分子生物学

Membrane-less organelles play essential roles in both physiological and pathological processes by compartmentalizing biomolecules through phase separation to form dynamic hubs. These hubs enable rapid responses to cellular stress and help maintain cellular homeostasis. However, a straightforward and efficient method for detecting and illustrating the distribution and diversity of RNA species within membrane-less organelles is still highly sought after. In this study, we present a detailed protocol for in situ profiling of RNA subcellular localization using Target Transcript Amplification and Sequencing (TATA-seq). Specifically, TATA-seq employs a primary antibody against a marker protein of the target organelle to recruit a secondary antibody conjugated with streptavidin, which binds an oligonucleotide containing a T7 promoter. This design enables targeted, in situ reverse transcription of RNAs with minimal background noise, a key advantage further refined during data analysis by subtracting signals obtained from a parallel IgG control experiment. The subsequent T7 RNA polymerase-mediated linear amplification ensures high-fidelity RNA amplification from low-input material, which directly contributes to optimized sequencing metrics, including a duplication rate of no more than 25% and a mapping ratio of approximately 90%. Furthermore, the modular design of TATA-seq provides broad compatibility with diverse organelles. While initially developed for membrane-less organelles, the protocol can be readily adapted to profile RNA in other subcellular compartments, such as nuclear speckles and paraspeckles, under both normal and pathogenic conditions, offering a versatile tool for spatial transcriptomics.

Thousands of RNAs are localized to specific subcellular locations, and these localization patterns are often required for optimal cell function. However, the sequences within RNAs that direct their transport are unknown for almost all localized transcripts. Similarly, the RNA content of most subcellular locations remains unknown. To facilitate the study of subcellular transcriptomes, we developed the RNA proximity labeling method OINC-seq. OINC-seq utilizes photoactivatable, spatially restricted RNA oxidation to specifically label RNA in proximity to a subcellularly localized bait protein. After labeling, these oxidative RNA marks are then read out via high-throughput sequencing due to their ability to induce predictable misincorporation events by reverse transcriptase. These induced mutations are then quantitatively assessed for each gene using our software package PIGPEN. The observed mutation rate for a given RNA species is therefore related to its proximity to the localized bait protein. This protocol describes procedures for assaying RNA localization via OINC-seq experiments as well as computational procedures for analyzing the resulting data using PIGPEN.

Cellular communication relies on the intricate interplay of signaling molecules, which come together to form the cell–cell interaction (CCI) network that orchestrates tissue behavior. Researchers have shown that shallow neural networks can effectively reconstruct the CCI from the abundant molecular data captured in spatial transcriptomics (ST). However, in scenarios characterized by sparse connections and excessive noise within the CCI, shallow networks are often susceptible to inaccuracies, leading to suboptimal reconstruction outcomes. To achieve a more comprehensive and precise CCI reconstruction, we propose a novel method called triple-enhancement-based graph neural network (TENET). The TENET framework has been implemented and evaluated on both real and synthetic ST datasets. This protocol primarily introduces our network architecture and its implementation.

Information on RNA localisation is essential for understanding physiological and pathological processes, such as gene expression, cell reprogramming, host–pathogen interactions, and signalling pathways involving RNA transactions at the level of membrane-less or membrane-bounded organelles and extracellular vesicles. In many cases, it is important to assess the topology of RNA localisation, i.e., to distinguish the transcripts encapsulated within an organelle of interest from those merely attached to its surface. This allows establishing which RNAs can, in principle, engage in local molecular interactions and which are prevented from interacting by membranes or other physical barriers. The most widely used techniques interrogating RNA localisation topology are based on the treatment of isolated organelles with RNases with subsequent identification of the surviving transcripts by northern blotting, qRT-PCR, or RNA-seq. However, this approach produces incoherent results and many false positives. Here, we describe Controlled Level of Contamination coupled to deep sequencing (CoLoC-seq), a more refined subcellular transcriptomics approach that overcomes these pitfalls. CoLoC-seq starts by the purification of organelles of interest. They are then either left intact or lysed and subjected to a gradient of RNase concentrations to produce unique RNA degradation dynamics profiles, which can be monitored by northern blotting or RNA-seq. Through straightforward mathematical modelling, CoLoC-seq distinguishes true membrane-enveloped transcripts from degradable and non-degradable contaminants of any abundance. The method has been implemented in the mitochondria of HEK293 cells, where it outperformed alternative subcellular transcriptomics approaches. It is applicable to other membrane-bounded organelles, e.g., plastids, single-membrane organelles of the vesicular system, extracellular vesicles, or viral particles.

Key features

• Tested on human mitochondria; potentially applicable to cell cultures, non-model organisms, extracellular vesicles, enveloped viruses, tissues; does not require genetic manipulations or highly pure organelles.

• In the case of human cells, the required amount of starting material is ~2,500 cm² of 80% confluent cells (or ~3 × 10⁸ HEK293 cells).

• CoLoC-seq implements a special RNA-seq strategy to selectively capture intact transcripts, which requires RNases generating 5′-hydroxyl and 2′/3′-phosphate termini (e.g., RNase A, RNase I).

• Relies on nonlinear regression software with customisable exponential functions.

Graphical overview