Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions

Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.     

叶绿体蛋白质组学研究进展

蛋白质组分析是鉴定蛋白质种类和功能的有力工具之一。叶绿体作为光合作用的重要细胞器,叶绿体蛋白质组学成为了研究的热点,涉及的领域包括叶绿体的总蛋白质组学、亚细胞蛋白质组学、差异蛋白质组学和蛋白质的功能等。现主要介绍蛋白质组学的常用技术以及叶绿体蛋白质组学的最新研究进展。     

A Foundation for Reliable Spatial Proteomics Data Analysis

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.     

Global approaches to study Golgi function

Enormous insights into Golgi function have been provided by yeast genetics, biochemical assays and immuno-labeling methods and the emerging picture is of a very complex organelle with multiple levels of regulation. Despite many elegant experimental approaches, it remains unclear what mechanisms transport secretory proteins and lipids through the Golgi, and even the basic structure of the organelle is debated. Recently, new, global approaches such as proteomics and functional genomics have been applied to study the Golgi and its matrix. The data produced reveals great complexity and has potential to help address major unresolved questions concerning Golgi function.     

Comparison of Golgi apparatus and endoplasmic reticulum proteins from livers of juvenile and aged rats using a novel technique for separation and enrichment of organelles.

The broad dynamic range of protein abundances, which can vary from about 10(6) for cells to 10(10) for tissues in complex proteomes, continues to challenge proteomics research. Proteome analysis, in particular organelle proteomics, using current approaches, requires extensive fractionation, separation, and enrichment. Over the years, organelle separation was achieved through the use of differential and density-gradient ultracentrifugation. However, the traditional fixed-volume process is a time-consuming and labor-intensive method, especially with large quantities of sample. Here, we present a novel tool for subcellular fractionation of biologically complex mixtures: continuous-flow ultracentrifugation of tissue homogenates to obtain both organelle separation and extensive organelle enrichment at the same time. In this study, rat liver tissues from two different age groups (3-8 wk and greater than 1 y old) were homogenized by blending. After removing nuclei, the resulting homogenates were further fractionated at the subcellular level by the use of sucrose gradient continuous-flow ultracentrifugation. Each organelle's enriched fractions were identified by Western blot analysis. To study the possible effects of aging on the endoplasmic reticulum and Golgi apparatus, we compared the organelle protein profiles of the two groups of rat liver tissues using two-dimensional gel electrophoresis, digitized imaging of two-dimensional gel electrophoresis, and mass spectrometry. Significant differences in the protein profiles of both organelles were observed between the two groups of rat tissues. The technique described here for fractionation and enrichment of organelles demonstrated a useful tool for proteomics research, including identification of low-abundance proteins and post-translational modifications.     

Localization of organelle proteins by isotope tagging (LOPIT)

We describe a proteomics method for determining the subcellular localization of membrane proteins. Organelles are partially separated using centrifugation through self-generating density gradients. Proteins from each organelle co-fractionate and therefore exhibit similar distributions in the gradient. Protein distributions can be determined through a series of pair-wise comparisons of gradient fractions, using cleavable ICAT to enable relative quantitation of protein levels by MS. The localization of novel proteins is determined using multivariate data analysis techniques to match their distributions to those of proteins that are known to reside in specific organelles. Using this approach, we have simultaneously demonstrated the localization of membrane proteins in both the endoplasmic reticulum and the Golgi apparatus in Arabidopsis. Localization of organelle proteins by isotope tagging is a new tool for high-throughput protein localization, which is applicable to a wide range of research areas such as the study of organelle function and protein trafficking.     

Total variance should drive data handling strategies in third generation proteomic studies

Quantitative proteomics is entering its “third generation,” where intricate experimental designs aim to increase the spatial and temporal resolution of protein changes. This paper re‐analyses multiple internally consistent proteomic datasets generated from whole cell homogenates and fractionated brain tissue samples providing a unique opportunity to explore the different factors influencing experimental outcomes. The results clearly indicate that improvements in data handling are required to compensate for the increased mean CV associated with complex study design and intricate upstream tissue processing. Furthermore, applying arbitrary inclusion thresholds such as fold change in protein abundance between groups can lead to unnecessary exclusion of important and biologically relevant data.     

Design and analysis of experiments with high throughput biological assay data

The design and analysis of experiments using gene expression microarrays is a topic of considerable current research, and work is beginning to appear on the analysis of proteomics and metabolomics data by mass spectrometry and NMR spectroscopy. The literature in this area is evolving rapidly, and commercial software for analysis of array or proteomics data is rarely up to date, and is essentially nonexistent for metabolomics data. In this paper, I review some of the issues that should concern any biologists planning to use such high-throughput biological assay data in an experimental investigation. Technical details are kept to a minimum, and may be found in the referenced literature, as well as in the many excellent papers which space limitations prevent my describing. There are usually a number of viable options for design and analysis of such experiments, but unfortunately, there are even more non-viable ones that have been used even in the published literature. This is an area in which up-to-date knowledge of the literature is indispensable for efficient and effective design and analysis of these experiments. In general, we concentrate on relatively simple analyses, often focusing on identifying differentially expressed genes and the comparable issues in mass spectrometry and NMR spectroscopy (consistent differences in peak heights or areas for example). Complex multivariate and pattern recognition methods also need much attention, but the issues we describe in this paper must be dealt with first. The literature on analysis of proteomics and metabolomics data is as yet sparse, so the main focus of this paper will be on methods devised for analysis of gene expression data that generalize to proteomics and metabolomics, with some specific comments near the end on analysis of metabolomics data by mass spectrometry and NMR spectroscopy.     

