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

The role of the AP-1 adaptor complex in outgoing and incoming membrane traffic

The AP-1 adaptor complex is found in all eukaryotes, but it has been implicated in different pathways in different organisms. To look directly at AP-1 function, we generated stably transduced HeLa cells coexpressing tagged AP-1 and various tagged membrane proteins. Live cell imaging showed that AP-1 is recruited onto tubular carriers trafficking from the Golgi apparatus to the plasma membrane, as well as onto transferrin-containing early/recycling endosomes. Analysis of single AP-1 vesicles showed that they are a heterogeneous population, which starts to sequester cargo 30 min after exit from the ER. Vesicle capture showed that AP-1 vesicles contain transmembrane proteins found at the TGN and early/recycling endosomes, as well as lysosomal hydrolases, but very little of the anterograde adaptor GGA2. Together, our results support a model in which AP-1 retrieves proteins from post-Golgi compartments back to the TGN, analogous to COPI's role in the early secretory pathway. We propose that this is the function of AP-1 in all eukaryotes.

OCaR1 endows exocytic vesicles with autoregulatory competence by preventing uncontrolled Ca2+ release, exocytosis, and pancreatic tissue damage

Regulated exocytosis is initiated by increased Ca2+ concentrations in close spatial proximity to secretory granules, which is effectively prevented when the cell is at rest. Here we showed that exocytosis of zymogen granules in acinar cells was driven by Ca2+ directly released from acidic Ca2+ stores including secretory granules through NAADP-activated two-pore channels (TPCs). We identified OCaR1 (encoded by Tmem63a) as an organellar Ca2+ regulator protein integral to the membrane of secretory granules that controlled Ca2+ release via inhibition of TPC1 and TPC2 currents. Deletion of OCaR1 led to extensive Ca2+ release from NAADP-responsive granules under basal conditions as well as upon stimulation of GPCR receptors. Moreover, OCaR1 deletion exacerbated the disease phenotype in murine models of severe and chronic pancreatitis. Our findings showed OCaR1 as a gatekeeper of Ca2+ release that endows NAADP-sensitive secretory granules with an autoregulatory mechanism preventing uncontrolled exocytosis and pancreatic tissue damage.

Enhancing subcellular protein localization mapping analysis using Sc2promap utilizing attention mechanisms

BACKGROUND

Aberrant protein localization is a prominent feature in many human diseases and can have detrimental effects on the function of specific tissues and organs. High-throughput technologies, which continue to advance with iterations of automated equipment and the development of bioinformatics, enable the acquisition of large-scale data that are more pattern-rich, allowing for the use of a wider range of methods to extract useful patterns and knowledge from them.

METHODS

The proposed sc2promap (Spatial and Channel for SubCellular Protein Localization Mapping) model, designed to proficiently extract meaningful features from a vast repository of single-channel grayscale protein images for the purposes of protein localization analysis and clustering. Sc2promap incorporates a prediction head component enriched with supplementary protein annotations, along with the integration of a spatial-channel attention mechanism within the encoder to enables the generation of high-resolution protein localization maps that encapsulate the fundamental characteristics of cells, including elemental cellular localizations such as nuclear and non-nuclear domains.

RESULTS

Qualitative and quantitative comparisons were conducted across internal and external clustering evaluation metrics, as well as various facets of the clustering results. The study also explored different components of the model. The research outcomes conclusively indicate that, in comparison to previous methods, Sc2promap exhibits superior performance.

CONCLUSIONS

The amalgamation of the attention mechanism and prediction head components has led the model to excel in protein localization clustering and analysis tasks.

GENERAL SIGNIFICANCE

The model effectively enhances the capability to extract features and knowledge from protein fluorescence images.

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

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

Orthogonal Enzyme-Substrate Design Strategy for Discovery of Human Protein Palmitoyltransferase Substrates

Protein palmitoylation, with more than 5000 substrates, is the most prevalent form of protein lipidation. Palmitoylated proteins participate in almost all areas of cellular physiology and have been linked to several human diseases. Twenty-three zDHHC enzymes catalyze protein palmitoylation with extensive overlap among the substrates of each zDHHC member. Currently, there is no global strategy to delineate the physiological substrates of individual zDHHC enzymes without perturbing the natural cellular pool. Here, we outline a general approach to accomplish this on the basis of synthetic orthogonal substrates that are only compatible with engineered zDHHC enzymes. We demonstrate the utility of this strategy by validating known substrates and use it to identify novel substrates of two human zDHHC enzymes. Finally, we employ this method to discover and explore conserved palmitoylation in a family of host restriction factors against pathogenic viruses, including SARS-CoV-2.

