System-wide analysis of RNA and protein subcellular localization dynamics

Understanding the molecular diversity of synapses

Synapses are composed of thousands of proteins, providing the potential for extensive molecular diversity to shape synapse type-specific functional specializations. In this Review, we explore the landscape of synaptic diversity and describe the mechanisms that expand the molecular complexity of synapses, from the genotype to the regulation of gene expression to the production of specific proteoforms and the formation of localized protein complexes. We emphasize the importance of examining every molecular layer and adopting a systems perspective to understand how these interconnected mechanisms shape the diverse functional and structural properties of synapses. We explore current frameworks for classifying synapses and methodologies for investigating different synapse types at varying scales, from synapse-type-specific proteomics to advanced imaging techniques with single-synapse resolution. We highlight the potential of synapse-type-specific approaches for integrating molecular data with cellular functions, circuit organization and organismal phenotypes to enable a more holistic exploration of neuronal phenomena across different scales.

Heat shock responsive genes in Brassicaceae: genome-wide identification, phylogeny, and evolutionary associations within and between genera

Heat stress affects the growth and development of Brassicaceae crops. Plant breeders aim to mitigate the effects of heat stress by selecting for heat stress tolerance, but the genes responsible for heat stress in Brassicaceae remain largely unknown. During heat stress, heat shock proteins (HSPs) function as molecular chaperones to aid in protein folding, and heat shock transcription factors (HSFs) serve as transcriptional regulators of HSP expression. We identified 5002 heat shock related genes, including HSPs and HSFs, across 32 genomes in Brassicaceae. Among these, 3347 genes were duplicated, with segmented duplication primarily contributing to their expansion. We identified 466 physical gene clusters, including 240 homogenous clusters and 226 heterogeneous clusters, shedding light on the organization of heat shock related genes. Notably, 37 genes were co-located with published thermotolerance quantitative trait loci, which supports their functional role in conferring heat stress tolerance. This study provides a comprehensive resource for the identification of functional Brassicaceae heat shock related genes, elucidates their clustering and duplication patterns and establishes the genomic foundation for future heat tolerance research. We hypothesise that genetic variants in HSP and HSF genes in certain species have potential for improving heat stress tolerance in Brassicaceae crops.

SubCELL: the landscape of subcellular compartment-specific molecular interactions

The subcellular compartment-specific molecular interactions (SCSIs) are the building blocks for most molecular functions, biological processes and disease pathogeneses. Extensive experiments have therefore been conducted to accumulate the valuable information of SCSIs, but none of the available databases has been constructed to describe those data. In this study, a novel knowledge base SubCELL is thus introduced to depict the landscape of SCSIs among DNAs/RNAs/proteins. This database is UNIQUE in (a) providing, for the first time, the experimentally-identified SCSIs, (b) systematically illustrating a large number of SCSIs inferred based on well-established method and (c) collecting experimentally-determined subcellular locations for the DNAs/RNAs/proteins of diverse species. Given the essential physiological/pathological role of SCSIs, the SubCELL is highly expected to have great implications for modern molecular biological study, which can be freely accessed with no login requirement at: https://idrblab.org/subcell/.

Spatial omics advances for in situ RNA biology

Spatial regulation of RNA plays a critical role in gene expression regulation and cellular function. Understanding spatially resolved RNA dynamics and translation is vital for bringing new insights into biological processes such as embryonic development, neurobiology, and disease pathology. This review explores past studies in subcellular, cellular, and tissue-level spatial RNA biology driven by diverse methodologies, ranging from cell fractionation, in situ and proximity labeling, imaging, spatially indexed next-generation sequencing (NGS) approaches, and spatially informed computational modeling. Particularly, recent advances have been made for near-genome-scale profiling of RNA and multimodal biomolecules at high spatial resolution. These methods enabled new discoveries into RNA's spatiotemporal kinetics, RNA processing, translation status, and RNA-protein interactions in cells and tissues. The evolving landscape of experimental and computational strategies reveals the complexity and heterogeneity of spatial RNA biology with subcellular resolution, heralding new avenues for RNA biology research.

Advances in spatial proteomics: Mapping proteome architecture from protein complexes to subcellular localizations

Proteins are responsible for most intracellular functions, which they perform as part of higher-order molecular complexes, located within defined subcellular niches. Localization is both dynamic and context specific and mislocalization underlies a multitude of diseases. It is thus vital to be able to measure the components of higher-order protein complexes and their subcellular location dynamically in order to fully understand cell biological processes. Here, we review the current range of highly complementary approaches that determine the subcellular organization of the proteome. We discuss the scale and resolution at which these approaches are best employed and the caveats that should be taken into consideration when applying them. We also look to the future and emerging technologies that are paving the way for a more comprehensive understanding of the functional roles of protein isoforms, which is essential for unraveling the complexities of cell biology and the development of disease treatments.

Mapping subcellular RNA localization with proximity labeling

The subcellular localization of RNA is critical to a variety of physiological and pathological processes. Dissecting the spatiotemporal regulation of the transcriptome is key to understanding cell function and fate. However, it remains challenging to effectively enrich and catalogue RNAs from various subcellular structures using traditional approaches. In recent years, proximity labeling has emerged as an alternative strategy for efficient isolation and purification of RNA from these intricate subcellular compartments. This review focuses on examining RNA-related proximity labeling tools and exploring their application in elucidating the spatiotemporal regulation of RNA at the subcellular level.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

Protein Condensate Atlas from predictive models of heteromolecular condensate composition

Biomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates.

A Review for Artificial Intelligence Based Protein Subcellular Localization

Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer's disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions

The spatial and temporal distributions of proteins are critical to protein function, but cannot be directly assessed by measuring protein bundance. Here we describe a mass spectrometry-based proteomics strategy, Simultaneous Proteome Localization and Turnover (SPLAT), to measure concurrently protein turnover rates and subcellular localization in the same experiment. Applying the method, we find that unfolded protein response (UPR) has different effects on protein turnover dependent on their subcellular location in human AC16 cells, with proteome-wide slowdown but acceleration among stress response proteins in the ER and Golgi. In parallel, UPR triggers broad differential localization of proteins including RNA-binding proteins and amino acid transporters. Moreover, we observe newly synthesized proteins including EGFR that show a differential localization under stress than the existing protein pools, reminiscent of protein trafficking disruptions. We next applied SPLAT to an induced pluripotent stem cell derived cardiomyocyte (iPSC-CM) model of cancer drug cardiotoxicity upon treatment with the proteasome inhibitor carfilzomib. Paradoxically, carfilzomib has little effect on global average protein half-life, but may instead selectively disrupt sarcomere protein homeostasis. This study provides a view into the interactions of protein spatial and temporal dynamics and demonstrates a method to examine protein homeostasis regulations in stress and drug response.