Spatial Proteomics Data Research Articles

EPCO-46. SPATIAL TRANSCRIPTOMICS ANALYSIS OF GLIOBLASTOMA REVEALS THREE DISTINCT REGIONAL PROGRAMS OF T-CELL INFILTRATION

Abstract Glioblastoma (GBM) resists T-cell killing by developing an immunosuppressive tumor microenvironment. Transcriptomic technologies, such as single-cell RNA sequencing (scRNA-seq), have characterized immune cell phenotypes and putative cell-cell interactions in GBM. However, the tumor microenvironment surrounding T-cells is still not well characterized, motivating the need for spatial analyses. Two publicly-available GBM spatial transcriptomic datasets were analyzed, with one discovery cohort (N=18) and one validation cohort (N=12). Using cell deconvolution with a scRNA-seq reference dataset, areas of high T-cell infiltration were identified and clustered into regional programs based on transcriptomic signatures. To highlight which ligand-receptor interactions are most prominent in these regional programs, a novel spatial enrichment algorithm was applied. The impact of spatially enriched ligand-receptor interactions on overall survival was evaluated using bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA). Three distinct T-cell regional programs (transitional, immunosuppressive, and infiltrative) were found across both the discovery and validation cohort. The infiltrative T-cell regional program resided on the periphery of the tumor with a neural progenitor phenotype (NRGN, VSNL1, SLC17A7). In contrast, the immunosuppressive T-cell region had a higher proportion of malignant cells and a greater mesenchymal tumor signature (ANXA1, CD44, complement pathway). Furthermore, ligand-receptor enrichment analysis showed that the immunosuppressive region had higher tumor and tumor-associated macrophage (TAM) signaling towards CD4 T-cells along the collagen, midkine, and osteopontin pathways. TCGA survival analysis showed that higher collagen and midkine signals were associated with worse overall and disease free survival. Our results identified three distinct regional programs in GBM and highlight a set of signaling pathways associated with T-cell suppression. Our findings suggest that the disruption of these pathways could reduce immunosuppressive signals from tumor and TAMs to T-cells. Subsequent validation with spatial proteomics data in glioblastoma is ongoing.

Graph Fourier transform for spatial omics representation and analyses of complex organs

Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.

Integration of Clinical Trial Spatial Multiomics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery.

Due to the lack of treatment options, there remains a need to advance new therapeutics in hepatocellular carcinoma (HCC). The traditional approach moves from initial molecular discovery through animal models to human trials to advance novel systemic therapies that improve treatment outcomes for patients with cancer. Computational methods that simulate tumors mathematically to describe cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico, potentially greatly accelerating delivery of new therapeutics to patients. To facilitate the design of dosing regimens and identification of potential biomarkers for immunotherapy, we developed a new computational model to track tumor progression at the organ scale while capturing the spatial heterogeneity of the tumor in HCC. This computational model of spatial quantitative systems pharmacology was designed to simulate the effects of combination immunotherapy. The model was initiated using literature-derived parameter values and fitted to the specifics of HCC. Model validation was done through comparison with spatial multiomics data from a neoadjuvant HCC clinical trial combining anti-PD1 immunotherapy and a multitargeted tyrosine kinase inhibitor cabozantinib. Validation using spatial proteomics data from imaging mass cytometry demonstrated that closer proximity between CD8 T cells and macrophages correlated with nonresponse. We also compared the model output with Visium spatial transcriptomics profiling of samples from posttreatment tumor resections in the clinical trial and from another independent study of anti-PD1 monotherapy. Spatial transcriptomics data confirmed simulation results, suggesting the importance of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. Our findings demonstrate that incorporating mathematical modeling and computer simulations with high-throughput spatial multiomics data provides a novel approach for patient outcome prediction and biomarker discovery. Significance: Incorporating mathematical modeling and computer simulations with high-throughput spatial multiomics data provides an effective approach for patient outcome prediction and biomarker discovery.

Role of ferroptosis in neuroimmunity and neurodegeneration in multiple sclerosis revealed by multi-omics data.