Microtubule-associated septin complexes modulate kinesin and dynein motility with differential specificities

Long-range membrane traffic is guided by microtubule-associated proteins and posttranslational modifications, which collectively comprise a traffic code. The regulatory principles of this code and how it orchestrates the motility of kinesin and dynein motors are largely unknown. Septins are a large family of GTP-binding proteins, which assemble into complexes that associate with microtubules. Using single-molecule in vitro motility assays, we tested how the microtubule-associated SEPT2/6/7, SEPT2/6/7/9, and SEPT5/7/11 complexes affect the motilities of the constitutively active kinesins KIF5C and KIF1A and the dynein-dynactin-bicaudal D (DDB) motor complex. We found that microtubule-associated SEPT2/6/7 is a potent inhibitor of DDB and KIF5C, preventing mainly their association with microtubules. SEPT2/6/7 also inhibits KIF1A by obstructing stepping along microtubules. On SEPT2/6/7/9-coated microtubules, KIF1A inhibition is dampened by SEPT9, which alone enhances KIF1A, showing that individual septin subunits determine the regulatory properties of septin complexes. Strikingly, SEPT5/7/11 differs from SEPT2/6/7, in permitting the motility of KIF1A and immobilizing DDB to the microtubule lattice. In hippocampal neurons, filamentous SEPT5 colocalizes with somatodendritic microtubules that underlie Golgi membranes and lack SEPT6. Depletion of SEPT5 disrupts Golgi morphology and polarization of Golgi ribbons into the shaft of somato-proximal dendrites, which is consistent with the tethering of DDB to microtubules by SEPT5/7/11. Collectively, these results suggest that microtubule-associated complexes have differential specificities in the regulation of the motility and positioning of microtubule motors. We posit that septins are an integral part of the microtubule-based code that spatially controls membrane traffic.

Deep and fast label-free Dynamic Organellar Mapping

The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we enhance the performance of DOMs through data-independent acquisition (DIA) mass spectrometry. DIA-DOMs achieve twice the depth of our previous workflow in the same mass spectrometry runtime, and substantially improve profiling precision and reproducibility. We leverage this gain to establish flexible map formats scaling from high-throughput analyses to extra-deep coverage. Furthermore, we introduce DOM-ABC, a powerful and user-friendly open-source software tool for analyzing profiling data. We apply DIA-DOMs to capture subcellular localization changes in response to starvation and disruption of lysosomal pH in HeLa cells, which identifies a subset of Golgi proteins that cycle through endosomes. An imaging time-course reveals different cycling patterns and confirms the quantitative predictive power of our translocation analysis. DIA-DOMs offer a superior workflow for label-free spatial proteomics as a systematic phenotype discovery tool.

Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling

Repeated measures experimental designs, which quantify proteins in biological subjects repeatedly over multiple experimental conditions or times, are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects and increase the statistical power of detecting within-subject changes in protein abundance. Meanwhile, proteomics experiments increasingly incorporate tandem mass tag (TMT) labeling, a multiplexing strategy that gains both relative protein quantification accuracy and sample throughput. However, combining repeated measures and TMT multiplexing in a large-scale investigation presents statistical challenges due to unique interplays of between-mixture, within-mixture, between-subject, and within-subject variation. This manuscript proposes a family of linear mixed-effects models for differential analysis of proteomics experiments with repeated measures and TMT multiplexing. These models decompose the variation in the data into the contributions from its sources as appropriate for the specifics of each experiment, enable statistical inference of differential protein abundance, and recognize a difference in the uncertainty of between-subject versus within-subject comparisons. The proposed family of models is implemented in the R/Bioconductor package MSstatsTMT v2.2.0. Evaluations of four simulated datasets and four investigations answering diverse biological questions demonstrated the value of this approach as compared to the existing general-purpose approaches and implementations.

DeepSP: A Deep Learning Framework for Spatial Proteomics

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

Proteomic analysis of antiviral innate immunity

The capacity of host cells to detect and restrict an infecting virus rests on an array of cell-autonomous antiviral effectors and innate immune receptors that can trigger inflammatory processes at tissue and organismal levels. Dynamic changes in protein abundance, subcellular localisation, post-translational modifications and interactions with other biomolecules govern these processes. Proteomics is therefore an ideal experimental tool to discover novel mechanisms of host antiviral immunity. Additional information can be gleaned both about host and virus by systematic analysis of viral immune evasion strategies. In this review, we summarise recent advances in proteomic technologies and their application to antiviral innate immunity.

Analysis of Yeast Peroxisomes via Spatial Proteomics

Peroxisomes are ubiquitous organelles with essential functions in numerous cellular processes such as lipid metabolism, detoxification of reactive oxygen species, and signaling. Knowledge of the peroxisomal proteome including multi-localized proteins and, most importantly, changes of its composition induced by altering cellular conditions or impaired peroxisome biogenesis and function is of paramount importance for a holistic view on peroxisomes and their diverse functions in a cellular context. In this chapter, we provide a spatial proteomics protocol specifically tailored to the analysis of the peroxisomal proteome of baker's yeast that enables the definition of the peroxisomal proteome under distinct conditions and to monitor dynamic changes of the proteome including the relocation of individual proteins to a different cellular compartment. The protocol comprises subcellular fractionation by differential centrifugation followed by Nycodenz density gradient centrifugation of a crude peroxisomal fraction, quantitative mass spectrometric measurements of subcellular and density gradient fractions, and advanced computational data analysis, resulting in the establishment of organellar maps on a global scale.

nuPRISM: Microfluidic Genome-Wide Phenotypic Screening Platform for Cellular Nuclei

Genome-wide loss-of-function screens are critical tools to identify novel genetic regulators of intracellular proteins. However, studying the changes in the organelle-specific expression profile of intracellular proteins can be challenging due to protein localization differences across the whole cell, hindering context-dependent protein expression and activity analyses. Here, we describe nuPRISM, a microfluidics chip specifically designed for large-scale isolated nuclei sorting. The new device enables rapid genome-wide loss-of-function phenotypic CRISPR-Cas9 screens directed at intranuclear targets. We deployed this technology to identify novel genetic regulators of β-catenin nuclear accumulation, a phenotypic hallmark of APC-mutated colorectal cancer. nuPRISM expands our ability to capture aberrant nuclear morphological and functional traits associated with distinctive signal transduction and subcellular localization-driven functional processes with substantial resolution and high throughput.

