescent DNA-binding dye, DRAQ5, which upon excitation drives the photo-oxidation and precipitation of diaminobenzidine polymers on the surface of DNA/nucleosome particles that are visible in the electron microscope when stained with osmium. Here, we describe a detailed protocol for ChromEMT, including DRAQ5 staining, photo-oxidation, sample preparation and multi-tilt EMT that can be applied broadly to reconstruct genomic DNA structure and 3D interactions in cells and tissues and different kingdoms of life. The entire procedure takes ~9 days and requires expertise in electron microscopy sample sectioning and acquisition of multi-tilt EMT data sets.