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

Size-classifiable quantification of nanoplastic by rate zonal centrifugation coupled with pyrolysis-gas chromatography-mass spectrometry

Particle size is an important indicator to evaluate the environmental risk and biotoxicity of nanoplastic (NP, particle diameter <1000 nm). The methods available to determine size classes of NP in environmental samples are few and are rare to achieve efficient separation and recycling of NP with close particle sizes. Here, we show that rate-zonal centrifugation (RZC) can quickly and efficiently collect NP of different sizes based on their sedimentation coefficients. When combined with cloud-point extraction (CPE) and pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS), our method can quantify three NP particle-size classes separately (including 100 nm, 300 nm, and 600 nm) in aqueous samples with high recovery (81.4 %-89.4 %), limits of detections (LODs, 33.5-53.4 μg/L), and limits of quantifications (LOQs, 110.6-167.2 μg/L). Compared with the conventional sample pretreatment process, our method can effectively extract and determine the NP with different sizes. Our approach is highly scalable and can be effectively applied to NP in a wide range of aquatic environments. Meanwhile, our approach is highly scalable to incorporate diverse assays to study the environmental behaviours and ecological risks of NP.

A molecular atlas of plastid and mitochondrial proteins reveals organellar remodeling during plant evolutionary transitions from algae to angiosperms

Algae and plants carry 2 organelles of endosymbiotic origin that have been co-evolving in their host cells for more than a billion years. The biology of plastids and mitochondria can differ significantly across major lineages and organelle changes likely accompanied the adaptation to new ecological niches such as the terrestrial habitat. Based on organelle proteome data and the genomes of 168 phototrophic (Archaeplastida) versus a broad range of 518 non-phototrophic eukaryotes, we screened for changes in plastid and mitochondrial biology across 1 billion years of evolution. Taking into account 331,571 protein families (or orthogroups), we identify 31,625 protein families that are unique to primary plastid-bearing eukaryotes. The 1,906 and 825 protein families are predicted to operate in plastids and mitochondria, respectively. Tracing the evolutionary history of these protein families through evolutionary time uncovers the significant remodeling the organelles experienced from algae to land plants. The analyses of gained orthogroups identifies molecular changes of organelle biology that connect to the diversification of major lineages and facilitated major transitions from chlorophytes en route to the global greening and origin of angiosperms.

Substantial downregulation of mitochondrial and peroxisomal proteins during acute kidney injury revealed by data-independent acquisition proteomics

Acute kidney injury (AKI) manifests as a major health concern, particularly for the elderly. Understanding AKI-related proteome changes is critical for prevention and development of novel therapeutics to recover kidney function and to mitigate the susceptibility for recurrent AKI or development of chronic kidney disease. In this study, mouse kidneys were subjected to ischemia-reperfusion injury, and the contralateral kidneys remained uninjured to enable comparison and assess injury-induced changes in the kidney proteome. A ZenoTOF 7600 mass spectrometer was optimized for data-independent acquisition (DIA) to achieve comprehensive protein identification and quantification. Short microflow gradients and the generation of a deep kidney-specific spectral library allowed for high-throughput, comprehensive protein quantification. Upon AKI, the kidney proteome was completely remodeled, and over half of the 3945 quantified protein groups changed significantly. Downregulated proteins in the injured kidney were involved in energy production, including numerous peroxisomal matrix proteins that function in fatty acid oxidation, such as ACOX1, CAT, EHHADH, ACOT4, ACOT8, and Scp2. Injured kidneys exhibited severely damaged tissues and injury markers. The comprehensive and sensitive kidney-specific DIA-MS assays feature high-throughput analytical capabilities to achieve deep coverage of the kidney proteome, and will serve as useful tools for developing novel therapeutics to remediate kidney function.

Dicarboxylic Acid Dietary Supplementation Protects against AKI

SIGNIFICANCE STATEMENT

In this study, we demonstrate that a common, low-cost compound known as octanedioic acid (DC 8 ) can protect mice from kidney damage typically caused by ischemia-reperfusion injury or the chemotherapy drug cisplatin. This compound seems to enhance peroxisomal activity, which is responsible for breaking down fats, without adversely affecting mitochondrial function. DC 8 is not only affordable and easy to administer but also effective. These encouraging findings suggest that DC 8 could potentially be used to assist patients who are at risk of experiencing this type of kidney damage.

