Proteomics methods for subcellular proteome analysis

Subcellular western blotting of single cells

Although immunoassays are the de facto standard for determining subcellular protein localization in individual cells, antibody probe cross-reactivity and fixation artifacts remain confounding factors. To enhance selectivity while providing single-cell resolution, we introduce a subcellular western blotting technique capable of separately assaying proteins in the 14 pL cytoplasm and 2 pL nucleus of individual cells. To confer precision fluidic control, we describe a passive multilayer microdevice that leverages the rapid transport times afforded by miniaturization. After isolating single cells in microwells, we apply single-cell differential detergent fractionation to lyse and western blot the cytoplasmic lysate, whereas the nucleus remains intact in the microwell. Subsequently, we lyse the intact nucleus and western blot the nuclear lysate. To index each protein analysis to the originating subcellular compartment, we utilize bi-directional electrophoresis, a multidimensional separation that assays the lysate from each compartment in a distinct region of the separation axis. Single-cell bi-directional electrophoresis eliminates the need for semi-subjective image segmentation algorithms required in immunocytochemistry. The subcellular, single-cell western blot is demonstrated for six targets per cell, and successfully localizes spliceosome-associated proteins solubilized from large protein and RNA complexes, even for closely sized proteins (a 7 kDa difference). Measurement of NF-κB translocation dynamics in unfixed cells at 15-min intervals demonstrates reduced technical variance compared with immunofluorescence. This chemical cytometry assay directly measures the nucleocytoplasmic protein distribution in individual unfixed cells, thus providing insight into protein signaling in heterogeneous cell populations.

Profiling Cell Lines Nuclear Sub-proteome

Proteins are very dynamic within the cell and their localization and trafficking between subcellular compartments are critical for their correct function. Indeed, the abnormal localization of a protein might lead to the pathogenesis of several diseases. The association of cell fractionation methods and mass spectrometry based proteomic methods allow both the localization and quantification of proteins in different sub-compartments. Here we present a detailed protocol for enrichment, identification, and quantitation of the nuclear proteome in cell lines combining nuclear subproteome enrichment by differential centrifugation and high-throughput proteomics.

Protein Interaction Network of Human Protein Kinase D2 Revealed by Chemical Cross-Linking/Mass Spectrometry

We investigated the interaction network of human PKD2 in the cytosol and in Golgi-enriched subcellular protein fractions by an affinity enrichment strategy combined with chemical cross-linking/mass spectrometry (MS). Analysis of the subproteomes revealed the presence of distinct proteins in the cytosolic and Golgi fractions. The covalent fixation of transient or weak interactors by chemical cross-linking allowed capturing interaction partners that might otherwise disappear during conventional pull-down experiments. In total, 31 interaction partners were identified for PKD2, including glycogen synthase kinase-3 beta (GSK3B), 14-3-3 protein gamma (YWHAG), and the alpha isoform of 55 kDa regulatory subunit B of protein phosphatase 2A (PPP2R2A). Remarkably, the entire seven-subunit Arp2/3 complex (ARPC1B, ARPC2, ARPC3, ARPC4, ARPC5, ACTR3, ACTR2) as well as ARPC1A and ARPC5L, which are putative substitutes of ARPC1B and ARPC5, were identified. We provide evidence of a direct protein-protein interaction between PKD2 and Arp2/3. Our findings will pave the way for further structural and functional studies of PKD2 complexes, especially the PKD2/Arp2/3 interaction, to elucidate the role of PKD2 for transport processes at the trans-Golgi network. Data are available via ProteomeXchange with identifiers PXD003909 (enrichment from cytosolic fractions), PXD003913 (enrichment from Golgi fractions), and PXD003917 (subcellular fractionation).

MetaMass, a tool for meta-analysis of subcellular proteomics data

We report a tool for the analysis of subcellular proteomics data, called MetaMass, based on the use of standardized lists of subcellular markers. We analyzed data from 11 studies using MetaMass, mapping the subcellular location of 5,970 proteins. Our analysis revealed large variations in the performance of subcellular fractionation protocols as well as systematic biases in protein annotation databases. The Excel and R versions of MetaMass should enhance transparency and reproducibility in subcellular proteomics.

