Accounting for Protein Subcellular Localization: A Compartmental Map of the Rat Liver Proteome

FUT10 and FUT11 are protein O-fucosyltransferases that modify protein EMI domains

O-Fucosylation plays crucial roles in various essential biological events. Alongside the well-established O-fucosylation of epidermal growth factor-like repeats by protein O-fucosyltransferase 1 (POFUT1) and thrombospondin type 1 repeats by POFUT2, we recently identified a type of O-fucosylation on the elastin microfibril interface (EMI) domain of Multimerin-1 (MMRN1). Here, using AlphaFold2 screens, co-immunoprecipitation, enzymatic assays combined with mass spectrometric analysis and CRISPR-Cas9 knockouts, we demonstrate that FUT10 and FUT11, originally annotated in UniProt as α1,3-fucosyltransferases, are actually POFUTs responsible for modifying EMI domains; thus, we renamed them as POFUT3 and POFUT4, respectively. Like POFUT1/2, POFUT3/4 function in the endoplasmic reticulum, require folded domain structures for modification and participate in a non-canonical endoplasmic reticulum quality control pathway for EMI domain-containing protein secretion. This finding expands the O-fucosylation repertoire and provides an entry point for further exploration in this emerging field of O-fucosylation.

Advances in spatial proteomics: Mapping proteome architecture from protein complexes to subcellular localizations

Proteins are responsible for most intracellular functions, which they perform as part of higher-order molecular complexes, located within defined subcellular niches. Localization is both dynamic and context specific and mislocalization underlies a multitude of diseases. It is thus vital to be able to measure the components of higher-order protein complexes and their subcellular location dynamically in order to fully understand cell biological processes. Here, we review the current range of highly complementary approaches that determine the subcellular organization of the proteome. We discuss the scale and resolution at which these approaches are best employed and the caveats that should be taken into consideration when applying them. We also look to the future and emerging technologies that are paving the way for a more comprehensive understanding of the functional roles of protein isoforms, which is essential for unraveling the complexities of cell biology and the development of disease treatments.

Improving quantitative prediction of protein subcellular locations in fluorescence images through deep generative models

Machine learning has been employed in recognizing protein localization at the subcellular level, which highly facilitates the protein function studies, especially for those multi-label proteins that localize in more than one organelle. However, existing works mostly study the qualitative classification of protein subcellular locations, ignoring fraction of one multi-label protein in different locations. Actually, about 50 % proteins are multi-label proteins, and the ignorance of quantitative information highly restricts the understanding of their spatial distribution and functional mechanism. One reason of the lack of quantitative study is the insufficiency of quantitative annotations. To address the data shortage problem, here we proposed a generative model, PLocGAN, which could generate cell images with conditional quantitative annotation of the fluorescence distribution. The model was a conditional generative adversarial network, in which the condition learning utilized partial label learning to overcome the lack of training labels and allowed training with only qualitative labels. Meanwhile, it used contrastive learning to enhance diversity of the generated images. We assessed the PLocGAN on four pixel-fused synthetic datasets and one real dataset, and demonstrated that the model could generate images with good fidelity and diversity, outperforming existing state-of-the-art generative methods. To verify the utility of PLocGAN in the quantitative prediction of protein subcellular locations, we replaced the training images with generated quantitative images and built prediction models, and found that they had a boosting effect on the quantitative estimation. This work demonstrates the effectiveness of deep generative models in bioimage analysis, and provides a new solution for quantitative subcellular proteomics.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

Subcellular Localization of Thioredoxin/Thioredoxin Reductase System-A Missing Link in Endoplasmic Reticulum Redox Balance

The lumen of the endoplasmic reticulum (ER) is usually considered an oxidative environment; however, oxidized thiol-disulfides and reduced pyridine nucleotides occur there parallelly, indicating that the ER lumen lacks components which connect the two systems. Here, we investigated the luminal presence of the thioredoxin (Trx)/thioredoxin reductase (TrxR) proteins, capable of linking the protein thiol and pyridine nucleotide pools in different compartments. It was shown that specific activity of TrxR in the ER is undetectable, whereas higher activities were measured in the cytoplasm and mitochondria. None of the Trx/TrxR isoforms were expressed in the ER by Western blot analysis. Co-localization studies of various isoforms of Trx and TrxR with ER marker Grp94 by immunofluorescent analysis further confirmed their absence from the lumen. The probability of luminal localization of each isoform was also predicted to be very low by several in silico analysis tools. ER-targeted transient transfection of HeLa cells with Trx1 and TrxR1 significantly decreased cell viability and induced apoptotic cell death. In conclusion, the absence of this electron transfer chain may explain the uncoupling of the redox systems in the ER lumen, allowing parallel presence of a reduced pyridine nucleotide and a probably oxidized protein pool necessary for cellular viability.

