A Mass Spectrometry-Based Approach for Mapping Protein Subcellular Localization Reveals the Spatial Proteome of Mouse Primary Neurons

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection

The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a protein's sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyperLOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identified in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches.

Molecular basis for KDEL-mediated retrieval of escaped ER-resident proteins - SWEET talking the COPs

Protein localisation in the cell is controlled through the function of trafficking receptors, which recognise specific signal sequences and direct cargo proteins to different locations. The KDEL receptor (KDELR) was one of the first intracellular trafficking receptors identified and plays an essential role in maintaining the integrity of the early secretory pathway. The receptor recognises variants of a canonical C-terminal Lys-Asp-Glu-Leu (KDEL) signal sequence on ER-resident proteins when these escape to the Golgi, and targets these proteins to COPI- coated vesicles for retrograde transport back to the ER. The empty receptor is then recycled from the ER back to the Golgi by COPII-coated vesicles. Crystal structures of the KDELR show that it is structurally related to the PQ-loop family of transporters that are found in both pro- and eukaryotes, and shuttle sugars, amino acids and vitamins across cellular membranes. Furthermore, analogous to PQ-loop transporters, the KDELR undergoes a pH-dependent and ligand-regulated conformational cycle. Here, we propose that the striking structural similarity between the KDELR and PQ-loop transporters reveals a connection between transport and trafficking in the cell, with important implications for understanding trafficking receptor evolution and function.

Summary: The structure of the KDEL receptor gives new insights into the close connection between trafficking and transport in the cell.

Surfaceome dynamics reveal proteostasis-independent reorganization of neuronal surface proteins during development and synaptic plasticity

Neurons are highly compartmentalized cells with tightly controlled subcellular protein organization. While brain transcriptome, connectome and global proteome maps are being generated, system-wide analysis of temporal protein dynamics at the subcellular level are currently lacking. Here, we perform a temporally-resolved surfaceome analysis of primary neuron cultures and reveal dynamic surface protein clusters that reflect the functional requirements during distinct stages of neuronal development. Direct comparison of surface and total protein pools during development and homeostatic synaptic scaling demonstrates system-wide proteostasis-independent remodeling of the neuronal surface, illustrating widespread regulation on the level of surface trafficking. Finally, quantitative analysis of the neuronal surface during chemical long-term potentiation (cLTP) reveals fast externalization of diverse classes of surface proteins beyond the AMPA receptor, providing avenues to investigate the requirement of exocytosis for LTP. Our resource (neurosurfaceome.ethz.ch) highlights the importance of subcellular resolution for systems-level understanding of cellular processes.

Cell surface proteins contribute to neuronal development and activity-dependent synaptic plasticity. Here, the authors perform a time-resolved surfaceome analysis of developing primary neurons and in response to homeostatic synaptic scaling and chemical long-term potentiation (cLTP), revealing surface proteome remodeling largely independent of global proteostasis.

Pinpointing Cell Identity in Time and Space

Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.

Small Molecule Enhancers of Endosome-to-Cytosol Import Augment Anti-tumor Immunity

Cross-presentation of antigens by dendritic cells (DCs) is critical for initiation of anti-tumor immune responses. Yet, key steps involved in trafficking of antigens taken up by DCs remain incompletely understood. Here, we screen 700 US Food and Drug Administration (FDA)-approved drugs and identify 37 enhancers of antigen import from endolysosomes into the cytosol. To reveal their mechanism of action, we generate proteomic organellar maps of control and drug-treated DCs (focusing on two compounds, prazosin and tamoxifen). By combining organellar mapping, quantitative proteomics, and microscopy, we conclude that import enhancers undergo lysosomal trapping leading to membrane permeation and antigen release. Enhancing antigen import facilitates cross-presentation of soluble and cell-associated antigens. Systemic administration of prazosin leads to reduced growth of MC38 tumors and to a synergistic effect with checkpoint immunotherapy in a melanoma model. Thus, inefficient antigen import into the cytosol limits antigen cross-presentation, restraining the potency of anti-tumor immune responses and efficacy of checkpoint blockers.