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.

Combinatorial and antagonistic effects of tubulin glutamylation and glycylation on katanin microtubule severing

Microtubules have spatiotemporally complex posttranslational modification patterns. How cells interpret this tubulin modification code is largely unknown. We show that C. elegans katanin, a microtubule severing AAA ATPase mutated in microcephaly and critical for cell division, axonal elongation, and cilia biogenesis, responds precisely, differentially, and combinatorially to three chemically distinct tubulin modifications-glycylation, glutamylation, and tyrosination-but is insensitive to acetylation. Glutamylation and glycylation are antagonistic rheostats with glycylation protecting microtubules from severing. Katanin exhibits graded and divergent responses to glutamylation on the α- and β-tubulin tails, and these act combinatorially. The katanin hexamer central pore constrains the polyglutamate chain patterns on β-tails recognized productively. Elements distal to the katanin AAA core sense α-tubulin tyrosination, and detyrosination downregulates severing. The multivalent microtubule recognition that enables katanin to read multiple tubulin modification inputs explains in vivo observations and illustrates how effectors can integrate tubulin code signals to produce diverse functional outcomes.

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.

Isobaric labeling: Expanding the breadth, accuracy, depth, and diversity of sample multiplexing

Isobaric labeling has rapidly become a predominant strategy for proteome-wide abundance measurements. Coupled to mass spectrometry, sample multiplexing techniques using isobaric labeling are unparalleled for profiling proteins and posttranslational modifications across multiple samples in a single experiment. Here, I highlight aspects of isobaric labeling in the context of expanding the breadth of multiplexing, improving quantitative accuracy and proteome depth, and developing a wide range of diverse applications. I underscore two facets that enhance quantitative accuracy and reproducibility, specifically the availability of quality control standards for isobaric labeling experiments and the evolution of data acquisition methods. I also emphasize the necessity for standardized methodologies, particularly for emerging high-throughput workflows. Future developments in sample multiplexing will further strengthen the importance of isobaric labeling for comprehensive proteome profiling.

Mass spectrometry and the cellular surfaceome

The collection of exposed plasma membrane proteins, collectively termed the surfaceome, is involved in multiple vital cellular processes, such as the communication of cells with their surroundings and the regulation of transport across the lipid bilayer. The surfaceome also plays key roles in the immune system by recognizing and presenting antigens, with its possible malfunctioning linked to disease. Surface proteins have long been explored as potential cell markers, disease biomarkers, and therapeutic drug targets. Despite its importance, a detailed study of the surfaceome continues to pose major challenges for mass spectrometry-driven proteomics due to the inherent biophysical characteristics of surface proteins. Their inefficient extraction from hydrophobic membranes to an aqueous medium and their lower abundance compared to intracellular proteins hamper the analysis of surface proteins, which are therefore usually underrepresented in proteomic datasets. To tackle such problems, several innovative analytical methodologies have been developed. This review aims at providing an extensive overview of the different methods for surfaceome analysis, with respective considerations for downstream mass spectrometry-based proteomics.

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

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

Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics

With increasing sensitivity and accuracy in mass spectrometry, the tumor phosphoproteome is getting into reach. However, the selection of quantitation techniques best-suited to the biomedical question and diagnostic requirements remains a trial and error decision as no study has directly compared their performance for tumor tissue phosphoproteomics. We compared label-free quantification (LFQ), spike-in-SILAC (stable isotope labeling by amino acids in cell culture), and tandem mass tag (TMT) isobaric tandem mass tags technology for quantitative phosphosite profiling in tumor tissue. Compared to the classic SILAC method, spike-in-SILAC is not limited to cell culture analysis, making it suitable for quantitative analysis of tumor tissue samples. TMT offered the lowest accuracy and the highest precision and robustness toward different phosphosite abundances and matrices. Spike-in-SILAC offered the best compromise between these features but suffered from a low phosphosite coverage. LFQ offered the lowest precision but the highest number of identifications. Both spike-in-SILAC and LFQ presented susceptibility to matrix effects. Match between run (MBR)-based analysis enhanced the phosphosite coverage across technical replicates in LFQ and spike-in-SILAC but further reduced the precision and robustness of quantification. The choice of quantitative methodology is critical for both study design such as sample size in sample groups and quantified phosphosites and comparison of published cancer phosphoproteomes. Using ovarian cancer tissue as an example, our study builds a resource for the design and analysis of quantitative phosphoproteomic studies in cancer research and diagnostics.

