RESUMEN
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.
Asunto(s)
Proteómica , Transcriptoma , Proteínas , Proteómica/métodos , ARNRESUMEN
The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a protein's sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyperLOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identified in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches.
Asunto(s)
Teorema de Bayes , Proteínas de Saccharomyces cerevisiae/metabolismo , Fracciones Subcelulares/metabolismo , Algoritmos , Animales , Conjuntos de Datos como Asunto , Humanos , Aprendizaje Automático , Espectrometría de Masas , Ratones , ProteómicaRESUMEN
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.
Asunto(s)
Inflamación/metabolismo , Leucemia/metabolismo , Leucemia/patología , Lipopolisacáridos/farmacología , Proteómica , Algoritmos , Antiinfecciosos/metabolismo , Antiinflamatorios/metabolismo , Presentación de Antígeno , Autofagosomas/metabolismo , Teorema de Bayes , Puntos de Control del Ciclo Celular , Membrana Celular/metabolismo , Núcleo Celular/metabolismo , Forma de la Célula , Humanos , Inmunidad , Inflamación/patología , Leucemia/inmunología , Activación de Linfocitos/inmunología , Lisosomas/metabolismo , Proteínas de Neoplasias/metabolismo , Transporte de Proteínas , Proteoma/metabolismo , Transducción de Señal , Linfocitos T/inmunología , Células THP-1 , Factores de Tiempo , Vesículas Transportadoras/metabolismo , Regulación hacia Arriba , Proteínas de Unión al GTP rho/metabolismoRESUMEN
Subcellular protein localisation is essential for the mechanisms that govern cellular homeostasis. The ability to understand processes leading to this phenomenon will therefore enhance our understanding of cellular function. Here we review recent developments in this field with regard to mass spectrometry, fluorescence microscopy and computational prediction methods. We highlight relative strengths and limitations of current methodologies focussing particularly on studies in the yeast Saccharomyces cerevisiae. We further present the first cell-wide spatial proteome map of S. cerevisiae, generated using hyperLOPIT, a mass spectrometry-based protein correlation profiling technique. We compare protein subcellular localisation assignments from this map, with two published fluorescence microscopy studies and show that confidence in localisation assignment is attained using multiple orthogonal methods that provide complementary data.
Asunto(s)
Proteómica/métodos , Proteínas de Saccharomyces cerevisiae/análisis , Saccharomyces cerevisiae/citología , Espectrometría de Masas/métodos , Microscopía Fluorescente/métodos , Saccharomyces cerevisiae/química , Saccharomyces cerevisiae/ultraestructuraRESUMEN
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.
Asunto(s)
Proteoma/análisis , Proteómica/métodos , Fraccionamiento Celular , Línea Celular Tumoral , Centrifugación por Gradiente de Densidad/métodos , Humanos , Espectrometría de Masas/métodos , Análisis Espacial , UltracentrifugaciónRESUMEN
The organization of eukaryotic cells into distinct subcompartments is vital for all functional processes, and aberrant protein localization is a hallmark of many diseases. Microscopy methods, although powerful, are usually low-throughput and dependent on the availability of fluorescent fusion proteins or highly specific and sensitive antibodies. One method that provides a global picture of the cell is localization of organelle proteins by isotope tagging (LOPIT), which combines biochemical cell fractionation using density gradient ultracentrifugation with multiplexed quantitative proteomics mass spectrometry, allowing simultaneous determination of the steady-state distribution of hundreds of proteins within organelles. Proteins are assigned to organelles based on the similarity of their gradient distribution to those of well-annotated organelle marker proteins. We have substantially re-developed our original LOPIT protocol (published by Nature Protocols in 2006) to enable the subcellular localization of thousands of proteins per experiment (hyperLOPIT), including spatial resolution at the suborganelle and large protein complex level. This Protocol Extension article integrates all elements of the hyperLOPIT pipeline, including an additional enrichment strategy for chromatin, extended multiplexing capacity of isobaric mass tags, state-of-the-art mass spectrometry methods and multivariate machine-learning approaches for analysis of spatial proteomics data. We have also created an open-source infrastructure to support analysis of quantitative mass-spectrometry-based spatial proteomics data (http://bioconductor.org/packages/pRoloc) and an accompanying interactive visualization framework (http://www. bioconductor.org/packages/pRolocGUI). The procedure we outline here is applicable to any cell culture system and requires â¼1 week to complete sample preparation steps, â¼2 d for mass spectrometry data acquisition and 1-2 d for data analysis and downstream informatics.
Asunto(s)
Proteoma/análisis , Proteómica/métodos , Análisis Espacial , Fraccionamiento Celular/métodos , Centrifugación por Gradiente de Densidad/métodos , Células Eucariotas/química , Espectrometría de Masas/métodosRESUMEN
Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.
Asunto(s)
Imagen Molecular , Orgánulos/química , Orgánulos/metabolismo , Mapas de Interacción de Proteínas , Proteoma/análisis , Proteoma/metabolismo , Análisis de la Célula Individual , Línea Celular , Conjuntos de Datos como Asunto , Femenino , Humanos , Masculino , Espectrometría de Masas , Microscopía Fluorescente , Mapeo de Interacción de Proteínas , Proteoma/genética , Reproducibilidad de los Resultados , Fracciones Subcelulares , TranscriptomaRESUMEN
Knowledge of the subcellular distribution of proteins is vital for understanding cellular mechanisms. Capturing the subcellular proteome in a single experiment has proven challenging, with studies focusing on specific compartments or assigning proteins to subcellular niches with low resolution and/or accuracy. Here we introduce hyperLOPIT, a method that couples extensive fractionation, quantitative high-resolution accurate mass spectrometry with multivariate data analysis. We apply hyperLOPIT to a pluripotent stem cell population whose subcellular proteome has not been extensively studied. We provide localization data on over 5,000 proteins with unprecedented spatial resolution to reveal the organization of organelles, sub-organellar compartments, protein complexes, functional networks and steady-state dynamics of proteins and unexpected subcellular locations. The method paves the way for characterizing the impact of post-transcriptional and post-translational modification on protein location and studies involving proteome-level locational changes on cellular perturbation. An interactive open-source resource is presented that enables exploration of these data.