Cross-platform clinical proteomics using the Charité open standard for plasma proteomics (OSPP)

Domain: chemistry
Published: 2025-12-22
Editorial outcome: major_revision

Abstract
The role of plasma and serum proteomics in characterizing human disease, identifying biomarkers, and advancing diagnostic technologies is rapidly increasing. However, there is an ongoing need to improve proteomic workflows in terms of accuracy, reproducibility, and cost-effectiveness, and to achieve cross-platform transferability. Based on large serum and plasma proteome studies, we generate the Charité Open Peptide Standard for Plasma Proteomics (OSPP), an open, versatile peptide internal standard for targeted and untargeted mass spectrometry-based proteomic studies. The OSPP includes 211 concentration-matched stable-isotope-labeled peptides selected for consistent quantification across a large number of plasma and serum proteome studies, and synthetic accessibility. We show they are consistently quantified across serum and EDTA, citrate, and heparin plasma using multiple LC-MS platforms. Despite being selected for technical parameters, the OSPP peptides represent proteins that function in a wide range of biological processes, are used in routine clinical tests, or are targets of FDA-approved drugs, making OSPP able to serve as an expandable clinical marker panel. We demonstrate the utility of OSPP in a COVID-19 inpatient cohort study for improving analytical performances, for cross-platform alignment of proteomic data, disease stratification, and biomarker discovery.

Introduction
The analysis of human plasma and serum proteomes is of increasing importance for biomedical research due to the minimally invasive sample collection of plasma and serum combined with the properties of the plasma proteome to directly reflect human physiological and/or pathophysiological states1–4. Plasma proteomes reflect various physiological processes, and plasma proteins can function as biomarkers for different conditions. This includes well-established biomarkers such as cardiac troponin T, C-reactive protein, procalcitonin, and cystatin C, supporting decision-making related to diagnosis, prognosis, and intervention monitoring5–9.

Liquid chromatography-mass spectrometry (LC-MS) allows the simultaneous quantification of large numbers of proteins, elucidating the functional implications of specific proteins and unveiling changes within pertinent pathways while maintaining relatively low operational costs3,10–12. Nevertheless, the routine implementation of LC-MS, especially discovery proteomics, still faces challenges arising from technical variances in acquisition batches and methods, analytical platforms, and processing pipelines. The presence of these variations results in incomparable relative quantification of proteins across platforms, adding complications to the data analysis and the routine implementation of the proteomic workflows.

A strategy for addressing these challenges is to include different types of analytical standards and to aim for absolute rather than relative quantification methods. In mass spectrometry, stable isotope-labeled internal standards (SIL-IS) have become increasingly popular since the 1990s and allow the normalization of endogenous signals with absolute reference values13,14. In bottom-up proteomics, the spike in SIL-IS with 13C, and 15N labels is commonly used15–19, and serves for quality control procedures, batch correction, retention time normalization, and for estimating absolute quantities at the peptide level. Further, these standards can help to achieve cross-platform and cross-laboratory reproducibility16,20–22. Commercially available peptide standards, such as the PQ500 standard (Biognosys, Switzerland)23–25 or the PeptiQuant 270-protein human plasma MRM panel (MRM Proteomics Inc., Canada)26,27, are designed for targeted analysis of broad sets of proteins. However, there is a continuing need to optimize peptide selection for constant peptide detection across platforms, studies, and matrices; for costs per sample; and to improve data analysis strategies to efficiently make use of internal standards not only in targeted but also in discovery proteomics.

In this work, to respond the above mentioned challenges, we extensively mined a large number of plasma and serum proteomic studies to select peptides with ideal properties for a broadly applicable and expandable SIL-IS and generated the Charité Open Peptide Standard for Plasma Proteomics (OSPP). Consisting of 211 isotope-labelled peptides, the OSPP peptides exhibit high analytical performance across various analytical platforms and blood matrices. Additionally, they are selected to be easily and economically synthesized and be broadly applicable across various clinical contexts. Moreover, we report their constant detection at low technical variance across platforms. As the OSPP peptides are derived from proteins involved in a wide range of biological processes, many of which are targets of FDA-approved drugs or routinely used biomarkers, quantifying the OSPP peptides directly captures biological responses and can thus be used as a basis and expanded into biomarker panels.