Previous studies have found that ferroptosis plays an important role in a variety of neurological diseases. However, the precise role of ferroptosis in the multiple sclerosis patients remains uncertain. We defined and validated a computational metric of ferroptosis levels. The ferroptosis scores were computed using the AUCell method, which reflects the enrichment scores of ferroptosis-related genes through gene ranking. The reliability of the ferroptosis score was assessed using various methods, involving cells induced to undergo ferroptosis by six different ferroptosis inducers. Through a comprehensive approach integrating snRNA-seq, spatial transcriptomics, and spatial proteomics data, we explored the role of ferroptosis in multiple sclerosis. Our findings revealed that among seven sampling regions of different white matter lesions, the edges of active lesions exhibited the highest ferroptosis score, which was associated with activation of the phagocyte system. Remyelination lesions exhibit the lowest ferroptosis score. In the cortex, ferroptosis score were elevated in neurons, relevant to a variety of neurodegenerative disease-related pathways. Spatial transcriptomics demonstrated a significant co-localization among ferroptosis score, neurodegeneration and microglia, which was verified by spatial proteomics. Furthermore, we established a diagnostic model of multiple sclerosis based on 24 ferroptosis-related genes in the peripheral blood. Ferroptosis might exhibits a dual role in the context of multiple sclerosis, relevant to both neuroimmunity and neurodegeneration, thereby presenting a promising and novel therapeutic target. Ferroptosis-related genes in the blood that could potentially serve as diagnostic and prognostic markers for multiple sclerosis.

MAPS: pathologist-level cell type annotation from tissue images through machine learning

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

A review on deep learning applications in highly multiplexed tissue imaging data analysis.

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

Cancer-associated fibroblast classification in single-cell and spatial proteomics data

Cancer-associated fibroblasts (CAFs) are a diverse cell population within the tumour microenvironment, where they have critical effects on tumour evolution and patient prognosis. To define CAF phenotypes, we analyse a single-cell RNA sequencing (scRNA-seq) dataset of over 16,000 stromal cells from tumours of 14 breast cancer patients, based on which we define and functionally annotate nine CAF phenotypes and one class of pericytes. We validate this classification system in four additional cancer types and use highly multiplexed imaging mass cytometry on matched breast cancer samples to confirm our defined CAF phenotypes at the protein level and to analyse their spatial distribution within tumours. This general CAF classification scheme will allow comparison of CAF phenotypes across studies, facilitate analysis of their functional roles, and potentially guide development of new treatment strategies in the future.

Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis.

Spatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and utilization. Taking inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival. The code for this work can be found at gitlab.com/enable-medicine-public/spatsurv.

SpatialSort: a Bayesian model for clustering and cell population annotation of spatial proteomics data.

Recent advances in spatial proteomics technologies have enabled the profiling of dozens of proteins in thousands of single cells in situ. This has created the opportunity to move beyond quantifying the composition of cell types in tissue, and instead probe the spatial relationships between cells. However, most current methods for clustering data from these assays only consider the expression values of cells and ignore the spatial context. Furthermore, existing approaches do not account for prior information about the expected cell populations in a sample. To address these shortcomings, we developed SpatialSort, a spatially aware Bayesian clustering approach that allows for the incorporation of prior biological knowledge. Our method is able to account for the affinities of cells of different types to neighbour in space, and by incorporating prior information about expected cell populations, it is able to simultaneously improve clustering accuracy and perform automated annotation of clusters. Using synthetic and real data, we show that by using spatial and prior information SpatialSort improves clustering accuracy. We also demonstrate how SpatialSort can perform label transfer between spatial and nonspatial modalities through the analysis of a real world diffuse large B-cell lymphoma dataset. Source code is available on Github at: https://github.com/Roth-Lab/SpatialSort.

DeepSP: A Deep Learning Framework for Spatial Proteomics.

The study of protein subcellular localization (PSL) is a fundamental step toward understanding the mechanism of protein function. The recent development of mass spectrometry (MS)-based spatial proteomics to quantify the distribution of proteins across subcellular fractions provides us a high-throughput approach to predict unknown PSLs based on known PSLs. However, the accuracy of PSL annotations in spatial proteomics is limited by the performance of existing PSL predictors based on traditional machine learning algorithms. In this study, we present a novel deep learning framework named DeepSP for PSL prediction of an MS-based spatial proteomics data set. DeepSP constructs the new feature map of a difference matrix by capturing detailed changes between different subcellular fractions of protein occupancy profiles and uses the convolutional block attention module to improve the prediction performance of PSL. DeepSP achieved significant improvement in accuracy and robustness for PSL prediction in independent test sets and unknown PSL prediction compared to current state-of-the-art machine learning predictors. As an efficient and robust framework for PSL prediction, DeepSP is expected to facilitate spatial proteomics studies and contributes to the elucidation of protein functions and the regulation of biological processes.

