DOI: 10.25345/c54f1mx7j
title: MassIVE MSV000099758 - A multi-tiered uDicer with hierarchical blades achieves protein-preserving microdissection down to 10 um
title: Biological tissues are heterogeneous. To study this heterogeneity in the sub-millimeter scale, laser capture microdissection (LCM) has been the prevailing method for isolating cells or regions of interest for downstream molecular profiling. However, as the target microtissues approach cellular dimensions (~10 um), laser-induced photothermal damage, reduced protein extraction efficiency, and challenges in capturing small microtissues in conventional LCM can compromise protein preservation and quantitative fidelity. Here, we report multi-tiered micro dicers (uDicers) to mechanically dissect tissue slices into uniform microtissues down to 10 um. The hierarchical blade architecture limits instantaneous blade-tissue engagement and lowers the cutting force relative to single-tier designs. To benchmark the multi-tiered uDicers, we performed proteomic analysis on microtissues from ethanol-fixed squamous cell carcinoma (SCC) slices generated by uDicers vs. LCM. Under identical nanoPOTS-LC-MS conditions, uDicer outperformed LCM by yielding more peptides and proteins, with the most significant improvements observed at 10-20 um spatial resolution. Confocal imaging showed catapult-associated cavities in microtissues generated by LCM. This material loss, along with membrane-limited protein extraction, likely contributed to the reduced protein coverage compared with uDicer samples. These results demonstrate that multi-tiered uDicers provide consistent and reproducible microdissection at 10 um while maintaining high protein coverage. With spatial registration of microtissues under development, uDicer has potential to complement LCM for next-generation proteomic workflows. Proteomics sample preparation using a nanoPOTS platform: uDicer and LCM generated microtissues were processed in parallel on the nanoPOTS platform for mass spectrometry analysis. A robotic liquid-handling platform, described elsewhere was used for preparation of proteomics sample preparation. Extraction of proteins followed by reduction was performed using a lysis buffer containing 0.1 % (v/v) n-dodecyl-beta D-maltoside, 1 mM tris(2-carboxyethyl)phosphine and 50 mM ammonium bicarbonate (ABC) (pH 8.0). 200 nL of lysis buffer was dispensed into each well and incubated for 1 hour at 70C. Next 50 nL of 10 mM of 2-iodoacetamide in 50 mM ABC buffer was added to each well and incubated in the dark for 30 minutes at room temperature. Following alkylation, the proteins were digested overnight using Lys-C and Trypsin. After overnight digestion, the reaction was quenched using 5% formic acid. The droplets were then evaporated in a vacuum desiccator and stored at -20C. LC-MS/MS analysis: A home-built nanoPOTS autosampler platform was used to sequentially perform sample collection, cleanup and chromatographic separation. An in-house column packed with C18 was used as solid phase extraction column. For chromatographic separation, an in-house packed LC column was used. A column heater was used to maintain a constant column temperature of 50C. Buffer A (0.1 % FA in water) was used to dissolve the dried peptides on the nanoPOTS chips and injected into the solid phase extraction column. The LC-MS system consisted of an UltiMate 3000 RSLCnano System coupled with an Orbitrap Fusion Lumos Tribrid MS. The peptides were then separated using a gradient of buffer B (0.1% FA in acetonitrile). A 30 minute linear gradient of buffer B followed by a 9 minute linear gradient of buffer B was used for separation. Eluted peptides were ionized using an electrospray ionization source. A discrete field asymmetric waveform ion mobility spectrometry (FAIMS pro) interface was used to enhance the sensitivity of the analysis by filtering out singly charged species. Spectral libraries were constructed by sequentially analyzing digested peptides from dissociated SCC tissues at three different compensation voltages (-45, -60 and -70) in three separate LC-MS runs. Fractionated peptide ions were first analyzed using the orbitrap. Precursor ions with charges ranging from +2 to +6 were isolated and fragmented with HCD. MS2 fragment ions were then analyzed in an ion trap. For analysis of the samples, three FAIMS compensation voltages (-45, -60 and -75) were used in three consecutive scans for each LC-MS run. Peptide precursor ions with charge states ranging from +2 to +6 were selected for MS2 analysis. For MS Data Analysis, FragPipe v23.1 was used, equipped with MSFragger, Philosopher for false discovery rate (FDR) filtering and reporting, and IonQuant for label-free-match-between-runs (MBR) and quantification. Peptides were identified and matched to the reference library using retention time, m/z, and FAIMS CV. An FDR rate of 0.05 was used during peptide search. Data cleanup, statistical analysis, and visualization were performed using R Studio.