A Set of Organelle-Localizable Reactive Molecules for Mitochondrial Chemical Proteomics in Living Cells and Brain Tissues

Protein functions are tightly regulated by their subcellular localization in live cells, and quantitative evaluation of dynamically altered proteomes in each organelle should provide valuable information. Here, we describe a novel method for organelle-focused chemical proteomics using spatially limited reactions. In this work, mitochondria-localizable reactive molecules (MRMs) were designed that penetrate biomembranes and spontaneously concentrate in mitochondria, where protein labeling is facilitated by the condensation effect. The combination of this selective labeling and liquid chromatography-mass spectrometry (LC-MS) based proteomics technology facilitated identification of mitochondrial proteomes and the profile of the intrinsic reactivity of amino acids tethered to proteins expressed in live cultured cells, primary neurons and brain slices. Furthermore, quantitative profiling of mitochondrial proteins whose expression levels change significantly during an oxidant-induced apoptotic process was performed by combination of this MRMs-based method with a standard quantitative MS technique (SILAC: stable isotope labeling by amino acids in cell culture). The use of a set of MRMs represents a powerful tool for chemical proteomics to elucidate mitochondria-associated biological events and diseases.

Ion Channels in Plant Bioenergetic Organelles, Chloroplasts and Mitochondria: From Molecular Identification to Function

Recent technical advances in electrophysiological measurements, organelle-targeted fluorescence imaging, and organelle proteomics have pushed the research of ion transport a step forward in the case of the plant bioenergetic organelles, chloroplasts and mitochondria, leading to the molecular identification and functional characterization of several ion transport systems in recent years. Here we focus on channels that mediate relatively high-rate ion and water flux and summarize the current knowledge in this field, focusing on targeting mechanisms, proteomics, electrophysiology, and physiological function. In addition, since chloroplasts evolved from a cyanobacterial ancestor, we give an overview of the information available about cyanobacterial ion channels and discuss the evolutionary origin of chloroplast channels. The recent molecular identification of some of these ion channels allowed their physiological functions to be studied using genetically modified Arabidopsis plants and cyanobacteria. The view is emerging that alteration of chloroplast and mitochondrial ion homeostasis leads to organelle dysfunction, which in turn significantly affects the energy metabolism of the whole organism. Clear-cut identification of genes encoding for channels in these organelles, however, remains a major challenge in this rapidly developing field. Multiple strategies including bioinformatics, cell biology, electrophysiology, use of organelle-targeted ion-sensitive probes, genetics, and identification of signals eliciting specific ion fluxes across organelle membranes should provide a better understanding of the physiological role of organellar channels and their contribution to signaling pathways in plants in the future.

Vision-based Nano Robotic System for High-throughput Non-embedded Cell Cutting

Cell cutting is a significant task in biology study, but the highly productive non-embedded cell cutting is still a big challenge for current techniques. This paper proposes a vision-based nano robotic system and then realizes automatic non-embedded cell cutting with this system. First, the nano robotic system is developed and integrated with a nanoknife inside an environmental scanning electron microscopy (ESEM). Then, the positions of the nanoknife and the single cell are recognized, and the distance between them is calculated dynamically based on image processing. To guarantee the positioning accuracy and the working efficiency, we propose a distance-regulated speed adapting strategy, in which the moving speed is adjusted intelligently based on the distance between the nanoknife and the target cell. The results indicate that the automatic non-embedded cutting is able to be achieved within 1–2 mins with low invasion benefiting from the high precise nanorobot system and the sharp edge of nanoknife. This research paves a way for the high-throughput cell cutting at cell’s natural condition, which is expected to make significant impact on the biology studies, especially for the in-situ analysis at cellular and subcellular scale, such as cell interaction investigation, neural signal transduction and low invasive cell surgery.