SubCellBarCode: integrated workflow for robust spatial proteomics by mass spectrometry

The molecular functions of a protein are defined by its inherent properties in relation to its environment and interaction network. Within a cell, this environment and network are defined by the subcellular location of the protein. Consequently, it is crucial to know the localization of a protein to fully understand its functions. Recently, we have developed a mass spectrometry- (MS) and bioinformatics-based pipeline to generate a proteome-wide resource for protein subcellular localization across multiple human cancer cell lines ( www.subcellbarcode.org ). Here, we present a detailed wet-lab protocol spanning from subcellular fractionation to MS-sample preparation and analysis. A key feature of this protocol is that it includes all generated cell fractions without discarding any material during the fractionation process. We also describe the subsequent quantitative MS-data analysis, machine learning-based classification, differential localization analysis and visualization of the output. For broad applicability, we evaluated the pipeline by using MS data generated by two different peptide pre-fractionation approaches, namely high-resolution isoelectric focusing and high-pH reverse-phase fractionation, as well as direct analysis without pre-fractionation by using long-gradient liquid chromatography-MS. Moreover, an R package covering the dry-lab part of the method was developed and made available through Bioconductor. The method is straightforward and robust, and the entire protocol, from cell harvest to classification output, can be performed within 1-2 weeks. The protocol enables accurate classification of proteins to 15 compartments and 4 neighborhoods, visualization of the output data and differential localization analysis including treatment-induced protein relocalization, condition-dependent localization or cell type-specific localization. The SubCellBarCode package is freely available at https://bioconductor.org/packages/devel/bioc/html/SubCellBarCode.html .

Spatial centrosome proteome of human neural cells uncovers disease-relevant heterogeneity

The centrosome provides an intracellular anchor for the cytoskeleton, regulating cell division, cell migration, and cilia formation. We used spatial proteomics to elucidate protein interaction networks at the centrosome of human induced pluripotent stem cell-derived neural stem cells (NSCs) and neurons. Centrosome-associated proteins were largely cell type-specific, with protein hubs involved in RNA dynamics. Analysis of neurodevelopmental disease cohorts identified a significant overrepresentation of NSC centrosome proteins with variants in patients with periventricular heterotopia (PH). Expressing the PH-associated mutant pre-mRNA-processing factor 6 (PRPF6) reproduced the periventricular misplacement in the developing mouse brain, highlighting missplicing of transcripts of a microtubule-associated kinase with centrosomal location as essential for the phenotype. Collectively, cell type-specific centrosome interactomes explain how genetic variants in ubiquitous proteins may convey brain-specific phenotypes.

Proteomic characterization of post-mortem human brain tissue following ultracentrifugation-based subcellular fractionation

Proteomic characterization of human brain tissue is increasingly utilized to identify potential novel biomarker and drug targets for a variety of neurological diseases. In whole tissue studies, results may be driven by changes in the proportion of the largest and most abundant organelles or tissue cell-type composition. Spatial proteomics approaches enhance our knowledge of disease mechanisms and changing signaling pathways at the subcellular level by taking into account the importance of cellular localization, which critically influences protein function. Density gradient-based ultracentrifugation methods allow for subcellular fractionation and have been utilized in cell lines, mouse, and human brain tissue to quantify thousands of proteins in specific enriched organelles such as the pre- and post-synapse. Serial ultra-centrifugation methods allow for the analysis of multiple cellular organelles from the same biological sample, and to our knowledge have not been previously applied to frozen post-mortem human brain tissue. The use of frozen human tissue for tissue fractionation faces two major challenges, the post-mortem interval, during which proteins may leach from their usual location into the cytosol, and freezing, which results in membrane breakdown. Despite these challenges, in this proof-of-concept study, we show that the majority of proteins segregate reproducibly into crude density-based centrifugation fractions, that the fractions are enriched for the appropriate organellar markers, and that significant differences in protein localization can be observed between tissue from individuals with Alzheimer’s Disease and control individuals.

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

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

Secretomics—A Key to a Comprehensive Picture of Unconventional Protein Secretion

For a long time, leaderless secreted proteins (LLSP) were neglected as artifacts derived from dying cells. It is now generally accepted that secretion of LLSP–as a part of the collective term unconventional protein secretion (UPS) - is an evolutionarily conserved process and that these LLSP are actively and selectively secreted from living cells bypassing the classical endoplasmic reticulum-Golgi pathway. However, the mechanism of UPS pathways, as well as the number of LLSP and which part of a protein is involved in the selection of LLSPs for secretion, are still enigmatic and await clarification. Secretomics-a proteomics-based approach to identify and quantify all proteins secreted by a cell-is inherently unbiased toward a particular secretion pathway and offers the opportunity to shed light on the UPS. Here, we will evaluate and present recent results of proteomic workflows allowing to obtain high-confident secretome data. Additionally, we address that cell culture conditions largely affect the composition of the secretome. This has to be kept in mind to control cell culture induced artifacts and adaptation stress in serum free conditions. Evaluation of click chemistry for secretome analysis of cells under serum-containing conditions showed a significant change in the cellular proteome with longer incubation time upon treatment with non-canonical amino acid azidohomoalanine. Finally, we showed that the number of LLSP far exceeds the number of secreted proteins annotated in Uniprot and ProteinAtlas. Thus, secretomics in combination with sophisticated microbioanalytical and sample preparation methods is well suited to provide a comprehensive picture of UPS.

