Mapping organelle proteins and protein complexes in Drosophila melanogaster

Cytoplasmic FKBPs are involved in molting and metamorphosis through regulating the nuclear localization of EcR

Molting and metamorphosis are important physiological processes in insects that are tightly controlled by ecdysone receptor (EcR) through the 20-hydroxyecdysone (20E) signaling pathway. EcR is a steroid nuclear receptor (SR). Several FK506-binding proteins (FKBPs) have been identified from the mammal SR complex, and are thought to be involved in the subcellular trafficking of SR. However, their roles in insects are poorly understood. To explore whether FKBPs are involved in insect molting or metamorphosis, we injected an FKBP inhibitor (FK506) into a lepidopteran insect, Spodoptera litura, and found that molting was inhibited in 61.11% of the larvae, and that the time for larvae to pupate was significantly extended. A total of 10 FKBP genes were identified from the genome of S. litura and were clustered into 2 distinct groups, according to their subcellular localization, with FKBP13 and FKBP14 belonging to the endoplasmic reticulum (ER) group and with the other members belonging to the cytoplasmic (Cy) group. All the CyFKBPs were significantly upregulated in the prepupal or pupal stages, with the opposite being observed for the ER group members. FK506 completely blocked the transfer of EcR to the nucleus under 20E induction, and significantly downregulated the transcriptional expression of many 20E signaling genes. A similar phenomenon was observed after RNA interference of 2 CyFKBPs (FKBP45 and FKBP12b), but not for FKBP13. Taken together, our data indicate that the cytoplasmic FKBPs, especially FKBP45 and FKBP12b, mediate the nuclear localization of EcR, thereby regulating the 20E signaling and ultimately affecting molting and metamorphosis in insects.

Engineering of TurboID-Wingless for the identification of Wingless interactors through in vivo proximity labelling

Wnt signalling coordinates growth and cell fate decisions during development and mis-regulation of Wnt signalling in adults is associated with a range of conditions, including cancer and neurodegenerative diseases. Therefore, means of modulating Wnt proteins and/or cofactors could have significant therapeutic potential. As a first step towards enumerating the Wnt interactome, we devised an in vivo proximity labelling strategy to identify proteins that interact with Wingless (Wg), the main Drosophila Wnt. We engineered the wingless locus to express a functional TurboID-Wg fusion at endogenous levels and identified in vivo interactors by streptavidin pull-down from embryos, followed by mass spectrometry. Further analysis may in future extend the screen coverage and deliver functional validation of the newly identified interactors.

A new metabolic model of Drosophila melanogaster and the integrative analysis of Parkinson's disease

High conservation of the disease-associated genes between flies and humans facilitates the common use of Drosophila melanogaster to study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model of Drosophila using an orthology-based approach. The gene coverage and metabolic information of the draft model derived from a reference human model were expanded via Drosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Furthermore, we performed literature-based curations to improve gene-reaction associations, subcellular metabolite locations, and various metabolic pathways. The performance of the resulting Drosophila model (8,230 reactions, 6,990 metabolites, and 2,388 genes), iDrosophila1 (https://github.com/SysBioGTU/iDrosophila), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated the transcriptome-based prediction capacity of iDrosophila1, where differential metabolic pathways during Parkinson's disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate system-level metabolic alterations in response to genetic and environmental perturbations.

Mapping diversity in African trypanosomes using high resolution spatial proteomics

African trypanosomes are dixenous eukaryotic parasites that impose a significant human and veterinary disease burden on sub-Saharan Africa. Diversity between species and life-cycle stages is concomitant with distinct host and tissue tropisms within this group. Here, the spatial proteomes of two African trypanosome species, Trypanosoma brucei and Trypanosoma congolense, are mapped across two life-stages. The four resulting datasets provide evidence of expression of approximately 5500 proteins per cell-type. Over 2500 proteins per cell-type are classified to specific subcellular compartments, providing four comprehensive spatial proteomes. Comparative analysis reveals key routes of parasitic adaptation to different biological niches and provides insight into the molecular basis for diversity within and between these pathogen species.

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.

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.

