INTRODUCTION Data-Independent Analysis (DIA or MSE) has shown distinct promise for improving the consistency of peptide identifications as well as increasing protein sequence coverage in complex samples. In the MSE implementation, the collision energy is alternated between low energy and high energy ramp in order to produce precursor- and product-ion spectra, respectively. However, with complex proteomes there are always co-eluting peptides for which retention time alignment is inadequate to deconvolve the MSE spectra. One approach to reduce MSE spectral complexity is to include an ion mobility separation prior to peptide fragmentation, providing both retention time and mobility coordinates for assigning precursor/product ion relationships. This approach is called Ion Mobility-Assisted DIA or HDMSE. Label-free alignment of DIA and DDA data then allows the complementary identifications provided by all methods to be aggregated.

METHODS

Tryptic digests were analyzed using either a nanoAcquity nanoflow LC or 2D nanoAcquity coupled to a Synapt G2 mass spectrometer (Waters, Milford, MA). Yeast samples were purchased from NIST and other protein samples were provided by investigators at Duke School of Medicine. All samples were solubilized in 0.1% or 0.25% Rapigest, reduced with 10 mM DTT, alkylated with 20 mM iodoacetamide, and digested with trypsin (Promega Sequencing Grade) at a 1:50 w/w ratio overnight at 37C. Samples were acidified to 1% TFA, dried, then resuspended in 200 mM pH10 Ammonium Formate. For 2DLC, either 5 or 8 fractions were generated at pH 10 ammonium formate (RPLC) from a Symmetry C18 at pre-defined acetonitrile steps as previously described1, and trapped in real-time following 10-fold post-column dilution onto a 5 um Xbridge C18 column. The second dimension separation was performed using a 150 mm x 75 um 1.7 um BEH column in 0.1% formic acid. Gradients were either 7 to 35% MeCN in 36 minutes at 0.5 uL/min (5-fraction) or 5 to 40% MeCN in 60 minutes at 0.4 uL/min (8-fraction). We conducted data-independent analysis (MSE), mobility-assisted data-independent analysis (HDMSE) and data-dependent analyses (DDA) of select samples. Protein identification was performed in ProteinLynx Global Server v2.5 (HDMSE) or Mascot (DDA). Data were searched against concatenated forward/reverse databases to control for false discovery, with 10 ppm precursor and 0.04 Da product ion tolerances. Rosetta Elucidator® v3.3 (Rosetta Biosoftware) with PeakTeller algorithm was utilized for peak detection and label-free alignment. The Elucidator implementation of PeptideProphet allowed for annotation across search-engine types with consistent and unbiased false-positive rate determination.

• Panel A: Peptides are evenly split between fractions 1-6, with a slight bias to fractions 7 and 8.

• Panel B: 85% of peptides quantified are only in a single fraction using this approach, showing high separation efficiency in the pH 10 RPLC first-dimension.

• Panel C: Peptides seen in multilple fractions follow traditional RPLC carryover, where the most abundant peptides are much more likely to be seen in multiple fractions.

Ion Mobility-Assisted Data Independent Analysis with Inter-Analysis Alignment Provides Improved Depth of Proteome Coverage

J. Will Thompson 1; Scott Geromanos2; Martha D. Stapels2; Laura G Dubois1; Keith Fadgen 2; Cindy Chepanoske3; Erik J Soderblom 1; M. Arthur Moseley1 1Duke University School of Medicine, Durham , NC; 2Waters Corporation, Milford, MA; 3Ceiba Solutions, Seattle, WA

www.genome.duke.edu/proteomics/

FUNDING: The authors would like to gratefully acknowledge the National Institutes of Health and Duke University School of Medicine for the support of this research.

RP/RP LC Separation

Alignment of All Data (AMRT) to a Merged Image for Quantitation

Data-Dependent Analysis (Qualitative)

+ Data-Independent Analysis, MSE

(Qual/Quant)

+ Ion Mobility-Assisted Data-Independent

Analysis, HDMSE

References: 1. Gilar, M et. Al. Two-dimensional separation of peptides using RP-RP-HPLC system with

different pH in first and second separation dimensions. J Sep Sci. 2005 Sep;28(14):1694-703.