Multiclassifier combinatorial proteomics of organelle shadows at the example of mitochondria in chromatin data

Subcellular localization is an important aspect of protein function, but the protein composition of many intracellular compartments is poorly characterized. For example, many nuclear bodies are challenging to isolate biochemically and thus remain inaccessible to proteomics. Here, we explore covariation in proteomics data as an alternative route to subcellular proteomes. Rather than targeting a structure of interest biochemically, we target it by machine learning. This becomes possible by taking data obtained for one organelle and searching it for traces of another organelle. As an extreme example and proof‐of‐concept we predict mitochondrial proteins based on their covariation in published interphase chromatin data. We detect about ⅓ of the known mitochondrial proteins in our chromatin data, presumably most as contaminants. However, these proteins are not present at random. We show covariation of mitochondrial proteins in chromatin proteomics data. We then exploit this covariation by multiclassifier combinatorial proteomics to define a list of mitochondrial proteins. This list agrees well with different databases on mitochondrial composition. This benchmark test raises the possibility that, in principle, covariation proteomics may also be applicable to structures for which no biochemical isolation procedures are available.

The biochemical and mass spectrometric profiling of the dystrophin complexome from skeletal muscle

The development of advanced mass spectrometric methodology has decisively enhanced the analytical capabilities for studies into the composition and dynamics of multi-subunit protein complexes and their associated components. Large-scale complexome profiling is an approach that combines the systematic isolation and enrichment of protein assemblies with sophisticated mass spectrometry-based identification methods. In skeletal muscles, the membrane cytoskeletal protein dystrophin of 427 kDa forms tight interactions with a variety of sarcolemmal, cytosolic and extracellular proteins, which in turn associate with key components of the extracellular matrix and the intracellular cytoskeleton. A major function of this enormous assembly of proteins, including dystroglycans, sarcoglycans, syntrophins, dystrobrevins, sarcospan, laminin and cortical actin, is postulated to stabilize muscle fibres during the physical tensions of continuous excitation-contraction-relaxation cycles. This article reviews the evidence from recent proteomic studies that have focused on the characterization of the dystrophin-glycoprotein complex and its central role in the establishment of the cytoskeleton-sarcolemma-matrisome axis. Proteomic findings suggest a close linkage of the core dystrophin complex with a variety of protein species, including tubulin, vimentin, desmin, annexin, proteoglycans and collagens. Since the almost complete absence of dystrophin is the underlying cause for X-linked muscular dystrophy, a more detailed understanding of the composition, structure and plasticity of the dystrophin complexome may have considerable biomedical implications.

Closing the gap between brain banks and proteomics to advance the study of neurodegenerative diseases

Neurodegenerative diseases (NDs), such as Alzheimer's disease and Parkinson's disease, are among the most debilitating neurological disorders, and as life expectancy rises quickly around the world, the scientific and clinical challenges of dealing with them will also increase dramatically, putting increased pressure on the biomedical community to come up with innovative solutions for the understanding, diagnosis, and treatment of these conditions. Despite several decades of intensive research, there is still little that can be done to prevent, cure, or even slow down the progression of NDs in most patients. There is an urgent need to develop new lines of basic and applied research that can be quickly translated into clinical application. One way to do this is to apply the tools of proteomics to well-characterized samples of human brain tissue, but a closer partnership must still be forged between proteomic scientists, brain banks, and clinicians to explore the maximum potential of this approach. Here, we analyze the challenges and potential benefits of using human brain tissue for proteomics research toward NDs.

Loss of histone deacetylase Hdac1 disrupts metabolic processes in intestinal epithelial cells

By using acetyl-CoA as a substrate, acetyltransferases and histone deacetylases regulate protein acetylation by adding or removing an acetyl group on lysines. Nuclear-located Hdac1 is a regulator of intestinal homeostasis. We have previously shown that Hdac1 define specific intestinal epithelial cell basal and inflammatory-dependent gene expression patterns and control cell proliferation. We show here that Hdac1 depletion in cellulo leads to increased histone acetylation after metabolic stresses, and to metabolic disturbances resulting in impaired responses to oxidative stresses, AMPK kinase activation and mitochondrial biogenesis. Thus, nuclear Hdac1 may control intestinal epithelial cell metabolism by regulating the supply of acetyl groups.