Organellar Maps Through Proteomic Profiling - A Conceptual Guide

Protein subcellular localization is an essential and highly regulated determinant of protein function. Major advances in mass spectrometry and imaging have allowed the development of powerful spatial proteomics approaches for determining protein localization at the whole cell scale. Here, a brief overview of current methods is presented, followed by a detailed discussion of organellar mapping through proteomic profiling. This relatively simple yet flexible approach is rapidly gaining popularity, because of its ability to capture the localizations of thousands of proteins in a single experiment. It can be used to generate high-resolution cell maps, and as a tool for monitoring protein localization dynamics. This review highlights the strengths and limitations of the approach and provides guidance to designing and interpreting profiling experiments.

Isotopically Encoded Nanotags for Multiplexed Ion Beam Imaging

High-dimensional profiling of markers and analytes using approaches, such as barcoded fluorescent imaging with repeated labeling and mass cytometry has allowed visualization of biological processes at the single-cell level. To address limitations of sensitivity and mass-channel capacity, a nanobarcoding platform is developed for multiplexed ion beam imaging (MIBI) using secondary ion beam spectrometry that utilizes fabricated isotopically encoded nanotags. Use of combinatorial isotope distributions in 100 nm sized nanotags expands the labeling palette to overcome the spectral bounds of mass channels. As a proof-of-principle, a four-digit (i.e., 0001-1111) barcoding scheme is demonstrated to detect 16 variants of 2H, 19F, 79/81Br, and 127I elemental barcode sets that are encoded in silica nanoparticle matrices. A computational debarcoding method and an automated machine learning analysis approach are developed to extract barcodes for accurate quantification of spatial nanotag distributions in large ion beam imaging areas up to 0.6 mm2. Isotopically encoded nanotags should boost the performance of mass imaging platforms, such as MIBI and other elemental-based bioimaging approaches.

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.

Moving Profiling Spatial Proteomics Beyond Discrete Classification

The spatial subcellular proteome is a dynamic environment; one that can be perturbed by molecular cues and regulated by post-translational modifications. Compartmentalization of this environment and management of these biomolecular dynamics allows for an array of ancillary protein functions. Profiling spatial proteomics has proved to be a powerful technique in identifying the primary subcellular localization of proteins. The approach has also been refashioned to study multi-localization and localization dynamics. Here, the analytical approaches that have been applied to spatial proteomics thus far are critiqued, and challenges particularly associated with multi-localization and dynamic relocalization is identified. To meet some of the current limitations in analytical processing, it is suggested that Bayesian modeling has clear benefits over the methods applied to date and should be favored whenever possible. Careful consideration of the limitations and challenges, and development of robust statistical frameworks, will ensure that profiling spatial proteomics remains a valuable technique as its utility is expanded.

Comparative Analysis of Quantitative Mass Spectrometric Methods for Subcellular Proteomics

Knowledge of intracellular location can provide important insights into the function of proteins and their respective organelles, and there is interest in combining classical subcellular fractionation with quantitative mass spectrometry to create global cellular maps. To evaluate mass spectrometric approaches specifically for this application, we analyzed rat liver differential centrifugation and Nycodenz density gradient subcellular fractions by tandem mass tag (TMT) isobaric labeling with reporter ion measurement at the MS2 and MS3 level and with two different label-free peak integration approaches, MS1 and data independent acquisition (DIA). TMT-MS2 provided the greatest proteome coverage, but ratio compression from contaminating background ions resulted in a narrower accurate dynamic range compared to TMT-MS3, MS1, and DIA, which were similar. Using a protein clustering approach to evaluate data quality by assignment of reference proteins to their correct compartments, all methods performed well, with isobaric labeling approaches providing the highest quality localization. Finally, TMT-MS2 gave the lowest percentage of missing quantifiable data when analyzing orthogonal fractionation methods containing overlapping proteomes. In summary, despite inaccuracies resulting from ratio compression, data obtained by TMT-MS2 assigned protein localization as well as other methods but achieved the highest proteome coverage with the lowest proportion of missing values.

Spatial proteomics: a powerful discovery tool for cell biology

Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.