Biochemical Separation of Cytoplasmic and Nuclear Fraction for Downstream Molecular Analysis

Biochemical fractionation is a technique used to isolate and separate distinct cellular compartments, critical for dissecting cellular mechanisms and molecular pathways. Herein we outline a biochemical fraction methodology for isolation of ultra-pure nuclei and cytoplasm. This protocol utilizes hypotonic lysis buffer to suspend cells, coupled with a calibrated centrifugation strategy, for enhanced separation of cytoplasm from the nuclear fraction. Subsequent purification steps ensure the integrity of the isolated nuclear fraction. Overall, this method facilitates accurate protein localization, essential for functional studies, demonstrating its efficacy in separating cellular compartments. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Biochemical fractionation Support Protocol 1: Protein quantification using Bradford assay Support Protocol 2: SDS/PAGE and Western blotting.

Analysis of the Mouse Hepatic Peroxisome Proteome-Identification of Novel Protein Constituents Using a Semi-Quantitative SWATH-MS Approach

Ongoing technical and bioinformatics improvements in mass spectrometry (MS) allow for the identifying and quantifying of the enrichment of increasingly less-abundant proteins in individual fractions. Accordingly, this study reassessed the proteome of mouse liver peroxisomes by the parallel isolation of peroxisomes from a mitochondria- and a microsome-enriched prefraction, combining density-gradient centrifugation with a semi-quantitative SWATH-MS proteomics approach to unveil novel peroxisomal or peroxisome-associated proteins. In total, 1071 proteins were identified using MS and assessed in terms of their distribution in either high-density peroxisomal or low-density gradient fractions, containing the bulk of organelle material. Combining the data from both fractionation approaches allowed for the identification of specific protein profiles characteristic of mitochondria, the ER and peroxisomes. Among the proteins significantly enriched in the peroxisomal cluster were several novel peroxisomal candidates. Five of those were validated by colocalization in peroxisomes, using confocal microscopy. The peroxisomal import of HTATIP2 and PAFAH2, which contain a peroxisome-targeting sequence 1 (PTS1), could be confirmed by overexpression in HepG2 cells. The candidates SAR1B and PDCD6, which are known ER-exit-site proteins, did not directly colocalize with peroxisomes, but resided at ER sites, which frequently surrounded peroxisomes. Hence, both proteins might concentrate at presumably co-purified peroxisome-ER membrane contacts. Intriguingly, the fifth candidate, OCIA domain-containing protein 1, was previously described as decreasing mitochondrial network formation. In this work, we confirmed its peroxisomal localization and further observed a reduction in peroxisome numbers in response to OCIAD1 overexpression. Hence, OCIAD1 appears to be a novel protein, which has an impact on both mitochondrial and peroxisomal maintenance.

Physiological Temperature and Osmotic Changes Drive Dynamic Proteome Alterations in the Leptospiral Outer Membrane and Enhance Protein Export Systems

Leptospirosis, a remerging zoonosis, has no effective vaccine or an unambiguous early diagnostic reagent. Proteins differentially expressed (DE) under pathogenic conditions will be useful candidates for antileptospiral measures. We employed a multipronged approach comprising high-resolution TMT-labeled LC-MS/MS-based proteome analysis coupled with bioinformatics on leptospiral proteins following Triton X-114 subcellular fractionation of leptospires treated under physiological temperature and osmolarity that mimic infection. Although there were significant changes in the DE proteins at the level of the entire cell, there were notable changes in proteins at the subcellular level, particularly on the outer membrane (OM), that show the significance of subcellular proteome analysis. The detergent-enriched proteins, representing outer membrane proteins (OMPs), exhibited a dynamic nature and upregulation under various physiological conditions. It was found that pathogenic proteins showed a higher proportion of upregulation compared to the nonpathogenic proteins in the OM. Further analysis identified 17 virulent proteins exclusively upregulated in the outer membrane during infection that could be useful for vaccine and diagnostic targets. The DE proteins may aid in metabolic adaptation and are enriched in pathways related to signal transduction and antibiotic biosynthesis. Many upregulated proteins belong to protein export systems such as SEC translocase, T2SSs, and T1SSs, indicating their sequential participation in protein transport to the outer leaflet of the OM. Further studies on OM-localized proteins may shed light on the pathogenesis of leptospirosis and serve as the basis for effective countermeasures.

Mapping diversity in African trypanosomes using high resolution spatial proteomics

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

Multi-Cell Line Analysis of Lysosomal Proteomes Reveals Unique Features and Novel Lysosomal Proteins

Lysosomes, the main degradative organelles of mammalian cells, play a key role in the regulation of metabolism. It is becoming more and more apparent that they are highly active, diverse, and involved in a large variety of processes. The essential role of lysosomes is exemplified by the detrimental consequences of their malfunction, which can result in lysosomal storage disorders, neurodegenerative diseases, and cancer. Using lysosome enrichment and mass spectrometry, we investigated the lysosomal proteomes of HEK293, HeLa, HuH-7, SH-SY5Y, MEF, and NIH3T3 cells. We provide evidence on a large scale for cell type-specific differences of lysosomes, showing that levels of distinct lysosomal proteins are highly variable within one cell type, while expression of others is highly conserved across several cell lines. Using differentially stable isotope-labeled cells and bimodal distribution analysis, we furthermore identify a high confidence population of lysosomal proteins for each cell line. Multi-cell line correlation of these data reveals potential novel lysosomal proteins, and we confirm lysosomal localization for six candidates. All data are available via ProteomeXchange with identifier PXD020600.