Single-nucleus and Spatially Resolved Intratumor Subtype Heterogeneity in Bladder Cancer

BackgroundCurrent bulk transcriptomic classification systems for bladder cancer do not consider the level of intratumor subtype heterogeneity. ObjectiveTo investigate the extent and possible clinical impact of intratumor subtype heterogeneity across early and more advanced stages of bladder cancer. Design, setting, and participantsWe performed single-nucleus RNA sequencing (RNA-seq) of 48 bladder tumors and additional spatial transcriptomics for four of these tumors. Total bulk RNA-seq and spatial proteomics data were available from the same tumors for comparison, along with detailed clinical follow-up of the patients. Outcome measurements and statistical analysisThe primary outcome was progression-free survival for non–muscle-invasive bladder cancer. Cox regression analysis, log-rank tests, Wilcoxon rank-sum tests, Spearman correlation, and Pearson correlation were used for statistical analysis. Results and limitationsWe found that the tumors exhibited varying levels of intratumor subtype heterogeneity and that the level of subtype heterogeneity can be estimated from both single-nucleus and bulk RNA-seq data, with high concordance between the two. We found that a higher class 2a weight estimated from bulk RNA-seq data is associated with worse outcome for patients with molecular high-risk class 2a tumors. The sparsity of the data generated using the DroNc-seq sequencing protocol is a limitation. ConclusionsOur results indicate that discrete subtype assignments from bulk RNA-seq data may lack biological granularity and that continuous class scores may improve clinical risk stratification of patients with bladder cancer. Patient summaryWe found that several molecular subtypes can exist within a single bladder tumor and that continuous subtype scores can be used to identify a subgroup of patients with poor outcomes. Use of these subtype scores may improve risk stratification for patients with bladder cancer, which can help in making decisions on treatment.

Abstract 4623: Single-cell spatial proteomic analysis of the tumor microenvironment in treatment-naive NSCLC samples with immunotherapy treatment and response data

Abstract The tumor microenvironment (TME) represents a complex network comprising a variety of cell types including immune, stromal, and extracellular elements in addition to malignant tumor cells. Growing evidence suggests that the spatial relationships of these components influence tumor progression and response/resistance to immunotherapeutic agents. However, the precise mechanisms driving this relationship to clinical outcomes remains poorly understood. This is in part because generating comprehensive spatial datasets has only recently been made possible due to innovations in instrumentation, laboratory techniques, and data storage and processing. Thus, there has been little integration between various types of -omics datasets with spatial data. Here, we present a workflow for analyzing multi-omic data of the TME with the aim of providing mechanistic insights into signatures of response to immunotherapy. The workflow starts with data generation and proceeds through cell segmentation, quality filtering, phenotype assignment through unsupervised clustering, analysis of pairwise interactions and higher order multicellular structures (i.e., “neighborhoods”), and finally association with patient clinical and molecular data allowing for meaningful comparisons to be made. This analysis can be performed through the Enable Medicine Cloud Platform, a framework that facilitates the association of a multitude of data types to samples, rapid processing of multiplex images to deliver single cell spatial proteomic data, and graphical interfaces for each analysis step. To demonstrate the utility of this workflow, we generated spatial proteomic data for a tissue microarray (TMA) comprising a cohort of treatment naïve NSCLC samples (n=42) with second-line immune checkpoint inhibitor-treatment and clinical follow-up information. The spatial proteomics data was generated with the Akoya PhenoCycler-Fusion multiplexed tissue imaging platform, with an optimized panel of 51 oligonucleotide-conjugated antibodies covering a wide variety of immune, epithelial, stromal, and malignant cell markers developed by Enable Medicine. This proteomic data was then integrated with GeoMX DSP, bulk RNAseq, and clinical information using the Enable Platform. This framework for analysis allows for the comprehensive integration of both clinical and biological parameters to enable biomarker discovery and insight into signatures of response to immunotherapy. Acknowledgement: Pasteur Hospital, 30 Voie Romaine, 06000 Nice, France for NSCLC tumor samples. Citation Format: Marie Cumberbatch, Geoffrey Ivison, Amy Lam, Aaron Mayer, Milan Bhagat. Single-cell spatial proteomic analysis of the tumor microenvironment in treatment-naive NSCLC samples with immunotherapy treatment and response data. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4623.