Dissecting intercellular signaling with mass spectrometry-based proteomics

Physiological functions depend on a coordinated interplay of numerous different cell types. Proteins serve as major signaling molecules between cells; however, their comprehensive investigation in physiologically relevant settings has remained challenging. Mass spectrometry (MS)-based shotgun proteomics is emerging as a powerful technology for the systematic analysis of protein-mediated intercellular signaling and regulated post-translational modifications. Here, we discuss recent advancements in cell biological, chemical, and biochemical MS-based approaches for the profiling of cellular messengers released by sending cells, receptors expressed on the cell surface, and their interactions. We highlight methods tailored toward the mapping of dynamic signal transduction mechanisms at cellular interfaces and approaches to dissect communication cell specifically in heterocellular systems. Thereby, MS-based proteomics contributes a unique systems biology perspective for the identification of intercellular signaling pathways deregulated in disease.

Classification of mouse B cell types using surfaceome proteotype maps

System-wide quantification of the cell surface proteotype and identification of extracellular glycosylation sites is challenging when samples are limited. Here, we miniaturize and automate the previously described Cell Surface Capture (CSC) technology, increasing sensitivity, reproducibility and throughput. We use this technology, which we call autoCSC, to create population-specific surfaceome maps of developing mouse B cells and use targeted flow cytometry to uncover developmental cell subpopulations.

Analysis of the cell surface proteome (surfaceome) is essential for cell classification but is technically challenging. Here the authors miniaturize and automate the Cell Surface Capture method to increase sensitivity, reproducibility and throughput, and use it to create population-specific surfaceome maps of developing mouse B cells.

Rapid affinity purification of intracellular organelles using a twin strep tag

Cells are internally organized into compartmentalized organelles that execute specialized functions. To understand the functions of individual organelles and their regulations, it is critical to resolve the compositions of individual organelles, which relies on a rapid and efficient isolation method for specific organellar populations. Here, we introduce a robust affinity purification method for rapid isolation of intracellular organelles (e.g. lysosomes, mitochondria and peroxisomes) by taking advantage of the extraordinarily high affinity between the twin strep tag and streptavidin variants. With this method, we can isolate desired organelles with high purity and yield in 3 min from the post-nuclear supernatant of mammalian cells or less than 8 min for the whole purification process. Using lysosomes as an example, we show that the rapid procedure is especially useful for studying transient and fast cellular activities, such as organelle-initiated signaling and organellar contents of small-molecular metabolites. Therefore, our method offers a powerful tool to dissect spatiotemporal regulation and functions of intracellular organelles.

Adaptor protein complexes and disease at a glance

Adaptor protein (AP) complexes are heterotetramers that select cargo for inclusion into transport vesicles. Five AP complexes (AP-1 to AP-5) have been described, each with a distinct localisation and function. Furthermore, patients with a range of disorders, particularly involving the nervous system, have now been identified with mutations in each of the AP complexes. In many cases this has been correlated with aberrantly localised membrane proteins. In this Cell Science at a Glance article and the accompanying poster, we summarize what is known about the five AP complexes and discuss how this helps to explain the clinical features of the different genetic disorders.

An overview on enrichment methods for cell surface proteome profiling

Cell surface proteins are essential for many important biological processes, including cell-cell interactions, signal transduction, and molecular transportation. With the characteristics of low abundance, high hydrophobicity, and high heterogeneity, it is difficult to get a comprehensive view of cell surface proteome by direct analysis. Thus, it is important to selectively enrich the cell surface proteins before liquid chromatography with mass spectrometry analysis. In recent years, a variety of enrichment methods have been developed. Based on the separation mechanism, these methods could be mainly classified into three types. The first type is based on their difference in the physicochemical property, such as size, density, charge, and hydrophobicity. The second one is based on the bimolecular affinity interaction with lectin or antibody. And the third type is based on the chemical covalent coupling to free side groups of surface-exposed proteins or carbohydrate chains, such as primary amines, carboxyl groups, glycan side chains. In addition, metabolic labeling and enzymatic reaction-based methods have also been employed to selectively isolate cell surface proteins. In this review, we will provide a comprehensive overview of the enrichment methods for cell surface proteome profiling.

