Development of a Sensitive, Scalable Method for Spatial, Cell-Type-Resolved Proteomics of the Human Brain

Abstract

While nearly comprehensive proteome coverage can be achieved from bulk tissue or cultured cells, the data usually lacks spatial resolution. As a result, tissue based proteomics averages protein abundance across multiple cell types and/or localizations. With proteomics platforms lacking sensitivity and throughput to undertake deep single-cell proteome studies in order to resolve spatial or cell type dependent protein expression gradients within tissue, proteome analysis has been combined with sorting techniques to enrich for certain cell populations. However, the spatial resolution and context is lost after cell sorting. Here, we report an optimized method for the proteomic analysis of neurons isolated from post-mortem human brain by laser capture microdissection (LCM). We tested combinations of sample collection methods, lysis buffers and digestion methods to maximize the number of identifications and quantitative performance, identifying 1500 proteins from 60 000 μm 2 of 10 μm thick cerebellar molecular layer with excellent reproducibility. To demonstrate the ability of our workflow to resolve cell type specific proteomes within human brain tissue, we isolated sets of individual Betz and Purkinje cells. Both neuronal cell types are involved in motor coordination and were found to express highly specific proteomes to a depth of 2800 to 3600 proteins.

Improved Reproducibility of Protein Detection in Neuromelanin-containing Human Substantia Nigra Tissue enriched by Laser Microdissection

“…Davis et al used a SP3 protocol for brain tissue of a cerebellar cortex enriched by laser microdissection (28). Between three replicates they did not measure a high SD as in our study.…”

“…Davis et al also tested combinations of lysis buffers and digestion methods to maximize the number of identi cations and quantitative performance for single-cell proteome studies (28). They analyzed 60000 µm 2 of laser microdissected cerebellum tissue.…”

A streamlined mass spectrometry-based proteomics workflow for large scale FFPE tissue analysis

“…Organic solvents such as 2,2,2trifluoroethanol (TFE) and acetonitrile (ACN) have overall good MS compatibility and yield high peptide and protein identifications. This is particular attractive for the analysis of lowinput samples [18,20]. As a proof-of-concept, we applied our workflow to an adenoma cohort of more than 100 samples, thereby validating its applicability and value to reveal new insights into disease biology.…”

“…Following peptide clean-up, we found a 2.3fold higher peptide yield using the TFE-based protocol ( Fig. S1A), indicating both improved protein and peptide recovery [18,20] and protein loss during detergent clean-up steps. Next, we injected 0.5 µg of each peptide sample on a quadrupole Orbitrap mass spectrometer (Q Exactive HF-X) and analyzed them in 100 min single-run data dependent acquisition (DDA) mode (top15).…”

“…S2C), indicating that differences in lysine methylation across samples should only marginally affect global proteome quantification. Despite its beneficial chemical properties for improved protein and peptide extraction [18,20], TFE is a hazardous substance posing a higher safety risk compared to most other protein extraction methods. ACN is an alternative organic solvent, which shares similar physico-chemical properties while being less hazardous.…”