BACKGROUND

Proximal tubules are rich in peroxisomes, which are damaged during AKI. Previous studies demonstrated that increasing peroxisomal fatty acid oxidation (FAO) is renoprotective, but no therapy has emerged to leverage this mechanism.

METHODS

Mice were fed with either a control diet or a diet enriched with dicarboxylic acids, which are peroxisome-specific FAO substrates, then subjected to either ischemia-reperfusion injury-AKI or cisplatin-AKI models. Biochemical, histologic, genetic, and proteomic analyses were performed.

RESULTS

Both octanedioic acid (DC 8 ) and dodecanedioic acid (DC 12 ) prevented the rise of AKI markers in mice that were exposed to renal injury. Proteomics analysis demonstrated that DC 8 preserved the peroxisomal and mitochondrial proteomes while inducing extensive remodeling of the lysine succinylome. This latter finding indicates that DC 8 is chain shortened to the anaplerotic substrate succinate and that peroxisomal FAO was increased by DC 8 .

CONCLUSIONS

DC 8 supplementation protects kidney mitochondria and peroxisomes and increases peroxisomal FAO, thereby protecting against AKI.

Keep in touch: a perspective on the mitochondrial social network and its implication in health and disease

Mitochondria have been the focus of extensive research for decades since their dysfunction is linked to more than 150 distinct human disorders. Despite considerable efforts, researchers have only been able to skim the surface of the mitochondrial social complexity and the impact of inter-organelle and inter-organ communication alterations on human health. While some progress has been made in deciphering connections among mitochondria and other cytoplasmic organelles through direct (i.e., contact sites) or indirect (i.e., inter-organelle trafficking) crosstalk, most of these efforts have been restricted to a limited number of proteins involved in specific physiological pathways or disease states. This research bottleneck is further narrowed by our incomplete understanding of the cellular alteration timeline in a specific pathology, which prevents the distinction between a primary organelle dysfunction and the defects occurring due to the disruption of the organelle's interconnectivity. In this perspective, we will (i) summarize the current knowledge on the mitochondrial crosstalk within cell(s) or tissue(s) in health and disease, with a particular focus on neurodegenerative disorders, (ii) discuss how different large-scale and targeted approaches could be used to characterize the different levels of mitochondrial social complexity, and (iii) consider how investigating the different expression patterns of mitochondrial proteins in different cell types/tissues could represent an important step forward in depicting the distinctive architecture of inter-organelle communication.

Enablers and challenges of spatial omics, a melting pot of technologies

Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.

Unique Properties of Apicomplexan Mitochondria

Apicomplexan parasites constitute more than 6,000 species infecting a wide range of hosts. These include important pathogens such as those causing malaria and toxoplasmosis. Their evolutionary emergence coincided with the dawn of animals. Mitochondrial genomes of apicomplexan parasites have undergone dramatic reduction in their coding capacity, with genes for only three proteins and ribosomal RNA genes present in scrambled fragments originating from both strands. Different branches of the apicomplexans have undergone rearrangements of these genes, with Toxoplasma having massive variations in gene arrangements spread over multiple copies. The vast evolutionary distance between the parasite and the host mitochondria has been exploited for the development of antiparasitic drugs, especially those used to treat malaria, wherein inhibition of the parasite mitochondrial respiratory chain is selectively targeted with little toxicity to the host mitochondria. We describe additional unique characteristics of the parasite mitochondria that are being investigated and provide greater insights into these deep-branching eukaryotic pathogens.

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.

Microbial Pathogenesis in the Era of Spatial Omics

The biology of a cell, whether it is a unicellular organism or part of a multicellular network, is influenced by cell type, temporal changes in cell state, and the cell's environment. Spatial cues play a critical role in the regulation of microbial pathogenesis strategies. Information about where the pathogen is-in a tissue or in proximity to a host cell-regulates gene expression and the compartmentalization of gene products in the microbe and the host. Our understanding of host and pathogen identity has bloomed with the accessibility of transcriptomics and proteomics techniques. A missing piece of the puzzle has been our ability to evaluate global transcript and protein expression in the context of the subcellular niche, primary cell, or native tissue environment during infection. This barrier is now lower with the advent of new spatial omics techniques to understand how location regulates cellular functions. This review will discuss how recent advances in spatial proteomics and transcriptomics approaches can address outstanding questions in microbial pathogenesis.