Spatial proteomics finds CD155 and Endophilin-A1 as mediators of growth and invasion in medulloblastoma

The composition of the plasma membrane (PM)-associated proteome of tumor cells determines cell-cell and cell-matrix interactions and the response to environmental cues. Whether the PM-associated proteome impacts the phenotype of Medulloblastoma (MB) tumor cells and how it adapts in response to growth factor cues is poorly understood. Using a spatial proteomics approach, we observed that hepatocyte growth factor (HGF)-induced activation of the receptor tyrosine kinase c-MET in MB cells changes the abundance of transmembrane and membrane-associated proteins. The depletion of MAP4K4, a pro-migratory effector kinase downstream of c-MET, leads to a specific decrease of the adhesion and immunomodulatory receptor CD155 and of components of the fast-endophilin-mediated endocytosis (FEME) machinery in the PM-associated proteome of HGF-activated MB cells. The decreased surface expression of CD155 or of the fast-endophilin-mediated endocytosis effector endophilin-A1 reduces growth and invasiveness of MB tumor cells in the tissue context. These data thus describe a novel function of MAP4K4 in the control of the PM-associated proteome of tumor cells and identified two downstream effector mechanisms controlling proliferation and invasiveness of MB cells.

Proximity-Dependent Biotinylation Approaches to Explore the Dynamic Compartmentalized Proteome

In recent years, proximity-dependent biotinylation approaches, including BioID, APEX, and their derivatives, have been widely used to define the compositions of organelles and other structures in cultured cells and model organisms. The associations between specific proteins and given compartments are regulated by several post-translational modifications (PTMs); however, these effects have not been systematically investigated using proximity proteomics. Here, we discuss the progress made in this field and how proximity-dependent biotinylation strategies could elucidate the contributions of PTMs, such as phosphorylation, to the compartmentalization of proteins.

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

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

Multi-omics analysis to reveal disorders of cell metabolism and integrin signaling pathways induced by PM2.5

Atmospheric fine particle pollution is known to cause many adverse health effects. However, the potential mechanisms of PM_2.5-induced cytotoxicity still needs further understanding. Herein, we integrated cytotoxicity, component profiling, metabolomics and proteomics data to deeply explain the biological responses of human bronchial epithelial cells exposed to PM_2.5. We observed that PM_2.5 caused cell cycle arrest, calcium influx, cell damage and further induced cell apoptosis. The contents of heavy metals and 4-6 rings PAHs in PM_2.5 were positively correlated with intracellular ROS, indicating that they might be the important components to induce the above cytotoxicity. Integrated metabolomics and proteomics analysis revealed the significant alterations of many metabolic processes, such as glycolysis, the citric acid cycle, amino acid metabolism and lipid metabolism. Notably, we found that PM_2.5 inhibited the integrin signaling pathway, including down-regulating the protein expression of integrins and the phosphorylation of downstream signaling kinases, which might ultimately affect cell cycle progression, cell metabolism and apoptosis. This study provided a comprehensive data resource for the deep understanding of biological toxicity mechanisms caused by atmospheric fine particles in human lung-bronchial epithelium cells.

PSL-LCCL: a resource for subcellular protein localization in liver cancer cell line SK_HEP1

The characterization of subcellular protein localization provides a basis for further understanding cellular behaviors. A delineation of subcellular localization of proteins on cytosolic membrane-bound organelles in human liver cancer cell lines (hLCCLs) has yet to be performed. To obtain its proteome-wide view, we isolated and enriched six cytosolic membrane-bound organelles in one of the hLCCLs (SK_HEP1) and quantified their proteins using mass spectrometry. The vigorous selection of marker proteins and a machine-learning-based algorithm were implemented to localize proteins at cluster and neighborhood levels. We validated the performance of the proposed method by comparing the predicted subcellular protein localization with publicly available resources. The profiles enabled investigating the correlation of protein domains with their subcellular localization and colocalization of protein complex members. A subcellular proteome database for SK_HEP1, including (i) the subcellular protein localization and (ii) the subcellular locations of protein complex members and their interactions, was constructed. Our research provides resources for further research on hLCCLs proteomics.

Database URL: http://www.igenetics.org.cn/project/PSL-LCCL/

Computer-aided design of reversible hybridization chain reaction (CAD-HCR) enables multiplexed single-cell spatial proteomics imaging

In situ spatial proteomics analysis of a single cell has not been achieved yet, mainly because of insufficient throughput and sensitivity of current techniques. Recent progress on immuno-nucleic acid amplification technology presents tremendous opportunities to address this issue. Here, we report an innovative hybridization chain reaction (HCR) technique that involves computer-aided design (CAD) and reversible assembly. CAD enables highly multiplexed HCR with a sequence database that can work in parallel, while reversible assembly enables the switching of HCR between a working state and a resting state. Thus, CAD-HCR has been successfully adopted for single-cell spatial proteomics analysis. The fluorescence signal of CAD-HCR is comparable with conventional immunofluorescence, and it is positively correlated with the abundance of target proteins, which is beneficial for the visualization of proteins. The method developed here expands the toolbox of single-cell analysis and proteomics studies, as well as the performance and application of HCR.

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.

