Ultra-fast proteomics with Scanning SWATH

Abstract

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min^-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

Figure 1. Scanning SWATH replaces the stepwise precursor selection with a continuously moving quadrupole and thereby adds another dimension to the data and shortens duty cycles

a. In conventional SWATH-MS/DIA-MS, a quadrupole selects a relatively wide mass range, and the detector collects MS/MS spectra for a defined accumulation time. The windows are stepped and are overlapping (to compensate for edge effects). The collision cell needs to be emptied after each step. b. In Scanning SWATH, the isolation window slides over the precursor mass range and MS/MS spectra are continuously acquired. The continuous movement of the quadrupole results in a time dependency of the fragment intensity. Fragment signals appear when the leading edge of the quadrupole passes the precursor m/z and they disappear when the precursor m/z falls out of the quadrupole isolation window. c. The acquired raw data is sectioned into bins of a defined m/z size. Data from TOF pulses that overlap with a certain m/z bin are summed together and written into the respective bin (e.g. all TOF pulses labeled in red on the diagram are summed together in the respective bin). Therefore, the highest signal for a fragment is in the bin which includes the respective precursor mass. In contrast to conventional SWATH, data from each TOF pulse is written into more than one bin, resulting in a Q1 profile of a triangular shape. d,e. The Q1 profile provides a 4^th dimension in the Scanning SWATH data. In conventional SWATH each fragment mass (mass dimension) has a certain intensity (intensity dimension) that is measured along the chromatographic time (retention time dimension). e. In Scanning SWATH data, each fragment gives rise also to a Q1 profile (Q1 dimension) f. Different fragments from the same precursor show correlating Q1 profiles (e.g. green, orange and purple fragments). The apex of the Q1 profile corresponds to the precursor mass and thus fragments from different precursors can be distinguished (e.g. green, orange and purple fragments belong to a different precursor than the pink fragment).

Figure 2. Scanning SWATH improves peptide identification in short gradients

a. 10 µg human cell line digest was acquired with a Scanning SWATH method (10 m/z window) and a conventional stepped SWATH method using a 5-minute high-flow (800 µL/min) LC gradient. The data were analyzed with DIA-NN using a two-species library, that contains human and Arabidopsis thaliana precursors, to validate the FDR experimentally (methods) ^,. b. left panel: Q1 profiles of fragments corresponding to a true-positive target precursor (human) with a mass of 443.8 m/z (AVVIVDDR(2+)). b. right panel: Q1 profile of fragments corresponding to a false target (Arabidopsis thaliana precursor) with the precursor mass 799.1 m/z (FDGALNVDVTEFQTNLVPYPR(3+)). c. Number of protein groups identified (1% FDR) in a K562 cell lysate with Scanning SWATH and conventional stepped SWATH, using 5, 3, 1 minute and 30 second chromatographic gradients and adjusted duty cycles (Table S1, S2, S3). d. Number of precursors (peptides ionized to a specific charge) and peptides (stripped sequences) identified (1% FDR) in human cell lysates measured with different acquisition schemes and platforms. A K562 digest was analyzed with 5 and 1-minute gradient Scanning SWATH (“5-min sSWATH” and “1-min sSWATH”) and 5-minute conventional stepped SWATH (“5 min SWATH”). 10 µg was injected for the 5-minute gradients and 5 µg for the 1-minute gradient. To put the results into context, we compared them to a publicly available 5-minute gradient human cell line (HeLa) DIA dataset as recorded with an Evosep One system coupled to an Orbitrap Exploris 480 with FAIMS (“5-min DIA-FAIMS”) (PXD016662). Project-specific libraries and the same software settings were used for raw data analysis (Methods). Data are presented as mean +/- standard deviation (n=3 replicate injections). e. The number of protein groups (1% FDR) with at least one or two peptide identifications, respectively. Data are presented as mean +/- standard deviation (n=3 replicate injections). f. Number of precursors (left) and protein groups (right) quantified with a coefficient of variation (CV) below 20% and below 10% in triplicate injections. CV values were calculated from n = 3 replicate injections.