Evolutionary Divergence of Phosphorylation to Regulate Interactive Protein Networks in Lower and Higher Species

The phosphorylation of proteins affects their functions in extensively documented circumstances. However, the role of phosphorylation in many interactive networks of proteins remains very elusive due to the experimental limits of exploring the transient interaction in a large complex of assembled proteins induced by stimulation. Previous studies have suggested that phosphorylation is a recent evolutionary process that differently regulates ortholog proteins in numerous lineages of living organisms to create new functions. Despite the fact that numerous phospho-proteins have been compared between species, little is known about the organization of the full phospho-proteome, the role of phosphorylation to orchestrate large interactive networks of proteins, and the intertwined phospho-landscape in these networks. In this report, we aimed to investigate the acquired role of phosphate addition in the phenomenon of protein networking in different orders of living organisms. Our data highlighted the acquired status of phosphorylation in organizing large, connected assemblages in Homo sapiens. The protein networking guided by phosphorylation turned out to be prominent in humans, chaotic in yeast, and weak in flies. Furthermore, the molecular functions of GO annotation enrichment regulated by phosphorylation were found to be drastically different between flies, yeast, and humans, suggesting an evolutionary drift specific to each species.

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.

Green Synthesis of Selenium Nanoparticles Mediated by Nilgirianthus ciliates Leaf Extracts for Antimicrobial Activity on Foodborne Pathogenic Microbes and Pesticidal Activity Against Aedes aegypti with Molecular Docking

The present study deals with the synthesis of selenium nanoparticles (SeNPs) using Nilgirianthus ciliatus leaf extracts, characterized by UV-Vis spectrophotometer, XRD, FTIR, FE-SEM, HR-TEM, DLS, and zeta potential analysis. The antimicrobial activity against Staphylococcus aureus (MTCC96), Escherichia coli (MTCC443), and Salmonella typhi (MTCC98) showed the remarkable inhibitory effect at 25 µl/mL concentration level. Furthermore, the characterized SeNPs showed a great insecticidal activity against Aedes aegypti in the early larval stages with the median Lethal Concentration (LC₅₀) of 0.92 mg/L. Histopathological observations of the SeNPs treated midgut and caeca regions of Ae. aegypti 4th instar larvae showed damaged epithelial layer and fragmented peritrophic membrane. In order to provide a mechanistic approach for further studies, molecular docking studies using Auto Dock Vina were performed with compounds of N. ciliatus within the active site of AeSCP2. Overall, the N. ciliates leaf-mediated biogenic SeNPs was promisingly evidenced to have potential larvicidal and food pathogenic bactericidal activity in an eco-friendly approach.

Localization of Organelle Proteins by Isotope Tagging: Current status and potential applications in drug discovery research

Spatial proteomics has provided important insights into the relationship between protein function and subcellular location. Localization of Organelle Proteins by Isotope Tagging (LOPIT) and its variants are proteome-wide techniques, not matched in scale by microscopy-based or proximity tagging-based techniques, allowing holistic mapping of protein subcellular location and re-localization events downstream of cellular perturbations. LOPIT can be a powerful and versatile tool in drug discovery for unlocking important information on disease pathophysiology, drug mechanism of action, and off-target toxicity screenings. Here, we discuss technical concepts of LOPIT with its potential applications in drug discovery and development research.

SUBCELLULAR TRANSCRIPTOMICS & 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.

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Subcellular information of protein and RNA give insights into molecular function.

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This review discusses strategies available to measure subcellular information.

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Hybridization of methods shows promise for exploring the composition of organelles.

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Advances are aiding understanding of the organisation and dynamics of cells.

Construction of Messenger RNA (mRNA) Probes Delivered By Lipid Nanoparticles to Visualize Intracellular Protein Expression and Localization at Organelles

Organelles are specialized compartments, where various proteins reside and play crucial roles to maintain essential cellular structures and functions in mammalian cells. A comprehensive understanding of protein expressions and subsequent localizations at each organelle is of great benefit to the development of organelle-based therapies. Herein, a set of single or dual organelle labeling messenger RNAs (SOLAR or DOLAR) is designed as novel imaging probes, which encode fluorescent proteins with various organelle localization signals. These mRNA probes enable to visualize the protein localizations at different organelles and investigate their trafficking from ribosomal machinery to specific organelles. According to the in vitro results, SOLAR probes show organelle targeting capabilities consistent with the design. Moreover, DOLAR probes with different linkers display distinct targeting properties depending on different organelle localization signals. Additionally, these mRNA probes also exhibit organelle labeling ability in vivo when delivered by lipid nanoparticles (LNPs). Therefore, these mRNA-based probes provide a unique tool to study cell organelles and may facilitate the design of organelle-based therapies.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

Microbial pathogens have evolved numerous mechanisms to hijack host's systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one's analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.

