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bioRxiv · 10.1101/2025.11.06.686925

High-Throughput Spatial Proteomics at Cellular and Subcellular Resolution via a Hanging Droplet Workflow

Chen, H.·Guzman, U.·Olsen, J. V.
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Abstract

Formalin-fixed paraffin-embedded (FFPE) samples are essential for clinical research and spatial proteomics (SP) but are technically challenging to analyze by liquid-chromatography coupled mass-spectrometry (LC-MS) due to losses during protein extraction, evaporation during decrosslinking and low-throughput sample preparation methods. Here, we present a streamlined micro-FFPE proteomics sample preparation protocol for laser-capture micro-dissection (LMD) that enables scalable and high-throughput processing in 96-well format, completing the entire process from tissue lysis to tryptic peptide mixtures on LC-MS-ready Evotips in just 2-hours. Our method integrates whole-slide imaging, LMD, and ultra-sensitive narrow-window data-independent acquisition (nDIA)-MS and facilitates enhanced lysis, decrosslinking, and proteolytic digestion directly within Teflon-based EVO96 chips. By utilizing nano-wells and the hanging droplet concept, our approach significantly reduces surface adsorption, thereby improving processing efficiency for both higher- and very low-input FFPE samples. This enables quantification of >5,500 protein groups (PGs) by nDIA-MS from 50,000 {micro}m{superscript 2} ([~]250 cells) with robust performance maintained down to 1000 {micro}m2 ([~]5 cells) with [~]2,500 PGs identified. Application to a panel of breast cancer (BC) tissues precisely discerned BC subtypes and their tumor micro-environment (TME) based on differential MS-based protein abundance of biomarkers including TP53, HER2 and PGR. Individually excised subcellular regions ([~]60 {micro}m{superscript 2}) yielded [~]1,500 PGs, demonstrating reliable micro-scale spatial proteomics profiling approaching near-organelle resolution. TeaserStreamlined micro-FFPE protocol enables scalable and high-throughput processing of LMD samples, from lysis to mass spectrometry in 2 hours.

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Chen, H., Guzman, U., Olsen, J. V.. 2025-11-07. High-Throughput Spatial Proteomics at Cellular and Subcellular Resolution via a Hanging Droplet Workflow. https://doi.org/10.1101/2025.11.06.686925

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