Cryptosporidium uses multiple distinct secretory organelles to interact with and modify its host cell

Cryptosporidium is a leading cause of diarrheal disease in children and an important contributor to early childhood mortality. The parasite invades and extensively remodels intestinal epithelial cells, building an elaborate interface structure. How this occurs at the molecular level and the contributing parasite factors are largely unknown. Here, we generated a whole-cell spatial proteome of the Cryptosporidium sporozoite and used genetic and cell biological experimentation to discover the Cryptosporidium-secreted effector proteome. These findings reveal multiple organelles, including an original secretory organelle, and generate numerous compartment markers by tagging native gene loci. We show that secreted proteins are delivered to the parasite-host interface, where they assemble into different structures including a ring that anchors the parasite into its unique epicellular niche. Cryptosporidium thus uses a complex set of secretion systems during and following invasion that act in concert to subjugate its host cell.

Substantial Downregulation of Mitochondrial and Peroxisomal Proteins during Acute Kidney Injury revealed by Data-Independent Acquisition Proteomics

Acute kidney injury (AKI) manifests as a major health concern, particularly for the elderly. Understanding AKI-related proteome changes is critical for prevention and development of novel therapeutics to recover kidney function and to mitigate the susceptibility for recurrent AKI or development of chronic kidney disease. In this study, mouse kidneys were subjected to ischemia-reperfusion injury, and the contralateral kidneys remained uninjured to enable comparison and assess injury-induced changes in the kidney proteome. A fast-acquisition rate ZenoTOF 7600 mass spectrometer was introduced for data-independent acquisition (DIA) for comprehensive protein identification and quantification. Short microflow gradients and the generation of a deep kidney-specific spectral library allowed for high-throughput, comprehensive protein quantification. Upon AKI, the kidney proteome was completely remodeled, and over half of the 3,945 quantified protein groups changed significantly. Downregulated proteins in the injured kidney were involved in energy production, including numerous peroxisomal matrix proteins that function in fatty acid oxidation, such as ACOX1, CAT, EHHADH, ACOT4, ACOT8, and Scp2. Injured mice exhibited severely declined health. The comprehensive and sensitive kidney-specific DIA assays highlighted here feature high-throughput analytical capabilities to achieve deep coverage of the kidney proteome and will serve as useful tools for developing novel therapeutics to remediate kidney function.

Spatial proteomics of vesicular trafficking: coupling mass spectrometry and imaging approaches in membrane biology

In plants, membrane compartmentalization requires vesicle trafficking for communication among distinct organelles. Membrane proteins involved in vesicle trafficking are highly dynamic and can respond rapidly to changes in the environment and to cellular signals. Capturing their localization and dynamics is thus essential for understanding the mechanisms underlying vesicular trafficking pathways. Quantitative mass spectrometry and imaging approaches allow a system-wide dissection of the vesicular proteome, the characterization of ligand-receptor pairs and the determination of secretory, endocytic, recycling and vacuolar trafficking pathways. In this review, we highlight major proteomics and imaging methods employed to determine the location, distribution and abundance of proteins within given trafficking routes. We focus in particular on methodologies for the elucidation of vesicle protein dynamics and interactions and their connections to downstream signalling outputs. Finally, we assess their biological applications in exploring different cellular and subcellular processes.

Reconstruction of Plastid Proteomes of Apicomplexans and Close Relatives Reveals the Major Evolutionary Outcomes of Cryptic Plastids

Apicomplexans and related lineages comprise many obligate symbionts of animals; some of which cause notorious diseases such as malaria. They evolved from photosynthetic ancestors and transitioned into a symbiotic lifestyle several times, giving rise to species with diverse non-photosynthetic plastids. Here, we sought to reconstruct the evolution of the cryptic plastids in the apicomplexans, chrompodellids, and squirmids (ACS clade) by generating five new single-cell transcriptomes from understudied gregarine lineages, constructing a robust phylogenomic tree incorporating all ACS clade sequencing datasets available, and using these to examine in detail, the evolutionary distribution of all 162 proteins recently shown to be in the apicoplast by spatial proteomics in Toxoplasma. This expanded homology-based reconstruction of plastid proteins found in the ACS clade confirms earlier work showing convergence in the overall metabolic pathways retained once photosynthesis is lost, but also reveals differences in the degrees of plastid reduction in specific lineages. We show that the loss of the plastid genome is common and unexpectedly find many lineage- and species-specific plastid proteins, suggesting the presence of evolutionary innovations and neofunctionalizations that may confer new functional and metabolic capabilities that are yet to be discovered in these enigmatic organelles.

