DOI: 10.25345/c5nz8127s
title: MassIVE MSV000097300 - Novel approach for spatial single-cell proteomics using laser capture microdissection microscopy coupled with microfluidic sample preparation
title: Spatially resolved proteomics, which reveals the proteome in its spatial context, is an invaluable tool for gaining comprehensive insights into biological systems. Laser capture microdissection (LCM) is a widely used method in spatial biology, which allows for the precise and controlled isolation of specific cell populations from within tissue sections. However, current LCM-based cell isolation methods often encounter limited spatial resolution due to the low collection efficiency for tissue sizes smaller than 50 um. Herein, we developed a semi-cut approach that is highly efficient a isolating single cells directly from tissue sections with 5 um spatial resolution. By integrating this method with our nanoPOTS (nanodroplet processing in one pot for trace samples) single-cell proteomics workflow, we established a robust platform for spatially resolved single-cell proteomics (SSCP). Using the SSCP method, we can robustly quantify an average of 115, 710, and 1,573 proteins from pancreatic tissue samples with volumes of 5x5x10 um3, 10x10x10 um3, and 20x20x10 um3, respectively. Furthermore, this approach enabled high-resolution mapping across tissue at single-cell resolution, facilitating the identification of new protein markers and providing a highly efficient strategy for biomarker discovery. Additionally, the semi-cut technology can be easily adaptable in other laboratories without the need for additional device development, which has a great potential to advance the field of spatially resolved single-cell omics. NanoLC-MS/MS analysis: A home-built nanoPOTS LC system was employed for sample injection, online sample cleanup and chromatographic separation. The solid-phase extraction (SPE) column (100 um i.d., 4 cm long) and analytical column (50 um i.d., 25 cm long) were packed in-house with 5 um, C18 packing material (300 Angstrom pore size; Phenomenex) and 1.7 um, C18 packing material (BEH 130 Angstrom pore size; Waters), respectively. An UltiMate 3000 RSLCnano System (Thermo Fisher Scientific) was utilized for the delivery of mobile phase A (0.1 % FA in water) and mobile phase B (0.1% FA in acetonitrile) at a flowrate of 100 nL/min. A column heater was used to maintain the analytical column temperature at 50 deg C. The dried samples on nanoPOTS chips were injected and transferred to the SPE column for 5 min for sample cleanup using a loading buffer containing 5% acetonitrile. The peptides were then eluted and separated on the analytical column with a gradient elution. MS detection was performed using an Orbitrap Fusion Lumos Tribrid MS with a field asymmetric ion mobility spectrometry (FAIMS) interface. For the construction of the spectral library, 50 ng of library samples were analyzed using discrete FAIMS compensation voltage (CV, -45, -60, or -75 V) for each LC-MS run. For low-input tissue sample analysis, three FAIMS CVs (-45, -60 and -75 V) were applied in a single LC-MS run. Data-dependent acquisition (DDA) mode was employed to automatically trigger precursor scan and MS/MS scans. The MS1 was scanned at a resolution of 120,000, with an automatic gain control (AGC) target of 1E6, and a maximum injection time of 118 ms for library samples and 500 ms for low-input tissue samples. For MS/MS acquisition, precursor ions with +2 to +6 charges and intensities &gt;1E4 were isolated using a 1.4 m/z window and fragmented by 30% high energy dissociation (HCD). Fragmented ions were scanned in the ion trap with an AGC target of 2E4 and a maximum injection time of 86 ms for library samples and 150 ms for low-input tissue samples. The cycle time was set to 2 seconds for library samples and 0.8 seconds for low-input tissue samples. Data analysis: Raw files were processed using FragPipe v22.0 (coupled with MSFragger v4.1, Philosopher v5.1.1, IonQuant v1.10.2) for feature detection, database searching, and protein/peptide quantification using transferring identification based on the FAIMS filtering (TIFF) method. Peptides were identified by matching to the reference library through LC retention time, accurate m/z, and FAIMS CV. MS spectra were searched against the UniProt human (homo sapiens) database (downloaded May 2024, containing 20,419 reviewed protein sequences). Strict trypsin was set as the cleavage enzyme. 1% false discovery rate (FDR) was used for identifications at both unique peptide level and protein level. Match-Between-Runs (MBR) FDR was 5%.