Rapid nuclear deadenylation of mammalian messenger RNA

Poly(A) tails protect RNAs from degradation and their deadenylation rates determine RNA stability. Although poly(A) tails are generated in the nucleus, deadenylation of tails has mostly been investigated within the cytoplasm. Here, we combined long-read sequencing with metabolic labeling, splicing inhibition and cell fractionation experiments to quantify, separately, the genesis and trimming of nuclear and cytoplasmic tails in vitro and in vivo. We present evidence for genome-wide, nuclear synthesis of tails longer than 200 nt, which are rapidly shortened after transcription. Our data suggests that rapid deadenylation is a nuclear process, and that different classes of transcripts and even transcript isoforms have distinct nuclear tail lengths. For example, many long-noncoding RNAs retain long poly(A) tails. Modeling deadenylation dynamics predicts nuclear deadenylation about 10 times faster than cytoplasmic deadenylation. In summary, our data suggests that nuclear deadenylation might be a key mechanism for regulating mRNA stability, abundance, and subcellular localization.

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.

Membrane compartmentalisation of the ubiquitin system

We now have a comprehensive inventory of ubiquitin system components. Understanding of any system also needs an appreciation of how components are organised together. Quantitative proteomics has provided us with a census of their relative populations in several model cell types. Here, by examining large scale unbiased data sets, we seek to identify and map those components, which principally reside on the major organelles of the endomembrane system. We present the consensus distribution of > 50 ubiquitin modifying enzymes, E2s, E3s and DUBs, that possess transmembrane domains. This analysis reveals that the ER and endosomal compartments have a diverse cast of resident E3s, whilst the Golgi and mitochondria operate with a more restricted palette. We describe key functions of ubiquitylation that are specific to each compartment and relate this to their signature complement of ubiquitin modifying components.

Newly identified disorder of copper metabolism caused by variants in CTR1, a high-affinity copper transporter

The high-affinity copper transporter CTR1 is encoded by CTR1 (SLC31A1), a gene locus for which no detailed genotype-phenotype correlations have previously been reported. We describe identical twin male infants homozygous for a novel missense variant NM_001859.4:c.284 G > A (p.Arg95His) in CTR1 with a distinctive autosomal recessive syndrome of infantile seizures and neurodegeneration, consistent with profound central nervous system copper deficiency. We used clinical, biochemical and molecular methods to delineate the first recognized examples of human CTR1 deficiency. These included clinical phenotyping, brain imaging, assays for copper, cytochrome c oxidase (CCO), and mitochondrial respiration, western blotting, cell transfection experiments, confocal and electron microscopy, protein structure modeling and fetal brain and cerebral organoid CTR1 transcriptome analyses. Comparison with two other critical mediators of cellular copper homeostasis, ATP7A and ATP7B, genes associated with Menkes disease and Wilson disease, respectively, revealed that expression of CTR1 was highest. Transcriptome analyses identified excitatory neurons and radial glia as brain cell types particularly enriched for copper transporter transcripts. We also assessed the effects of Copper Histidinate in the patients' cultured cells and in the patients, under a formal clinical protocol. Treatment normalized CCO activity and enhanced mitochondrial respiration in vitro, and was associated with modest clinical improvements. In combination with present and prior studies, these infants' clinical, biochemical and molecular phenotypes establish the impact of this novel variant on copper metabolism and cellular homeostasis and illuminate a crucial role for CTR1 in human brain development. CTR1 deficiency represents a newly defined inherited disorder of brain copper metabolism.

A Cytological Atlas of the Human Liver Proteome from PROTEOMESKY-LIVERHu 2.0, a Publicly Available Database

The liver plays a unique role as a metabolic center of the body, and also performs other important functions such as detoxification and immune response. Here, we establish a cell type-resolved healthy human liver proteome including hepatocytes (HCs), hepatic stellate cells (HSCs), Kupffer cells (KCs), and liver sinusoidal endothelial cells (LSECs) by high-resolution mass spectrometry. Overall, we quantify total 8354 proteins for four cell types and over 6000 proteins for each cell type. Analysis of this data set and regulatory pathway reveals the cellular labor division in the human liver follows the pattern that parenchymal cells make the main components of pathways, but nonparenchymal cells trigger these pathways. Human liver cells show some novel molecular features: HCs maintain KCs and LSECs homeostasis by producing cholesterol and ketone bodies; HSCs participate in xenobiotics metabolism as an agent deliverer; KCs and LSECs mediate immune response through MHC class II-TLRs and MHC class I-TGFβ cascade, respectively; and KCs play a central role in diurnal rhythms regulation through sensing diurnal IGF and temperature flux. Together, this work expands our understandings of liver physiology and provides a useful resource for future analyses of normal and diseased livers.