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.

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.

Arginine kinase interacts with 2MIT and is involved in Drosophila melanogaster short-term memory

Mushroom bodies are a higher order center for sensory integration, learning and memory of the insect brain. Memory is generally subdivided into different phases. In the model organism Drosophila melanogaster, mushroom bodies have been shown to play a central role in both short- and long-term memory. In D. melanogaster, the gene 2mit codes a transmembrane protein carrying an extracellular Leucin-rich-repeat domain, which is highly transcribed in the mushroom and ellipsoid bodies of the adult fly brain and has a role in the early phase of memory. Utilizing coimmunoprecipitation experiments and mass spectrometry analyses, we have shown that 2MIT interacts with Arginine kinase in adult fly heads. Arginine kinase belongs to the family of Phosphagen kinases and plays a fundamental role in energy homeostasis. Using the GAL4/UAS binary system, we demonstrated that a downregulation of Arginine kinase mainly driven in the mushroom bodies affects short-term memory of Drosophila adult flies, in a courtship conditioning paradigm. As 2mit ^c03963 hypomorphic mutants showed comparable results when analyzed with the same assay, these data suggest that 2MIT and Arginine kinase are both involved in the same memory phenotype, likely interacting at the level of mushroom bodies. 2MIT and Arginine kinase are conserved among insects, the implications of which, along with their potential roles in other insect taxa are also discussed.

Leaf Extract of Dillenia indica as a Source of Selenium Nanoparticles with Larvicidal and Antimicrobial Potential toward Vector Mosquitoes and Pathogenic Microbes

Chikungunya, dengue, Zika, malaria, Japanese encephalitis, filariasis, West Nile, etc. are mosquito transmitted diseases that have killed millions of people worldwide, and millions of people are at risk of these diseases. Control of the mosquitoes, such as Aedes aegypti and Culex quinquefasciatus, is challenging due to their development of resistance to synthetic insecticides. The habitats of the young mosquitoes are also the habitats for foodborne pathogens like Staphylococcus aureus (MTCC96) and Serratia marcescens (MTCC4822). The present study was aimed at synthesizing eco-friendly green nanoparticles using Dillenia indica leaf broth and analyzing its efficacy in controlling the vector mosquitoes A. aegypti and C. quinquefasciatus, as well as the microbial pathogens St. aureus and Se. marcescens. The formation of selenium nanoparticles (SeNps) was confirmed using UV-Vis spectroscopy (absorption peak at 383.00 nm), Fourier transform infrared radiation (FTIR spectrum peaks at 3177, 2114, 1614, 1502, 1340, 1097, 901, 705, and 508 cm−1), X-ray diffraction (diffraction peaks at 23.3 (100), 29.6 (101), 43.5 (012), and 50.05 (201)), and scanning electron microscopy (oval shaped). The size of the nanoparticles and their stability were analyzed using dynamic light scattering (Z-Average value of 248.0 nm) and zeta potential (−13.2 mV). The SeNps disorganized the epithelial layers and have broken the peritrophic membrane. Histopathological changes were also observed in the midgut and caeca regions of the SeNPs treated A. aegypti and C. quinquefasciatus larvae. The SeNps were also active on both the bacterial species showing strong inhibitory zones. The present results will explain the ability of SeNps in controlling the mosquitoes as well as the bacteria and will contribute to the development of multi potent eco-friendly compounds.

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.

Cell-Surface Proteomic Profiling in the Fly Brain Uncovers Wiring Regulators

Molecular interactions at the cellular interface mediate organized assembly of single cells into tissues and, thus, govern the development and physiology of multicellular organisms. Here, we developed a cell-type-specific, spatiotemporally resolved approach to profile cell-surface proteomes in intact tissues. Quantitative profiling of cell-surface proteomes of Drosophila olfactory projection neurons (PNs) in pupae and adults revealed global downregulation of wiring molecules and upregulation of synaptic molecules in the transition from developing to mature PNs. A proteome-instructed in vivo screen identified 20 cell-surface molecules regulating neural circuit assembly, many of which belong to evolutionarily conserved protein families not previously linked to neural development. Genetic analysis further revealed that the lipoprotein receptor LRP1 cell-autonomously controls PN dendrite targeting, contributing to the formation of a precise olfactory map. These findings highlight the power of temporally resolved in situ cell-surface proteomic profiling in discovering regulators of brain wiring.