In Vitro Transcription/Translation System: A Versatile Tool in the Search for Missing Proteins

Approximately 18% of all human genes purported to encode proteins have not been directly evidenced at the protein level, according to the validation criteria established by neXtProt, and are considered to be "missing" proteins. One of the goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to identify as many of these missing proteins as possible in human samples using mass spectrometry-based methods. To further this goal, a consortium of C-HPP teams (chromosomes 5, 10, 16, and 19) has joined forces to devise new strategies to identify missing proteins by use of a cell-free in vitro transcription/translation system (IVTT). The proposed strategy employs LC-MS/MS data-dependent acquisition (DDA) and targeted selective reaction monitoring (SRM) methods to scrutinize low-complexity samples derived from IVTT. The optimized assays are then applied to identify missing proteins in human cells and tissues. We describe the approach and show proof-of-concept results for development of LC-SRM assays for identification of 18 missing proteins. We believe that the IVTT system, when coupled with downstream mass spectrometric identification, can be applied to identify proteins that have eluded more traditional methods of detection.

Simultaneous Pathoproteomic Evaluation of the Dystrophin-Glycoprotein Complex and Secondary Changes in the mdx-4cv Mouse Model of Duchenne Muscular Dystrophy

In skeletal muscle, the dystrophin-glycoprotein complex forms a membrane-associated assembly of relatively low abundance, making its detailed proteomic characterization in normal versus dystrophic tissues technically challenging. To overcome this analytical problem, we have enriched the muscle membrane fraction by a minimal differential centrifugation step followed by the comprehensive label-free mass spectrometric analysis of microsomal membrane preparations. This organelle proteomic approach successfully identified dystrophin and its binding partners in normal versus dystrophic hind limb muscles. The introduction of a simple pre-fractionation step enabled the simultaneous proteomic comparison of the reduction in the dystrophin-glycoprotein complex and secondary changes in the mdx-4cv mouse model of dystrophinopathy in a single analytical run. The proteomic screening of the microsomal fraction from dystrophic hind limb muscle identified the full-length dystrophin isoform Dp427 as the most drastically reduced protein in dystrophinopathy, demonstrating the remarkable analytical power of comparative muscle proteomics. Secondary pathoproteomic expression patterns were established for 281 proteins, including dystrophin-associated proteins and components involved in metabolism, signalling, contraction, ion-regulation, protein folding, the extracellular matrix and the cytoskeleton. Key findings were verified by immunoblotting. Increased levels of the sarcolemmal Na⁺/K⁺-ATPase in dystrophic leg muscles were also confirmed by immunofluorescence microscopy. Thus, the reduction of sample complexity in organelle-focused proteomics can be advantageous for the profiling of supramolecular protein complexes in highly intricate systems, such as skeletal muscle tissue.

Plasma membrane proteomic analysis of human Gastric Cancer tissues: revealing flotillin 1 as a marker for Gastric Cancer

Background

Gastric cancer remains the second leading cause of cancer-related deaths in the world. Successful early gastric cancer detection is hampered by lack of highly sensitive and specific biomarkers. Plasma membrane proteins participate and/or have a central role in the metastatic process of cancer cells and are potentially useful for cancer therapy due to easy accessibility of the targets.

Methods

In the present research, TMT method followed by mass spectrometry analysis was used to compare the relative expression levels of plasma membrane proteins between noncancer and gastric cancer tissues.

Results

Of a total data set that included 501 identified proteins, about 35% of the identified proteins were found to be plasma membrane and associated proteins. Among them, 82 proteins were at least 1.5-fold up- or down-regulated in gastric cancer compared with the adherent normal tissues.