A Method to Estimate the Distribution of Proteins across Multiple Compartments Using Data from Quantitative Proteomics Subcellular Fractionation Experiments

Knowledge of cellular location is key to understanding the biological function of proteins. One commonly used large-scale method to assign cellular locations is subcellular fractionation, followed by quantitative mass spectrometry to identify proteins and estimate their relative distribution among centrifugation fractions. In most of such subcellular proteomics studies, each protein is assigned to a single cellular location by comparing its distribution to those of a set of single-compartment reference proteins. However, in many cases, proteins reside in multiple compartments. To accurately determine the localization of such proteins, we previously introduced constrained proportional assignment (CPA), a method that assigns each protein a fractional residence over all reference compartments (Jadot Mol. Cell Proteomics 2017, 16(2), 194-212. 10.1074/mcp.M116.064527). In this Article, we describe the principles underlying CPA, as well as data transformations to improve accuracy of assignment of proteins and protein isoforms, and a suite of R-based programs to implement CPA and related procedures for analysis of subcellular proteomics data. We include a demonstration data set that used isobaric-labeling mass spectrometry to analyze rat liver fractions. In addition, we describe how these programs can be readily modified by users to accommodate a wide variety of experimental designs and methods for protein quantitation.

SNAT7 regulates mTORC1 via macropinocytosis

Mammalian target of rapamycin complex 1 (mTORC1) senses amino acids to control cell growth, metabolism, and autophagy. Some amino acids signal to mTORC1 through the Rag GTPase, whereas glutamine and asparagine activate mTORC1 through a Rag GTPase-independent pathway. Here, we show that the lysosomal glutamine and asparagine transporter SNAT7 activates mTORC1 after extracellular protein, such as albumin, is macropinocytosed. The N terminus of SNAT7 forms nutrient-sensitive interaction with mTORC1 and regulates mTORC1 activation independently of the Rag GTPases. Depletion of SNAT7 inhibits albumin-induced mTORC1 lysosomal localization and subsequent activation. Moreover, SNAT7 is essential to sustain KRAS-driven pancreatic cancer cell growth through mTORC1. Thus, SNAT7 links glutamine and asparagine signaling from extracellular protein to mTORC1 independently of the Rag GTPases and is required for macropinocytosis-mediated mTORC1 activation and pancreatic cancer cell growth.

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens

The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic “Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens,” this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.

Current methods to analyze lysosome morphology, positioning, motility and function

Since the discovery of lysosomes more than 70 years ago, much has been learned about the functions of these organelles. Lysosomes were regarded as exclusively degradative organelles, but more recent research has shown that they play essential roles in several other cellular functions, such as nutrient sensing, intracellular signalling and metabolism. Methodological advances played a key part in generating our current knowledge about the biology of this multifaceted organelle. In this review, we cover current methods used to analyze lysosome morphology, positioning, motility and function. We highlight the principles behind these methods, the methodological strategies and their advantages and limitations. To extract accurate information and avoid misinterpretations, we discuss the best strategies to identify lysosomes and assess their characteristics and functions. With this review, we aim to stimulate an increase in the quantity and quality of research on lysosomes and further ground-breaking discoveries on an organelle that continues to surprise and excite cell biologists.

p38-MAPK recruits the proteolytic pathways in Caenorhabditis elegans during bacterial infection

In eukaryotic organisms, cell-signalling completely relies on Post Translational Modifications (PTMs) that can function as regulatory switches. Phosphorylation is a fundamental and frequently occurring PTM in almost all eukaryotes. Herein, we have studied the importance of protein phosphorylation using classical proteomic techniques in C. elegans upon bacterial infection. The differentially regulated proteins during bacterial infection were excised from SDS-PAGE (One-Dimensional) gel (TiO₂ column elutes) and subjected to MALDI-TOF-MS which ended up in identifying 220 proteins kinetically. KEGG pathway analysis of those proteins suggested that MAPK pathway was part of the innate immunity. Thus, we have characterized p38-MAPK (one of the crucial downstream MAPKs) using immunoblotting, subcellular fractionation, coimmunoprecipitation, LC-MS/MS, bioinformatics studies and qPCR. Meanwhile, KU25 strain (pmk-1 mutant) exhibited an earlier mortality during infection suggesting the crucial role of p38-MAPK during host-pathogen interaction. Interestingly, Reactome pathway analysis of p38 interactors (CoIP coupled to LC-MS/MS) revealed the involvement of various proteolytic pathway players (ubiquitination, SUMOylation and Neddylation) during bacterial infection. Further, the regulation of SUMOylation and Neddylation was identified and validated using immunoblotting and qPCR analyses, respectively. Concisely, our study indicated that bacterial infection triggers the MAPK cascade to elicit innate immunity which in turn recruits proteolytic pathways to counteract the invading pathogen.