Results
Selection of analytically robust peptide standards from large-scale proteomic datasets
To identify peptides with ideal properties to be combined into an SIL-IS standard panel addressing neat serum and plasma proteomics, we exploited proteomic data produced in 1505 control injections, which were measured alongside the analysis of 15,617 plasma and serum proteomes at the Charité High-throughput Mass Spectrometry Core Facility from 2020 to 2022. Data was analyzed using DIA-NN28 using a publicly avalaible spectral library (DiOGenes study)29.

To prioritize the most reliably quantified precursors from tryptic peptides that are proteotypic to the respective protein, we introduced a relative rank metric. This metric was calculated by first defining the precursor weight as the ratio of the precursor’s detection frequency/percentage of presence (PPres(p,n)) to its coefficient of variation (CV(p,n)) evaluated on an intensity scale. The weight of the precursor thus corresponds to its signal-to-noise ratio (S=N=1=CV), then multiplied by its presence.

Weight(p,n) = PPres(p,n) = CV(p,n): (1)

Here, p stands for precursor and n for a study pool series. Further, the rank of a precursor is assigned by its weight. Here and below, the index of a function indicates the variable to which the function is applied.

Rank(p,n) = rank {Weight(p,n)}: (2)

The number of precursors was different between studies, ranging from 4653 to 6361, with an average of 5390 precursors in all studies (Supplementary Data 1—selection cohort). To account for differences in the total number of precursors across studies, we calculated a relative rank (RelRank(p,n)) for each precursor in each study, defined as the rank of a precursor (Rank(p,n)) divided by the maximum rank observed in that study (max {Rank(p,n)}).

RelRank(p,n) = Rank(p,n) = max {Rank(p,n)} (3)

To select the best-performing, non-project-specific precursors for every protein, we calculated the average (over considered studies) relative rank RelRank(p) and set the lower cutoff as 0.5.

RelRank(p) = mean {RelRank(p,n)} (4)

(RelRank(p) >= 0:5) (5)

The proteotypic peptides from the best-performing, non-project-specific precursors were further selected to be quantified in at least half of the examined studies and covered at least two blood matrices.

Proteotypic(p) = 1, PresInNProjects(p) >= 4, PresInNMatrices(p) >= 2 (6)

To allow coverage of a larger protein concentration range, not more than three top-ranked peptides were selected for each protein (Fig. 1). Eventually, this selection process identified 382 consistently quantified precursors. We further selected these according to their suitability as internal standards, such as a peptide length between 6-25 amino acids, a minimal likelihood of missed cleavages, and low susceptibility to chemical modifications (Fig. 1). Among these, we prioritized peptides with high synthesis efficiency according to the Peptide Analyzing Tool (Thermo Scientific)30. We also incorporated 24 out of 50 peptides from a previous peptide panel, which showed excellent cross-platform analytical performances31,32.

Eventually, this selection procedure converged on 187 new peptides, expanded by 24 peptides that were already part of a previous SIL-IS panel designed to assess disease severity in COVID-19 and MPox patients31,32. These 211 peptides are derived from 131 proteins, of which 71 proteins are represented by one internal standard peptide, 44 proteins are represented by 2 internal standard peptides, 13 proteins by 3 internal standard peptides, 2 proteins, APOA1 and APOB, represented by 4 internal standard peptides, and SERPINA3 is represented by 5 internal standard peptides (Supplementary Data 1—peptide properties, Supplementary Fig. 1). These proteins span over four orders of magnitude in the concentration range of human plasma (Fig. 2a). Moreover, they have been found to be associated with several disease types (Fig. 2b, upper panel), encompassing enzymes, transporters, and cytokines (Fig. 2b, middle panel), and cover a broad range of 35 FDA-approved drug targets33. Notably, a fraction of these proteins, selected for purely technical reasons, already serve as routine clinical chemistry biomarkers used in different matrices (serum, citrate-, heparin-, or EDTA-plasma) (Fig. 2b, lower panel).

Assessment of selected peptide standards
The selected peptides were synthesized in both unlabeled (native) and isotopically labeled (SIL) forms, with heavy labeling of the C-terminal arginine or lysine using 13C 15N. Validation of the synthesized peptides involved analysis by LC-UV/VIS by the peptide manufacturer (Pepmic, Inc.) and LC-MS analysis. Subsequently, unlabeled and SIL peptides were prepared in groups of 11 based on their abundance in EDTA plasma and analyzed with a 5µl/min microflow-rate reversed-phase chromatographic gradient by Zeno SWATH DIA on a ZenoTOF 7600 instrument (SCIEX)34. Quality criteria entailed the co-elution of SIS with their respective unlabeled forms and the absence of signals from the unlabeled standard peptides. Reassuringly, the selected peptides all have well-distributed hydrophobicity scales, achieving a balanced elution distribution across the entire retention time range in a 20 min active gradient microflow chromatography (Fig. 2c, Supplementary Data 1—peptide properties).