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.

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.

Annotation of gene product function from high-throughput studies using the Gene Ontology

High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community.

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.

Proteomics of nucleocytoplasmic partitioning

The partitioning of the proteome between nucleus and cytoplasm affects nearly every aspect of eukaryotic biology. Despite this central role, we still have a poor understanding of which proteins localize in the nucleus and how this varies in different cell types and conditions. Recent advances in quantitative proteomics and high-throughput imaging are starting to close this knowledge gap. Studies on protein interaction are beginning to reveal the spectrum of cargos of nuclear import and export receptors. We anticipate that it will soon be possible to predict each protein's nucleocytoplasmic localization based on its importin/exportin interactions and its estimated diffusion rate through the nuclear pore. This insight is likely to provide us with a fundamental understanding of how cells use nucleocytoplasmic partitioning to encode and relay information.

The SubCons webserver: A user friendly web interface for state-of-the-art subcellular localization prediction

SubCons is a recently developed method that predicts the subcellular localization of a protein. It combines predictions from four predictors using a Random Forest classifier. Here, we present the user-friendly web-interface implementation of SubCons. Starting from a protein sequence, the server rapidly predicts the subcellular localizations of an individual protein. In addition, the server accepts the submission of sets of proteins either by uploading the files or programmatically by using command line WSDL API scripts. This makes SubCons ideal for proteome wide analyses allowing the user to scan a whole proteome in few days. From the web page, it is also possible to download precalculated predictions for several eukaryotic organisms. To evaluate the performance of SubCons we present a benchmark of LocTree3 and SubCons using two recent mass-spectrometry based datasets of mouse and drosophila proteins. The server is available at http://subcons.bioinfo.se/.

Organellar Omics-A Reviving Strategy to Untangle the Biomolecular Complexity of the Cell

A eukaryotic cell encompasses many membrane-enclosed organelles, each of these holding several types of biomolecules that exhibit tremendous diversity in terms of their localization and expression. Despite the development of increasingly sensitive analytical tools, the enormous biomolecular complexity that exists within a cell cannot yet be fully resolved as low abundant molecules often remain unrecognized. Moreover, a drawback of whole cell analysis is that it does not provide spatial information and therefore it is not capable of assigning distinct biomolecules to specific compartments or analyzing changes in the composition of these compartments. Reduction of the biomolecular complexity of a sample helps to identify low abundant molecules, but such a reductionist approach requires methods that enable proper isolation and purification of individual cellular organelles. Decades of research have led to the development of a plethora of isolation methods for a broad range of subcellular organelles; yet, in particular, intrinsically dynamic compartments belonging to the endocytic machinery, including the plasma membrane, remain difficult to isolate in a sufficiently pure fraction. In this review, we discuss various methods that are commonly used to isolate subcellular organelles from cells and evaluate their advantages and disadvantages.

Destruxin A Induces and Binds HSPs in Bombyx mori Bm12 Cells

Destruxin A (DA) is a cyclodepsipeptidic mycotoxin isolated from the entomopathogenic fungus, Metarhizium anisopliae. It has insecticidal activity against host insect's innate immunity system, but the molecular mechanism is not yet elucidated. In our previous experiment, four HSPs (heat shock proteins, BmHSP70-3, BmHSP75, BmHSP83, and BmHSCP) were characterized from the specific protein electrophoretic bands of Bombyx mori Bm12 cell line treated with DA in the test of drug affinity responsive target stability (DARTS), which implied that these HSPs might be kinds of DA-affinity proteins, or DA induces them up-regulated expression. Therefore, in current research, the interactions of DA and HSPs were explored through analysis of bio-layer interferometry (BLI) employing FortBio OcteteQK. The expression levels of HSPs genes were surveyed by quantitative real-time polymerase chain reaction (qPCR). The results indicated that DA had no interactions with BmHSP70-3, BmHSP75, and BmHSP83, but had affinity to BmHSCP with a K_D value of 88.1 μM, in BLI analysis. However, the expression levels of all HSPs genes were significantly up-regulated after the Bm12 cells were treated by DA. In conclusion, DA can induce the four HSPs expression in Bm12 cells, but DA only binds to BmHSCP. Our research provides new insights on understanding of the action mechanisms of destruxins.