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.

Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line

Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community.

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.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

ECHDC1 knockout mice accumulate ethyl-branched lipids and excrete abnormal intermediates of branched-chain fatty acid metabolism

The cytosolic enzyme ethylmalonyl-CoA decarboxylase (ECHDC1) decarboxylates ethyl- or methyl-malonyl-CoA, two side products of acetyl-CoA carboxylase. These CoA derivatives can be used to synthesize a subset of branched-chain fatty acids (FAs). We previously found that ECHDC1 limits the synthesis of these abnormal FAs in cell lines, but its effects in vivo are unknown. To further evaluate the effects of ECHDC1 deficiency, we generated knockout mice. These mice were viable, fertile, showed normal postnatal growth, and lacked obvious macroscopic and histologic changes. Surprisingly, tissues from wild-type mice already contained methyl-branched FAs due to methylmalonyl-CoA incorporation, but these FAs were only increased in the intraorbital glands of ECHDC1 knockout mice. In contrast, ECHDC1 knockout mice accumulated 16–20-carbon FAs carrying ethyl-branches in all tissues, which were undetectable in wild-type mice. Ethyl-branched FAs were incorporated into different lipids, including acylcarnitines, phosphatidylcholines, plasmanylcholines, and triglycerides. Interestingly, we found a variety of unusual glycine-conjugates in the urine of knockout mice, which included adducts of ethyl-branched compounds in different stages of oxidation. This suggests that the excretion of potentially toxic intermediates of branched-chain FA metabolism might prevent a more dramatic phenotype in these mice. Curiously, ECHDC1 knockout mice also accumulated 2,2-dimethylmalonyl-CoA. This indicates that the broad specificity of ECHDC1 might help eliminate a variety of potentially dangerous branched-chain dicarboxylyl-CoAs. We conclude that ECHDC1 prevents the formation of ethyl-branched FAs and that urinary excretion of glycine-conjugates allows mice to eliminate potentially deleterious intermediates of branched-chain FA metabolism.

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.

Data mining for traffic information

Modern cell biology is now rich with data acquired at the whole genome and proteome level. We can add value to this data through integration and application of specialist knowledge. To illustrate, we will focus on the SNARE and RAB proteins; key regulators of intracellular fusion specificity and organelle identity. We examine published mass spectrometry data to gain an estimate of protein copy number and organelle distribution in HeLa cells for each family member. We also survey recent global CRISPR/Cas9 screens for essential genes from these families. We highlight instances of co-essentiality with other genes across a large panel of cell lines that allows for the identification of functionally coherent clusters. Examples of such correlations include RAB10 with the SNARE protein Syntaxin4 (STX4) and RAB7/RAB21 with the WASH and the CCC (COMMD/CCDC22/CCDC93) complexes, both of which are linked to endosomal recycling pathways.

Large-scale Proteomic and Phosphoproteomic Analyses of Maize Seedling Leaves During De-etiolation

De-etiolation consists of a series of developmental and physiological changes that a plant undergoes in response to light. During this process light, an important environmental signal, triggers the inhibition of mesocotyl elongation and the production of photosynthetically active chloroplasts, and etiolated leaves transition from the "sink" stage to the "source" stage. De-etiolation has been extensively studied in maize (Zea mays L.). However, little is known about how this transition is regulated. In this study, we described a quantitative proteomic and phosphoproteomic atlas of the de-etiolation process in maize. We identified 16,420 proteins in proteome, among which 14,168 proteins were quantified. In addition, 8746 phosphorylation sites within 3110 proteins were identified. From the combined proteomic and phosphoproteomic data, we identified a total of 17,436 proteins. Only 7.0% (998/14,168) of proteins significantly changed in abundance during de-etiolation. In contrast, 26.6% of phosphorylated proteins exhibited significant changes in phosphorylation level; these included proteins involved in gene expression and homeostatic pathways and rate-limiting enzymes involved in photosynthetic light and carbon reactions. Based on phosphoproteomic analysis, 34.0% (1057/3110) of phosphorylated proteins identified in this study contained more than 2 phosphorylation sites, and 37 proteins contained more than 16 phosphorylation sites, indicating that multi-phosphorylation is ubiquitous during the de-etiolation process. Our results suggest that plants might preferentially regulate the level of posttranslational modifications (PTMs) rather than protein abundance for adapting to changing environments. The study of PTMs could thus better reveal the regulation of de-etiolation.

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.

