Understanding molecular mechanisms of disease through spatial proteomics

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/.

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.

Recent advances in proteomic-based diagnostics of cystic fibrosis

INTRODUCTION

Cystic fibrosis (CF) is a genetic disease characterized by thick and sticky mucus accumulation, which may harm numerous internal organs. Various variables such as gene modifiers, environmental factors, age of diagnosis, and CF transmembrane conductance regulator (CFTR) gene mutations influence phenotypic disease diversity. Biomarkers that are based on genomic information may not accurately represent the underlying mechanism of the disease as well as its lethal complications. Therefore, recent advancements in mass spectrometry (MS)-based proteomics may provide deep insights into CF mechanisms and cellular functions by examining alterations in the protein expression patterns from various samples of individuals with CF.

AREAS COVERED

We present current developments in MS-based proteomics, its application, and findings in CF. In addition, the future roles of proteomics in finding diagnostic and prognostic novel biomarkers.

EXPERT OPINION

Despite significant advances in MS-based proteomics, extensive research in a large cohort for identifying and validating diagnostic, prognostic, predictive, and therapeutic biomarkers for CF disease is highly needed.

Apicobasal Surfaceome Architecture Encodes for Polarized Epithelial Functionality and Depends on Tumor Suppressor PTEN

The loss of apicobasal polarity during the epithelial-to-mesenchymal transition (EMT) is a hallmark of cancer and metastasis. The key feature of this polarity in epithelial cells is the subdivision of the plasma membrane into apical and basolateral domains, with each orchestrating specific intra- and extracellular functions. Epithelial transport and signaling capacities are thought to be determined largely by the quality, quantity, and nanoscale organization of proteins residing in these membrane domains, the apicobasal surfaceomes. Despite its implications for cancer, drug uptake, and infection, our current knowledge of how the polarized surfaceome is organized and maintained is limited. Here, we used chemoproteomic surfaceome scanning to establish proteotype maps of apicobasal surfaceomes and reveal quantitative distributions of, i.e., surface proteases, phosphatases, and tetraspanins as potential key regulators of polarized cell functionality. We show further that the tumor suppressor PTEN regulates polarized surfaceome architecture and uncover a potential role in collective cell migration. Our differential surfaceome analysis provides a molecular framework to elucidate polarized protein networks regulating epithelial functions and PTEN-associated cancer progression.

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

Monitoring Protein Import into the Endoplasmic Reticulum in Living Cells with Proximity Labeling

The proper trafficking of eukaryotic proteins is essential to cellular function. Genetic, environmental, and other stresses can induce protein mistargeting and, in turn, threaten cellular protein homeostasis. Current methods for measuring protein mistargeting are difficult to translate to living cells, and thus the role of cellular signaling networks in stress-dependent protein mistargeting processes, such as ER pre-emptive quality control (ER pQC), is difficult to parse. Herein, we use genetically encoded peroxidases to characterize protein import into the endoplasmic reticulum (ER). We show that the ^ERHRP/^cytAPEX pair provides good selectivity and sensitivity for both multiplexed protein labeling and for identifying protein mistargeting, using the known ER pQC substrate transthyretin (TTR). Although ^ERHRP labeling induces formation of detergent-resistant TTR aggregates, this is minimized by using low ^ERHRP expression, without loss of labeling efficiency. ^cytAPEX labeling recovers TTR that is mistargeted as a consequence of Sec61 inhibition or ER stress-induced ER pQC. Furthermore, we discover that stress-free activation of the ER stress-associated transcription factor ATF6 recapitulates the TTR import deficiency of ER pQC. Hence, proximity labeling is an effective strategy for characterizing factors that influence ER protein import in living cells.

New Views of Old Proteins: Clarifying the Enigmatic Proteome

All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide a >10 years of projection for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we describe a next generation of approaches to define, quantify, and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.

Proteomic analysis of the IPF mesenchymal progenitor cell nuclear proteome identifies abnormalities in key nodal proteins that underlie their fibrogenic phenotype

IPF is a progressive fibrotic lung disease whose pathogenesis remains incompletely understood. We have previously discovered pathologic mesenchymal progenitor cells (MPCs) in the lungs of IPF patients. IPF MPCs display a distinct transcriptome and create sustained interstitial fibrosis in immune deficient mice. However, the precise pathologic alterations responsible for this fibrotic phenotype remain to be uncovered. Quantitative mass spectrometry and interactomics is a powerful tool that can define protein alterations in specific subcellular compartments that can be implemented to understand disease pathogenesis. We employed quantitative mass spectrometry and interactomics to define protein alterations in the nuclear compartment of IPF MPCs compared to control MPCs. We identified increased nuclear levels of PARP1, CDK1, and BACH1. Interactomics implicated PARP1, CDK1, and BACH1 as key hub proteins in the DNA damage/repair, differentiation, and apoptosis signaling pathways respectively. Loss of function and inhibitor studies demonstrated important roles for PARP1 in DNA damage/repair, CDK1 in regulating IPF MPC stemness and self-renewal, and BACH1 in regulating IPF MPC viability. Our quantitative mass spectrometry studies combined with interactomic analysis uncovered key roles for nuclear PARP1, CDK1, and BACH1 in regulating IPF MPC fibrogenicity.