Next, we tested the analytical performance of the SIL-IS peptides in Heparin-, Citrate-, and EDTA-plasma, as well as in serum samples obtained from healthy volunteers (Supplementary Data 4—metadata for 4 matrix). As an initial analysis, we added an equal concentration of each SIL-IS peptide (“Single-conc. Standard”, 100pg per peptide, 21.1ng total peptide amount) into 1.5µg of total protein digest from each matrix, analyzed using analytical flow rate chromatography (500µl/min, 5min gradient with a timsTOF HT mass spectrometer (Bruker) and analyzed using diaPASEF35). Despite inherent abundance changes e.g., due to age and sex differences (Supplementary Fig. 2), 93% (197/211) of peptides (both endogenous and SIL-IS peptides) were consistently detected in over 66% of samples. Moreover, 147 endogenous peptides were consistently detected in all four blood matrices, 174 in at least three matrices, and 9 endogenous peptides can be identified in a single matrix only (Supplementary Data 1—peptide properties, Peptide quantities in different matrices; Supplementary Fig. 2b).

To further assess peptide-specific abundance differences across matrices, we performed pairwise Wilcoxon signed-rank tests to evaluate rank shifts of the respective endogenous peptides between sample matrices (Supplementary Data 1—pairwise Wilcoxon signed-rank tests). A subset of peptides maintained consistent relative abundance, with serum and heparin plasma showing the highest similarity—148 out of 188 peptides did not change rank significantly (p.adjust > 0.05). In contrast, rank shifts were observed across other matrix comparisons. The largest difference was seen between serum and citrate plasma, where 143 out of 194 peptides changed rank (p.adjust < 0.05).

To account for the large concentration differences of proteins in plasma, we adjusted each selected peptide to align within one order of magnitude of its endogenous counterpart in the pooled EDTA plasma samples. Biological variation increased overall CVs, but endogenous peptides matching the OSPP remained more precise: PQ500 endogenous peptides showed median CVs of 32% (CRC) and 27% (controls), while OSPP endogenous peptides showed median CVs of 31% (CRC) and 25% (controls) (Supplementary Data 1—PXD036594_output; PXD036594_CV_comparewithPQ500_peptide). Thus, although PQ500 includes more peptides, the extensive selection of OSPP peptides from large study data had resulted in peptides more consistently detected and also more precisely quantified, even with a more sensitive acquisition method, in both our own, and external datasets.

Evaluation of the OSPP application in a case study of the human host response to SARS-CoV-2 infection
To demonstrate the usability of our SIL-IS, we applied OSPP as internal standards in a clinical cohort study. We chose a well-characterized clinical cohort of severely ill COVID-19 patients31,37–39 (n=45, Fig. 3a, Supplementary Data 4—meta data for study cohort). This cohort, enrolled at Charité between March 1–26, 2020, included individuals across the WHO ordinal scale of treatment escalation, which serves as a proxy of disease severity40. This reference cohort enrolled patients with WHO levels 0, 3, 4–5, and 6–7, ranging from uninfected to critically ill. Upon proteomics sample preparation using a semi-automated workflow41, 1µl (40.4ng, total peptide amount overall 211 peptides) of the OSPP was spiked into 1.5µg of total citrate plasma protein digest. The samples were first separated using 20min, 5µl/min µflow chromatography on an ACQUITY UPLC M-Class system (Waters), coupled with a ZenoTOF 7600 system (SCIEX). We used a targeted method, Zeno MRM-HR, to compare peptide concentrations across disease states, classified using the WHO ordinal scale for clinical symptom40 (Supplementary Data 4—WHO score & Severity). Data was processed using Skyline42, including manual inspection of peak integration.

