A new LOPIT on the block

22 January 2019

Example data demonstrating LOPIT-DC protein localisation classifications agree with those from hyperLOPIT.

The Lilley Group have published a new paper in Nature Communications that combines the LOPIT-DC and hyperLOPIT spatial proteomics approaches.

Proteins can adopt multiple functions which are controlled by their subcellular location, binding partners, and post-transcriptional and post-translational modification status, significantly increasing the functionality of the proteome over what is encoded within the genome. The processes governing these features are highly dynamic, and our ability to chart changes in the dynamic proteome upon perturbation, such as drug treatment or cell stress, is of paramount importance to our understanding of cellular mechanisms.

The study of protein localisation has greatly benefited from high-throughput methods utilising cellular fractionation and proteomic profiling. Such methods are very reliant on high degrees of accuracy and precision. Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method in this area developed by the Lilley Group1-3. It achieves high-resolution separation of organelles and subcellular compartments, but is relatively time- and resource-intensive. As a simpler alternative, the Lilley Group recently published the Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) method, based on differential centrifugation rather than equilibrium density centrifugation. The LOPIT-DC protocol requires less sample amount and reagents, and reduces the time taken to produce spatial proteomics data without compromising the sub-cellular resolution afforded by hyperLOPIT4.

In their new publication, the Lilley Group compared LOPIT-DC to the density gradient-based hyperLOPIT approach. Both approaches facilitate identification of isoform-specific localisations and high-confidence localisation assignments for proteins in suborganellar structures, protein complexes and signalling pathways. By combining these two approaches, which separate subcellular structures based on complementary physical attributes, the authors present a comprehensive high-resolution dataset of human protein localisations and deliver a flexible set of protocols for subcellular proteomics.

1Christoforou et al., Nat. Commun. 7:8992 (2016).

2Mulvey et al., Nat. Protoc. 12:1110 (2017).

3Thul et al., Science26:356 (2017).

4Geladaki et al., Nat. Commun.10:331 (2019).