STAR Protocols最新文献

We introduce a protocol for spatial proteomics using thin cryotome sections of mouse skeletal muscle tissue. We describe steps for preparing muscle sections and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses to generate spatial protein profiles along the longitudinal skeletal muscle axis. We detail procedures for scanning longitudinal protein profiles and replacing missing data using a sliding window approach. This protocol has potential for applications in spatial proteomics to other tissues with asymmetric patterns such as the brain and heart tissue. For complete details on the use and execution of this protocol, please refer to Schmidt et al.¹.

The genome of the most recent common ancestor is generally not available but can greatly facilitate the inference of demographic history and the detection of local adaptations. Here, we present a protocol for applying local ancestry inference in present-day samples to reconstruct ancestral genomes. We describe steps for estimating haplotypes, inferring local ancestry, and assembling ancestral haplotypes. This protocol describes the analytic steps of reconstructing ancestral genomes using the example data of the Miao and She target populations. For complete details on the use and execution of this protocol, please refer to Gao et al.¹.

Non-invasive prenatal testing (NIPT) not only enables the detection of chromosomal anomalies in fetuses but also generates vast amounts of ultra-low-depth sequencing data, which can be leveraged for population genomic studies. Here, we present a protocol designed for massive ultra-low-depth sequencing datasets. We detail the steps for data processing, quality control, and genotype imputation, followed by genome-wide association study (GWAS) and post-GWAS analyses. This protocol applies to a wide range of ultra-low-depth sequencing studies, extending beyond data from NIPT. For complete details on the use and execution of this profile, please refer to Xiao et al.¹.

Here, we present a protocol for 3D photogrammetry and morphological digitization of skulls, including complex ones with tusks, antlers, and horns, which are challenging to reconstruct digitally. We describe steps for setting up specimens for image acquisition, including camera and lighting configurations, and the subsequent image processing to generate high-quality 3D models. We also outline the extraction of morphological data for accurate geometric morphometric analyses.

Drugs that target specific proteins often have off-target effects. We present a protocol using artificial neural networks to model cellular transcriptional responses to drugs, aiming to understand their mechanisms of action. We detail steps for predicting transcriptional activities, inferring drug-target interactions, and explaining the off-target mechanism of action. As a case study, we analyze the off-target effects of lestaurtinib on FOXM1 in the A375 cell line. For complete details on the use and execution of this protocol, please refer to Meimetis et al.¹.

The eukaryotic cell division cycle is a highly conserved process, featuring fluctuations in protein localization and abundance required for key cell cycle transitions. Here, we present a protocol for the spatiotemporal analysis of the proteome during the budding yeast cell division cycle using live-cell imaging. We describe steps for strain construction, cell cultivation, microscopy, and image analysis. Variations of this protocol can be applied for the spatiotemporal analysis of the proteome in different contexts, such as genetic and environmental perturbations. For complete details on the use and execution of this protocol, please refer to Litsios et al.¹.

This protocol describes the steps to determine an airway microbiome signature for identifying Mycobacterium tuberculosis infection status. We outline procedures for processing microbiome data, calculating diversity measures, and fitting Dirichlet multinomial mixture models. Additionally, we provide steps for analyzing taxonomic relative and differential abundances, as well as identifying potential biomarkers associated with infection status. For complete details on the use and execution of this protocol, please refer to Kayongo et al.¹.

Analyzing host-microbe interactions is essential for understanding how microbiota changes disrupt host homeostasis. Here, we present a protocol for predicting host-microbe protein-protein interactions and their downstream effects using MicrobioLink. We describe steps for setting up the environment, installing software, and preparing human transcriptomic and bacterial proteomic data. The protocol outlines procedures for predicting protein-protein interactions through domain-motif interactions, integrating multi-omic datasets to map downstream effects, performing network analyses to identify key regulatory pathways, and visualizing multi-layered networks for systems-level data interpretation. For complete details on the use and execution of this protocol, please refer to Gul et al.¹ and Poletti et al.².

Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is an open-source, powerful simulator with a customizable platform for extensive Langevin dynamics simulations. Here, we present a protocol for using LAMMPS to develop coarse-grained models of polymeric systems with macromolecular crowding, an integral part of any soft matter or biophysical system. We describe steps for installing software, using LAMMPS basic commands and code, and translocating polymers. This protocol has potential applications in developing mathematical models for DNA sequencing, controlled drug delivery, and cellular transport processes. For complete details on the use and execution of this protocol, please refer to Garg et al.¹.