Peptides detected in more than two-thirds of samples were included for subsequent analysis, and this criterion was met by 202 of the 211 peptides. Among those peptides, when only using the endogenous quantified value, more than half (106/202) were responsive depending on the treatment escalation level (WHO grade). With the application of the OSPP as internal standards, technical bias and noise were better controlled, leading to more precise and reliable results (Supplementary Data 5—Kendall´s Tau trend test_WHO): The improved measurement precision revealed 34 additional peptides with significant changes when quantified with MRM-HR, while 2 peptides that were associated with disease severity without the OSPP normalization lost the significant association—likely these were false positive associations called with the less precise method. As a result, more than two-thirds of peptides quantified with the OSPP (138 out of 202) responded according to COVID-19 severity.

A principal component analysis of peptide quantities (normalized by the OSPP, i.e., endogenous/OSPP, “ratio”) revealed the separation of patients according to their treatment escalation levels. The first principal component explained 31.2% of the variance and primarily reflected the treatment escalation level (Fig. 3b). A highlight of the top 15 most increased and decreased peptide levels (Fig. 3c) reflects processes important for the host response in COVID-19. For example, peptides derived from tetranectin43 (CLEC3B) that modulate the biological function and inflammatory process during infection are significantly decreased in severe COVID-19, while complement factor C3-derived peptides31,38 are induced with severity. Additionally, other peptides exhibited distinct signals corresponding to specific disease escalations. For example, a peptide derived from the enzyme inhibitor ITIH344 is strongly associated with infection itself, as is a peptide from the acute phase protein AHSG39. Others, such as peptides from the kidney and inflammation marker CST331,38, changed drastically during critical COVID-19 cases, indicating the possibility of kidney dysfunction in severe infection6 and peptides associated with the calcium-regulated, actin-modulating protein gelsolin (GSN45) whose reduced abundances were associated with worse outcomes41 (Fig. 3c, d; Supplementary Data 5—Kendall´s Tau trend test_WHO). Consistent with previous observations, most of the apolipoproteins classically associated with HDLs were less abundant in COVID-19 HDL particles, including APOA-II46. In contrast, well-known key acute-phase plasma proteins such as Orosomucoid 1 protein (ORM1)47 and Serpin A3 Protein (SERPINA3)48 were increased depend on the severity of COVID-19, in agreement with our previous studies and other reports39,49,50 (Supplementary Fig. 4).

In summary, peptide relative quantities normalized by OSPP using a targeted proteome method (Zeno MRM-HR) responded according to the treatment escalation levels in a cohort of COVID-19 patients from the early pandemic. Furthermore, this targeted method successfully identified protein markers, covered by OSPP, that were associated with specific disease state transitions.

Application of the OSPP for cross-method proteome analysis in human plasma
To compare the quantitative performance of OSPP across targeted and untargeted acquisition methods, we measured the same samples in both targeted (MRM-HR) and untargeted (Zeno SWATH DIA34) acquisition methods on the same acquisition platform (ZenoTOF 7600) with the same 20min, 5µl/min reverse-phase chromatographic gradient. The data processing pipeline involved DIA-NN28, using the DiOGenes spectral library29 modified by adding isotopic labels (13C 15N) specifically on OSPP peptides and excluded b-ions for quantification. The majority of peptides (207/211), both the native and its matched SIL-IS form, were quantified in over two-thirds of patient samples using Zeno SWATH DIA, with 199/211 of these pairs quantified consistently in both Zeno SWATH DIA and Zeno MRM-HR, using the same fragment ions for quantification (Fig. 4a). On the spectral level, the OSPP SIL-IS peptides behaved similarly to endogenous peptides under both Zeno MRM-HR and Zeno SWATH DIA acquisition, with the median fold change of each shared fragment signal from peptide (endogenous/OSPP) being close to 1 (0.9927 for MRM-HR and 0.9938 for Zeno SWATH DIA). This indicated strong spectral agreement and a similar analytical performance between the standard peptides and the respective endogenous peptides. To validate whether OSPP peptides introduced interference in discovery-mode DIA data, we evaluated the distribution of fragment ion intensities within each peptide between acquisition methods. The fold changes of each fragment percentage between MRM-HR and Zeno SWATH DIA were close to 1 (median fold change: 0.9783 for endogenous and 0.9803 for OSPP peptides), demonstrating that both endogenous and OSPP fragment profiles remained consistent across acquisition methods. (Fig. 4b, Supplementary Data 6—Fragment distribution).