Contribution of Mass Spectrometry-Based Proteomics to Discoveries in Developmental Biology

Understanding multicellular organism development from a molecular perspective is no small feat, yet this level of comprehension affords clinician-scientists the ability to identify root causes and mechanisms of congenital diseases. Inarguably, the maturation of molecular biology tools has significantly contributed to the identification of genetic loci that underlie normal and aberrant developmental programs. In combination with cell biology approaches, these tools have begun to elucidate the spatiotemporal expression and function of developmentally-regulated proteins. The emergence of quantitative mass spectrometry (MS) for biological applications has accelerated the pace at which these proteins can be functionally characterized, driving the construction of an increasingly detailed systems biology picture of developmental processes. Here, we review the quantitative MS-based proteomic technologies that have contributed significantly to understanding the role of proteome regulation in developmental processes. We provide a brief overview of these methodologies, focusing on their ability to provide precise and accurate proteome measurements. We then highlight the use of discovery-based and targeted mass spectrometry approaches in model systems to study cellular differentiation states, tissue phenotypes, and spatiotemporal subcellular organization. We also discuss the current application and future perspectives of MS proteomics to study PTM coordination and the role of protein complexes during development.

Ribosome Abundance Control Via the Ubiquitin-Proteasome System and Autophagy

Ribosomes are central to the life of a cell, as they translate the genetic code into the amino acid language of proteins. Moreover, ribosomal abundance within the cell is coordinated with protein production required for cell function or processes such as cell division. As such, it is not surprising that these elegant machines are both highly regulated at the level of both their output of newly translated proteins but also at the level of ribosomal protein expression, ribosome assembly, and ribosome turnover. In this review, we focus on mechanisms that regulate ribosome abundance through both the ubiquitin-proteasome system and forms of autophagy referred to as "ribophagy." We discussed mechanisms employed in both yeast and mammalian cells, including the various machineries that are important for recognition and degradation of ribosomal components. In addition, we discussed controversies in the field and how the development of new approaches for examining flux through the proteasomal and autophagic systems in the context of a systematic inventory of ribosomal components is necessary to fully understand how ribosome abundance is controlled under various physiological conditions.

Cargo Sorting at the trans-Golgi Network for Shunting into Specific Transport Routes: Role of Arf Small G Proteins and Adaptor Complexes

The trans-Golgi network (TGN) is responsible for selectively recruiting newly synthesized cargo into transport carriers for delivery to their appropriate destination. In addition, the TGN is responsible for receiving and recycling cargo from endosomes. The membrane organization of the TGN facilitates the sorting of cargoes into distinct populations of transport vesicles. There have been significant advances in defining the molecular mechanism involved in the recognition of membrane cargoes for recruitment into different populations of transport carriers. This machinery includes cargo adaptors of the adaptor protein (AP) complex family, and monomeric Golgi-localized γ ear-containing Arf-binding protein (GGA) family, small G proteins, coat proteins, as well as accessory factors to promote budding and fission of transport vesicles. Here, we review this literature with a particular focus on the transport pathway(s) mediated by the individual cargo adaptors and the cargo motifs recognized by these adaptors. Defects in these cargo adaptors lead to a wide variety of diseases.

Circadian rhythms and proteomics: It's all about posttranslational modifications!

The circadian clock is a molecular endogenous timekeeping system and allows organisms to adjust their physiology and behavior to the geophysical time. Organized hierarchically, the master clock in the suprachiasmatic nuclei, coordinates peripheral clocks, via direct, or indirect signals. In peripheral organs, such as the liver, the circadian clock coordinates gene expression, notably metabolic gene expression, from transcriptional to posttranslational level. The metabolism in return feeds back on the molecular circadian clock via posttranslational-based mechanisms. During the last two decades, circadian gene expression studies have mostly been relying primarily on genomics or transcriptomics approaches and transcriptome analyses of multiple organs/tissues have revealed that the majority of protein-coding genes display circadian rhythms in a tissue specific manner. More recently, new advances in mass spectrometry offered circadian proteomics new perspectives, that is, the possibilities of performing large scale proteomic studies at cellular and subcellular levels, but also at the posttranslational modification level. With important implications in metabolic health, cell signaling has been shown to be highly relevant to circadian rhythms. Moreover, comprehensive characterization studies of posttranslational modifications are emerging and as a result, cell signaling processes are expected to be more deeply characterized and understood in the coming years with the use of proteomics. This review summarizes the work studying diurnally rhythmic or circadian gene expression performed at the protein level. Based on the knowledge brought by circadian proteomics studies, this review will also discuss the role of posttranslational modification events as an important link between the molecular circadian clock and metabolic regulation. This article is categorized under: Laboratory Methods and Technologies > Proteomics Methods Physiology > Mammalian Physiology in Health and Disease Biological Mechanisms > Cell 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.