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.

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.

Reduced mitochondria provide an essential function for the cytosolic methionine cycle

The loss of mitochondria in oxymonad protists has been associated with the redirection of the essential Fe-S cluster assembly to the cytosol. Yet as our knowledge of diverse free-living protists broadens, the list of functions of their mitochondrial-related organelles (MROs) expands. We revealed another such function in the closest oxymonad relative, Paratrimastix pyriformis, after we solved the proteome of its MRO with high accuracy, using localization of organelle proteins by isotope tagging (LOPIT). The newly assigned enzymes connect to the glycine cleavage system (GCS) and produce folate derivatives with one-carbon units and formate. These are likely to be used by the cytosolic methionine cycle involved in S-adenosyl methionine recycling. The data provide consistency with the presence of the GCS in MROs of free-living species and its absence in most endobionts, which typically lose the methionine cycle and, in the case of oxymonads, the mitochondria.

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.

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.

A metabolic, phylogenomic and environmental atlas of diatom plastid transporters from the model species Phaeodactylum

Diatoms are an important group of algae, contributing nearly 40% of total marine photosynthetic activity. However, the specific molecular agents and transporters underpinning the metabolic efficiency of the diatom plastid remain to be revealed. We performed in silico analyses of 70 predicted plastid transporters identified by genome-wide searches of Phaeodactylum tricornutum. We considered similarity with Arabidopsis thaliana plastid transporters, transcriptional co-regulation with genes encoding core plastid metabolic pathways and with genes encoded in the mitochondrial genomes, inferred evolutionary histories using single-gene phylogeny, and environmental expression trends using Tara Oceans meta-transcriptomics and meta-genomes data. Our data reveal diatoms conserve some of the ion, nucleotide and sugar plastid transporters associated with plants, such as non-specific triose phosphate transporters implicated in the transport of phosphorylated sugars, NTP/NDP and cation exchange transporters. However, our data also highlight the presence of diatom-specific transporter functions, such as carbon and amino acid transporters implicated in intricate plastid-mitochondria crosstalk events. These confirm previous observations that substrate non-specific triose phosphate transporters (TPT) may exist as principal transporters of phosphorylated sugars into and out of the diatom plastid, alongside suggesting probable agents of NTP exchange. Carbon and amino acid transport may be related to intricate metabolic plastid-mitochondria crosstalk. We additionally provide evidence from environmental meta-transcriptomic/meta- genomic data that plastid transporters may underpin diatom sensitivity to ocean warming, and identify a diatom plastid transporter (J43171) whose expression may be positively correlated with temperature.

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.

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 .

Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data

Localization of organelle proteins by isotope tagging (LOPIT) maps are a coordinate-directed representation of proteome data that can aid in biological interpretation. Analysis of organellar association for proteins as displayed using LOPIT is evaluated and interpreted for two types of proteomic data sets. First, test and control group protein abundances and fold change data obtained in a proximity labeling experiment are plotted on a LOPIT map to evaluate the likelihood of true protein interactions. Selection of true positives based on co-localization of proteins in the organellar space is shown to be consistent with carboxylase enrichment which serves as a positive control for biotinylation in streptavidin affinity selected proteome data sets. The mapping in organellar space facilitates discrimination between the test and control groups and aids in identification of proteins of interest. The same representation of proteins in organellar space is used in the analysis of extracellular vesicle proteomes for which protein abundance and fold change data are evaluated. Vesicular protein organellar localization patterns provide information about the subcellular origin of the proteins in the samples which are isolates from the extracellular milieu. The organellar localization patterns are indicative of the provenance of the vesicular proteome origin and allow discrimination between proteomes prepared using different enrichment methods. The patterns in LOPIT displays are easy to understand and compare which aids in the biological interpretation of proteome data.

