Spatially Resolved Proteome Mapping of Laser Capture Microdissected Tissue with Automated Sample Transfer to Nanodroplets

Current mass spectrometry (MS)-based proteomics approaches are ineffective for mapping protein expression in tissue sections with high spatial resolution because of the limited overall sensitivity of conventional workflows. Here we report an integrated and automated method to advance spatially resolved proteomics by seamlessly coupling laser capture microdissection (LCM) with a recently developed nanoliter-scale sample preparation system termed nanoPOTS (Nanodroplet Processing in One pot for Trace Samples). The workflow is enabled by prepopulating nanowells with DMSO, which serves as a sacrificial capture liquid for microdissected tissues. The DMSO droplets efficiently collect laser-pressure catapulted LCM tissues as small as 20 μm in diameter with success rates >87%. We also demonstrate that tissue treatment with DMSO can significantly improve proteome coverage, likely due to its ability to dissolve lipids from tissue and enhance protein extraction efficiency. The LCM-nanoPOTS platform was able to identify 180, 695, and 1827 protein groups on average from 12-μm-thick rat brain cortex tissue sections having diameters of 50, 100, and 200 μm, respectively. We also analyzed 100-μm-diameter sections corresponding to 10–18 cells from three different regions of rat brain and comparatively quantified ∼1000 proteins, demonstrating the potential utility for high-resolution spatially resolved mapping of protein expression in tissues. Current mass spectrometry (MS)-based proteomics approaches are ineffective for mapping protein expression in tissue sections with high spatial resolution because of the limited overall sensitivity of conventional workflows. Here we report an integrated and automated method to advance spatially resolved proteomics by seamlessly coupling laser capture microdissection (LCM) with a recently developed nanoliter-scale sample preparation system termed nanoPOTS (Nanodroplet Processing in One pot for Trace Samples). The workflow is enabled by prepopulating nanowells with DMSO, which serves as a sacrificial capture liquid for microdissected tissues. The DMSO droplets efficiently collect laser-pressure catapulted LCM tissues as small as 20 μm in diameter with success rates >87%. We also demonstrate that tissue treatment with DMSO can significantly improve proteome coverage, likely due to its ability to dissolve lipids from tissue and enhance protein extraction efficiency. The LCM-nanoPOTS platform was able to identify 180, 695, and 1827 protein groups on average from 12-μm-thick rat brain cortex tissue sections having diameters of 50, 100, and 200 μm, respectively. We also analyzed 100-μm-diameter sections corresponding to 10–18 cells from three different regions of rat brain and comparatively quantified ∼1000 proteins, demonstrating the potential utility for high-resolution spatially resolved mapping of protein expression in tissues. Biological tissues are often highly heterogeneous, consisting of a variety of cell types, subpopulations, and substructures (1Satija R. Farrell J.A. Gennert D. Schier A.F. Regev A. Spatial reconstruction of single-cell gene expression data.Nat. Biotechnol. 2015; 33: 495-502Crossref PubMed Scopus (2052) Google Scholar). Tissue cells often generate distinct microenvironments to execute biological functions, providing varied response to external stimuli, and often resulting in distinct pathology. Spatially resolved and multiplexed molecular imaging of tissue sections is of key importance for understanding biological function and pathogenesis (2Crosetto N. Bienko M. van Oudenaarden A. Spatially resolved transcriptomics and beyond.Nat. Rev. Genet. 2014; 16: 57-66Crossref PubMed Scopus (287) Google Scholar, 3Lein E. Borm L.E. Linnarsson S. 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Although approaches likely proteome by using MS are that are likely to for liquid are by and to smaller extraction of tissue regions of is also to with the of the tissues to with small and regions of in microdissection (LCM) used capture in pot for used capture in pot for an for because can tissue regions as small as single cells and with as by high-resolution J.F. M. of from spatially distinct and of and tissue sections by laser Chem. 2016; 88: PubMed Scopus Google Scholar). LCM has been integrated for spatially resolved P. Mann M. to the analysis of microdissected tissues 10: PubMed Scopus Google Scholar) comparatively analyzed protein expression of and and and G. Piehowski P.D. J.A. Moore R.J. J.A. N. analysis of laser capture microdissected tissue 2016; PubMed Scopus Google Scholar) proteome of tissues during and distinct biological and D. of automated for small sample amounts and its to tissue 2016; PubMed Scopus Google Scholar) an proteomics workflow to and a of protein was To sample losses during sample preparation and improve proteome coverage, approaches developed including the of species P. Mann M. to the analysis of microdissected tissues 10: PubMed Scopus Google G. Piehowski P.D. J.A. Moore R.J. J.A. N. analysis of laser capture microdissected tissue 2016; PubMed Scopus Google Scholar) and with D. of automated for small sample amounts and its to tissue 2016; PubMed Scopus Google Scholar). tissue cells to proteome laser capture microdissection in pot for cortex laser capture microdissection in pot for cortex We have recently developed an proteome and analysis platform termed nanoPOTS (Nanodroplet Processing in