Assessing sub-cellular resolution in spatial proteomics experiments

The sub-cellular localisation of a protein is vital in defining its function, and a protein's mis-localisation is known to lead to adverse effect. As a result, numerous experimental techniques and datasets have been published, with the aim of deciphering the localisation of proteins at various scales and resolutions, including high profile mass spectrometry-based efforts. Here, we present a meta-analysis assessing and comparing the sub-cellular resolution of 29 such mass spectrometry-based spatial proteomics experiments using a newly developed tool termed QSep. Our goal is to provide a simple quantitative report of how well spatial proteomics resolve the sub-cellular niches they describe to inform and guide developers and users of such methods.

The subcellular organisation of Saccharomyces cerevisiae

Subcellular protein localisation is essential for the mechanisms that govern cellular homeostasis. The ability to understand processes leading to this phenomenon will therefore enhance our understanding of cellular function. Here we review recent developments in this field with regard to mass spectrometry, fluorescence microscopy and computational prediction methods. We highlight relative strengths and limitations of current methodologies focussing particularly on studies in the yeast Saccharomyces cerevisiae. We further present the first cell-wide spatial proteome map of S. cerevisiae, generated using hyperLOPIT, a mass spectrometry-based protein correlation profiling technique. We compare protein subcellular localisation assignments from this map, with two published fluorescence microscopy studies and show that confidence in localisation assignment is attained using multiple orthogonal methods that provide complementary data.

Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics

The study of protein localisation has greatly benefited from high-throughput methods utilising cellular fractionation and proteomic profiling. Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method in this area. It achieves high-resolution separation of organelles and subcellular compartments but is relatively time- and resource-intensive. As a simpler alternative, we here develop Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) and compare this method to the density gradient-based hyperLOPIT approach. We confirm that high-resolution maps can be obtained using differential centrifugation down to the suborganellar and protein complex level. HyperLOPIT and LOPIT-DC yield highly similar results, facilitating the identification of isoform-specific localisations and high-confidence localisation assignment for proteins in suborganellar structures, protein complexes and signalling pathways. By combining both approaches, we present a comprehensive high-resolution dataset of human protein localisations and deliver a flexible set of protocols for subcellular proteomics.

Mapping the Saccharomyces cerevisiae Spatial Proteome with High Resolution Using hyperLOPIT

The subcellular localization of proteins is a posttranslational modification of paramount importance. The ability to study subcellular and organelle proteomes improves our understanding of cellular homeostasis and cellular dynamics. In this chapter, we describe a protocol for the unbiased and high-throughput study of protein subcellular localization in the yeast Saccharomyces cerevisiae: hyperplexed localization of organelle proteins by isotope tagging (hyperLOPIT), which involves biochemical fractionation of Saccharomyces cerevisiae and high resolution mass spectrometry-based protein quantitation using TMT 10-plex isobaric tags. This protocol enables the determination of the subcellular localizations of thousands of proteins in parallel in a single experiment and thereby deep sampling and high-resolution mapping of the spatial proteome.

Dynamic Organellar Maps for Spatial Proteomics

Eukaryotic cells are highly compartmentalized and protein subcellular localization critically influences protein function. Identification of the subcellular localizations of proteins and their translocation events upon perturbation has mostly been confined to targeted studies or laborious microscopy-based methods. Here we describe a systematic mass spectrometry-based method for spatial proteomics. The approach uses simple fractionation profiling and has two applications: Firstly it can be used to infer subcellular protein localization on a proteome-wide scale, resulting in a protein map of the cell. Secondly, the method permits identification of changes in protein localization, by comparing maps made under different conditions, as a tool for unbiased systems cell biology. © 2018 by John Wiley & Sons, Inc.