Spatial-proteomics reveals phospho-signaling dynamics at subcellular resolution

Dynamic change in subcellular localization of signaling proteins is a general concept that eukaryotic cells evolved for eliciting a coordinated response to stimuli. Mass spectrometry-based proteomics in combination with subcellular fractionation can provide comprehensive maps of spatio-temporal regulation of protein networks in cells, but involves laborious workflows that does not cover the phospho-proteome level. Here we present a high-throughput workflow based on sequential cell fractionation to profile the global proteome and phospho-proteome dynamics across six distinct subcellular fractions. We benchmark the workflow by studying spatio-temporal EGFR phospho-signaling dynamics in vitro in HeLa cells and in vivo in mouse tissues. Finally, we investigate the spatio-temporal stress signaling, revealing cellular relocation of ribosomal proteins in response to hypertonicity and muscle contraction. Proteomics data generated in this study can be explored through https://SpatialProteoDynamics.github.io .

A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation

Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.

Methodologies for Measuring Protein Trafficking across Cellular Membranes

Nearly all proteins are synthesized in the cytosol. The majority of this proteome must be trafficked elsewhere, such as to membranes, to subcellular compartments, or outside of the cell. Proper trafficking of nascent protein is necessary for protein folding, maturation, quality control and cellular and organismal health. To better understand cellular biology, molecular and chemical technologies to properly characterize protein trafficking (and mistrafficking) have been developed and applied. Herein, we take a biochemical perspective to review technologies that enable spatial and temporal measurement of protein distribution, focusing on both the most widely adopted methodologies and exciting emerging approaches.

A proteomic view on lysosomes

Lysosomes are the main degradative organelles of almost all eukaryotic cells. They fulfil a crucial function in cellular homeostasis, and impairments in lysosomal function are connected to a continuously increasing number of pathological conditions. In recent years, lysosomes are furthermore emerging as control centers of cellular metabolism, and major regulators of cellular signaling were shown to be activated at the lysosomal surface. To date, >300 proteins were demonstrated to be located in/at the lysosome, and the lysosomal proteome and interactome is constantly growing. For the identification of these proteins, and their involvement in cellular mechanisms or disease progression, mass spectrometry (MS)-based proteomics has proven its worth in a large number of studies. In this review, we are recapitulating the application of MS-based approaches for the investigation of the lysosomal proteome, and their application to a diverse set of research questions. Numerous strategies were applied for the enrichment of lysosomes or lysosomal proteins and their identification by MS-based methods. This allowed for the characterization of the lysosomal proteome, the investigation of lysosome-related disorders, the utilization of lysosomal proteins as biomarkers for diseases, and the characterization of lysosome-related cellular mechanisms. While these >60 studies provide a comprehensive picture of the lysosomal proteome across several model organisms and pathological conditions, various proteomics approaches have not been applied to lysosomes yet, and a large number of questions are still left unanswered.

Synaptic proteins associated with cognitive performance and neuropathology in older humans revealed by multiplexed fractionated proteomics

Alzheimer's disease (AD) is defined by the presence of abundant amyloid-β (Aβ) and tau neuropathology. While this neuropathology is necessary for AD diagnosis, it is not sufficient for causing cognitive impairment. Up to one third of community dwelling older adults harbor intermediate to high levels of AD neuropathology at death yet demonstrate no significant cognitive impairment. Conversely, there are individuals who exhibit dementia with no gross explanatory neuropathology. In prior studies, synapse loss correlated with cognitive impairment. To understand how synaptic composition changes in relation to neuropathology and cognition, multiplexed liquid chromatography mass-spectrometry was used to quantify enriched synaptic proteins from the parietal association cortex of 100 subjects with contrasting levels of AD pathology and cognitive performance. 123 unique proteins were significantly associated with diagnostic category. Functional analysis showed enrichment of serotonin release and oxidative phosphorylation categories in normal (cognitively unimpaired, low neuropathology) and "resilient" (unimpaired despite AD pathology) individuals. In contrast, frail individuals, (low pathology, impaired cognition) showed a metabolic shift towards glycolysis and increased presence of proteasome subunits.

Microscopic image-based covariation network analysis for actin scaffold-modified insulin signaling

To infer a "live" protein network in single cells, we developed a novel Protein Localization and Modification-based Covariation Network (PLOM-CON) analysis method using a large set of quantitative data on the abundance (quantity), post-translational modification state (quality), and localization/morphological information of target proteins from microscope immunostained images. The generated network exhibited synchronized time-dependent behaviors of the target proteins to visualize how a live protein network develops or changes in cells under specific experimental conditions. As a proof of concept for PLOM-CON analysis, we applied this method to elucidate the role of actin scaffolds, in which actin fibers and signaling molecules accumulate and form membrane-associated protein condensates, in insulin signaling in rat hepatoma cells. We found that the actin scaffold in cells may function as a platform for glycogenesis and protein synthesis upon insulin stimulation.