One pot for Trace Y. Piehowski P.D. Zhao R. J. Y. Moore R.J. platform for and proteome of PubMed Scopus Google Scholar). significantly the overall sensitivity of proteomics by the sample to the thus the protein and losses to the of proteins was for as as which is a of proteome for than thousands of The nanoPOTS platform was also used to laser microdissected sections of single However, a challenge with the nanoPOTS platform was the to sample tissues from a to the nanowells using a a which limited the tissue to μm, and in a of spatial for the the the success rates of sample and the was was also to protein from tissues or during the the of automated sample from LCM to the nanoPOTS is for of tissue with high spatial resolution and proteome Here we the of nanoPOTS with LCM by prepopulating the nanowells with DMSO droplets The DMSO serves as a highly sacrificial capture for tissue as small as single of for rat brain and tissue a 12-μm-thick of rat brain as a we demonstrate that the LCM-nanoPOTS workflow by DMSO as capture can identify and comparatively quantify ∼1000 proteins with a spatial resolution of μm, corresponding to 10–18 brain the direct of LCM with nanoPOTS using DMSO droplets for tissue Image of a nanoPOTS with an of DMSO of a nanoPOTS on a for a LCM tissue and the corresponding tissue in nanowells with from 20 μm to 200 12-μm-thick rat brain was used as Image from a system was used and from and in and from and from from The nanoPOTS of three including a a and a The was with as Y. 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The and high capture with sample such as mapping of tissues. also that tissue with a diameter of 20 μm to single cells in demonstrating the potential of the for single-cell isolation and To DMSO tissue we analyzed rat cortex tissue with DMSO droplets and with those using DMSO we a and in average and resulting in a corresponding and in protein DMSO was used for tissue of protein that of the proteins from in that the of DMSO droplets proteome for small tissue on the for is that DMSO overall protein extraction by the tissues. extraction from tissue was to than for cells G. S. M. J. of extraction methods for the analysis of brain proteome using mass PubMed Scopus Google for tissues with high such as approaches have been developed to challenge by A. Nagaraj N. Mann M. sample preparation method for proteome Methods. PubMed Scopus Google Scholar) or I. 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To the potential utility of the LCM-nanoPOTS system for spatially resolved tissue we a by and three different rat brain regions cortex and from a 12-μm-thick Tissue as regions with a diameter of μm, corresponding to an of The spatial to from μm to μm the brain and from μm to μm across different brain regions each and analyzed by To the number of proteins, we used the of Y. Zhao R. Piehowski P.D. Moore R.J. Lim S. L. of MS and analysis on proteome for J. PubMed Scopus Google Scholar, S. Cox J. The platform for mass 2016; PubMed Scopus Google Scholar) the based on and to the by L. of mass for protein Chem. 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We also the high expression of proteins and and protein Spatially resolved proteome mapping of or tissue sections can or The ability to map the proteome of thousands of with high spatial resolution across tissue regions a to tissue and from a proteome can integrated with other spatially resolved methods such as immunohistochemistry staining, spatial transcriptomics (4Ståhl P.L. Salmén F. Vickovic S. Lundmark A. Navarro J.F. Magnusson J. Giacomello S. Asp M. Westholm J.O. Huss M. Mollbrink A. Linnarsson S. Codeluppi S. Borg Å Pontén F Costea P.I. Sahlén P. Mulder J. Bergmann O. Lundeberg J. Frisén J. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.Science. 2016; 353: 78-82Crossref PubMed Scopus (971) Google and imaging (5Van de Plas R. Yang J. Spraggins J. Caprioli R.M. Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping.Nat. Methods. 2015; 12: 366-372Crossref PubMed Scopus (171) Google Scholar) to a molecular of tissue The LCM-nanoPOTS platform significantly advances spatially resolved proteomics by improving the spatial resolution and the The of DMSO droplets to efficiently capture tissue as small as 20 μm in diameter also significantly the proteome The workflow can automated and thus sample and protein are The to tissues by and sample We platform an in tissue characterization at the proteome The also broadly to other such as and advances of sensitivity, we proteome mapping at single cell resolution the LCM-nanoPOTS platform to other tissue isolation and processing, such as and with and the bottom-up proteomics is in as each analysis at the for mapping of tissue sections at high the LCM-nanoPOTS platform is for spatially resolved of tissue because of the of proteome in by using K. Zhao R. Y. Moore R.J. automated system for in Chem. PubMed Scopus Google sample based on chemical D. of automated for small sample amounts and its to tissue 2016; PubMed Scopus Google or by using and high-resolution ion separations in of Moore R.J. X. R. bottom-up and measurements with ion 2015; PubMed Scopus Google Scholar, L. X. J.A. resolution ion separations in a for ion Chem. 2016; 88: PubMed Scopus Google Scholar). to and imaging techniques M. D. R. M. spectrometry laser capture and of the tissue 2017; 16: PubMed Scopus Google Scholar) can with the LCM-nanoPOTS platform by regions and tissue substructures for proteome The mass spectrometry proteomics have been to the the with the and with