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- W2945771858 abstract "The ability to target discrete features within tissue using liquid surface extractions enables the identification of proteins while maintaining the spatial integrity of the sample. Here, we present a liquid extraction surface analysis (LESA) workflow, termed microLESA, that allows proteomic profiling from discrete tissue features of ∼110 μm in diameter by integrating nondestructive autofluorescence microscopy and spatially targeted liquid droplet micro-digestion. Autofluorescence microscopy provides the visualization of tissue foci without the need for chemical stains or the use of serial tissue sections. Tryptic peptides are generated from tissue foci by applying small volume droplets (∼250 pL) of enzyme onto the surface prior to LESA. The microLESA workflow reduced the diameter of the sampled area almost 5-fold compared to previous LESA approaches. Experimental parameters, such as tissue thickness, trypsin concentration, and enzyme incubation duration, were tested to maximize proteomics analysis. The microLESA workflow was applied to the study of fluorescently labeled Staphylococcus aureus infected murine kidney to identify unique proteins related to host defense and bacterial pathogenesis. Proteins related to nutritional immunity and host immune response were identified by performing microLESA at the infectious foci and surrounding abscess. These identifications were then used to annotate specific proteins observed in infected kidney tissue by MALDI FT-ICR IMS through accurate mass matching." @default.
- W2945771858 created "2019-05-29" @default.
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- W2945771858 date "2019-05-13" @default.
- W2945771858 modified "2023-10-16" @default.
- W2945771858 title "MicroLESA: Integrating Autofluorescence Microscopy, In Situ Micro-Digestions, and Liquid Extraction Surface Analysis for High Spatial Resolution Targeted Proteomic Studies" @default.
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- W2945771858 doi "https://doi.org/10.1021/acs.analchem.8b05889" @default.
- W2945771858 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6652190" @default.
- W2945771858 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31149808" @default.
- W2945771858 hasPublicationYear "2019" @default.
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