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

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.

TransGCN: a semi-supervised graph convolution network-based framework to infer protein translocations in spatio-temporal proteomics

Protein subcellular localization (PSL) is very important in order to understand its functions, and its movement between subcellular niches within cells plays fundamental roles in biological process regulation. Mass spectrometry-based spatio-temporal proteomics technologies can help provide new insights of protein translocation, but bring the challenge in identifying reliable protein translocation events due to the noise interference and insufficient data mining. We propose a semi-supervised graph convolution network (GCN)-based framework termed TransGCN that infers protein translocation events from spatio-temporal proteomics. Based on expanded multiple distance features and joint graph representations of proteins, TransGCN utilizes the semi-supervised GCN to enable effective knowledge transfer from proteins with known PSLs for predicting protein localization and translocation. Our results demonstrate that TransGCN outperforms current state-of-the-art methods in identifying protein translocations, especially in coping with batch effects. It also exhibited excellent predictive accuracy in PSL prediction. TransGCN is freely available on GitHub at https://github.com/XuejiangGuo/TransGCN.

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

The functions of proteins depend on their spatial and temporal distributions, which are not directly measured by static protein abundance. Under endoplasmic reticulum (ER) stress, the unfolded protein response (UPR) pathway remediates proteostasis in part by altering the turnover kinetics and spatial distribution of proteins. A global view of these spatiotemporal changes has yet to emerge and it is unknown how they affect different cellular compartments and pathways. Here we describe a mass spectrometry-based proteomics strategy and data analysis pipeline, termed Simultaneous Proteome Localization and Turnover (SPLAT), to measure concurrently the changes in protein turnover and subcellular distribution in the same experiment. Investigating two common UPR models of thapsigargin and tunicamycin challenge in human AC16 cells, we find that the changes in protein turnover kinetics during UPR varies across subcellular localizations, with overall slowdown but an acceleration in endoplasmic reticulum and Golgi proteins involved in stress response. In parallel, the spatial proteomics component of the experiment revealed an externalization of amino acid transporters and ion channels under UPR, as well as the migration of RNA-binding proteins toward an endosome co-sedimenting compartment. The SPLAT experimental design classifies heavy and light SILAC labeled proteins separately, allowing the observation of differential localization of new and old protein pools and capturing a partition of newly synthesized EGFR and ITGAV to the ER under stress that suggests protein trafficking disruptions. Finally, application of SPLAT toward human induced pluripotent stem cell derived cardiomyocytes (iPSC-CM) exposed to the cancer drug carfilzomib, identified a selective disruption of proteostasis in sarcomeric proteins as a potential mechanism of carfilzomib-mediated cardiotoxicity. Taken together, this study provides a global view into the spatiotemporal dynamics of human cardiac cells and demonstrates a method for inferring the coordinations between spatial and temporal proteome regulations in stress and drug response.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

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.

Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE

The steady-state localisation of proteins provides vital insight into their function. These localisations are context specific with proteins translocating between different subcellular niches upon perturbation of the subcellular environment. Differential localisation, that is a change in the steady-state subcellular location of a protein, provides a step towards mechanistic insight of subcellular protein dynamics. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we describe a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein differentially localises upon cellular perturbation. Extensive simulation studies demonstrate that BANDLE reduces the number of both type I and type II errors compared to existing approaches. Application of BANDLE to several datasets recovers well-studied translocations. In an application to cytomegalovirus infection, we obtain insights into the rewiring of the host proteome. Integration of other high-throughput datasets allows us to provide the functional context of these data.

Spatial Proteomics Reveals Differences in the Cellular Architecture of Antibody-Producing CHO and Plasma Cell-Derived Cells

Most of the recombinant biotherapeutics employed today to combat severe illnesses, for example, various types of cancer or autoimmune diseases, are produced by Chinese hamster ovary (CHO) cells. To meet the growing demand of these pharmaceuticals, CHO cells are under constant development in order to enhance their stability and productivity. The last decades saw a shift from empirical cell line optimization toward rational cell engineering using a growing number of large omics datasets to alter cell physiology on various levels. Especially proteomics workflows reached new levels in proteome coverage and data quality because of advances in high-resolution mass spectrometry instrumentation. One type of workflow concentrates on spatial proteomics by usage of subcellular fractionation of organelles with subsequent shotgun mass spectrometry proteomics and machine learning algorithms to determine the subcellular localization of large portions of the cellular proteome at a certain time point. Here, we present the first subcellular spatial proteome of a CHO-K1 cell line producing high titers of recombinant antibody in comparison to the spatial proteome of an antibody-producing plasma cell-derived myeloma cell line. Both cell lines show colocalization of immunoglobulin G chains with chaperones and proteins associated in protein glycosylation within the endoplasmic reticulum compartment. However, we report differences in the localization of proteins associated to vesicle-mediated transport, transcription, and translation, which may affect antibody production in both cell lines. Furthermore, pairing subcellular localization data with protein expression data revealed elevated protein masses for organelles in the secretory pathway in plasma cell-derived MPC-11 (Merwin plasma cell tumor-11) cells. Our study highlights the potential of subcellular spatial proteomics combined with protein expression as potent workflow to identify characteristics of highly efficient recombinant protein-expressing cell lines. Data are available via ProteomeXchange with identifier PXD029115.

