Comprehensive Single-Shot Proteomics with FAIMS on a Hybrid Orbitrap Mass Spectrometer

Abstract

Liquid chromatography (LC) prefractionation is often implemented to increase proteomic coverage; however, while effective, this approach is laborious, requires considerable sample amount, and can be cumbersome. We describe how interfacing a recently described high-field asymmetric waveform ion mobility spectrometry (FAIMS) device between a nanoelectrospray ionization (nanoESI) emitter and an Orbitrap hybrid mass spectrometer (MS) enables the collection of single-shot proteomic data with comparable depth to that of conventional two-dimensional LC approaches. This next generation FAIMS device incorporates improved ion sampling at the ESI-FAIMS interface, increased electric field strength, and a helium-free ion transport gas. With fast internal compensation voltage (CV) stepping (25 ms/transition), multiple unique gas-phase fractions may be analyzed simultaneously over the course of an MS analysis. We have comprehensively demonstrated how this device performs for bottom-up proteomics experiments as well as characterized the effects of peptide charge state, mass loading, analysis time, and additional variables. We also offer recommendations for the number of CVs and which CVs to use for different lengths of experiments. Internal CV stepping experiments increase protein identifications from a single-shot experiment to >8000, from over 100 000 peptide identifications in as little as 5 h. In single-shot 4 h label-free quantitation (LFQ) experiments of a human cell line, we quantified 7818 proteins with FAIMS using intra-analysis CV switching compared to 6809 without FAIMS. Single-shot FAIMS results also compare favorably with LC fractionation experiments. A 6 h single-shot FAIMS experiment generates 8007 protein identifications, while four fractions analyzed for 1.5 h each produce 7776 protein identifications.

Integrated Proteomics Reveals Brain-Based Cerebrospinal Fluid Biomarkers in Asymptomatic and Symptomatic Alzheimer’s Disease

“…Therefore, following TMT labeling, we analyzed our CSF2 samples by "single-shot" LC-MS/MS integrated with high-Field Asymmetric Waveform Ion Mobility Mass Spectrometry (FAIMS). When combined with synchronous precursor selection MS3-based quantitation (SPS-MS3), this FAIMS-based strategy is especially useful for enhancing accuracy of protein quantification in samples with high dynamic ranges of protein abundance, such as albumin-and immunoglobulin-rich spinal fluid [51]. Its ion separation step helps to limit the signal interference of albumin and other overwhelmingly abundant proteins, allowing for enhanced quantification of detected low-abundance proteins.…”

“…The gradient was 1% to 8% B over 3 min, then 8% to 40% B over 160 min, then 40% to 99% B over 10 min, and finally 99% B for 10 min. The mass spectrometer was set to acquire data in positive ion mode using data-dependent acquisition and three (-50, -65 and -85) different compensation voltages (CV) [51]. Each of the three experiments consisted of a single CV and 1s cycles.…”

A Consensus Proteomic Analysis of Alzheimer’s Disease Brain and Cerebrospinal Fluid Reveals Early Changes in Energy Metabolism Associated with Microglia and Astrocyte Activation

“…The gradient was from 1% to 8% buffer B over 3 min, then from 8% to 40% over 160 min, then from 40% to 99% over 10 min, and then held at 99% B for 10 min. The mass spectrometer was set to acquire data in positive ion mode using data dependent acquisition and three (-50, -65 and -85 V) different compensation voltages (CV)134 . Data were acquired at each CV for 1 s during each cycle.…”

Microscaled Proteogenomic Methods for Precision Oncology

“…Analysis time could be reduced with automation of sample processing, the use of faster instrumentation and orthogonal gas phase fraction such as FAIMS [47][48][49] . Furthermore, the protocol as presented can be readily adapted for use as a diagnostic tool by redirecting some of the denatured protein obtained using the BioTExt procedure to PRM assays developed for targets delineated in larger clinical discovery datasets, and, as illustrated for ERBB2 ( Supplementary Figure 6).…”