Enrichment of microsomes from Chinese hamster ovary cells by subcellular fractionation for its use in proteomic analysis

Chinese hamster ovary cells have been the workhorse for the production of recombinant proteins in mammalian cells. Since biochemical, cellular and omics studies are usually affected by the lack of suitable fractionation procedures to isolate compartments from these cells, differential and isopycnic centrifugation based techniques were characterized and developed specially for them. Enriched fractions in intact nuclei, mitochondria, peroxisomes, cis-Golgi, trans-Golgi and endoplasmic reticulum (ER) were obtained in differential centrifugation steps and subsequently separated in discontinuous sucrose gradients. Nuclei, mitochondria, cis-Golgi, peroxisomes and smooth ER fractions were obtained as defined bands in 30-60% gradients. Despite the low percentage represented by the microsomes of the total cell homogenate (1.7%), their separation in a novel sucrose gradient (10-60%) showed enough resolution and efficiency to quantitatively separate their components into enriched fractions in trans-Golgi, cis-Golgi and ER. The identity of these organelles belonging to the classical secretion pathway that came from 10-60% gradients was confirmed by proteomics. Data are available via ProteomeXchange with identifier PXD019778. Components from ER and plasma membrane were the most frequent contaminants in almost all obtained fractions. The improved sucrose gradient for microsomal samples proved being successful in obtaining enriched fractions of low abundance organelles, such as Golgi apparatus and ER components, for biochemical and molecular studies, and suitable for proteomic research, which makes it a useful tool for future studies of this and other mammalian cell lines.

Organellar Maps Through Proteomic Profiling - A Conceptual Guide

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

Moving Profiling Spatial Proteomics Beyond Discrete Classification

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

Comparative Analysis of Quantitative Mass Spectrometric Methods for Subcellular Proteomics

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

Spatial proteomics: a powerful discovery tool for cell biology

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

Global variability analysis of mRNA and protein concentrations across and within human tissues

Genes and proteins show variable expression patterns throughout the human body. However, it is not clear whether relative differences in mRNA concentrations are retained on the protein level. Furthermore, inter-individual protein concentration variability within single tissue types has not been comprehensively explored. Here, we used the Gini index for in-depth concentration variability analysis of publicly available transcriptomics and proteomics data, and of an in-house proteomics dataset of human liver and jejunum from 38 donors. We found that the transfer of concentration variability from mRNA to protein is limited, that established 'reference genes' for data normalization vary markedly at the protein level, that protein concentrations cover a wide variability spectrum within single tissue types, and that concentration variability analysis can be a convenient starting point for identifying disease-associated proteins and novel biomarkers. Our results emphasize the importance of considering individual concentration levels, as opposed to population averages, for personalized systems biology analysis.

Lysosomal protein thermal stability does not correlate with cellular half-life: global observations and a case study of tripeptidyl-peptidase 1

Late-infantile neuronal ceroid lipofuscinosis (LINCL) is a neurodegenerative lysosomal storage disorder caused by mutations in the gene encoding the protease tripeptidyl-peptidase 1 (TPP1). Progression of LINCL can be slowed or halted by enzyme replacement therapy, where recombinant human TPP1 is administered to patients. In this study, we utilized protein engineering techniques to increase the stability of recombinant TPP1 with the rationale that this may lengthen its lysosomal half-life, potentially increasing the potency of the therapeutic protein. Utilizing multiple structure-based methods that have been shown to increase the stability of other proteins, we have generated and evaluated over 70 TPP1 variants. The most effective mutation, R465G, increased the melting temperature of TPP1 from 55.6°C to 64.4°C and increased its enzymatic half-life at 60°C from 5.4 min to 21.9 min. However, the intracellular half-life of R465G and all other variants tested in cultured LINCL patient-derived lymphoblasts was similar to that of WT TPP1. These results provide structure/function insights into TPP1 and indicate that improving in vitro thermal stability alone is insufficient to generate TPP1 variants with improved physiological stability. This conclusion is supported by a proteome-wide analysis that indicates that lysosomal proteins have higher melting temperatures but also higher turnover rates than proteins of other organelles. These results have implications for similar efforts where protein engineering approaches, which are frequently evaluated in vitro, may be considered for improving the physiological properties of proteins, particularly those that function in the lysosomal environment.

TcellSubC: An Atlas of the Subcellular Proteome of Human T Cells

We have curated an in-depth subcellular proteomic map of primary human CD4+ T cells, divided into cytosolic, nuclear and membrane fractions generated by an optimized fractionation and HiRIEF-LC-MS/MS workflow for limited amounts of primary cells. The subcellular proteome of T cells was mapped under steady state conditions, as well as upon 15 min and 1 h of T cell receptor (TCR) stimulation, respectively. We quantified the subcellular distribution of 6,572 proteins and identified a subset of 237 potentially translocating proteins, including both well-known examples and novel ones. Microscopic validation confirmed the localization of selected proteins with previously known and unknown localization, respectively. We further provide the data in an easy-to-use web platform to facilitate re-use, as the data can be relevant for basic research as well as for clinical exploitation of T cells as therapeutic targets.