Regulation of microtubule plus end dynamics by septin 9

Septins are GTP-binding proteins that associate with the microtubule (MT) and actin cytoskeleton. Septins affect MT organization and posttranslational modifications, but their role in MT dynamics is less understood. Here, we reconstituted MT dynamics in the presence of the MT-binding septin (SEPT9) using an in vitro cell-free assay, which images the polymerization of tubulin from guanosine-5'-[(α,β)-methyleno]triphosphate (GMPCPP)-stabilized MT seeds. We found that submicromolar concentrations of SEPT9 suppress MT catastrophe and enhance the growth of MT plus ends to great lengths, while low micromolar concentrations of SEPT9 stabilize MTs by inhibiting dynamic instability. We show that SEPT9 associates preferentially with the lattice of GMPCPP-stabilized MT seeds and surprisingly recruits soluble tubulin to the MT lattice. Notably, the effects of SEPT9 on MT dynamics are dependent on its G-G dimerization interface, which is formed by the pockets of the GTP-binding domains. A mutation (H530D) that disrupts G-G dimerization abrogates the effects of SEPT9 on MT dynamics and diminishes its ability to recruit tubulin to the MT lattice. Taken together, these results suggest that SEPT9 promotes the formation and maintenance of long stable MTs through a mechanism that may involve recruitment of unpolymerized tubulin to the MT lattice.

Regulation of FGF10 Signaling in Development and Disease

Fibroblast Growth Factor 10 (FGF10) is a multifunctional mesenchymal-epithelial signaling growth factor, which is essential for multi-organ development and tissue homeostasis in adults. Furthermore, FGF10 deregulation has been associated with human genetic disorders and certain forms of cancer. Upon binding to FGF receptors with heparan sulfate as co-factor, FGF10 activates several intracellular signaling cascades, resulting in cell proliferation, differentiation, and invasion. FGF10 activity is modulated not only by heparan sulfate proteoglycans in the extracellular matrix, but also by hormones and other soluble factors. Despite more than 20 years of research on FGF10 functions, context-dependent regulation of FGF10 signaling specificity remains poorly understood. Emerging modes of FGF10 signaling regulation will be described, focusing on the role of FGF10 trafficking and sub-cellular localization, heparan sulfate proteoglycans, and miRNAs. Systems biology approaches based on quantitative proteomics will be considered for globally investigating FGF10 signaling specificity. Finally, current gaps in our understanding of FGF10 functions, such as the relative contribution of receptor isoforms to signaling activation, will be discussed in the context of genetic disorders and tumorigenesis.

Targeted LC-MS/MS Proteomics-Based Strategy To Characterize in Vitro Models Used in Drug Metabolism and Transport Studies

Subcellular fractionation of tissue homogenate provides enriched in vitro models (e.g., microsomes, cytosol, or membranes), which are routinely used in the drug metabolism or transporter activity and protein abundance studies. However, batch-to-batch or interlaboratory variability in the recovery, enrichment, and purity of the subcellular fractions can affect performance of in vitro models leading to inaccurate in vitro to in vivo extrapolation (IVIVE) of drug clearance. To evaluate the quality of subcellular fractions, we developed a simple, targeted, and sensitive LC-MS/MS proteomics-based strategy, which relies on determination of protein markers of various cellular organelles, i.e., plasma membrane, cytosol, nuclei, mitochondria, endoplasmic reticulum (ER), lysosomes, peroxisomes, cytoskeleton, and exosomes. Application of the quantitative proteomics method confirmed a significant effect of processing variables (i.e., homogenization method and centrifugation speed) on the recovery, enrichment, and purity of isolated proteins in microsomes and cytosol. Particularly, markers of endoplasmic reticulum lumen and mitochondrial lumen were enriched in the cytosolic fractions as a result of their release during homogenization. Similarly, the relative recovery and composition of the total membrane fraction isolated from cell vs tissue samples was quantitatively different and should be considered in IVIVE. Further, analysis of exosomes isolated from sandwich-cultured hepatocyte media showed the effect of culture duration on compositions of purified exosomes. Therefore, the quantitative proteomics-based strategy developed here can be applied for efficient and simultaneous determination of multiple protein markers of various cellular organelles when compared to antibody- or activity-based assays and can be used for quality control of subcellular fractionation procedures including in vitro model development for drug metabolism and transport studies.

