Ultra Sensitive LC-MS Workflow for Single Cell Proteomic Analysis

Importance of the Topic

The ability to characterize proteomes at the single-cell level addresses fundamental questions in biology and medicine by revealing cellular heterogeneity, rare cell populations, and limited clinical samples such as needle biopsies and exosomes. Ultra-sensitive workflows are key to mapping protein expression in individual cells, guiding advances in cancer research, immunology, and drug development.

Study Objectives and Overview

This whitepaper presents an integrated ultra-sensitive liquid chromatography–tandem mass spectrometry (LC-MS/MS) workflow tailored for single-cell proteomic analysis. It combines nanodroplet-scale sample processing (nanoPOTS), high-performance chromatographic separation, advanced FAIMS ion mobility, and state-of-the-art Orbitrap platforms (Eclipse Tribrid MS and Exploris 480 MS) with real-time database searching and isobaric labeling strategies to maximize depth, accuracy, and throughput.

Methodology and Instrumentation

• Sample Preparation: Fluorescence-activated cell sorting or laser capture microdissection isolates single cells. NanoPOTS platform carries out lysis, reduction/alkylation, and proteolysis in nanoliter droplets, minimizing losses.
• Chromatographic Separation: Low-flow nanoLC systems (20–30 µm i.d. columns) and trap-and-elute configurations deliver sharp peaks from picogram to nanogram loads.
• Mass Spectrometry: Orbitrap Eclipse Tribrid supports MSn up to MS10 with CID, HCD, ETD, EThcD, UVPD, and Proton Transfer Charge Reduction. QR5 segmented quadrupole and optional High Mass Range (HMRn) and PTCR enhance selectivity. Exploris 480 offers 480 000 resolution, robust design, and FAIMS Pro interface for interference removal.
• Data Acquisition and Analysis: Real-time search (Comet-based) dynamically triggers SPS MS3 scans for Tandem Mass Tag (TMT) quantitation. Software pipelines include Proteome Discoverer and MaxQuant.

Main Results and Discussion

• Single HeLa Cells: Label-free LC-MS on Eclipse Tribrid identified ~550 proteins per cell; FAIMS addition boosted IDs by ~50% to ~830 proteins.
• Nanogram Loads: From 0.5 ng HeLa digest, ~750 protein groups were identified, demonstrating sub-nanogram sensitivity.
• TMT Multiplexing: Real-time search SPS MS3 doubled throughput relative to classic SPS MS3, maintaining quantification precision (CV <1%) while enabling analysis of up to 16 channels with TMTpro reagents.
• Reproducibility: Five-repeat injections at 200 ng produced <1% CV in protein IDs and <2% CV in peptide quantitation on Eclipse + FAIMS.
• Throughput: Exploris 480 achieved 3 000–7 500 protein IDs in 90–120 min gradients for 10–1 000 ng HeLa loads; FAIMS and intra-analysis CV stepping further improved coverage.

Benefits and Practical Applications

The described workflow offers robust characterization of cellular heterogeneity, detection of rare cell types, and quantitative proteome profiling from minimal sample inputs. It supports cancer subpopulation studies, immune cell phenotyping, and biopharmaceutical research by delivering high sensitivity, accuracy, and reproducibility.

Future Trends and Potential Applications

Ongoing developments will focus on increasing multiplexing capacity (beyond 16-plex), integrating machine-learning approaches for real-time decision making, and further miniaturizing separation platforms. Coupling with spatial proteomics, single-cell multi-omics, and clinical diagnostics will expand impact in precision medicine.

Conclusion

This ultra-sensitive LC-MS/MS workflow merges nanoliter sample prep, advanced ion mobility, high-resolution Orbitrap analysis, and intelligent data acquisition to achieve unprecedented depth and quantification in single-cell proteomics. It lays a foundation for transformative discoveries in life sciences.

Used Instrumentation

• Thermo Scientific Orbitrap Eclipse Tribrid MS
• Thermo Scientific Orbitrap Exploris 480 MS
• FAIMS Pro interface
• nanoPOTS nanodroplet processing platform
• UltiMate 3000 RSLCnano system

Reference

• Zhu et al., Nature Communications 2018, 9, 882
• Pfammatter et al., Molecular & Cellular Proteomics 2018
• Eng et al., Proteomics 2013, 13(1):22–24
• Budnik et al., Genome Biology 2018, 19, 161
• S. Schweppe, Q. Yu, S. Gygi; J. Proteome Res. 2019, 18(3), 1299–1306