Nature Protocols最新文献

The persistent disparity between organ donation rates and clinical demand has driven the increasing use of extended-criteria donor livers. However, conventional static cold storage inadequately preserves extended-criteria donor with severe ischemia-reperfusion injury (IRI), contributing to high rates of mortality and morbidity. Although different machine perfusion technologies have been used to reduce IRI in clinical practice, organ ischemia remains unavoidable throughout the entire transplantation procedure. To minimize IRI to the greatest extent possible, we developed a novel ischemia-free liver transplantation (IFLT) method based on surgical innovation and continuous normothermic machine perfusion. IFLT not only effectively preserves graft quality but also expands the donor pool, making it possible to utilize high-risk livers. Classic IFLT increases the complexity of donor liver procurement and prolongs the anhepatic phase during implantation. Here we develop a simplified IFLT (SIFLT) technique. By streamlining the donor liver retrieval procedure and optimizing the sequence of vascular anastomosis during implantation, the efficacy and safety data for SIFLT are comparable to those of classic IFLT, with similar rates of postoperative complications, graft survival and patient survival. Thus, SIFLT represents a more efficient, safer and widely applicable approach to minimize organ ischemia, offering a robust strategy to improve outcomes and maximize organ utilization.

Synthetic gene circuits are powerful tools for precisely programming gene expression and introducing novel cellular functions. However, their development and application in plants has lagged behind other systems, due mainly to the limited availability of modular genetic parts. We recently developed a CRISPR interference (CRISPRi)-based synthetic gene circuit system for programming gene expression in plants. Using a robust and high-throughput protoplast-based dual luciferase assay, we demonstrated the development, testing and functionality of these circuits in various plant species. Here we detail the key design principles and considerations for building and testing programmable and reversible CRISPRi-based gene circuits in plants. We also provide detailed procedures for isolating protoplasts from multiple plant species, including Arabidopsis thaliana, Brassica napus, Triticum aestivum and Physcomitrium patens. Furthermore, we provide step-by-step instructions for the 96-well plate-based protoplast transfection assay for testing genetic parts and synthetic circuits, using a dual luciferase assay. The detailed descriptions of these developed systems will enhance the efficiency and reproducibility of the construction, testing, and implementation of synthetic gene circuits in a variety of plant species. This protocol enables the design and testing of CRISPRi-based gene circuits in plants within ~4 weeks.

This protocol describes a standardized in vitro method to determine the digestibility and digestible indispensable amino acid score (DIAAS) of dietary proteins. This 'INFOGEST Quant' method is an extension of our previous INFOGEST static digestion model (INFOGEST 2.0) and adds a workflow for the quantification of total protein digestibility, individual amino acid digestibility and DIAAS. The protocol was validated using in vivo data obtained by digesting the same food samples, and the results showed a high degree of agreement, confirming its relevance for nutritional assessments. To establish the DIAAS of a protein source, nonabsorbable peptides and proteins are precipitated after in vitro digestion and the resulting absorbable fraction is analyzed using ultrahigh-performance liquid chromatography with ultraviolet detection (to assess total and individual amino acids). Two alternative quantification strategies are also described: protein titration using the Kjeldahl method (to assess total nitrogen) and spectrophotometric analysis with o-phthalaldehyde (to assess total amino groups). Both alternative methods are valid only for the calculation of total digestibility and the proxy-digestible indispensable amino acid ratio, which gives an approximation of the DIAAS of the tested protein sources. Compared to existing approaches, this protocol is suitable for routine application in nutrition and food science laboratories. The preparatory steps take ~6 d, whereas the full workflow can be completed in triplicate in ~8 d. Analysis of the digesta takes an additional 3-5 d, depending on the method. The procedure requires only standard laboratory equipment and reagents and can be performed by anyone with basic training in biochemistry or a related discipline.