AP-4 vesicles contribute to spatial control of autophagy via RUSC-dependent peripheral delivery of ATG9A

Adaptor protein 4 (AP-4) is an ancient membrane trafficking complex, whose function has largely remained elusive. In humans, AP-4 deficiency causes a severe neurological disorder of unknown aetiology. We apply unbiased proteomic methods, including 'Dynamic Organellar Maps', to find proteins whose subcellular localisation depends on AP-4. We identify three transmembrane cargo proteins, ATG9A, SERINC1 and SERINC3, and two AP-4 accessory proteins, RUSC1 and RUSC2. We demonstrate that AP-4 deficiency causes missorting of ATG9A in diverse cell types, including patient-derived cells, as well as dysregulation of autophagy. RUSC2 facilitates the transport of AP-4-derived, ATG9A-positive vesicles from the trans-Golgi network to the cell periphery. These vesicles cluster in close association with autophagosomes, suggesting they are the "ATG9A reservoir" required for autophagosome biogenesis. Our study uncovers ATG9A trafficking as a ubiquitous function of the AP-4 pathway. Furthermore, it provides a potential molecular pathomechanism of AP-4 deficiency, through dysregulated spatial control of autophagy.

Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data

Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry–based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.

Role of the AP-5 adaptor protein complex in late endosome-to-Golgi retrieval

The AP-5 adaptor protein complex is presumed to function in membrane traffic, but so far nothing is known about its pathway or its cargo. We have used CRISPR-Cas9 to knock out the AP-5 ζ subunit gene, AP5Z1, in HeLa cells, and then analysed the phenotype by subcellular fractionation profiling and quantitative mass spectrometry. The retromer complex had an altered steady-state distribution in the knockout cells, and several Golgi proteins, including GOLIM4 and GOLM1, were depleted from vesicle-enriched fractions. Immunolocalisation showed that loss of AP-5 led to impaired retrieval of the cation-independent mannose 6-phosphate receptor (CIMPR), GOLIM4, and GOLM1 from endosomes back to the Golgi region. Knocking down the retromer complex exacerbated this phenotype. Both the CIMPR and sortilin interacted with the AP-5–associated protein SPG15 in pull-down assays, and we propose that sortilin may act as a link between Golgi proteins and the AP-5/SPG11/SPG15 complex. Together, our findings suggest that AP-5 functions in a novel sorting step out of late endosomes, acting as a backup pathway for retromer. This provides a mechanistic explanation for why mutations in AP-5/SPG11/SPG15 cause cells to accumulate aberrant endolysosomes, and highlights the role of endosome/lysosome dysfunction in the pathology of hereditary spastic paraplegia and other neurodegenerative disorders.

Eukaryotic cells contain multiple membrane-bound compartments, each with a distinct function and molecular composition. Proteins are transported from one compartment to another by vesicular carriers. Formation of these carriers requires coat proteins, which both shape the membrane into a vesicle and select the proteins that are to be included as cargo. In many cases, cargo selection is facilitated by an adaptor protein (AP) complex, of which 5 have been identified. The most recently identified complex, AP-5, localises to a late endosomal/lysosomal compartment, and patients with mutations in AP-5 have a form of hereditary spastic paraplegia characterised by aberrant lysosomes. However, the precise function of AP-5, including its cargo and its pathway, has until now been unclear. In the present study, we have used unbiased subcellular proteomics to look for changes in the localisation of thousands of different proteins in cells from which AP-5 has been deleted by gene editing. We found that there are defects in the retrieval of several proteins from late endosomes back to the Golgi apparatus. Thus, we propose that AP-5 facilitates a novel late-acting retrieval pathway, which contributes to normal lysosomal homeostasis.

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.

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.