Analysis of Brain and Cerebrospinal Fluid from Mouse Models of the Three Major Forms of Neuronal Ceroid Lipofuscinosis Reveals Changes in the Lysosomal Proteome

Treatments are emerging for the neuronal ceroid lipofuscinoses (NCLs), a group of similar but genetically distinct lysosomal storage diseases. Clinical ratings scales measure long-term disease progression and response to treatment but clinically useful biomarkers have yet to be identified in these diseases. We have conducted proteomic analyses of brain and cerebrospinal fluid (CSF) from mouse models of the most frequently diagnosed NCL diseases: CLN1 (infantile NCL), CLN2 (classical late infantile NCL) and CLN3 (juvenile NCL). Samples were obtained at different stages of disease progression and proteins quantified using isobaric labeling. In total, 8303 and 4905 proteins were identified from brain and CSF, respectively. We also conduced label-free analyses of brain proteins that contained the mannose 6-phosphate lysosomal targeting modification. In general, we detect few changes at presymptomatic timepoints but later in disease, we detect multiple proteins whose expression is significantly altered in both brain and CSF of CLN1 and CLN2 animals. Many of these proteins are lysosomal in origin or are markers of neuroinflammation, potentially providing clues to underlying pathogenesis and providing promising candidates for further validation.

The synthesis of branched-chain fatty acids is limited by enzymatic decarboxylation of ethyl- and methylmalonyl-CoA

Most fatty acids (FAs) are straight chains and are synthesized by fatty acid synthase (FASN) using acetyl-CoA and malonyl-CoA units. Yet, FASN is known to be promiscuous as it may use methylmalonyl-CoA instead of malonyl-CoA and thereby introduce methyl-branches. We have recently found that the cytosolic enzyme ECHDC1 degrades ethylmalonyl-CoA and methylmalonyl-CoA, which presumably result from promiscuous reactions catalyzed by acetyl-CoA carboxylase on butyryl- and propionyl-CoA. Here, we tested the hypothesis that ECHDC1 is a metabolite repair enzyme that serves to prevent the formation of methyl- or ethyl-branched FAs by FASN. Using the purified enzyme, we found that FASN can incorporate not only methylmalonyl-CoA but also ethylmalonyl-CoA, producing methyl- or ethyl-branched FAs. Using a combination of gas-chromatography and liquid chromatography coupled to mass spectrometry, we observed that inactivation of ECHDC1 in adipocytes led to an increase in several methyl-branched FAs (present in different lipid classes), while its overexpression reduced them below wild-type levels. In contrast, the formation of ethyl-branched FAs was observed almost exclusively in ECHDC1 knockout cells, indicating that ECHDC1 and the low activity of FASN toward ethylmalonyl-CoA efficiently prevent their formation. We conclude that ECHDC1 performs a typical metabolite repair function by destroying methyl- and ethylmalonyl-CoA. This reduces the formation of methyl-branched FAs and prevents the formation of ethyl-branched FAs by FASN. The identification of ECHDC1 as a key modulator of the abundance of methyl-branched FAs opens the way to investigate their function.

A Protocol to Map the Spatial Proteome Using HyperLOPIT in Saccharomyces cerevisiae

The correct subcellular localization of proteins is vital for cellular function and the study of this process at the systems level will therefore enrich our understanding of the roles of proteins within the cell. Multiple methods are available for the study of protein subcellular localization, including fluorescence microscopy, organelle cataloging, proximity labeling methods, and whole-cell protein correlation profiling methods. We provide here a protocol for the systems-level study of the subcellular localization of the yeast proteome, using a version of hyperplexed Localization of Organelle Proteins by Isotope Tagging (hyperLOPIT) that has been optimized for use with Saccharomyces cerevisiae. The entire protocol encompasses cell culture, cell lysis by nitrogen cavitation, subcellular fractionation, monitoring of the fractionation using Western blotting, labeling of samples with TMT isobaric tags and mass spectrometric analysis. Also included is a brief explanation of downstream processing of the mass spectrometry data to produce a map of the spatial proteome. If required, the nitrogen cavitation lysis and Western blotting portions of the protocol may be performed independently of the mass spectrometry analysis. The protocol in its entirety, however, enables the unbiased, systems-level and high-resolution analysis of the localizations of thousands of proteins in parallel within a single experiment.

Multi-localized Proteins: The Peroxisome-Mitochondria Connection

Peroxisomes and mitochondria are dynamic, multifunctional organelles that play pivotal cooperative roles in the metabolism of cellular lipids and reactive oxygen species. Their functional interplay, the "peroxisome-mitochondria connection", also includes cooperation in anti-viral signalling and defence, as well as coordinated biogenesis by sharing key division proteins. In this review, we focus on multi-localised proteins which are shared by peroxisomes and mitochondria in mammals. We first outline the targeting and sharing of matrix proteins which are involved in metabolic cooperation. Next, we discuss shared components of peroxisomal and mitochondrial dynamics and division, and we present novel insights into the dual targeting of tail-anchored membrane proteins. Finally, we provide an overview of what is currently known about the role of shared membrane proteins in disease. What emerges is that sharing of proteins between these two organelles plays a key role in their cooperative functions which, based on new findings, may be more extensive than originally envisaged. Gaining a better insight into organelle interplay and the targeting of shared proteins is pivotal to understanding how organelle cooperation contributes to human health and disease.