Abstract 5636: Multiplex digital spatial profiling (DSP) of proteins in the tumor microenvironment in response to GSK-3 inhibition by 9-ING-41 (elraglusib) correlates with novel immunostimulatory effects observed in vivo

Abstract Glycogen synthase kinase 3 (GSK-3) is a serine/threonine kinase with key roles in myriad biological processes such as tumor progression, and inhibition of GSK-3 using the small molecule elraglusib has shown promising preclinical antitumor activity in multiple tumor types. Our preclinical experiments showed that elraglusib treatment increased tumor cell PD-L1 expression, downregulated angiogenic and immunosuppressive signaling pathways, and increased anti-tumor immune responses in vitro and in vivo. Subsequent studies showed that several circulating factors were predictive of response to PD-1/PD-L1 blockade and GSK-3 inhibition in a murine model of colorectal cancer. To determine the translational relevance of our prior results we evaluated tumor biopsies and plasma samples from patients with refractory solid tumors of multiple tissue origins enrolled in a Phase 1 clinical trial investigating elraglusib (NCT03678883). Plasma samples were collected from patients at baseline and 24 hours post-IV administration of elraglusib and were analyzed using Luminex technology. Paired FFPE tumor biopsies from patients with colorectal or pancreatic cancer before and after treatment were selected to analyze the tumor microenvironment using NanoString GeoMx DSP technology. The region of interest (ROI) selection strategy focused on mixed tumor and immune cell segments and ROIs were segmented using panCK+ and CD45+ morphology stains. Cytokine analysis revealed that elevated baseline plasma levels of IL-1 beta and reduced levels of VEGF correlated with improved progression-free survival (PFS) and overall survival (OS). PFS was also found to be positively correlated with elevated plasma levels of immunostimulatory analytes such as Granzyme B, IFN-gamma, and IL-2 at 24 hours post-treatment with elraglusib. CD45+ tumor-infiltrating immune cells had lower expression of VISTA, PD-1, and IDO-1 inhibitory checkpoint proteins and higher expression of OX40L and B7-H3 stimulatory checkpoint proteins in post-treatment biopsies as compared to pre-treatment biopsies. Moreover, time-on-study length negatively correlated with CD39 expression in PanCK+ segments and positively correlated with CD163 expression in CD45+ segments. This ongoing study, to our knowledge, represents the first digital spatial analysis of tumor biopsies from patients treated with elraglusib. These novel circulating biomarkers of response to GSK-3 inhibition could provide significant clinical utility and the spatial proteomics data may give us insights into the immunomodulatory mechanisms of GSK-3 inhibition. Citation Format: Kelsey E. Huntington, Christoph Schorl, Shaolei Lu, Daniel Newhouse, Benedito A. Carneiro, Wafik S. El-Deiry. Multiplex digital spatial profiling (DSP) of proteins in the tumor microenvironment in response to GSK-3 inhibition by 9-ING-41 (elraglusib) correlates with novel immunostimulatory effects observed in vivo. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5636.

Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics.

Understanding sub-cellular protein localisation is an essential component in the analysis of context specific protein function. Recent advances in quantitative mass-spectrometry (MS) have led to high resolution mapping of thousands of proteins to sub-cellular locations within the cell. Novel modelling considerations to capture the complex nature of these data are thus necessary. We approach analysis of spatial proteomics data in a non-parametric Bayesian framework, using K-component mixtures of Gaussian process regression models. The Gaussian process regression model accounts for correlation structure within a sub-cellular niche, with each mixture component capturing the distinct correlation structure observed within each niche. The availability of marker proteins (i.e. proteins with a priori known labelled locations) motivates a semi-supervised learning approach to inform the Gaussian process hyperparameters. We moreover provide an efficient Hamiltonian-within-Gibbs sampler for our model. Furthermore, we reduce the computational burden associated with inversion of covariance matrices by exploiting the structure in the covariance matrix. A tensor decomposition of our covariance matrices allows extended Trench and Durbin algorithms to be applied to reduce the computational complexity of inversion and hence accelerate computation. We provide detailed case-studies on Drosophila embryos and mouse pluripotent embryonic stem cells to illustrate the benefit of semi-supervised functional Bayesian modelling of the data.

