Online 2D-nanoLC hyphenated with high-resolution accurate-mass Orbitrap mass spectrometry for comprehensive and robust proteome profiling
Applications | 2019 | Thermo Fisher ScientificInstrumentation
This work addresses the critical need for high-depth, high-throughput proteome profiling of complex samples such as cell digests and body fluids. By integrating two-dimensional nanoLC separations online with high-resolution accurate-mass Orbitrap detection, the method overcomes limitations of sample loss, lengthy offline fractionation, and low MS utilization that traditionally constrain deep proteomic analysis.
The authors developed and evaluated an automated online 2D-nanoLC platform combining high-pH reversed-phase (first dimension) and low-pH reversed-phase (second dimension) separations on a Thermo Scientific UltiMate 3000 RSLCnano system coupled to an Orbitrap Exploris 480. They applied 2-, 4-, and 8-fraction methods to HeLa digest and crude human serum to assess proteome coverage, throughput, and scalability.
Sample preparation was simplified: HeLa digest was reconstituted in 0.1% formic acid, and crude serum underwent methanol precipitation followed by dual-step trypsin digestion without reduction/alkylation and SPE cleanup. Online 2D-nanoLC employed a PepSwift monolithic first-dimension column (pH 8 buffer) with alternating trapping on two nano-traps, followed by low-pH separation on an EASY-Spray column in 45 min cycles. Data were acquired in DDA mode on an Orbitrap Exploris 480 and processed in Proteome Discoverer (PD) 2.2 and 2.4 using SEQUEST HT and library search nodes; FDR was controlled at <1%.
The 8-fraction method (6.75 h) with 4 μg loading of HeLa digest yielded ~7 000 proteins and >70 000 peptides with 88% MS2 utilization. Orthogonality between dimensions was high, distributing unique peptides evenly across fractions. For serum, a 4-fraction method (3.75 h) identified ~329 proteins and ~3 700 peptides per 0.5 μL crude sample. Reprocessing with PD 2.4 and library search boosted identifications by ~27% (peptides) and ~14% (proteins).
Further expansions could include additional fractions for ultra-deep coverage, integration with isobaric labeling workflows, and application to single-cell or spatial proteomics. Advances in real-time instrument control and AI-driven MS acquisition may further enhance sensitivity and speed.
The online 2D-nanoLC-Orbitrap approach delivers robust, high-depth proteomic profiling with minimal sample loss, high MS utilization, and flexible scalability. It represents a powerful alternative to lengthy one-dimensional separations and labor-intensive offline workflows, enabling comprehensive analysis of cell and biofluid proteomes.
LC/HRMS, LC/MS, LC/MS/MS, LC/Orbitrap, 2D-LC
IndustriesProteomics
ManufacturerThermo Fisher Scientific
Summary
Significance of the topic
This work addresses the critical need for high-depth, high-throughput proteome profiling of complex samples such as cell digests and body fluids. By integrating two-dimensional nanoLC separations online with high-resolution accurate-mass Orbitrap detection, the method overcomes limitations of sample loss, lengthy offline fractionation, and low MS utilization that traditionally constrain deep proteomic analysis.
Objectives and study overview
The authors developed and evaluated an automated online 2D-nanoLC platform combining high-pH reversed-phase (first dimension) and low-pH reversed-phase (second dimension) separations on a Thermo Scientific UltiMate 3000 RSLCnano system coupled to an Orbitrap Exploris 480. They applied 2-, 4-, and 8-fraction methods to HeLa digest and crude human serum to assess proteome coverage, throughput, and scalability.
Methodology and instrumentation
Sample preparation was simplified: HeLa digest was reconstituted in 0.1% formic acid, and crude serum underwent methanol precipitation followed by dual-step trypsin digestion without reduction/alkylation and SPE cleanup. Online 2D-nanoLC employed a PepSwift monolithic first-dimension column (pH 8 buffer) with alternating trapping on two nano-traps, followed by low-pH separation on an EASY-Spray column in 45 min cycles. Data were acquired in DDA mode on an Orbitrap Exploris 480 and processed in Proteome Discoverer (PD) 2.2 and 2.4 using SEQUEST HT and library search nodes; FDR was controlled at <1%.
Used instrumentation
- UltiMate 3000 RSLCnano modules: NCS-3500RS, NCP-3200RS, WPS-3000TPL RS, VWD-3400RS with 10-port 2-pos valve
- Columns: PepSwift monolithic (100 μm × 250 mm), Acclaim PepMap trap, EASY-Spray analytical (75 μm × 150 mm)
- Mass spectrometer: Thermo Scientific Orbitrap Exploris 480
- Syringe and valve fluidics, nanoViper fittings
Main results and discussion
The 8-fraction method (6.75 h) with 4 μg loading of HeLa digest yielded ~7 000 proteins and >70 000 peptides with 88% MS2 utilization. Orthogonality between dimensions was high, distributing unique peptides evenly across fractions. For serum, a 4-fraction method (3.75 h) identified ~329 proteins and ~3 700 peptides per 0.5 μL crude sample. Reprocessing with PD 2.4 and library search boosted identifications by ~27% (peptides) and ~14% (proteins).
Benefits and practical applications
- Fully automated online workflow eliminates offline fractionation and manual handling
- High throughput with deep coverage in under 7 h per run
- Adaptable to varying fraction numbers and sample loads for customization
- Suitable for clinical cohorts and biomarker discovery in complex biofluids
Future trends and potential applications
Further expansions could include additional fractions for ultra-deep coverage, integration with isobaric labeling workflows, and application to single-cell or spatial proteomics. Advances in real-time instrument control and AI-driven MS acquisition may further enhance sensitivity and speed.
Conclusion
The online 2D-nanoLC-Orbitrap approach delivers robust, high-depth proteomic profiling with minimal sample loss, high MS utilization, and flexible scalability. It represents a powerful alternative to lengthy one-dimensional separations and labor-intensive offline workflows, enabling comprehensive analysis of cell and biofluid proteomes.
References
- Boychenko A., Pynn C., Arrey T., Zheng R., Decrop W., Jehle P. Tailored high-throughput low-flow LC-MS methods for large scale sample cohort analysis. Thermo Scientific Technical Note 73208.
- Osorio D., Rondón-Villarreal P., Torres R. Peptides: Calculate indices and theoretical physicochemical properties of peptides and protein sequences. R package version 2.2, 2014.
- R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2019.
- Carr D., Lewin-Koh N., Maechler M. Hexagonal Binning Routines. 2019.
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