Preclinical mouse models are indispensable in cancer research, providing insights into tumor biology and therapeutic responses. This protocol describes a minimally invasive blood-based tumor monitoring approach using secreted luciferases for longitudinal tracking of tumor burden in transplantable xenografts and genetically engineered mouse models. Unlike intracellular luciferases used in bioluminescence imaging, secreted luciferases are actively released into circulation, enabling precise quantification from microliter-scale blood samples. We describe a transplantable model, where tumor cells are labeled in vitro using lentiviral transduction before engraftment. Orthogonal secreted luciferases enable multiplexed analysis of distinct tumor populations within a single host, reducing animal numbers and enhancing data density. We also describe an autochthonous lung cancer model, where intratracheal adenoviral delivery of Cre recombinase and CRISPR nucleases induces tumorigenesis through somatic genome editing while activating a conditional secreted luciferase reporter transgene. Tumor-bearing mice undergo routine blood sampling, with luciferase activity measured ex vivo to quantify viable tumor burden. Compared to imaging techniques, this method eliminates anesthesia and contrast agents, minimizing animal stress and enabling frequent monitoring with superior temporal resolution and reduced logistical complexity. The protocol requires only standard molecular biology skills and basic mouse handling expertise. While tumor labeling and growth duration is model dependent, blood sampling requires ~5 min per animal, with all samples from one cohort processed and measured together within 2 h. This approach provides an accessible, cost-effective and scalable alternative to imaging-based tumor monitoring, that is aligned with the 3Rs principles, offering a powerful and ethically sound platform for preclinical cancer research.

Chemotherapeutics are widely used in cancer treatments, but their toxicity, bioavailability and solubility present challenges. RNA nanotechnology has emerged as a promising modality for targeted delivery of chemotherapeutics. Structurally, RNA is thermostable, while conformationally it is dynamic and flexible. RNA's unique deformability and motility lead to rapid spontaneous tumor accumulation and glomerular excretion, thus fast body clearance, while its anionic charge and favorable small size prevent accumulation in vital organs, resulting in undetectable toxicity. We developed branched 4-way junction (4WJ) nanoparticles that were stable with a melting temperature >80 °C, even when conjugated with 24 drugs per 4WJ. Each 4WJ RNA component strand can conjugate six molecules of hydrophobic chemotherapeutic drugs, such as camptothecin, paclitaxel and SN-38. Thus, each 4WJ carries a total of 24 drug molecules spaced to prevent aggregation. RNA conjugation improved paclitaxel water solubility 32,000-fold. This protocol describes the construction of 4WJ RNA drug complexes for cancer therapy. Specific procedures include the modification of chemical drugs, conjugation of multiple prodrug molecules to each synthesized RNA component strand, assembly of RNA nanoparticles and their purification and characterization. Prodrugs are conjugated to RNA nanoparticles via efficient click chemistry, creating an ester linker that is cleaved by esterases in tumor tissues or cells, allowing the prodrugs to return back to their original structures and chemistry upon delivery and release, minimizing toxicity. Inclusion of tumor targeting ligands demonstrated specific delivery of high payload chemotherapeutics to tumors, controlled release of chemical drugs and strong tumor inhibition.

The high spatiotemporal resolution of noninvasive magnetoencephalographic (MEG) functional brain imaging provides a rich portrayal of brain network dynamics and enhances its versatility in neuroscience and beyond. However, MEG is notably less used in research settings relative to other neuroimaging modalities such as functional MRI. Here we describe a protocol for integrating MEG into research studies, using the Dallas Hearts and Minds Study (DHMS) as an illustrative example. Existing resources on MEG research best practices have restricted focus to primarily data acquisition, processing and analysis steps. We extend upon these works by outlining strategies for four stages of MEG research: (1) planning, (2) piloting, (3) implementing the procedure and (4) maintaining quality assurance. In doing so, we describe methodological considerations that enhance MEG procedure efficiency, align MEG implementation with research goals, improve data quality, reduce participant burden and optimize the financial resources needed. We also discuss the special considerations appropriate for large, multidisciplinary, population-based studies. We include an analysis of the DHMS MEG pilot data and results, providing example data on a publicly available repository ( https://git.biohpc.swmed.edu/ansir/utsw-meg-study-repository ), and collate many other resources to facilitate adaptation of the protocol. This resource aims to support trainees, researchers and clinician scientists in deploying MEG effectively and encourage its broader use in diverse research settings. The MEG experimental procedure used in the DHMS (stage 3) requires ~2 h, and DHMS pilot and main study data acquisition spanned ~5 years. The entire protocol requires multiple months to years depending on study size.