SubcellulaRVis: a web-based tool to simplify and visualise subcellular compartment enrichment

Cells contain intracellular compartments, including membrane-bound organelles and the nucleus, and are surrounded by a plasma membrane. Proteins are localised to one or more of these cellular compartments; the correct localisation of proteins is crucial for their correct processing and function. Moreover, proteins and the cellular processes they partake in are regulated by relocalisation in response to various cellular stimuli. High-throughput ‘omics experiments result in a list of proteins or genes of interest; one way in which their functional role can be understood is through the knowledge of their subcellular localisation, as deduced through statistical enrichment for Gene Ontology Cellular Component (GOCC) annotations or similar. We have designed a bioinformatics tool, named SubcellulaRVis, that compellingly visualises the results of GOCC enrichment for quick interpretation of the localisation of a group of proteins (rather than single proteins). We demonstrate that SubcellulaRVis precisely describes the subcellular localisation of gene lists whose locations have been previously ascertained. SubcellulaRVis can be accessed via the web (http://phenome.manchester.ac.uk/subcellular/) or as a stand-alone app (https://github.com/JoWatson2011/subcellularvis). SubcellulaRVis will be useful for experimental biologists with limited bioinformatics expertise who want to analyse data related to protein (re)localisation and location-specific modules within the intracellular protein network.

Obtaining Complete Human Proteomes

Proteins are the molecular effectors of the information encoded in the genome. Proteomics aims at understanding the molecular functions of proteins in their biological context. In contrast to transcriptomics and genomics, the study of proteomes provides deeper insight into the dynamic regulatory layers encoded at the protein level, such as posttranslational modifications, subcellular localization, cell signaling, and protein-protein interactions. Currently, mass spectrometry (MS)-based proteomics is the technology of choice for studying proteomes at a system-wide scale, contributing to clinical biomarker discovery and fundamental molecular biology. MS technologies are continuously being developed to fulfill the requirements of speed, resolution, and quantitative accuracy, enabling the acquisition of comprehensive proteomes. In this review, we present how MS technology and acquisition methods have evolved to meet the requirements of cutting-edge proteomics research, which is describing the human proteome and its dynamic posttranslational modifications with unprecedented depth. Finally, we provide a perspective on studying proteomes at single-cell resolution. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

Eukaryotic cellular intricacies shape mitochondrial proteomic complexity

Mitochondria have been fundamental to the eco-physiological success of eukaryotes since the last eukaryotic common ancestor (LECA). They contribute essential functions to eukaryotic cells, above and beyond classical respiration. Mitochondria interact with, and complement, metabolic pathways occurring in other organelles, notably diversifying the chloroplast metabolism of photosynthetic organisms. Here, we integrate existing literature to investigate how mitochondrial metabolism varies across the landscape of eukaryotic evolution. We illustrate the mitochondrial remodelling and proteomic changes undergone in conjunction with major evolutionary transitions. We explore how the mitochondrial complexity of the LECA has been remodelled in specific groups to support subsequent evolutionary transitions, such as the acquisition of chloroplasts in photosynthetic species and the emergence of multicellularity. We highlight the versatile and crucial roles played by mitochondria during eukaryotic evolution, extending from its huge contribution to the development of the LECA itself to the dynamic evolution of individual eukaryote groups, reflecting both their current ecologies and evolutionary histories.

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.

Bleaching physiology: who's the 'weakest link' - host vs. symbiont?

Environmental stress, such as an increase in the sea surface temperature, triggers coral bleaching, a profound dysfunction of the mutualist symbiosis between the host cnidarians and their photosynthetic dinoflagellates of the Family Symbiodiniaceae. Because of climate change, mass coral bleaching events will increase in frequency and severity in the future, threatening the persistence of this iconic marine ecosystem at global scale. Strategies adapted to coral reefs preservation and restoration may stem from the identification of the succession of events and of the different molecular and cellular contributors to the bleaching phenomenon. To date, studies aiming to decipher the cellular cascade leading to temperature-related bleaching, emphasized the involvement of reactive species originating from compromised bioenergetic pathways (e.g. cellular respiration and photosynthesis). These molecules are responsible for damage to various cellular components causing the dysregulation of cellular homeostasis and the breakdown of symbiosis. In this review, we synthesize the current knowledge available in the literature on the cellular mechanisms caused by thermal stress, which can initiate or participate in the cell cascade leading to the loss of symbionts, with a particular emphasis on the role of each partner in the initiating processes.