Conclusions

A number of markers (e.g. annexin A6, caveolin 1, epidermal growth factor receptor, integrin beta 4) were previously reported as biomarkers of GC. Additionally, several potential biomarkers participated in endocytosis pathway and integrin signaling pathways were firstly identified as differentially expressed proteins in GC samples. Our findings also supported the notion that flotillin 1 is a potential biomarker that could be exploited for molecular imaging-based detection of gastric cancer. Together, the results show that subcellular proteomics of tumor tissue is a feasible and promising avenue for exploring oncogenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1343-5) contains supplementary material, which is available to authorized users.

Roseomics: a blank slate

Recent technological advances have led to an explosion in the system-wide profiling of biological processes in the study of herpesvirus biology, herein referred to as '-omics'. In many cases these approaches have revealed novel virus-induced changes to host cell biology that can be targeted with new antiviral therapeutics. Despite these successes, -omics approaches are not widely applied in the study of roseoloviruses. Here we describe examples of how -omics studies have shaped our understanding of herpesvirus biology, and discuss how these approaches might be used to identify host and viral factors that mediate roseolovirus pathogenesis.

Quantitative proteomics of the yeast Hsp70/Hsp90 interactomes during DNA damage reveal chaperone-dependent regulation of ribonucleotide reductase

The highly conserved molecular chaperones Hsp90 and Hsp70 are indispensible for folding and maturation of a significant fraction of the proteome, including many proteins involved in signal transduction and stress response. To examine the dynamics of chaperone-client interactions after DNA damage, we applied quantitative affinity-purification mass spectrometry (AP-MS) proteomics to characterize interactomes of the yeast Hsp70 isoform Ssa1 and Hsp90 isoform Hsp82 before and after exposure to methyl methanesulfonate. Of 256 proteins identified and quantified via (16)O(/18)O labeling and LC-MS/MS, 142 are novel Hsp70/90 interactors. Nearly all interactions remained unchanged or decreased after DNA damage, but 5 proteins increased interactions with Ssa1 and/or Hsp82, including the ribonucleotide reductase (RNR) subunit Rnr4. Inhibiting Hsp70 or 90 chaperone activity destabilized Rnr4 in yeast and its vertebrate homolog hRMM2 in breast cancer cells. In turn, pre-treatment of cancer cells with chaperone inhibitors sensitized cells to the RNR inhibitor gemcitabine, suggesting a novel chemotherapy strategy. All MS data have been deposited in the ProteomeXchange with identifier PXD001284.

BIOLOGICAL SIGNIFICANCE

This study provides the dynamic interactome of the yeast Hsp70 and Hsp90 under DNA damage which suggest key roles for the chaperones in a variety of signaling cascades. Importantly, the cancer drug target ribonucleotide reductase was shown to be a client of Hsp70 and Hsp90 in both yeast and breast cancer cells. As such, this study highlights the potential of a novel cancer therapeutic strategy that exploits the synergy of chaperone and ribonucleotide reductase inhibitors.

Recent advances in stable isotope labeling based techniques for proteome relative quantification

The large scale relative quantification of all proteins expressed in biological samples under different states is of great importance for discovering proteins with important biological functions, as well as screening disease related biomarkers and drug targets. Therefore, the accurate quantification of proteins at proteome level has become one of the key issues in protein science. Herein, the recent advances in stable isotope labeling based techniques for proteome relative quantification were reviewed, from the aspects of metabolic labeling, chemical labeling and enzyme-catalyzed labeling. Furthermore, the future research direction in this field was prospected.

Proteomic identification of the molecular basis of mammalian CNS growth cones

The growth cone, which is a unique structure with high motility that forms at the tips of extending axons and dendrites, is crucial to neuronal network formation. Axonal growth of the mammalian CNS is most likely achieved by the complicated coordination of cytoskeletal rearrangement and vesicular trafficking via many proteins. Before recent advances, no methods to identify numerous proteins existed; however, proteomics revolutionarily resolved such problems. In this review, I summarize the profiles of the mammalian growth cone proteins revealed by proteomics as the molecular basis of the growth cone functions, with molecular mapping. These results should be used as a basis for understanding the mechanisms of the complex mammalian CNS developmental process.

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.