Methods of quantitative proteomics and their application to plant organelle characterization

Many cell biologists wish to know the subcellular localization of proteins of interest. Proteomics methods have the potential to describe the entire protein content of organelles. However, practical limitations in organelle isolation and analysis of low abundance proteins have meant that organelle proteomics has had, until recently, only limited success. Some examples of quantitative proteomic methods and their use in the study of plant organelle proteomes are discussed here. It is concluded that 2D-difference gel electrophoresis (2D-DIGE) as well as differential isotope tagging strategies coupled to non-gel-based LC-MS are proving useful in this area of research.     

Building the mitochondrial proteome

Mitochondria are essential organelles for cellular homeostasis. A variety of pathologies including cancer, myopathies, diabetes, obesity, aging and neurodegenerative diseases are linked to mitochondrial dysfunction. Therefore, mapping the different components of mitochondria is of particular interest to gain further understanding of such diseases. In recent years, proteomics-based approaches have been developed in attempts to determine the complete set of mitochondrial proteins in yeast, plants and mammals. In addition, proteomics-based methods have been applied not only to the analysis of protein function in the organelle, but also to identify biomarkers for diagnosis and therapeutic targets of specific pathologies associated with mitochondria. Altogether, it is becoming clear that proteomics is a powerful tool not only to identify currently unknown components of the mitochondrion, but also to study the different roles of the organelle in cellular homeostasis.     

Building the mitochondrial proteome

Mitochondria are essential organelles for cellular homeostasis. A variety of pathologies including cancer, myopathies, diabetes, obesity, aging and neurodegenerative diseases are linked to mitochondrial dysfunction. Therefore, mapping the different components of mitochondria is of particular interest to gain further understanding of such diseases. In recent years, proteomics-based approaches have been developed in attempts to determine the complete set of mitochondrial proteins in yeast, plants and mammals. In addition, proteomics-based methods have been applied not only to the analysis of protein function in the organelle, but also to identify biomarkers for diagnosis and therapeutic targets of specific pathologies associated with mitochondria. Altogether, it is becoming clear that proteomics is a powerful tool not only to identify currently unknown components of the mitochondrion, but also to study the different roles of the organelle in cellular homeostasis.     

Proteomic analysis of clathrin-coated vesicles

For more than 50 years cell biologists have embraced the concept that biochemical and enzymatic analysis of isolated subcellular fractions provides insight into the function and machineries of cellular compartments including organelles. The utility of this approach has been significantly enhanced with the advent of mass spectrometry leading to the broad application of organelle proteomics. Clathrin-coated vesicles (CCVs) form at the plasma membrane where they select protein and lipid cargo for endocytic entry into cells. CCVs also form at the trans-Golgi network, where they function in protein transport from the secretory pathway to the endosomal/lysosomal system. Herein we will describe how organelle proteomics of CCVs has greatly expanded our knowledge of the machineries, mechanisms and sites of clathrin-mediated membrane trafficking.     

Advances and challenges in understanding the role of the lipid raft proteome in human health

ABSTRACT

Introduction: Phase separation as a biophysical principle drives the formation of liquid-ordered ‘lipid raft’ membrane microdomains in cellular membranes, including organelles. Given the critical role of cellular membranes in both compartmentalization and signaling, clarifying the roles of membrane microdomains and their mutual regulation of/by membrane proteins is important in understanding the fundamentals of biology, and has implications for health.

Areas covered: This article will consider the evidence for lateral membrane phase separation in model membranes and organellar membranes, critically evaluate the current methods for lipid raft proteomics and discuss the biomedical implications of lipid rafts.

Expert commentary: Lipid raft homeostasis is perturbed in numerous chronic conditions; hence, understanding the precise roles and regulation of the lipid raft proteome is important for health and medicine. The current technical challenges in performing lipid raft proteomics can be overcome through well-controlled experimental design and careful interpretation. Together with technical developments in mass spectrometry and microscopy, our understanding of lipid raft biology and function will improve through recognition of the similarity between organelle and plasma membrane lipid rafts and considered integration of published lipid raft proteomics data.     

Structural proteomics: methods in deriving protein structural information and issues in data management

Structural proteomics is an emerging paradigm that is gaining importance in the post-genomic era as a valuable discipline to process the protein target information being deciphered. The field plays a crucial role in assigning function to sequenced proteins, defining pathways in which the targets are involved, and understanding structure-function relationships of the protein targets. A key component of this research sector is accessing the three-dimensional structures of protein targets by both experimental and theoretical methods. This then leads to the question of how to store, retrieve, and manipulate vast amounts of sequence (1-D) and structural (3-D) information in a relational format so that extensive data analysis can be achieved. We at SBI have addressed both of these fundamental requirements of structural proteomics. We have developed an extensive collection of three-dimensional protein structures from sequence data and have implemented a relational architecture for data management. In this article we will discuss our approaches to structural proteomics and the tools that life science researchers can use in their discovery efforts.