Subcellular Proteomics as a Unified Approach of Experimental Localizations and Computed Prediction Data for Arabidopsis and Crop Plants

In eukaryotic organisms, subcellular protein location is critical in defining protein function and understanding sub-functionalization of gene families. Some proteins have defined locations, whereas others have low specificity targeting and complex accumulation patterns. There is no single approach that can be considered entirely adequate for defining the in vivo location of all proteins. By combining evidence from different approaches, the strengths and weaknesses of different technologies can be estimated, and a location consensus can be built. The Subcellular Location of Proteins in Arabidopsis database ( http://suba.live/ ) combines experimental data sets that have been reported in the literature and is analyzing these data to provide useful tools for biologists to interpret their own data. Foremost among these tools is a consensus classifier (SUBAcon) that computes a proposed location for all proteins based on balancing the experimental evidence and predictions. Further tools analyze sets of proteins to define the abundance of cellular structures. Extending these types of resources to plant crop species has been complex due to polyploidy, gene family expansion and contraction, and the movement of pathways and processes within cells across the plant kingdom. The Crop Proteins of Annotated Location database ( http://crop-pal.org/ ) has developed a range of subcellular location resources including a species-specific voting consensus for 12 plant crop species that offers collated evidence and filters for current crop proteomes akin to SUBA. Comprehensive cross-species comparison of these data shows that the sub-cellular proteomes (subcellulomes) depend only to some degree on phylogenetic relationship and are more conserved in major biosynthesis than in metabolic pathways. Together SUBA and cropPAL created reference subcellulomes for plants as well as species-specific subcellulomes for cross-species data mining. These data collections are increasingly used by the research community to provide a subcellular protein location layer, inform models of compartmented cell function and protein-protein interaction network, guide future molecular crop breeding strategies, or simply answer a specific question-where is my protein of interest inside the cell?

Multilocation proteins in organelle communication: Based on protein-protein interactions

Protein-protein interaction (PPI) plays a crucial role in most biological processes, including signal transduction and cell apoptosis. Importantly, the knowledge of PPIs can be useful for identification of multimeric protein complexes and elucidation of uncharacterized protein functions. Arabidopsis thaliana, the best-characterized dicotyledonous plant, the steadily increasing amount of information on the levels of its proteome and signaling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated. In this study, we performed an overview analysis of the 10 major organelles and their associated proteins of the dicotyledonous model plant Arabidopsis thaliana via PPI network, and found that PPI may play an important role in organelle communication. Further, multilocation proteins, especially phosphorylation-related multilocation proteins, can function as a "needle and thread" via PPIs and play an important role in organelle communication. Similar results were obtained in a monocotyledonous model crop, rice. Furthermore, we provide a research strategy for multilocation proteins by LOPIT technique, proteomics, and bioinformatics analysis and also describe their potential role in the field of plant science. The results provide a new view that the phosphorylation-related multilocation proteins play an important role in organelle communication and provide new insight into PPIs and novel directions for proteomic research. The research of phosphorylation-related multilocation proteins may promote the development of organelle communication and provide an important theoretical basis for plant responses to external stress.

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

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

SUBCELLULAR TRANSCRIPTOMICS & PROTEOMICS: A COMPARATIVE METHODS REVIEW

The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics.

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

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This review discusses strategies available to measure subcellular information.

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

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

Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models

Advanced three-dimensional (3D) cell models have proven to be capable of depicting architectural and microenvironmental features of several tissues. By providing data of higher physiological and pathophysiological relevance, 3D cell models have been contributing to a better understanding of human development, pathology onset and progression mechanisms, as well as for 3D cell-based assays for drug discovery. Nonetheless, the characterization and interrogation of these tissue-like structures pose major challenges on the conventional analytical methods, pushing the development of spatially-resolved technologies. Herein, we review recent advances and pioneering technologies suitable for the interrogation of multicellular 3D models, while capable of retaining biological spatial information. We focused on imaging technologies and omics tools, namely transcriptomics, proteomics and metabolomics. The advantages and shortcomings of these novel methodologies are discussed, alongside the opportunities to intertwine data from the different tools.