A signal capture and proofreading mechanism for the KDEL-receptor explains selectivity and dynamic range in ER retrieval

ER proteins of widely differing abundance are retrieved from the Golgi by the KDEL-receptor. Abundant ER proteins tend to have KDEL rather than HDEL signals, whereas ADEL and DDEL are not used in most organisms. Here, we explore the mechanism of selective retrieval signal capture by the KDEL-receptor and how HDEL binds with 10-fold higher affinity than KDEL. Our results show the carboxyl-terminus of the retrieval signal moves along a ladder of arginine residues as it enters the binding pocket of the receptor. Gatekeeper residues D50 and E117 at the entrance of this pocket exclude ADEL and DDEL sequences. D50N/E117Q mutation of human KDEL-receptors changes the selectivity to ADEL and DDEL. However, further analysis of HDEL, KDEL, and RDEL-bound receptor structures shows that affinity differences are explained by interactions between the variable −4 H/K/R position of the signal and W120, rather than D50 or E117. Together, these findings explain KDEL-receptor selectivity, and how signal variants increase dynamic range to support efficient ER retrieval of low and high abundance proteins.

Quantitative Temporal Viromics

The abundance, localization, modifications, and protein-protein interactions of many host cell and virus proteins can change dynamically throughout the course of any viral infection. Studying these changes is critical for a comprehensive understanding of how viruses replicate and cause disease, as well as for the development of antiviral therapeutics and vaccines. Previously, we developed a mass spectrometry-based technique called quantitative temporal viromics (QTV), which employs isobaric tandem mass tags (TMTs) to allow precise comparative quantification of host and virus proteomes through a whole time course of infection. In this review, we discuss the utility and applications of QTV, exemplified by numerous studies that have since used proteomics with a variety of quantitative techniques to study virus infection through time. Expected final online publication date for the Annual Review of Virology, Volume 8 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

Proteogenomic interrogation of cancer cell lines: an overview of the field

Introduction: Cancer cell lines (CCLs) have been a major resource for cancer research. Over the past couple of decades, they have been instrumental in omic profiling method development and as model systems to generate new knowledge in cell and cancer biology. More recently, with the increasing amount of genomic, transcriptomic and proteomic data being generated in hundreds of CCLs, there is growing potential for integrative proteogenomic data analyses to be performed.Areas covered: In this review, we first describe the most commonly used proteome profiling methods in CCLs. We then discuss how these proteomics data can be integrated with genomics data for proteogenomics analyses. Finally, we highlight some of the recent biological discoveries that have arisen from proteogenomics analyses of CCLs.Expert opinion: Protegeonomics analyses of CCLs have so far enabled the discovery of novel proteins and proteoforms. It has also improved our understanding of biological processes including post-transcriptional regulation of protein abundance and the presentation of antigens by major histocompatibility complex alleles. With proteomics data to be generated in hundreds to thousands of CCLs in coming years, there will be further potential for large-scale proteogenomics analyses and data integration with the phenotypically well-characterized CCLs.

Advances in Proteomics Allow Insights Into Neuronal Proteomes

Protein–protein interaction networks and signaling complexes are essential for normal brain function and are often dysregulated in neurological disorders. Nevertheless, unraveling neuron- and synapse-specific proteins interaction networks has remained a technical challenge. New techniques, however, have allowed for high-resolution and high-throughput analyses, enabling quantification and characterization of various neuronal protein populations. Over the last decade, mass spectrometry (MS) has surfaced as the primary method for analyzing multiple protein samples in tandem, allowing for the precise quantification of proteomic data. Moreover, the development of sophisticated protein-labeling techniques has given MS a high temporal and spatial resolution, facilitating the analysis of various neuronal substructures, cell types, and subcellular compartments. Recent studies have leveraged these novel techniques to reveal the proteomic underpinnings of well-characterized neuronal processes, such as axon guidance, long-term potentiation, and homeostatic plasticity. Translational MS studies have facilitated a better understanding of complex neurological disorders, such as Alzheimer’s disease (AD), Schizophrenia (SCZ), and Autism Spectrum Disorder (ASD). Proteomic investigation of these diseases has not only given researchers new insight into disease mechanisms but has also been used to validate disease models and identify new targets for research.

Spatially resolved cell polarity proteomics of a human epiblast model

Critical early steps in human embryonic development include polarization of the inner cell mass, followed by formation of an expanded lumen that will become the epiblast cavity. Recently described three-dimensional (3D) human pluripotent stem cell-derived cyst (hPSC-cyst) structures can replicate these processes. To gain mechanistic insights into the poorly understood machinery involved in epiblast cavity formation, we interrogated the proteomes of apical and basolateral membrane territories in 3D human hPSC-cysts. APEX2-based proximity bioinylation, followed by quantitative mass spectrometry, revealed a variety of proteins without previous annotation to specific membrane subdomains. Functional experiments validated the requirement for several apically enriched proteins in cyst morphogenesis. In particular, we found a key role for the AP-1 clathrin adaptor complex in expanding the apical membrane domains during lumen establishment. These findings highlight the robust power of this proximity labeling approach for discovering novel regulators of epithelial morphogenesis in 3D stem cell-based models.