Figure 3. The proteome response in drug-treated Saccharomyces cerevisiae captured with 5-minute Scanning SWATH

Prototrophic Saccharomyces cerevisiae (S288c background) yeast cells were grown in minimal media and treated with 10 µM of the indicated drug. 5 µg peptides were injected and analyzed with Scanning SWATH and 5-minute water-to-acetonitrile chromatographic gradients (800 µL/min flow rate). a. Principal component analysis separates the samples according to drug class as well as potency. Proteins that are differentially expressed in at least one of the drug classes (compared to DMSO) were considered (two-sided t-test, adjusted p-value < 0.01, Benjamini-Hochberg multiple testing correction ). The quantities were log2 transformed and centered. Drugs that have > 20 differentially expressed proteins are shown. b. Pathway enrichment of proteomic data identifies the target pathways. Pathway enrichment among differentially expressed proteins (two-sided t-test, adjusted p-value < 0.01, Benjamini-Hochberg multiple testing correction ) was conducted using hypergeometric testing. c. The proteome responses are drug class-specific. Differentially expressed proteins in at least one drug class are illustrated as a heatmap. Clustering was performed row-wise but not column-wise. Drugs that have > 20 differentially expressed proteins are shown. d. Differential protein expression varies by drug class, and identifies the targeted pathways for azoles (left panel) and statins (right panel). Significance (-log10(adjusted p-value)) was calculated with a t-test (two-sided) and is plotted as a function of the log2 fold-changes (ratio of expression levels in treated and DMSO-treated cells). Proteins in the cholesterol pathway that have an adjusted p-value < 0.01 are highlighted and are labelled with the respective gene name. The Benjamini-Hochberg procedure was used for multiple testing correction . e. Treatments with azoles and statins result in down- and upregulation of the Squalene monooxygenase (gene product of ERG1), respectively. The expression levels are given as fold changes (ratio of expression levels in treated and DMSO-treated cells). The boxes show the first and third quartile as well as the median (middle) and the whiskers extend to the most extreme data point, which is no more than 1.5 times the interquartile range from the box. n = 5 azoles, n = 7 statins.

Figure 4. 1-minute gradients and Scanning SWATH identify biomarkers that classify COVID-19 patients

a. Plasma samples were taken from 30 hospitalized COVID-19 patients of different severity, as well as 15 healthy individuals. b. Plasma proteomes classify COVID-19 patients according to the severity. Centered and standardized quantities (z-scores) for 54 proteins that are significantly differentially expressed depending on COVID-19 severity are illustrated on a heatmap (Kendall's Tau test for the Theil-Sen trend estimator, adjusted p-value < 0.01, Benjamini-Hochberg for multiple testing ). Clustering was performed row-wise but not column-wise. Labels indicate the corresponding gene names. c. Principal component analysis separates patients according to their severity. Proteins found significantly differentially expressed depending on severity were considered. d. The 1-minute Scanning SWATH method gives similar quantities as conventional SWATH with 5 times shorter gradients. Boxplots comparing 5-minute conventional SWATH with 1-minute Scanning SWATH quantifying the COVID-19 severity biomarkers as a function of COVID-19 severity. Plots are labelled with gene names that encode the respective proteins: CFI (Complement factor I), GSN (Gelsolin) and ITIH4 (Inter-alpha-trypsin inhibitor heavy chain H4). The intensities were normalized to the mean values of each protein. n = 15 healthy patients, n = 5 mild patients, n = 4 severe patients, n= 8 critical patients. e. COVID-19 severity biomarkers, that have to our knowledge not been associated to COVID-19 severity by proteomics before. Plots are labelled with gene names that encode the respective proteins: A2M (Alpha-2-macroglobulin), C1QC (Complement C1q subcomponent subunit C), HPX (Hemopexin), IGHG2 (Immunoglobulin heavy constant gamma 2), IGKV4-1 (Immunoglobulin kappa variable 4-1), PON1 (Serum paraoxonase/arylesterase 1), PROS1 (Vitamin K-dependent protein S), SERPINA7 (Thyroxine-binding globulin), SERPINF2 (Alpha-2-antiplasmin), TMEM198 (Transmembrane protein 198), TTR (Transthyretin). Protein quantities (Log2 transformed) are plotted as a function of COVID-19 severity. The boxes in d. and e. show the first and third quartile, the median (middle) and the whiskers extend to the most extreme data point, which is no more than 1.5 times the interquartile range from the box. n = 15 healthy patients, n = 10 mild patients, n = 7 severe patients, n= 13 critical patients (Table S6).