To assess the quantitative precision of OSPP on both acquisition platforms, we first generated a study pool sample and analyzed it in quintuplicate, alongside the study samples. As expected, without normalization, the two methods returned different relative values (Supplementary Data 6—fold change). To correct the different quantification scales between methods, we then applied different normalization strategies. We first tested median normalization based on endogenous peptide quantities, referred to here as “norm_light”. After median normalization, the median fold change of all peptide relative quantities between methods increased from 0.006 (before normalization) to 1.18. Next, we used OSPP as the internal standard for normalization. Here, we calculated the ratio of the quantity of the endogenous peptide to that of its corresponding SIL-IS (“ratio”). The relative quantities represented by peptide ratios between methods further improved the 1.06 median fold change of all peptides, indicating improved comparability between methods (Supplementary Data 6—fold change). The use of OSPP in the DIA method also improved the overall precision. The median CV of all peptide quantities from quintuplicates of the study pool improved from 14% using the median normalization (“norm_light”) to 9% of the median CV with the OSPP normalized ratios (Fig. 4c, Supplementary Data 6—CV for study pools (MAD/median)).

We then explored quantitative similarities and differences of all samples between the targeted Zeno MRM-HR and the DIA method by fitting a linear regression model to each peptide quantity in each severity group. In our comparison, an r2 close to 1 indicates data acquired from Zeno SWATH DIA has a closer relation to Zeno MRM-HR data, and a slope close to 1 indicates that the quantities obtained from both methods are highly consistent. For example, the quantity for the Apolipoprotein A-II (APOA2)-derived peptide SPELQAEAK correlated with an r2 of 0.49 using median normalization; the correlation was improved to 0.98 using OSPP normalized ratios (Fig. 4d, Supplementary Data 6—R2-slope). Across the top 30 up- or down-regulated peptides that distinguish the COVID-19 patients in Zeno MRM-HR (as in Fig. 3c), the average r2 of the mild patient group was 0.53 (peptides that increased in concentration in more ill patients) or 0.27 (decreased) without the standard and improved to an average r2 of 0.88 (increased) or 0.74 (decreased) upon OSPP normalization. Also, across all peptides in all severity groups, the median correlation between the normalized quantities obtained with Zeno MRM-HR and Zeno SWATH DIA improved markedly from 0.39 (normal_light) to 0.83 (ratio). For quantity comparison between methods, we also observed the median slope being generally closer to 1 (0.91) when applying normalization to the OSPP internal standard (Fig. 4f, Supplementary Data 6—R2-slope).

Due to the higher technical precision upon normalization to the internal standard, more proteins were assigned to disease severity. For example, Apolipoprotein A-II (APOA2), a key component of high-density lipoprotein (HDL), has been reported to be dysregulated in patients with COVID-19, reflecting alterations in lipid metabolism and the host inflammatory response51–53. In our analysis, with normalization to the internal standard, a significant association of APOA2-derived peptide SPELQAEAK with both targeted and untargeted proteomic methods was detected (Fig. 4g). Overall, 132/199 peptides quantified by MRM-HR and 137/199 peptides from various OSPP concentration bins quantified by Zeno SWATH DIA show significant association with the WHO score (Supplementary Fig. 5, Supplementary Data 6—Kendall´s Tau trend test_Severity).

Use of the OSPP in comparing plasma proteomic data acquired on different DIA-MS platforms
One of the key motivations of including a SIL-IS is to achieve cross-platform comparability of the acquired data, which remains a key challenge in clinical proteomics. We thus expanded the evaluation to various LC-MS configurations, spanning from nano-flow chromatography (250nl/min) to an 800µl/min analytical flow rate chromatography used in high-throughput applications. These LC systems were coupled to different mass spectrometers, namely an Orbitrap Exploris 480 System (Thermo Scientific) and a ZenoTOF 7600 System (SCIEX). We injected samples from the aforementioned COVID-19 cohort, and data was acquired using DIA-MS on all platforms and processed with the afore-described DIA-NN-based pipeline.