SFPQ Promotes Lung Cancer Malignancy via Regulation of CD44 v6 Expression

Mesenchymal stem cells (MSCs) contribute to tumor pathogenesis and elicit antitumor immune responses in tumor microenvironments. Nuclear proteins might be the main players in these processes. In the current study, combining spatial proteomics with ingenuity pathway analysis (IPA) in lung non-small cell (NSC) cancer MSCs, we identify a key nuclear protein regulator, SFPQ (Splicing Factor Proline and Glutamine Rich), which is overexpressed in lung cancer MSCs and functions to promote MSCs proliferation, chemical resistance, and invasion. Mechanistically, the knockdown of SFPQ reduces CD44v6 expression to inhibit lung cancer MSCs stemness, proliferation in vitro, and metastasis in vivo. The data indicates that SFPQ may be a potential therapeutic target for limiting growth, chemotherapy resistance, and metastasis of lung cancer.

Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data

Localization of organelle proteins by isotope tagging (LOPIT) maps are a coordinate-directed representation of proteome data that can aid in biological interpretation. Analysis of organellar association for proteins as displayed using LOPIT is evaluated and interpreted for two types of proteomic data sets. First, test and control group protein abundances and fold change data obtained in a proximity labeling experiment are plotted on a LOPIT map to evaluate the likelihood of true protein interactions. Selection of true positives based on co-localization of proteins in the organellar space is shown to be consistent with carboxylase enrichment which serves as a positive control for biotinylation in streptavidin affinity selected proteome data sets. The mapping in organellar space facilitates discrimination between the test and control groups and aids in identification of proteins of interest. The same representation of proteins in organellar space is used in the analysis of extracellular vesicle proteomes for which protein abundance and fold change data are evaluated. Vesicular protein organellar localization patterns provide information about the subcellular origin of the proteins in the samples which are isolates from the extracellular milieu. The organellar localization patterns are indicative of the provenance of the vesicular proteome origin and allow discrimination between proteomes prepared using different enrichment methods. The patterns in LOPIT displays are easy to understand and compare which aids in the biological interpretation of proteome data.

Computer-aided design of reversible hybridization chain reaction (CAD-HCR) enables multiplexed single-cell spatial proteomics imaging

In situ spatial proteomics analysis of a single cell has not been achieved yet, mainly because of insufficient throughput and sensitivity of current techniques. Recent progress on immuno-nucleic acid amplification technology presents tremendous opportunities to address this issue. Here, we report an innovative hybridization chain reaction (HCR) technique that involves computer-aided design (CAD) and reversible assembly. CAD enables highly multiplexed HCR with a sequence database that can work in parallel, while reversible assembly enables the switching of HCR between a working state and a resting state. Thus, CAD-HCR has been successfully adopted for single-cell spatial proteomics analysis. The fluorescence signal of CAD-HCR is comparable with conventional immunofluorescence, and it is positively correlated with the abundance of target proteins, which is beneficial for the visualization of proteins. The method developed here expands the toolbox of single-cell analysis and proteomics studies, as well as the performance and application of HCR.

Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models

Advanced three-dimensional (3D) cell models have proven to be capable of depicting architectural and microenvironmental features of several tissues. By providing data of higher physiological and pathophysiological relevance, 3D cell models have been contributing to a better understanding of human development, pathology onset and progression mechanisms, as well as for 3D cell-based assays for drug discovery. Nonetheless, the characterization and interrogation of these tissue-like structures pose major challenges on the conventional analytical methods, pushing the development of spatially-resolved technologies. Herein, we review recent advances and pioneering technologies suitable for the interrogation of multicellular 3D models, while capable of retaining biological spatial information. We focused on imaging technologies and omics tools, namely transcriptomics, proteomics and metabolomics. The advantages and shortcomings of these novel methodologies are discussed, alongside the opportunities to intertwine data from the different tools.

Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line

Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community.

Subcellular proteomics

The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.

