X-ray computed tomography is a reliable technique for the detection and longitudinal monitoring of pulmonary nodules. In preclinical stages of diagnostic or therapeutic development, the miniaturized versions of the clinical computed tomography scanners are ideally suited for carrying out translationally-relevant research in conditions that closely mimic those found in the clinic. In this Protocol, we provide image acquisition parameters optimized for low radiation dose, high-resolution and high-throughput computed tomography imaging using three commercially available micro-computed tomography scanners, together with a detailed description of the image analysis tools required to identify a variety of lung tumor types, characterized by specific radiological features. For each animal, image acquisition takes 4-8 min, and data analysis typically requires 10-30 min. Researchers with basic training in animal handling, medical imaging and software analysis should be able to implement this protocol across a wide range of lung cancer models in mice for investigating the molecular mechanisms driving lung cancer development and the assessment of diagnostic and therapeutic agents.

Robust, reliable quantification of large sample cohorts is often essential for meaningful clinical or pharmaceutical proteomics investigations, but it is technically challenging. When analyzing very large numbers of samples, isotope labeling approaches may suffer from substantial batch effects, and even with label-free methods, it becomes evident that low-abundance proteins are not reliably measured owing to unsufficient reproducibility for quantification. The MS1-based quantitative proteomics pipeline IonStar was designed to address these challenges. IonStar is a label-free approach that takes advantage of the high sensitivity/selectivity attainable by ultrahigh-resolution (UHR)-MS1 acquisition (e.g., 120-240k full width at half maximum at m/z = 200) which is now widely available on ultrahigh-field Orbitrap instruments. By selectively and accurately procuring quantitative features of peptides within precisely defined, very narrow m/z windows corresponding to the UHR-MS1 resolution, the method minimizes co-eluted interferences and substantially enhances signal-to-noise ratio of low-abundance species by decreasing noise level. This feature results in high sensitivity, selectivity, accuracy and precision for quantification of low-abundance proteins, as well as fewer missing data and fewer false positives. This protocol also emphasizes the importance of well-controlled, robust experimental procedures to achieve high-quality quantification across a large cohort. It includes a surfactant cocktail-aided sample preparation procedure that achieves high/reproducible protein/peptide recoveries among many samples, and a trapping nano-liquid chromatography-mass spectrometry strategy for sensitive and reproducible acquisition of UHR-MS1 peptide signal robustly across a large cohort. Data processing and quality evaluation are illustrated using an example dataset ( http://proteomecentral.proteomexchange.org ), and example results from pharmaceutical project and one clinical project (patients with acute respiratory distress syndrome) are shown. The complete IonStar pipeline takes ~1-2 weeks for a sample cohort containing ~50-100 samples.

Honey bees are indispensable pollinators and model organisms for studying social behavior, development and cognition. However, their eusociality makes it difficult to use standard forward genetic approaches to study gene function. Most functional genomics studies in bees currently utilize double-stranded RNA (dsRNA) injection or feeding to induce RNAi-mediated knockdown of a gene of interest. However, dsRNA injection is laborious and harmful, and dsRNA feeding is difficult to scale cheaply. Further, both methods require repeated dsRNA administration to ensure a continued RNAi response. To fill this gap, we engineered the bee gut bacterium Snodgrassella alvi to induce a sustained host RNA interference response that reduces expression of a targeted gene. To employ this functional genomics using engineered symbionts (FUGUES) procedure, a dsRNA expression plasmid is cloned in Escherichia coli using Golden Gate assembly and then transferred to S. alvi. Adult worker bees are then colonized with engineered S. alvi. Finally, gene knockdown is verified through qRT-PCR, and bee phenotypes of interest can be further assessed. Expression of targeted genes is reduced by as much as 50-75% throughout the entire bee body by 5 d after colonization. This protocol can be accomplished in 4 weeks by bee researchers with microbiology and molecular cloning skills. FUGUES currently offers a streamlined and scalable approach for studying the biology of honey bees. Engineering other microbial symbionts to influence their hosts in ways that are similar to those described in this protocol may prove useful for studying additional insect and animal species in the future.