Using hyperLOPIT to perform high-resolution mapping of the spatial proteome

The organization of eukaryotic cells into distinct subcompartments is vital for all functional processes, and aberrant protein localization is a hallmark of many diseases. Microscopy methods, although powerful, are usually low-throughput and dependent on the availability of fluorescent fusion proteins or highly specific and sensitive antibodies. One method that provides a global picture of the cell is localization of organelle proteins by isotope tagging (LOPIT), which combines biochemical cell fractionation using density gradient ultracentrifugation with multiplexed quantitative proteomics mass spectrometry, allowing simultaneous determination of the steady-state distribution of hundreds of proteins within organelles. Proteins are assigned to organelles based on the similarity of their gradient distribution to those of well-annotated organelle marker proteins. We have substantially re-developed our original LOPIT protocol (published by Nature Protocols in 2006) to enable the subcellular localization of thousands of proteins per experiment (hyperLOPIT), including spatial resolution at the suborganelle and large protein complex level. This Protocol Extension article integrates all elements of the hyperLOPIT pipeline, including an additional enrichment strategy for chromatin, extended multiplexing capacity of isobaric mass tags, state-of-the-art mass spectrometry methods and multivariate machine-learning approaches for analysis of spatial proteomics data. We have also created an open-source infrastructure to support analysis of quantitative mass-spectrometry-based spatial proteomics data (http://bioconductor.org/packages/pRoloc) and an accompanying interactive visualization framework (http://www. bioconductor.org/packages/pRolocGUI). The procedure we outline here is applicable to any cell culture system and requires ∼1 week to complete sample preparation steps, ∼2 d for mass spectrometry data acquisition and 1-2 d for data analysis and downstream informatics.

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.

An antibody toolkit for the study of membrane traffic in Drosophila melanogaster

The use of Drosophila melanogaster as a model organism has been pivotal to understanding the developmental processes of metazoans. However, the use of flies for studying subcellular organization is hampered by a paucity of reliable reagents to label specific organelles. Here, we describe the generation of mouse monoclonal antibodies against a set of markers of the secretory and endocytic pathways, along with goat polyclonal antibodies against two Golgi proteins. We show that the monoclonal antibodies are highly specific and sufficiently sensitive to detect endogenous proteins in crude extracts by immunoblotting with little background staining. By immunofluorescence the major compartments of the membrane traffic system (including the endoplasmic reticulum, the Golgi, and early and late endosomes) are labeled by at least one antibody. Moreover, the antibodies can be used to label organelles in fly tissues including salivary glands and wing imaginal discs. We anticipate that these antibodies will provide a useful tool kit to facilitate the investigation of how the endomembrane system functions and varies in the diverse tissue types of metazoans.

Summary: We report the generation and characterization of set of monoclonal and polyclonal antibodies for labeling the major compartments of the secretory and endocytic pathways in Drosophila melanogaster.

Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.

Sub-cellular localisation of proteins is critical to their function in all cellular processes; proteins localising to their intended micro-environment, e.g organelles, vesicles or macro-molecular complexes, will meet the interaction partners and biochemical conditions suitable to pursue their molecular function. Therefore, sound data and methods to reliably and systematically study protein localisation, and hence their mis-localisation and the disruption of protein trafficking, that are relied upon by the cell biology community, are essential. Here we present a method to infer protein localisation relying on the optimal integration of experimental mass spectrometry-based data and auxiliary sources, such as GO annotation, outputs from third-party software, protein-protein interactions or immunocytochemistry data. We found that the application of transfer learning algorithms across these diverse data sources considerably improves on the quantity and reliability of sub-cellular protein assignment, compared to single data classifiers previously applied to infer sub-cellular localisation using experimental data only. We show how our method does not compromise biologically relevant experimental-specific signal after integration with heterogeneous freely available third-party resources. The integration of different data sources is an important challenge in the data intensive world of biology and we anticipate the transfer learning methods presented here will prove useful to many areas of biology, to unify data obtained from different but complimentary sources.