Genetic disruption of WASHC4 drives endo-lysosomal dysfunction and cognitive-movement impairments in mice and humans

Mutation of the Wiskott-Aldrich syndrome protein and SCAR homology (WASH) complex subunit, SWIP, is implicated in human intellectual disability, but the cellular etiology of this association is unknown. We identify the neuronal WASH complex proteome, revealing a network of endosomal proteins. To uncover how dysfunction of endosomal SWIP leads to disease, we generate a mouse model of the human WASHC4c.3056C>G mutation. Quantitative spatial proteomics analysis of SWIPP1019R mouse brain reveals that this mutation destabilizes the WASH complex and uncovers significant perturbations in both endosomal and lysosomal pathways. Cellular and histological analyses confirm that SWIPP1019R results in endo-lysosomal disruption and uncover indicators of neurodegeneration. We find that SWIPP1019R not only impacts cognition, but also causes significant progressive motor deficits in mice. A retrospective analysis of SWIPP1019R patients reveals similar movement deficits in humans. Combined, these findings support the model that WASH complex destabilization, resulting from SWIPP1019R, drives cognitive and motor impairments via endo-lysosomal dysfunction in the brain.

Unbiased proteomic profiling of host cell extracellular vesicle composition and dynamics upon HIV-1 infection

Cells release diverse types of extracellular vesicles (EVs), which transfer complex signals to surrounding cells. Specific markers to distinguish different EVs (e.g. exosomes, ectosomes, enveloped viruses like HIV) are still lacking. We have developed a proteomic profiling approach for characterizing EV subtype composition and applied it to human Jurkat T cells. We generated an interactive database to define groups of proteins with similar profiles, suggesting release in similar EVs. Biochemical validation confirmed the presence of preferred partners of commonly used exosome markers in EVs: CD81/ADAM10/ITGB1, and CD63/syntenin. We then compared EVs from control and HIV-1-infected cells. HIV infection altered EV profiles of several cellular proteins, including MOV10 and SPN, which became incorporated into HIV virions, and SERINC3, which was re-routed to non-viral EVs in a Nef-dependent manner. Furthermore, we found that SERINC3 controls the surface composition of EVs. Our workflow provides an unbiased approach for identifying candidate markers and potential regulators of EV subtypes. It can be widely applied to in vitro experimental systems for investigating physiological or pathological modifications of EV release.

Bacterial riboproteogenomics: the era of N-terminal proteoform existence revealed

With the rapid increase in the number of sequenced prokaryotic genomes, relying on automated gene annotation became a necessity. Multiple lines of evidence, however, suggest that current bacterial genome annotations may contain inconsistencies and are incomplete, even for so-called well-annotated genomes. We here discuss underexplored sources of protein diversity and new methodologies for high-throughput genome reannotation. The expression of multiple molecular forms of proteins (proteoforms) from a single gene, particularly driven by alternative translation initiation, is gaining interest as a prominent contributor to bacterial protein diversity. In consequence, riboproteogenomic pipelines were proposed to comprehensively capture proteoform expression in prokaryotes by the complementary use of (positional) proteomics and the direct readout of translated genomic regions using ribosome profiling. To complement these discoveries, tailored strategies are required for the functional characterization of newly discovered bacterial proteoforms.

From fuzziness to precision medicine: on the rapidly evolving proteomics with implications in mitochondrial connectivity to rare human disease

Mitochondrial (mt) dysfunction is linked to rare diseases (RDs) such as respiratory chain complex (RCC) deficiency, MELAS, and ARSACS. Yet, how altered mt protein networks contribute to these ailments remains understudied. In this perspective article, we identified 21 mt proteins from public repositories that associate with RCC deficiency, MELAS, or ARSACS, engaging in a relatively small number of protein-protein interactions (PPIs), underscoring the need for advanced proteomic and interactomic platforms to uncover the complete scope of mt connectivity to RDs. Accordingly, we discuss innovative untargeted label-free proteomics in identifying RD-specific mt or other macromolecular assemblies and mapping of protein networks in complex tissue, organoid, and stem cell-differentiated neurons. Furthermore, tag- and label-based proteomics, genealogical proteomics, and combinatorial affinity purification-mass spectrometry, along with advancements in detecting and integrating transient PPIs with single-cell proteomics and transcriptomics, collectively offer seminal follow-ups to enrich for RD-relevant networks, with implications in RD precision medicine.

Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers

Intracellular traffic between compartments of the secretory and endocytic pathways is mediated by vesicle-based carriers. The proteomes of carriers destined for many organelles are ill-defined because the vesicular intermediates are transient, low-abundance and difficult to purify. Here, we combine vesicle relocalisation with organelle proteomics and Bayesian analysis to define the content of different endosome-derived vesicles destined for the trans-Golgi network (TGN). The golgin coiled-coil proteins golgin-97 and GCC88, shown previously to capture endosome-derived vesicles at the TGN, were individually relocalised to mitochondria and the content of the subsequently re-routed vesicles was determined by organelle proteomics. Our findings reveal 45 integral and 51 peripheral membrane proteins re-routed by golgin-97, evidence for a distinct class of vesicles shared by golgin-97 and GCC88, and various cargoes specific to individual golgins. These results illustrate a general strategy for analysing intracellular sub-proteomes by combining acute cellular re-wiring with high-resolution spatial proteomics.

Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). Thus, it is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released. Even in this era of proteomic atlas at the single-cell level, researchers continue to develop new algorithms, aiming at accessing the impact of disease-causing mutations/cell type-specific alternative splicing, for example. In this article, we overview the entire field and discuss its future direction.