The Organelle Proteome of the DT40 Lymphocyte Cell Line

Abstract

A major challenge in eukaryotic cell biology is to understand the roles of individual proteins and the subcellular compartments in which they reside. Here, we use the localization of organelle proteins by isotope tagging technique to complete the first proteomic analysis of the major organelles of the DT40 lymphocyte cell line. This cell line is emerging as an important research tool because of the ease with which gene knockouts can be generated. We identify 1090 proteins through the analysis of preparations enriched for integral membrane or soluble and peripherally associated proteins and localize 223 proteins to the endoplasmic reticulum, Golgi, lysosome, mitochondrion, or plasma membrane by matching their density gradient distributions to those of known organelle residents. A striking finding is that within the secretory and The chicken pre-B cell line DT40 exhibits a remarkably high ratio of targeted to random integration for transfected DNA constructs. This property is unusual in vertebrate cell lines and enables targeted gene disruption experiments to be carried out with relative ease (1). Consequently, DT40 has become a major research tool for the molecular dissection of a wide range of cellular and biochemical mechanisms in a vertebrate context, including membrane traffic, signal transduction, and cell cycle (2).Proteins in eukaryotic cells are organized according to their functions within a dynamic network of membranes. Localization is therefore paramount in assigning functions to uncharacterized proteins and understanding the processes occurring in subcellular compartments. An increased knowledge of the protein localization within the DT40 cell line would be of great value. Traditional localization methods such as immunofluorescence microscopy are typically low throughput and are more suitably applied to the study of specific proteins of interest rather than the cataloguing of large numbers of proteins. Recent developments in proteomics have made it possible to analyze the protein composition of organelles using a variety of different approaches. Several groups have utilized label-free quantitative proteomics in the high throughput assignment of proteins to subcellular compartments. In one approach, protein correlation profiling, proteins from enriched organelle fractions are quantified by peptide ion intensity measurements (3, 4). Other similar methods employ quantitation by spectral counting, recording the number of ions detected per protein (5, 6). Localization of organelle proteins by isotope tagging (LOPIT) 1 is a complementary approach, which employs isotope labeling for quantitation (7-9). Rather than processing each sample separately as in label-free techniques, differentially labeled fractions are pooled early in the LOPIT protocol. This has the important advantage of reducing the points at which variation might be introduced into the data.LOPIT begins with the partial separation of organelles by density gradient centrifugation and relies on the assumption that proteins from each organelle c...

Proteomics of total membranes and subcellular membranes

“…LOPIT uses the iTR AQ technology to measure distribution patterns of proteins from self-generating iodixanol density gradients [66] and is summarized in Figure 2. This technique also allows simultaneous assignment of proteins to multiple organelles and has been used in the authors' laboratory to assign proteins to organelles in plants, Drosophila and animal cell culture [67][68][69]. Four or eight fractions, enriched with different organelles, are selected for comparison and protein distributions are determined by measuring their relative abundance using iTRAQ quantification.…”

Tackling centrosome biology through gene targeting in chicken B cells

“…The high homologous recombination rate together with the ease of propagation make DT40 ideal for characterizing the biochemical roles of protein domains and residues. Indeed, large molecular complexes, protein interactomes and even whole organelle proteomes have been successfully identified from wild type and various mutant DT40 cell lines (Hall, Hester, Griffin, Lilley, & Jackson, 2009;Mosedale et al, 2005;Ohta et al, 2010). In the context of centrosome biology, the use of DT40 cells have proved to be equally valuable, helped by the fact that centrosome morphology and centrosomal genes are highly conserved between vertebrates and mammals (Barr, Kilmartin, & Gergely, 2010;Dantas, Wang, Lalor, Dockery, & Morrison, 2011;Inanc et al, 2013;Shang et al, 2013;Sir et al, 2011Sir et al, , 2013Wang, Dantas, Lalor, Dockery, & Morrison, 2013).…”

A Foundation for Reliable Spatial Proteomics Data Analysis

“…Missing data and the impact of imputation have not been thoroughly addressed in proteomics, let alone in spatial proteomics. Published LOPIT studies (7,18,20) have excluded proteins that presented missing values across replicated experiments. PCP studies (8,21) have limited the computation of their 2 metric to pairs of fractions (defined as the squared deviation of the normalized profile for all peptides divided by the number of data points), thus increasing the bias by reducing the number of data points.…”

“…This is a useful exercise to undertake, especially in such an emerging field as spatial proteomics data analysis. The first applications of large-scale organelle proteomics data analysis were protein correlation profiling efforts (8,21) that calculated the 2 metric using in-house tools and LOPIT (7,18,20) that applied partial least squares discriminant analysis using the commercial SIMCA software (Umetrics, Umea, Sweden). Trotter et al (9) implemented custom R code (12) and used the SVM algorithm from the kernlab package (33), but no code for others to repeat this state-of-the-art procedure is provided.…”