A draft map of the mouse pluripotent stem cell spatial proteome

Knowledge of the subcellular distribution of proteins is vital for understanding cellular mechanisms. Capturing the subcellular proteome in a single experiment has proven challenging, with studies focusing on specific compartments or assigning proteins to subcellular niches with low resolution and/or accuracy. Here we introduce hyperLOPIT, a method that couples extensive fractionation, quantitative high-resolution accurate mass spectrometry with multivariate data analysis. We apply hyperLOPIT to a pluripotent stem cell population whose subcellular proteome has not been extensively studied. We provide localization data on over 5,000 proteins with unprecedented spatial resolution to reveal the organization of organelles, sub-organellar compartments, protein complexes, functional networks and steady-state dynamics of proteins and unexpected subcellular locations. The method paves the way for characterizing the impact of post-transcriptional and post-translational modification on protein location and studies involving proteome-level locational changes on cellular perturbation. An interactive open-source resource is presented that enables exploration of these data.

Visualization of proteomics data using R and bioconductor

Data visualization plays a key role in high-throughput biology. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience. Here, we provided a short overview of the application of the R software to the visualization of proteomics data. We present a summary of R's plotting systems and how they are used to visualize and understand raw and processed MS-based proteomics data.

The pathogenic human Torsin A in Drosophila activates the unfolded protein response and increases susceptibility to oxidative stress

Background

Dystonia1 (DYT1) dystonia is caused by a glutamic acid deletion (ΔE) mutation in the gene encoding Torsin A in humans (HTorA). To investigate the unknown molecular and cellular mechanisms underlying DYT1 dystonia, we performed an unbiased proteomic analysis.

Results

We found that the amount of proteins and transcripts of an Endoplasmic reticulum (ER) resident chaperone Heat shock protein cognate 3 (HSC3) and a mitochondria chaperone Heat Shock Protein 22 (HSP22) were significantly increased in the HTorA^ΔE– expressing brains compared to the normal HTorA (HTorA^WT) expressing brains. The physiological consequences included an increased susceptibility to oxidative and ER stress compared to normal HTorA^WT flies. The alteration of transcripts of Inositol-requiring enzyme-1 (IRE1)-dependent spliced X box binding protein 1(Xbp1), several ER chaperones, a nucleotide exchange factor, Autophagy related protein 8b (ATG8b) and components of the ER associated degradation (ERAD) pathway and increased expression of the Xbp1-enhanced Green Fluorescence Protein (eGFP) in HTorA^ΔE brains strongly indicated the activation of the unfolded protein response (UPR). In addition, perturbed expression of the UPR sensors and inducers in the HTorA^ΔEDrosophila brains resulted in a significantly reduced life span of the flies. Furthermore, the types and quantities of proteins present in the anti-HSC3 positive microsomes in the HTorA^ΔE brains were different from those of the HTorA^WT brains.

Conclusion

Taken together, these data show that HTorA^ΔE in Drosophila brains may activate the UPR and increase the expression of HSP22 to compensate for the toxic effects caused by HTorA^ΔE in the brains.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1518-0) contains supplementary material, which is available to authorized users.

A foundation for reliable spatial proteomics data analysis

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.

Identification of trans-golgi network proteins in Arabidopsis thaliana root tissue

Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.

Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata

MOTIVATION

Experimental spatial proteomics, i.e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools.

RESULTS

Here we present pRoloc, a complete infrastructure to support and guide the sound analysis of quantitative mass-spectrometry-based spatial proteomics data. It provides functionality for unsupervised and supervised machine learning for data exploration and protein classification and novelty detection to identify new putative sub-cellular clusters. The software builds upon existing infrastructure for data management and data processing.

Determining protein subcellular localization in mammalian cell culture with biochemical fractionation and iTRAQ 8-plex quantification

Protein subcellular localization is a fundamental feature of posttranslational functional regulation. Traditional microscopy based approaches to study protein localization are typically of limited throughput, and dependent on the availability of antibodies with high specificity and sensitivity, or fluorescent fusion proteins. In this chapter we describe how Localization of Organelle Proteins by Isotope Tagging (LOPIT), a mass spectrometry based workflow coupling biochemical fractionation and iTRAQ™ 8-plex quantification, can be applied for the high-throughput characterization of protein localization in a mammalian cell culture line.

