Comprehensive Summary
This article focuses on the development of a high-resolution spatially resolved proteomics framework that uses omics data to determine spatial protein patterns across entire tissues. This framework called PLATO was designed by first cryosectioning three consecutive tissue slices where the middle slice is used for generating reference omics data, and the first and last slices are used for microfluidics-based proteomic profiling at different angles. Each tissue slice is then covered by parallel microchannels and after on-chip digestion the peptides are collected for LC- MS/MS analysis which are also called parallel-flow projections. A transfer learning algorithm called Flow2Spatial was developed to reconstruct protein spatial distributions and one of the ways in which PLATO was validated was by assessing Flow2Spatial’s accuracy in reconstructing spatial patterns. The researchers found that PLATO was able to detect 85.32% of the proteins in the tissue sample with just 1 micro liter of mouse cerebellum tissue lysates. Furthermore, a linear correlation was observed between glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression and cell density and when doing an abundance ranking of proteins across different dilutions a high correlation was obtained. Therefore, these results demonstrated the reliability and reproducibility of PLATO. In addition, the researchers compared the performance of Flow2Spatial and Tomographer when it comes to spatial reconstruction, and they found that Flow2Spatial outperformed Tomographer particularly in detecting fine structures and clustering different region types. Overall, this paper introduces the PLATO framework and shows that it is versatile, easy to operate, can achieve high spatial resolution by requiring only a few parallel sampling projections, and has the capacity to map whole tissues.
Outcomes and Implications
This research is important because current methods for proteome-wide spatial protein profiling of focused tissue areas such as mass spectrometry and laser capture microdissection often necessitate highly specialized laboratory facilities. Furthermore, these methods are usually low throughput and not suited for examining the whole tissue. Therefore, PLATO allows for high spatial resolution proteome-scale mapping for the whole tissue without requiring sophisticated equipment. This work is relevant to medicine because omics data collected in the clinic needs to be analyzed and the framework introduced in this paper can allow for more efficient and higher quality analysis of this data. However, before this framework can be implemented in the clinic enhancing PLATO’s resolution so that single-cell resolution could be achieved and aligning cell- type information from spatial transcriptomics using deconvolution algorithms are some of the ways in which PLATO can be improved before clinical implementation.