Identification of Oocyst-Driven Toxoplasma gondii Infections in Humans and Animals through Stage-Specific Serology-Current Status and Future Perspectives

The apicomplexan zoonotic parasite Toxoplasma gondii has three infective stages: sporozoites in sporulated oocysts, which are shed in unsporulated form into the environment by infected felids; tissue cysts containing bradyzoites, and fast replicating tachyzoites that are responsible for acute toxoplasmosis. The contribution of oocysts to infections in both humans and animals is understudied despite being highly relevant. Only a few diagnostic antigens have been described to be capable of discriminating which parasite stage has caused an infection. Here we provide an extensive overview of the antigens and serological assays used to detect oocyst-driven infections in humans and animals according to the literature. In addition, we critically discuss the possibility to exploit the increasing knowledge of the T. gondii genome and the various 'omics datasets available, by applying predictive algorithms, for the identification of new oocyst-specific proteins for diagnostic purposes. Finally, we propose a workflow for how such antigens and assays based on them should be evaluated to ensure reproducible and robust results.

A Prioritized and Validated Resource of Mitochondrial Proteins in Plasmodium Identifies Unique Biology

Plasmodium species have a single mitochondrion that is essential for their survival and has been successfully targeted by antimalarial drugs. Most mitochondrial proteins are imported into this organelle, and our picture of the Plasmodium mitochondrial proteome remains incomplete. Many data sources contain information about mitochondrial localization, including proteome and gene expression profiles, orthology to mitochondrial proteins from other species, coevolutionary relationships, and amino acid sequences, each with different coverage and reliability. To obtain a comprehensive, prioritized list of Plasmodium falciparum mitochondrial proteins, we rigorously analyzed and integrated eight data sets using Bayesian statistics into a predictive score per protein for mitochondrial localization. At a corrected false discovery rate of 25%, we identified 445 proteins with a sensitivity of 87% and a specificity of 97%. They include proteins that have not been identified as mitochondrial in other eukaryotes but have characterized homologs in bacteria that are involved in metabolism or translation. Mitochondrial localization of seven Plasmodium berghei orthologs was confirmed by epitope labeling and colocalization with a mitochondrial marker protein. One of these belongs to a newly identified apicomplexan mitochondrial protein family that in P. falciparum has four members. With the experimentally validated mitochondrial proteins and the complete ranked P. falciparum proteome, which we have named PlasmoMitoCarta, we present a resource to study unique proteins of Plasmodium mitochondria. IMPORTANCE The unique biology and medical relevance of the mitochondrion of the malaria parasite Plasmodium falciparum have made it the subject of many studies. However, we actually do not have a comprehensive assessment of which proteins reside in this organelle. Many omics data are available that are predictive of mitochondrial localization, such as proteomics data and expression data. Individual data sets are, however, rarely complete and can provide conflicting evidence. We integrated a wide variety of available omics data in a manner that exploits the relative strengths of the data sets. Our analysis gave a predictive score for the mitochondrial localization to each nuclear encoded P. falciparum protein and identified 445 likely mitochondrial proteins. We experimentally validated the mitochondrial localization of seven of the new mitochondrial proteins, confirming the quality of the complete list. These include proteins that have not been observed mitochondria before, adding unique mitochondrial functions to P. falciparum.

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.

Proximity-dependent biotinylation approaches to study apicomplexan biology

In the last 10 years, proximity-dependent biotinylation (PDB) techniques greatly expanded the ability to study protein environments in the living cell that range from specific protein complexes to entire compartments. This is achieved by using enzymes such as BirA* and APEX that are fused to proteins of interest and biotinylate proteins in their proximity. PDB techniques are now also increasingly used in apicomplexan parasites. In this review, we first give an overview of the main PDB approaches and how they compare with other techniques that address similar questions. PDB is particularly valuable to detect weak or transient protein associations under physiological conditions and to study cellular structures that are difficult to purify or have a poorly understood protein composition. We also highlight new developments such as novel smaller or faster-acting enzyme variants and conditional PDB approaches, providing improvements in both temporal and spatial resolution which may offer broader application possibilities useful in apicomplexan research. In the second part, we review work using PDB techniques in apicomplexan parasites and how this expanded our knowledge about these medically important parasites.

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.