TRANSPIRE: A Computational Pipeline to Elucidate Intracellular Protein Movements from Spatial Proteomics Data Sets

Protein localization is paramount to protein function, and the intracellular movement of proteins underlies the regulation of numerous cellular processes. Given advances in spatial proteomics, the investigation of protein localization at a global scale has become attainable. Also becoming apparent is the need for dedicated analytical frameworks that allow the discovery of global intracellular protein movement events. Here, we describe TRANSPIRE, a computational pipeline that facilitates TRanslocation ANalysis of SPatIal pRotEomics data sets. TRANSPIRE leverages synthetic translocation profiles generated from organelle marker proteins to train a probabilistic Gaussian process classifier that predicts changes in protein distribution. This output is then integrated with information regarding co-translocating proteins and complexes and enriched gene ontology associations to discern the putative regulation and function of movement. We validate TRANSPIRE performance for predicting nuclear-cytoplasmic shuttling events. Analyzing an existing data set of nuclear and cytoplasmic proteomes during Kaposi Sarcoma-associated herpesvirus (KSHV)-induced cellular mRNA decay, we confirm that TRANSPIRE readily discerns expected translocations of RNA binding proteins. We next investigate protein translocations during infection with human cytomegalovirus (HCMV), a β-herpesvirus known to induce global organelle remodeling. We find that HCMV infection induces broad changes in protein localization, with over 800 proteins predicted to translocate during virus replication. Evident are protein movements related to HCMV modulation of host defense, metabolism, cellular trafficking, and Wnt signaling. For example, the low-density lipoprotein receptor (LDLR) translocates to the lysosome early in infection in conjunction with its degradation, which we validate by targeted mass spectrometry. Using microscopy, we also validate the translocation of the multifunctional kinase DAPK3, a movement that may contribute to HCMV activation of Wnt signaling.

Light at Night Exposure Effects on Differentiation and Cell Cycle in the Rat Liver With Autonomic Nervous System Denervation

Introduction: N Exposure to artificial light at night (LAN) affects human health and causes several functional modifications in the body. Obesity, diabetes, and hormonal changes are reported after exposure to LAN in humans. This study aims to highlight the critical features of biological terms that are affected in the liver of rats which received autonomic nervous system denervation. Methods: The liver gene expression profiles of 8 male Wistar rats that received sympathetic plus parasympathetic hepatic denervation and were exposed to LAN from Gene Expression Omnibus (GEO) for 1 hour were compared with 5 controls. The significant differentially-expressed genes (DEGs) were screened by the protein-protein interaction (PPI) network analysis STRING database (an application of Cytoscape software). Also, CuleGO and CleuDedia, the 2 applications of Cytoscape software, were used for more analysis. Results: Among 250 DEGs, 173 characterized genes with fold change more than 2 plus 100 added relevant genes were included in the PPI network. The analysis of the main connected component (MCC) led to introducing 15 hubs and 15 bottlenecks. CCT2, COPS7A, KAT2A, and ERCC1 were determined as hub-bottlenecks. Among hubs and bottlenecks, DHX15, KAT2A, CCT2, HSP90AB1, CCNE1, DHX16, LSM2, WEE1, CWC27, BAZ1B, RAB22A, DNM2, and DHX30 were linked to each other by various kinds of actions. CCT2 and KAT2A, the 2 hub-bottlenecks, were included in the interacted genes in the action map. Four classes of biological terms including negative regulation of non-motile cilium assembly, negative regulation of transforming growth factor beta activation, alpha-tubulin acetylation, and histamine-induced gastric acid secretion were identified as the critical biochemical pathways and biological processes. Conclusion: Several essential functions such as differentiation, cell cycle, ribosome assembly, and splicing are affected by LAN in rat livers with autonomic nervous system denervation.

Proteomics: a powerful tool to study plant responses to biotic stress

In recent years, mass spectrometry-based proteomics has provided scientists with the tremendous capability to study plants more precisely than previously possible. Currently, proteomics has been transformed from an isolated field into a comprehensive tool for biological research that can be used to explain biological functions. Several studies have successfully used the power of proteomics as a discovery tool to uncover plant resistance mechanisms. There is growing evidence that indicates that the spatial proteome and post-translational modifications (PTMs) of proteins directly participate in the plant immune response. Therefore, understanding the subcellular localization and PTMs of proteins is crucial for a comprehensive understanding of plant responses to biotic stress. In this review, we discuss current approaches to plant proteomics that use mass spectrometry, with particular emphasis on the application of spatial proteomics and PTMs. The purpose of this paper is to investigate the current status of the field, discuss recent research challenges, and encourage the application of proteomics techniques to further research.