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.

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 ∼1 week to complete sample preparation steps, ∼2 d for mass spectrometry data acquisition and 1-2 d for data analysis and downstream informatics.

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.

BackgroundMalignant transformation of normal gastric cells is a complex and multistep process, resulting in development of heterogeneous tumours. Susceptible genetic background, accumulation of genetic changes, and environmental factors play an important role in gastric carcinogenesis. Single nucleotide polymorphisms (SNPs) in mitotic segregation genes could be responsible for inducing the slow process of accumulation of genetic changes, leading to genome instability.Patients and methodsWe performed a case-control study of polymorphisms in mitotic kinases TTK rs151658 and BUB1B rs1031963 and rs1801376 to assess their effects on gastric cancer risk. We examined the TTK abundance in gastric cancer tissues using immunoblot analysis.ResultsC/G genotype of rs151658 was more frequent in patients with diffuse type of gastric cancer and G/G genotype was more common in intestinal types of gastric cancers (p = 0.049). Polymorphic genotype A/A of rs1801376 was associated with higher risk for developing diffuse type of gastric cancer in female population (p = 0.007), whereas A/A frequencies were increased in male patients with subserosa tumour cell infiltration (p = 0.009). T/T genotype of rs1031963 was associated with well differentiated tumours (p = 0.035). TT+CT genotypes of rs1031963 and GG+AG genotypes of rs1801376 were significantly associated with gastric cancer risk (dominant model; OR = 2,929, 95% CI: 1.281-6.700; p = 0.017 and dominant model; OR = 0,364, 95% CI: 0.192-0.691; p = 0.003 respectively).ConclusionsOur results suggest that polymorphisms in mitotic kinases TTK and BUB1B may contribute to gastric tumorigenesis and risk of tumour development. Further investigations on large populations and populations of different ethnicity are needed to determine their clinical utility.