A Bioconductor workflow for the Bayesian analysis of spatial proteomics

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

A Brief Survey of Machine Learning Methods in Protein Sub-Golgi Localization

Background:

The location of proteins in a cell can provide important clues to their functions in various biological processes. Thus, the application of machine learning method in the prediction of protein subcellular localization has become a hotspot in bioinformatics. As one of key organelles, the Golgi apparatus is in charge of protein storage, package, and distribution.

Objective:

The identification of protein location in Golgi apparatus will provide in-depth insights into their functions. Thus, the machine learning-based method of predicting protein location in Golgi apparatus has been extensively explored. The development of protein sub-Golgi apparatus localization prediction should be reviewed for providing a whole background for the fields.

Method:

The benchmark dataset, feature extraction, machine learning method and published results were summarized.

Results:

We briefly introduced the recent progresses in protein sub-Golgi apparatus localization prediction using machine learning methods and discussed their advantages and disadvantages.

Conclusion:

We pointed out the perspective of machine learning methods in protein sub-Golgi localization prediction.

Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics

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

Proteomics of nucleocytoplasmic partitioning

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

Marginal protein stability drives subcellular proteome isoelectric point

There exists a positive correlation between the pH of subcellular compartments and the median isoelectric point (pI) for the associated proteomes. Proteins in the human lysosome-a highly acidic compartment in the cell-have a median pI of ∼6.5, whereas proteins in the more basic mitochondria have a median pI of ∼8.0. Proposed mechanisms reflect potential adaptations to pH. For example, enzyme active site general acid/base residue pKs are likely evolved to match environmental pH. However, such effects would be limited to a few residues on specific proteins, and might not affect the proteome at large. A protein model that considers residue burial upon folding recapitulates the correlation between proteome pI and environmental pH. This correlation can be fully described by a neutral evolution process; no functional selection is included in the model. Proteins in acidic environments incur a lower energetic penalty for burying acidic residues than basic residues, resulting in a net accumulation of acidic residues in the protein core. The inverse is true under alkaline conditions. The pI distributions of subcellular proteomes are likely not a direct result of functional adaptations to pH, but a molecular spandrel stemming from marginal stability.

WDR41 supports lysosomal response to changes in amino acid availability

C9orf72 mutations are a major cause of amyotrophic lateral sclerosis and frontotemporal dementia. The C9orf72 protein undergoes regulated recruitment to lysosomes and has been broadly implicated in control of lysosome homeostasis. However, although evidence strongly supports an important function for C9orf72 at lysosomes, little is known about the lysosome recruitment mechanism. In this study, we identify an essential role for WDR41, a prominent C9orf72 interacting protein, in C9orf72 lysosome recruitment. Analysis of human WDR41 knockout cells revealed that WDR41 is required for localization of the protein complex containing C9orf72 and SMCR8 to lysosomes. Such lysosome localization increases in response to amino acid starvation but is not dependent on either mTORC1 inhibition or autophagy induction. Furthermore, WDR41 itself exhibits a parallel pattern of regulated association with lysosomes. This WDR41-dependent recruitment of C9orf72 to lysosomes is critical for the ability of lysosomes to support mTORC1 signaling as constitutive targeting of C9orf72 to lysosomes relieves the requirement for WDR41 in mTORC1 activation. Collectively, this study reveals an essential role for WDR41 in supporting the regulated binding of C9orf72 to lysosomes and solidifies the requirement for a larger C9orf72 containing protein complex in coordinating lysosomal responses to changes in amino acid availability.

Resolution Matters: Correlating Quantitative Proteomics and Nanoscale-Precision Microscopy for Reconstructing Synapse Identity

For more than a century, the precision at which any protein (or RNA) could be localized in living cells depends on the spatial resolution of microscopy. Light microscopy, even recently benchmarked laser-scanning microscopy, is inherently liable to the diffraction limit of visible light. Electron microscopy that had existed as the only alternative for decades is, in turn, of low throughput and sensitive to processing artefacts. Therefore, researchers have looked for alternative technologies particularly with ever-growing interest in resolving structural underpinnings of cellular heterogeneity in the human body. Computational ("in silico") predictions provided only partial solutions given the incompleteness of existing databases and erroneous assumptions on evolutionarily conserved sequence homology across species. A breakthrough that facilitates subcellular protein localization came with the introduction of "super-resolution" microscopy, which yields 20-60 nm resolution by overcoming diffraction-limited technologies. The ensuing combination of "super-resolution" microscopy with unbiased proteomics continues to produce never-before-seen gains by quantitatively addressing the distribution, interaction, turnover, and secretion of proteins in living cells. Here, we illustrate the power of this combined work flow by the example of transmembrane receptor localization at the neuronal synapse. We also discuss how dynamic analysis allows for inferences be made for cellular physiology and pathobiology.