# Protocol for the spatiotemporal profiling of RNA within nuclear compartments in human cell lines using SLAM-RT&Tag

## TL;DR

This paper introduces a protocol called SLAM-RT&Tag to study RNA dynamics in nuclear compartments of human cells.

## Contribution

The novel contribution is the integration of RNA metabolic labeling with proximity labeling to measure RNA dynamics in nuclear compartments.

## Key findings

- SLAM-RT&Tag enables quantification of RNA dynamics through T-to-C conversion events.
- The protocol provides detailed steps for library preparation and differential kinetic analysis.

## Abstract

Measuring RNA residence time within nuclear compartments provides insight into their roles as either storage sites or transient processing hubs. This protocol describes SLAM-RT&Tag, a genomic technique that integrates RNA metabolic labeling with RT&Tag, an RNA proximity labeling method. We detail steps for RNA labeling, library preparation, and computational quantification of T-to-C conversion events to infer RNA dynamics within nuclear compartments in human cell lines.

For complete details on the use and execution of this protocol, please refer to Khyzha et al.1

•Steps for quantifying localized RNA dynamics using SLAM-RT&Tag•Instructions for preparing SLAM-RT&Tag Illumina sequencing libraries•Procedures for differential kinetic analysis of SLAM-RT&Tag data

Steps for quantifying localized RNA dynamics using SLAM-RT&Tag

Instructions for preparing SLAM-RT&Tag Illumina sequencing libraries

Procedures for differential kinetic analysis of SLAM-RT&Tag data

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Measuring RNA residence time within nuclear compartments provides insight into their roles as either storage sites or transient processing hubs. This protocol describes SLAM-RT&Tag, a genomic technique that integrates RNA metabolic labeling with RT&Tag, an RNA proximity labeling method. We detail steps for RNA labeling, library preparation, and computational quantification of T-to-C conversion events to infer RNA dynamics within nuclear compartments in human cell lines.