Out of the 211 internal standard peptides of the OSPP, 187 pairs (88.6%) of the endogenous and its matched SIL-IS peptides were consistently identified and quantified across more than two-thirds of the samples, across three LC-MS platforms and five different acquisition and quantification methods (Fig. 5a, Supplementary Data 7—Detection of peptide in certain Severity Group). Retention times (RTs) were consistent between OSPP and respective endogenous peptides, with a median difference less than 200ms, indicating that the peptides were correctly identified (Supplementary Data 7—Retention time of each sample). Moreover, the RTs for each peptide remained stable across all samples, with RT fluctuations across all samples within the acquisition batch below 40 seconds and a median CV less than 2% in each platform (Supplementary Data 7—RT difference between light & OSPP). Also, fragment ion intensity distributions were comparable between endogenous peptides and their labeled counterparts (Fig. 5b, Supplementary Data 7—Fragment distribution). We performed inter-class coefficient correlation (ICC) of all samples across platforms. More than 90% of the fragments showed ICC > 0.75, and 85% exceeded 0.9 (median ICC of 0.967 for endogenous peptides and 0.987 for spiked OSPP) (Supplementary Data 7—ICC fragment percentage of all peptides). Furthermore, peptide-level analysis revealed that correlations between peptides mapping to the same protein showed diverging overall correlations; in each platform, over 40% of same-protein peptide pairs had an R² above 0.7 (Supplementary Data 7—correlation between peptides from same protein). It is worth speculating that the high isoform diversity in the human plasma is a main contributor to the diverging quantities of peptides derived from the same protein model.

To check the quantification consistency across platforms, we compared the peptide quantification across acquisition platforms for all COVID-19 clinical samples. Due to the different design of the instruments, as expected, peak areas, which represent relative quantities, differed between platforms without normalization to the internal standard. Upon forming ratios with the corresponding OSPP standard, the peptides were quantified across platforms with a median CV of just 13.2% (Supplementary Data 7—CV for study pools (MAD/median)).

In a principal components analysis (PCA) of OSPP-normalized ratios for 187 peptides quantified across all platforms, the data showed strong cross-platform consistency. The first principal component (PC1, 22.7%) corresponded to the major variation associated with disease severities, while PC2 (10.5%) was mostly driven by technical variance between platforms (Fig. 5c, left panel). Visualization using 95% confidence ellipses highlighted the substantial overlap of data points across platforms, indicating minimal technical bias and consistent peptide quantification between different LC-MS platforms after OSPP normalization (Fig. 5c, right panel). Variance partitioning analysis across peptides further confirmed that OSPP-based normalization substantially reduced technical variance attributs to cross-platform differences, from a median of 39.6% among all peptide quantities using median normalization down to 4.8% after OSPP normalization. (Supplementary Data 7—variance partitioning across peptides). All above analysis demonstrates that OSPP normalization effectively minimizes cross-platform technical variance and enables consistent quantification across DIA platforms. For instance, the OSPP normalized ratio of peptide SPELQAEAK (from Apolipoprotein A-II) showed consistent quantification across all DIA platforms, and this normalized ratio effectively allows comparable relative quantities and helps distinguish COVID-19 severity across platforms (Fig. 5d).

To further highlight the possibility for cross-platform data alignment using the OSPP internal standard, we evaluated whether data collected with different acquisition platforms can be mixed to produce comparable results to those from a single platform. We created an artificial “composite dataset” with peptide quantities randomly taken from datasets acquired from various platforms (OSPP normalized), reflecting the sample and severity distribution of the original clinical cohort (Supplementary Data 4—Composite dataset). To verify whether the artificial dataset showed similar peptide expression changes across severity levels, we compared it with data acquired using analytical flow on the ZT Scan DIA platform. Among the 159 peptides that were above the lower limit of quantification (LLOQ), 54 peptides showed substantial abundance changes in both the composite dataset and the single-platform cohort when normalized using the OSPP. Additionally, 10 peptides showed significant changes across severity groups (p.adjust < 0.05) only in the single-platform cohort, likely due to platform-specific sensitivity and outlier effects near the detection limit. Conversely, while 11 peptides did not show statistically significant changes across severity groups in the single-platform dataset, they displayed significant differences in the composite dataset. In general, normalization to the OSPP internal standard allowed cross-platform combination of the proteomic results. For example, the concentration of Apolipoprotein A-II (represented by peptide SPELQAEAK) is significantly increased during COVID-19 progression, as observed on the ZT Scan DIA platform, and this trend is similarly confirmed in the composite dataset (Fig. 6a, Supplementary Data 7—Statistics_composite dataset).