The multi-attribute method (MAM) is a liquid chromatography-mass spectrometry (LC-MS)-based method that is used to directly characterize and monitor numerous product quality attributes (PQAs) at the amino acid level of a biopharmaceutical product. MAM enables identity testing based on primary sequence verification, detection and quantitation of post-translational modifications and impurities. This ability to simultaneously and directly determine PQAs of therapeutic proteins makes MAM a more informative, streamlined and productive workflow than conventional chromatographic and electrophoretic assays. MAM relies on proteolytic digestion of the sample followed by reversed-phase chromatographic separation and high-resolution LC-MS analysis in two phases. First, a discovery study to determine quality attributes for monitoring is followed by the creation of a targeted library based on high-resolution retention time plus accurate mass analysis. The second aspect of MAM is the monitoring phase based on the target peptide library and new peak detection using differential analysis of the data to determine the presence, absence or change of any species that might affect the activity or stability of the biotherapeutic. The sample preparation process takes between 90 and 120 min, whereas the time spent on instrumental and data analyses might vary from one to several days for different sample sizes, depending on the complexity of the molecule, the number of attributes to be monitored and the information to be detailed in the final report. MAM is developed to be used throughout the product life cycle, from process development through upstream and downstream processes to quality control release or under current good manufacturing practices regulations enforced by regulatory agencies.

Erratum in Nat Protoc. 2023 Jan 18;:

Extrachromosomal circular DNA (eccDNA) was discovered more than half a century ago. However, its biogenesis and function have just begun to be elucidated. One hurdle that has prevented our understanding of eccDNA is the difficulty in obtaining pure eccDNA from cells. The current eccDNA purification methods mainly rely on depleting linear DNAs by exonuclease digestion after obtaining crude circles by alkaline lysis. Owing to eccDNA's low abundance and heterogeneous size, the current purification methods are not efficient in obtaining pure eccDNA. Here we describe a new three-step eccDNA purification (3SEP) procedure that adds a step to recover circular DNA, but not linear DNA that escape from the exonuclease digestion, whereby 3SEP results in eccDNA preparations with high purity and reproducibility. Additionally, we developed a full-length eccDNA sequencing technique by combining rolling-circle amplification with Nanopore sequencing. Accordingly, we developed a full-length eccDNA caller (Flec) to call the consensus sequence of multiple tandem copies of eccDNA contained within the debranched rolling-circle amplification product and map the consensus to its genomic origin. Collectively, our protocol will facilitate eccDNA identification and characterization, and has the potential for diagnostic and clinical applications. For a well-trained molecular biologist, it takes ~1-2 d to purify eccDNAs, another 5-6 d to carry out Nanopore library preparation and sequencing, and 1-5 d for an experienced bioinformatic scientist to analyze the data.

The large pores of functional mesoporous carbonaceous nanomaterials have broad accessibility, making them efficient substrates for the mass transport of chemicals in biomedical applications, gas separation, catalysis, sensing, and energy storage and conversion. Recently, the assembly of monomicelles has been used to control the nanostructure and mesoporosity of carbonaceous nanomaterials, where the structure-oriented unit is a single micelle made up of block copolymers/surfactants and of precursor species (via hydrogen bonds, Coulombic and/or other noncovalent interactions). Each monomicelle then represents a template for a single mesopore, and multiple monomicelles can be stacked like LEGO blocks. After polymerization of the precursor species (in this case dopamine), carbonization results in the carbonaceous nanomaterial. The micellar size, structure and shape can be easily tuned by altering the synthetic conditions, providing a high degree of control over the structure of the final product, which can therefore be shaped into original nanostructures otherwise difficult to synthesize using conventional templating methods. Here we provide a detailed procedure for the preparation of the monomicelles, the monomicellar assembly into mesostructured polymeric samples and the conversion of polymeric samples to carbonaceous frameworks. We describe the functional characterization of two mesoporous carbonaceous nanomaterials that demonstrate excellent sodium-ion storage performance and oxygen reduction reactivity, respectively. The monomicellar assembly process for the synthesis of the ordered mesoporous polymers requires ~5 h; the synthesis, including subsequent centrifugation, freeze drying and carbonization, requires 2 d, whereas the entire procedure, including the characterization of the nanomaterials, requires ~4 d.