Extracellular vesicles (EVs), present in blood as well as other biological fluids, encapsulate nucleic acid biomarkers used for diagnosis, prognosis and treatment monitoring of disease via minimally invasive liquid biopsy. EVs are a reliable source of biomarkers because their contents reflect the cells from which they are derived, and their lipid bilayer membranes protect nucleic acids from degradation. Previously, analyzing EVs in blood was difficult because of time-consuming, labor-intensive EV isolation methods. Here, we provide a protocol for an EV detection approach in which reagent-loaded liposomes fuse with EVs directly in patient blood to sensitively detect RNA within the EVs. In this 'liposome-EV fusion assay', antibodies capture EVs in blood, and reagent-loaded liposomes initiate liposome-EV fusion and CRISPR-based nucleic acid detection. We originally used this assay to detect EV-encapsulated viral RNA and accurately diagnose infectious diseases from patient plasma. It has since been adopted by many other research groups to detect mRNA, microRNA, DNA, DNA mutations and EV surface proteins in a variety of patient-derived tumor samples, incorporating enzymatic and nonenzymatic detection reagents and different diagnostic readouts. As a clinical and research tool, this approach has great potential for the diagnosis, treatment and study of cancer, infectious diseases and neurological dysfunction.

Cryogenic-electron tomography (cryo-ET) permits the in situ visualization of biological macromolecules at the molecular level. Owing to the variable thickness of cells, tissues and organisms, frozen specimens may need to be thinned by cryo-focused ion beam (FIB) milling to produce thin (<500 nm) cryo-lamellae suitable for cryo-ET. Locating regions of interest remains a challenge because untargeted milling can lead to inadvertent ablation and removal of regions of interest. Correlative light and electron microscopy, combined with cryo-FIB milling, can guide the identification of labeled targets in the cellular milieu. Multiple transfers between cryo-imaging instruments, cumbersome correlation algorithms, limited accuracy and low throughput have hindered the routine adoption of cryo-FIB milling within a multimodal correlative workflow for in situ structural biology. Here we present a workflow for 3D correlative cryo-fluorescence light microscopy-FIB-ET that streamlines fluorescence light microscopy-guided FIB milling, improving throughput while preserving both structural and contextual information. The complete integration of hardware and software described here minimizes sample contamination from cross-platform exchanges and greatly enhances the efficiency of 3D targeting in cryo-milling. We then describe procedures for implementing montage parallel array cryo-ET (MPACT), which can be easily adapted to any modern life-science transmission electron microscope. MPACT supports high-throughput cryo-ET acquisitions (10 tilt series in 1.5 h) for structure determination and comprehensive contextual understanding of macromolecules within their native surroundings. A complete session from sample preparation to MPACT data processing takes 5-7 d for an individual experienced in both cryo-EM and cryo-FIB milling.

Single-cell RNA sequencing quantifies biological samples at an unprecedented scale, allowing us to decipher biological differentiation dynamics such as normal development or disease progression. As conventional single-cell RNA sequencing experiments are destructive by nature, reconstructing cellular trajectories computationally is an essential aspect of analysis pipelines. To infer trajectories in a consistent and scalable manner, we have developed CellRank. In its first iteration, CellRank quantitatively recovered trajectories from RNA velocity estimates and transcriptomic similarity. Given these data views, CellRank constructed a cell-cell transition matrix, inducing a Markov chain to automatically infer terminal states and describe their lineage formation. However, CellRank did not enable incorporating complementary data views such as experimental time points, pseudotime or stemness potential. To facilitate these and future views, CellRank 2 generalizes CellRank's trajectory inference framework to multiview single-cell data, leading to a general and scalable framework for cellular fate mapping. Overall, the CellRank framework enables the consistent quantification of cellular fate, combining complementary views and analyzing lineage priming consistently. Here we provide detailed protocols on how to run exemplary CellRank analyses at scale and across different data views. Using CellRank requires basic apprehension and knowledge of single-cell omics data and the Python programming language.

As DNA sequencing technologies improve, it is becoming easier to sequence and assemble new genomes from non-model organisms. However, before a newly assembled genome sequence can be used as a reference, it must be annotated with genes and other features. This can be conducted by individual laboratories using publicly available software. Modern genome annotations integrate gene predictions from the assembled DNA sequence with gene homology information from other high-quality reference genomes and take into account functional evidence (e.g., protein sequences and RNA sequencing information). Many genome annotation pipelines exist but have varying accuracies, resource requirements and ease of use. This genome annotation Tutorial describes a streamlined genome annotation pipeline that can create high-quality genome annotations for animals in the laboratory. Our workflow integrates existing state-of-the-art genome annotation tools capable of annotating protein-coding and non-coding RNA genes. This Tutorial also guides the user on assigning gene symbols and annotating repeat regions. Finally, we describe additional tools to assess annotation quality and combine and format the results.