Intermittent bulk release of human cytomegalovirus

Human Cytomegalovirus (HCMV) can infect a variety of cell types by using virions of varying glycoprotein compositions. It is still unclear how this diversity is generated, but spatio-temporally separated envelopment and egress pathways might play a role. So far, one egress pathway has been described in which HCMV particles are individually enveloped into small vesicles and are subsequently exocytosed continuously. However, some studies have also found enveloped virus particles inside multivesicular structures but could not link them to productive egress or degradation pathways. We used a novel 3D-CLEM workflow allowing us to investigate these structures in HCMV morphogenesis and egress at high spatio-temporal resolution. We found that multiple envelopment events occurred at individual vesicles leading to multiviral bodies (MViBs), which subsequently traversed the cytoplasm to release virions as intermittent bulk pulses at the plasma membrane to form extracellular virus accumulations (EVAs). Our data support the existence of a novel bona fide HCMV egress pathway, which opens the gate to evaluate divergent egress pathways in generating virion diversity.

A Method to Estimate the Distribution of Proteins across Multiple Compartments Using Data from Quantitative Proteomics Subcellular Fractionation Experiments

Knowledge of cellular location is key to understanding the biological function of proteins. One commonly used large-scale method to assign cellular locations is subcellular fractionation, followed by quantitative mass spectrometry to identify proteins and estimate their relative distribution among centrifugation fractions. In most of such subcellular proteomics studies, each protein is assigned to a single cellular location by comparing its distribution to those of a set of single-compartment reference proteins. However, in many cases, proteins reside in multiple compartments. To accurately determine the localization of such proteins, we previously introduced constrained proportional assignment (CPA), a method that assigns each protein a fractional residence over all reference compartments (Jadot Mol. Cell Proteomics 2017, 16(2), 194-212. 10.1074/mcp.M116.064527). In this Article, we describe the principles underlying CPA, as well as data transformations to improve accuracy of assignment of proteins and protein isoforms, and a suite of R-based programs to implement CPA and related procedures for analysis of subcellular proteomics data. We include a demonstration data set that used isobaric-labeling mass spectrometry to analyze rat liver fractions. In addition, we describe how these programs can be readily modified by users to accommodate a wide variety of experimental designs and methods for protein quantitation.

Dual-channel detection of viscosity and pH with a near-infrared fluorescent probe for cancer visualization

Compared to ordinary cells, tumor cells have a unique microenvironment, characterized by high viscosity, low pH, high reactive oxygen species level and the overexpression of certain proteases. Therefore, viscosity and pH can be used as important parameters for visualizing cancer. We designed a spiro-oxazolidine compound (In-1) for the dual-channel detection of viscosity and pH, with the red channel for detecting viscosity and the blue channel for pH. Interestingly, In-1 can locate different organelles under different conditions. Under physiological conditions, In-1 efficiently targeted lysosomes and showed that the viscosity of lysosomes increases in cancer cells while the pH decreases, which can be used to distinguish and detect cancer cells and normal cells. When we treated HL-7702 cells with CCCP, the probe could effectively target the mitochondria, and the fluorescence intensity in the pH channel decreased. This indicates that In-1 can be used as a powerful tool to simultaneously monitor viscosity and pH in different organelles, and may have a guiding role in diseases caused by mitochondrial and lysosomal microenvironments.

Spatial proteomics reveals subcellular reorganization in human keratinocytes exposed to UVA light

The effects of UV light on the skin have been extensively investigated. However, systematic information about how the exposure to ultraviolet-A (UVA) light, the least energetic but the most abundant UV radiation reaching the Earth, shapes the subcellular organization of proteins is lacking. Using subcellular fractionation, mass-spectrometry-based proteomics, machine learning algorithms, immunofluorescence, and functional assays, we mapped the subcellular reorganization of the proteome of human keratinocytes in response to UVA light. Our workflow quantified and assigned subcellular localization for over 1,600 proteins, of which about 200 were found to redistribute upon UVA exposure. Reorganization of the proteome affected modulators of signaling pathways, cellular metabolism, and DNA damage response. Strikingly, mitochondria were identified as one of the main targets of UVA-induced stress. Further investigation demonstrated that UVA induces mitochondrial fragmentation, up-regulates redox-responsive proteins, and attenuates respiratory rates. These observations emphasize the role of this radiation as a potent metabolic stressor in the skin.

Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.

Subcellular Transcriptomics and Proteomics: A Comparative Methods Review

The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics.

Virus-host protein interactions as footprints of human cytomegalovirus replication

Human cytomegalovirus (HCMV) is a pervasive β-herpesvirus that causes lifelong infection. The lytic replication cycle of HCMV is characterized by global organelle remodeling and dynamic virus-host interactions, both of which are necessary for productive HCMV replication. With the advent of new technologies for investigating protein-protein and protein-nucleic acid interactions, numerous critical interfaces between HCMV and host cells have been identified. Here, we review temporal and spatial virus-host interactions that support different stages of the HCMV replication cycle. Understanding how HCMV interacts with host cells during entry, replication, and assembly, as well as how it interfaces with host cell metabolism and immune responses promises to illuminate processes that underlie the biology of infection and the resulting pathologies.

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.