Proteomics methods for subcellular proteome analysis

The elucidation of the subcellular distribution of proteins under different conditions is a major challenge in cell biology. This challenge is further complicated by the multicompartmental and dynamic nature of protein localization. To address this issue, quantitative proteomics workflows have been developed to reliably identify the protein complement of whole organelles, as well as for protein assignment to subcellular location and relative protein quantification based on different cell culture conditions. Here, we review quantitative MS-based approaches that combine cellular fractionation with proteomic analysis. The application of these methods to the characterization of organellar composition and to the determination of the dynamic nature of protein complexes is improving our understanding of protein functions and dynamics.

The effect of organelle discovery upon sub-cellular protein localisation

Prediction of protein sub-cellular localisation by employing quantitative mass spectrometry experiments is an expanding field. Several methods have led to the assignment of proteins to specific subcellular localisations by partial separation of organelles across a fractionation scheme coupled with computational analysis. Methods developed to analyse organelle data have largely employed supervised machine learning algorithms to map unannotated abundance profiles to known protein-organelle associations. Such approaches are likely to make association errors if organelle-related groupings present in experimental output are not included in data used to create a protein-organelle classifier. Currently, there is no automated way to detect organelle-specific clusters within such datasets. In order to address the above issues we adapted a phenotype discovery algorithm, originally created to filter image-based output for RNAi screens, to identify putative subcellular groupings in organelle proteomics experiments. We were able to mine datasets to a deeper level and extract interesting phenotype clusters for more comprehensive evaluation in an unbiased fashion upon application of this approach. Organelle-related protein clusters were identified beyond those sufficiently annotated for use as training data. Furthermore, we propose avenues for the incorporation of observations made into general practice for the classification of protein-organelle membership from quantitative MS experiments.

BIOLOGICAL SIGNIFICANCE

Protein sub-cellular localisation plays an important role in molecular interactions, signalling and transport mechanisms. The prediction of protein localisation by quantitative mass-spectrometry (MS) proteomics is a growing field and an important endeavour in improving protein annotation. Several such approaches use gradient-based separation of cellular organelle content to measure relative protein abundance across distinct gradient fractions. The distribution profiles are commonly mapped in silico to known protein-organelle associations via supervised machine learning algorithms, to create classifiers that associate unannotated proteins to specific organelles. These strategies are prone to error, however, if organelle-related groupings present in experimental output are not represented, for example owing to the lack of existing annotation, when creating the protein-organelle mapping. Here, the application of a phenotype discovery approach to LOPIT gradient-based MS data identifies candidate organelle phenotypes for further evaluation in an unbiased fashion. Software implementation and usage guidelines are provided for application to wider protein-organelle association experiments. In the wider context, semi-supervised organelle discovery is discussed as a paradigm with which to generate new protein annotations from MS-based organelle proteomics experiments.

Enabling technologies for yeast proteome analysis

Whilst the study of yeast genomes and transcriptomes is in an advanced state, there is still much to learn about the resulting proteins in terms of cataloging, characterization of post-translational modifications, turnover, and the dynamics of sub-cellular localization and interactions. Analysis of the transcripts gives little insight into function or diversity as changes in RNA levels do not always correlate with the resulting protein abundance. A number of global and targeted attempts have been made to catalog and characterize the yeast proteome and we describe here the methods used to gain a greater understanding of the yeast proteome. This comprehensive review also describes future approaches that will aid completion in identifying and characterizing the remaining 20% of the undetermined yeast proteome as well as giving new insight into protein dynamics.

Quantitative, high-resolution proteomics for data-driven systems biology

Systems biology requires comprehensive data at all molecular levels. Mass spectrometry (MS)-based proteomics has emerged as a powerful and universal method for the global measurement of proteins. In the most widespread format, it uses liquid chromatography (LC) coupled to high-resolution tandem mass spectrometry (MS/MS) to identify and quantify peptides at a large scale. This peptide intensity information is the basic quantitative proteomic data type. It is used to quantify proteins between different proteome states, including the temporal variation of the proteome, to determine the complete primary structure of proteins including posttranslational modifications, to localize proteins to organelles, and to determine protein interactions. Here, we describe the principles of analysis and the areas of biology where proteomics can make unique contributions. The large-scale nature of proteomics data and its high accuracy pose special opportunities as well as challenges in systems biology that have been largely untapped so far.