High/Low pH 2DLC Fractionation Results, Yeast Lysate

Linearity of Quantitation Comparing Data-Independent and Mobility-Assisted Data Independent Analysis

A B C

High Spectral Quality: Validation of Mobility-Assisted DIA Identifications by Performing Searches Against Alternative Search Engines (e.g. MASCOT)

DDA

HDMSE

Acquisition “Rules” +2 to +4 CS, Top 3, >400 m/z, dynamic exclusion, etc

No Acquisition “Rules”; Alternate 0.6 s MS with MSE

and Real-Time IMS

Mascot Distiller / PLGS Processing, Combine MS/MS spectra (.mgf / .pkl)

Apex4D Processing, RT/IMS alignment, .pkl FILTERING (>800 M+H, Perl)

ACQUIRE PROCESS Mascot SEARCH

MASCOT Search Results from 5-fraction RP/RPLC Analysis of 3 ug Platelet Lysate with DDA or HDMSE Acquisition (5 hours total)

Selectivity Gained from Orthogonal Ion-Mobility Separation can Produce Spectra Similar to Serial Precursor Isolation

DIA Spectra acquired by HDMSE DDA Spectra

Selectivity Gained from Orthogonal Ion-Mobility Separation Sometimes Resolves Chimeric DDA Spectra

DDA Spectrum, Mascot Ion Score 5 (clearly two peptides in MS/MS isolation window)

DIA Spectrum acquired by HDMSE, Mascot Ion Score 62

LC/LC-HDMSE Impact on Biomedical Applications (5-hour single-sample pilot studies)

Lung Epithelium, Cystic Fibrosis patient Proteins (0.7% FDR)

2 369 303

107 980 3627

Peptides (0.3% FDR)

DDA/Mascot (371)

HDMSE/IdentityE (672)

DDA/Mascot (1087)

HDMSE/IdentityE (4607)

Platelet Lysate, Healthy Volunteer Proteins (0.5% FDR)

1 343 262

122 1692 5161

Peptides (0.1% FDR)

DDA/Mascot (370)

HDMSE/IdentityE (605)

DDA/Mascot (1814)

HDMSE/IdentityE (6853)

Samples courtesy of Deepak Voora, MD

Duke Institute for Genome Sciences & Policy Samples courtesy of Bernie Fisher, DVM, PhD

Pediatric Pulmonary Medicine, Duke Univ Med Ctr.

Co-Alignment of DDA, MSE, and HDMSE using Accurate Mass and Retention Time (AMRT) Gives Maximum Information Content

• NIST Yeast Lysate, Analyzed by 8-fraction LC/LC • Alignment of DDA, MSE (15-40V), and HDMSE (27-50V) data collections in Rosetta Elucidator • All spectra processed through PeptideTeller and ProteinTeller Algorithms, 0.5% Peptide FDR

• Pilot studies are important feasibility-determining studies for basic science and clinical researchers

• These studies must be fast, low-cost, and with reasonable proteome coverage to allow the researcher to determine if a differential expression study is feasible given a particular sample type

• HDMSE acquisition has shown the ability to double the number of confidently identified proteins and triple to quadruple the number of confidently identified peptides over our traditional DDA approach

Qualitative Overlap in Peptide Identifications Quantitative Distribution of Identified Peptides

2242

2517

DDA, 3515 peptides (175 unique)

MSE 6371 peptides

HDMSE 9942 peptides

4872

1813

• Single 8-fraction LC/LC analysis of Yeast was utilized to compare quantitative linearity between MSE and HDMSE acquisition

• Quantitation is linear between HDMSE and MSE modes over 4 orders of magnitude

• Slight nonlinear quantitative bias (red box) noted for high abundance peaks in HDMSE mode, speculation is that this may be due to saturation of the Ion Mobility separation

MASCOT Qualitative Pipeline

574 1247 735

DDA/Mascot (1821)

HDMSE/Mascot (1982)

Mascot Peptide Identifiations • Whereas retention time-only aligned DIA spectra (MSE approach) do not search well against alternative search engines, the additional mobility separation in HDMSE produces spectra which resemble those generated by traditional MS/MS (precursor isolation)

• This allows HDMSE to many times outperform DDA even when using traditional search engines

Peptide YDPTIEDSYR RAP1A_HUMAN

Peptide SGTDVDAANLR CASP3_HUMAN

Peptide VELEDWNGR FIBG_HUMAN