Transcriptome-wide methods to map RNA–protein interactions in living cells provide powerful tools to unravel the complexity of ribonucleoprotein (RNP) assemblies and their roles in post-transcriptional regulation. Here, we present three interconnected cross-linking and immunoprecipitation (CLIP)-based procedures, irCLIP-RNP, irCLIPv2 and Re-CLIP, that enable the identification and characterization of RNA-dependent associated proteins (RDAPs) in proximity to a target RNA-binding protein (RBP). After the construction of the infrared (IR) biotinylated DNA adapter, these methods combine UV cross-linking, partial RNase digestion, protein-specific immunoprecipitation and 3′ DNA adapter ligation, followed by distinct downstream workflows: mass spectrometry for RDAP identification (irCLIP-RNP), RNA sequencing to map binding regions (irCLIPv2) or a second immunoprecipitation with sequencing for interaction validation (Re-CLIP). Custom bioinformatics pipelines analyze enriched RDAPs or significant binding sites, offering mechanistic insights into proximal RNA–protein interactions. Compared to conventional CLIP methods, these protocols resolve adjacent interactions, linking RBPs to their RDAPs with greater precision. The workflow is applicable across diverse contexts such as cell state transitions and stimulus responses. It requires expertise in RNA biochemistry, next-generation sequencing and computational analysis. The entire process, from cell harvesting to data analysis, can be completed in 7–10 d, providing a robust toolkit for investigating post-transcriptional regulatory networks. Ribonucleoprotein assemblies are crucial regulators of RNA processing. Here, three interconnected procedures enabling the detection and interrogation of these multi-protein assemblies using transcriptomics and proteomics are presented.
Human metabolism and human gut microbiome co-metabolism can be difficult to investigate in vivo owing to the complexity of the systems involved. Genome-scale metabolic modeling with human whole-body metabolic models (WBMs) offers a computational approach to address this challenge. However, personalization and analysis of WBMs requires comprehensive knowledge of metabolic modeling, which can be daunting for beginners. To reduce the computational expertise required and to standardize the process of working with and analyzing WBMs, we present Persephone: a personalization and evaluation pipeline for human WBMs. Persephone has functionalities to create community microbiome models from raw sequencing data or taxonomy-assigned read tables, use physiological and metabolomic data to create personalized WBMs, combine community microbiome models with WBMs or personalized WBMs to create community microbiome (personalized) WBMs, optimize WBMs according to user-defined metabolic reactions, perform statistical analyses to describe the data, and visualize significant results in various figures. Persephone saves intermediate results as well as a log file with all changes made to the model, data and any warnings encountered during the various steps. Persephone is designed for use by anyone with basic research-computing experience and access to modest computer hardware. Applications include the investigation of human–microbiome co-metabolism and the evaluation of the effects of various physiological parameters, such as weight, on human metabolism. Using a dataset of 100 metagenomic samples, it can take several days to a week to run Persephone on a single computer, depending on the number of reactions optimized and whether taxonomic assignment is performed. This protocol for using Persephone, an automated pipeline for evaluation and personalization of human whole-body metabolic models, combines them with community microbiome models to enable investigation of human and human gut microbiome co-metabolism.
Here we describe a protocol for image-derived mean squared displacement (iMSD) analysis, a fluctuation-based technique to quantify the average spatiotemporal dynamics of subcellular structures—such as proteins, organelles and nanoparticles—in living cells. Unlike single-particle tracking, which relies on discrete object localization and trajectory reconstruction, iMSD computes the spatiotemporal autocorrelation function of fluorescence intensity fluctuations directly from standard time-lapse image stacks. This approach overcomes single-particle tracking limitations in high-density or low signal-to-noise environments, providing a robust ensemble average without trajectory linking. The protocol details the optimization of image acquisition parameters, the computation of the spatiotemporal correlation function and the Gaussian fitting of the resulting correlation peak variance to derive the iMSD curve. Expected results include the simultaneous extraction of diffusion coefficients, anomalous exponents and confinement dimensions, allowing users to distinguish between Brownian, superdiffusive and confined motion across different temporal scales. The method’s temporal dynamic range depends on the imaging modality: standard confocal scanning resolves millisecond-scale dynamics typical of organelles and membrane proteins, whereas camera-based or line-scanning configurations are required to capture fast cytosolic diffusion. Uniquely, the method also yields information on the average size of the moving objects. The complete procedure takes ~4–6 h of active hands-on time for image acquisition and computational analysis. It requires basic experience in fluorescence microscopy and data processing. A protocol for image-derived mean squared displacement analysis, a fluctuation-based technique to quantify the average spatiotemporal dynamics of subcellular structures—such as proteins, organelles and nanoparticles—in living cells.
The self-assembly of peptides into well-defined nanostructures represents a versatile strategy for developing biomedical agents. However, cellular internalization of pre-assembled peptide nanostructures is often hampered by insufficient uptake and low selectivity toward target cells, leading to off-target concerns and limited therapeutic efficacy. The past few years have witnessed the development of in situ assembly in living cells as a new endeavor for optimizing the performance of peptide assemblies. In situ assembly of peptides refers to intracellular assembling processes regulated by biocompatible stimuli, thereby enhancing peptide internalization and locally forming assemblies at target sites. Recently, we have developed strategies for establishing in situ assembly of peptides in living cells dictated by stimuli-responsive unnatural amino acids. Here we detail the procedure for developing in situ peptide assembly systems in living cells through incorporating stimuli-responsive unnatural amino acids. We present a thorough workflow encompassing peptide design and synthesis as well as the characterization of peptide responsiveness to glutathione or sulfatase. Self-assembly conditions are described for experiments in solution, in murine macrophage cells, human cervical carcinoma cells and murine breast cancer cells. The applications of in situ-formed peptide assemblies for subcellular targeting and cancer therapy in murine breast tumor-bearing BALB/c mice are also presented. The described methodology provides a robust platform for developing in situ-formed peptide assemblies and advancing the clinical translation of peptide-based supramolecular therapeutics. Performance of this Protocol requires expertise in peptide chemistry, materials engineering and biomedical engineering, taking an expected 92–115 d to complete. Peptide assemblies exhibit potential in biomedical applications due to their excellent biocompatibility and bioactivity. In this Protocol, a versatile strategy for the establishment of in situ peptide assembly systems in living cells is described.
Genomic duplication is a common type of structural variation that has important roles in gene dosage regulation and contributes to the development of various human diseases including cancer and inherited disorders. However, current genome-editing technologies are limited in their ability to introduce precise and efficient DNA duplications. Amplification editing (AE) is a genome-editing strategy that enables efficient and precise duplication of genomic sequences ranging from tens of base pairs to chromosomal scales. AE offers high precision and efficiency and the ability to duplicate endogenous sequences across multiple targeting sites and in diverse cell types. Here, we describe detailed procedures for the design, execution and analysis of AE-based genomic duplication. These include protocols for duplicating endogenous loci across a range of fragment sizes, as well as workflows to quantify editing efficiency and validate duplication outcomes. In addition, we provide guidance for optimizing AE performance and extending its applicability across diverse genomic targets and experimental contexts. Most AE experiments can be completed within 2 weeks, supporting efficient implementation of targeted genomic duplication in mammalian cells. Here, the authors provide a protocol for amplification editing, a genome-editing strategy using prime editors that enables efficient and precise duplication of genomic sequences ranging from tens of base pairs to chromosomal scales in diverse cell types.