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.

Membrane marker selection for segmenting single cell spatial proteomics data

The ability to profile spatial proteomics at the single cell level enables the study of cell types, their spatial distribution, and interactions in several tissues and conditions. Current methods for cell segmentation in such studies rely on known membrane or cell boundary markers. However, for many tissues, an optimal set of markers is not known, and even within a tissue, different cell types may express different markers. Here we present RAMCES, a method that uses a convolutional neural network to learn the optimal markers for a new sample and outputs a weighted combination of the selected markers for segmentation. Testing RAMCES on several existing datasets indicates that it correctly identifies cell boundary markers, improving on methods that rely on a single marker or those that extend nuclei segmentations. Application to new spatial proteomics data demonstrates its usefulness for accurately assigning cell types based on the proteins expressed in segmented cells.

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.

A Bioconductor workflow for the Bayesian analysis of spatial proteomics.

Knowledge of the subcellular location of a protein gives valuable insight into its function. The field of spatial proteomics has become increasingly popular due to improved multiplexing capabilities in high-throughput mass spectrometry, which have made it possible to systematically localise thousands of proteins per experiment. In parallel with these experimental advances, improved methods for analysing spatial proteomics data have also been developed. In this workflow, we demonstrate using `pRoloc` for the Bayesian analysis of spatial proteomics data. We detail the software infrastructure and then provide step-by-step guidance of the analysis, including setting up a pipeline, assessing convergence, and interpreting downstream results. In several places we provide additional details on Bayesian analysis to provide users with a holistic view of Bayesian analysis for spatial proteomics data.

A Bayesian mixture modelling approach for spatial proteomics.

Analysis of the spatial sub-cellular distribution of proteins is of vital importance to fully understand context specific protein function. Some proteins can be found with a single location within a cell, but up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment. These considerations lead to uncertainty in associating a protein to a single location. Currently, mass spectrometry (MS) based spatial proteomics relies on supervised machine learning algorithms to assign proteins to sub-cellular locations based on common gradient profiles. However, such methods fail to quantify uncertainty associated with sub-cellular class assignment. Here we reformulate the framework on which we perform statistical analysis. We propose a Bayesian generative classifier based on Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus proteins have a probability distribution over sub-cellular locations, with Bayesian computation performed using the expectation-maximisation (EM) algorithm, as well as Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncertainty quantification, thus adding a further layer to the analysis of spatial proteomics. Our framework is flexible, allowing many different systems to be analysed and reveals new modelling opportunities for spatial proteomics. We find our methods perform competitively with current state-of-the art machine learning methods, whilst simultaneously providing more information. We highlight several examples where classification based on the support vector machine is unable to make any conclusions, while uncertainty quantification using our approach provides biologically intriguing results. To our knowledge this is the first Bayesian model of MS-based spatial proteomics data.

A Bioconductor workflow for processing and analysing spatial proteomics data.

Spatial proteomics is the systematic study of protein sub-cellular localisation. In this workflow, we describe the analysis of a typical quantitative mass spectrometry-based spatial proteomics experiment using the MSnbase and pRoloc Bioconductor package suite. To walk the user through the computational pipeline, we use a recently published experiment predicting protein sub-cellular localisation in pluripotent embryonic mouse stem cells. We describe the software infrastructure at hand, importing and processing data, quality control, sub-cellular marker definition, visualisation and interactive exploration. We then demonstrate the application and interpretation of statistical learning methods, including novelty detection using semi-supervised learning, classification, clustering and transfer learning and conclude the pipeline with data export. The workflow is aimed at beginners who are familiar with proteomics in general and spatial proteomics in particular.