Exploring the diversity of plant proteome

The tremendous functional, spatial, and temporal diversity of the plant proteome is regulated by multiple factors that continuously modify protein abundance, modifications, interactions, localization, and activity to meet the dynamic needs of plants. Dissecting the proteome complexity and its underlying genetic variation is attracting increasing research attention. Mass spectrometry (MS)-based proteomics has become a powerful approach in the global study of protein functions and their relationships on a systems level. Here, we review recent breakthroughs and strategies adopted to unravel the diversity of the proteome, with a specific focus on the methods used to analyze posttranslational modifications (PTMs), protein localization, and the organization of proteins into functional modules. We also consider PTM crosstalk and multiple PTMs temporally regulating the life cycle of proteins. Finally, we discuss recent quantitative studies using MS to measure protein turnover rates and examine future directions in the study of the plant proteome.

A Bayesian semi-parametric model for thermal proteome profiling

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

Closing the protein gap in plant chronobiology

Our modern understanding of diel cell regulation in plants stems from foundational work in the late 1990s that analysed the dynamics of selected genes and mutants in Arabidopsis thaliana. The subsequent rise of transcriptomics technologies such as microarrays and RNA sequencing has substantially increased our understanding of anticipatory (circadian) and reactive (light- or dark-triggered) diel events in plants. However, it is also becoming clear that gene expression data fail to capture critical events in diel regulation that can only be explained by studying protein-level dynamics. Over the past decade, mass spectrometry technologies and quantitative proteomic workflows have significantly advanced, finally allowing scientists to characterise diel protein regulation at high throughput. Initial proteomic investigations suggest that the diel transcriptome and proteome generally lack synchrony and that the timing of daily regulatory events in plants is impacted by multiple levels of protein regulation (e.g., post-translational modifications [PTMs] and protein-protein interactions [PPIs]). Here, we highlight and summarise how the use of quantitative proteomics to elucidate diel plant cell regulation has advanced our understanding of these processes. We argue that this new understanding, coupled with the extraordinary developments in mass spectrometry technologies, demands greater focus on protein-level regulation of, and by, the circadian clock. This includes hitherto unexplored diel dynamics of protein turnover, PTMs, protein subcellular localisation and PPIs that can be masked by simple transcript- and protein-level changes. Finally, we propose new directions for how the latest advancements in quantitative proteomics can be utilised to answer outstanding questions in plant chronobiology.

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.

The NANOTUMOR consortium - Towards the Tumor Cell Atlas

Cancer is a multi-step disease where an initial tumour progresses through critical steps shaping, in most cases, life-threatening secondary foci called metastases. The oncogenic cascade involves genetic, epigenetic, signalling pathways, intracellular trafficking and/or metabolic alterations within cancer cells. In addition, pre-malignant and malignant cells orchestrate complex and dynamic interactions with non-malignant cells and acellular matricial components or secreted factors within the tumour microenvironment that is instrumental in the progression of the disease. As our aptitude to effectively treat cancer mostly depends on our ability to decipher, properly diagnose and impede cancer progression and metastasis formation, full characterisation of molecular complexes and cellular processes at play along the metastasis cascade is crucial. For many years, the scientific community lacked adapted imaging and molecular technologies to accurately dissect, at the highest resolution possible, tumour and stromal cells behaviour within their natural microenvironment. In that context, the NANOTUMOR consortium is a French national multi-disciplinary workforce which aims at a providing a multi-scale characterisation of the oncogenic cascade, from the atomic level to the dynamic organisation of the cell in response to genetic mutations, environmental changes or epigenetic modifications. Ultimately, this program aims at identifying new therapeutic targets using innovative drug design.

A Comprehensive Subcellular Atlas of the Toxoplasma Proteome via hyperLOPIT Provides Spatial Context for Protein Functions

Apicomplexan parasites cause major human disease and food insecurity. They owe their considerable success to highly specialized cell compartments and structures. These adaptations drive their recognition, nondestructive penetration, and elaborate reengineering of the host's cells to promote their growth, dissemination, and the countering of host defenses. The evolution of unique apicomplexan cellular compartments is concomitant with vast proteomic novelty. Consequently, half of apicomplexan proteins are unique and uncharacterized. Here, we determine the steady-state subcellular location of thousands of proteins simultaneously within the globally prevalent apicomplexan parasite Toxoplasma gondii. This provides unprecedented comprehensive molecular definition of these unicellular eukaryotes and their specialized compartments, and these data reveal the spatial organizations of protein expression and function, adaptation to hosts, and the underlying evolutionary trajectories of these pathogens.