To assess quantification consistency between the composite dataset and individual dataset, we performed Bland–Altman analysis for each peptide, using all corresponding sample measurements from both datasets (Supplementary Data 7—Bland-Altman output). To illustrate a representative example, we choose the APOA2-derived peptide. Using the OSPP normalized ratio, we observed strong agreement on quantitative data obtained with both cohorts (Bias = 0.12), with only a few data points falling outside the limits of agreement (Fig. 6b). Bland–Altman analysis across all peptides that were above the limit of quantification showed a median absolute bias of 0.037 between datasets, with an average limits-of-agreement range of 1.716 units. Notably, 76.1% of peptides demonstrated differences within ±10%, indicating high concordance between quantification methods and 96.5% of the peptides showing bias between −1 and 1, signifying significant concordance in peptide ratios between the two datasets (Supplementary Data 7—bland-altman output); Although a small subset of peptides (n=6) showed clear bias (absolute bias > 1), these likely reflect a minority influenced by intrinsic technical or biological variability. The overall near-zero median bias supports robust quantification across the majority of peptides between platforms, affirming the reliability of the method despite inherent complexities.

Methods
Reagents
Water was obtained from Merck (LiChrosolv LC-MS grade; Cat# 115333), acetonitrile from Biosolve (LC-MS grade; Cat# 012078), trypsin (Sequence grade; Cat# V511X) from Promega, 1,4-Dithiothreitol (DTT; Cat# 6908.2) from Carl-Roth, iodoacetamide (IAA; Bioultra; Cat# I1149) and urea (puriss. P.a., reag. Ph.Eur.; Cat# 33247) from Sigma-Aldrich, ammonium bicarbonate (Eluent additive for LC-MS; Cat# 40867) and Dimethyl sulfoxide (DMSO; Cat# 41648) from Fluka, formic acid (LC-MS Grade; Eluent additive for LC-MS; Cat# 85178) from Thermo Scientific™, bovine serum albumin (BSA; Albumin Bovine Fraction V, Very Low Endotoxin, Fatty Acid-free; Cat# 47299) from Serva., commercial human plasma samples (Human Source Plasma, LOT# 20CILP1034) from zenbio.

Peptide selection and synthesis
To prioritize the most reliably quantified precursors and minimize the influence of such factors as precursor abundance, study cohort, MS setups, LC separations, and sample preparation procedures, we first selected a wide range of studies with different sample types (Supplementary Data 1—selection cohort). These samples were study pools prepared from human plasma and serum using a semi-automated workflow in 96-well plates, which involves a cleanup step using solid-phase extraction before being analyzed on a proteomic platform that uses analytical flow rate reverse-phase chromatography with water-to-acetonitrile gradients and a throughput of 5–8min/sample38. Proteomes were recorded using data-independent acquisition on two TripleTOF 6600+ instruments (Sciex) operating in SWATH68 or Scanning SWATH38 mode. Data was analyzed using DIA-NN28 with the DiOGenes spectral library29.

Then we introduced a relative rank metric, which was defined as follows. First, we defined precursor weight as a ratio of a precursor’s % presence PPres, to the coefficient of variation (CV), see Eq. (1), and a weight-based rank, see Eq. (2). Here, p stands for precursor and n for a study pool series. The weight thus corresponds to a precursor’s signal-to-noise ratio (S=N=1=CV) multiplied by its presence and reflect our desire to pick up precursors that are reliably detected (showing high presence) and reliably quantified (showing high S/N). The measurements are conducted on an intensity scale; therefore, to get an adequate evaluation of variation in the data, we used CV calculated on the intensity scale as the ratio of intensity standard deviation σ (I(p,s,n)) to its mean mean I(p,s,n):

CV(p,n) = σ (I(p,s,n)) = mean I(p,s,n) (7)

The number of precursors was changing from study to study, with min=4653 and max=6361 (the full peptidome consisted of 10560 precursors, of which 7621 were proteotypic). To minimize the influence of the total number of precursors on the ranking, we introduced relative rank RelRank(p,n), defined as the ratio of the precursor’s rank Rank(p,n) to the maximum rank value max {Rank(p,n)} in a study, see Eq. (3).

Finally, the precursor’s average (over considered studies) relative rank RelRank(p), see Eq. (4), was used to select the best „global“ (i.e. non-project-specific) precursors for every protein, while we also required that the lower cutoff of the relative rank be set as 0.5, see Eq. (5). This threshold guarantees that selected precursors on average have CV below the median CV.

Additionally, we only consider proteotypic peptides in our panel and for more reliable quantification, require those peptides to be quantified in at least half of the projects (≥ 4 projects) and to be present in at least 2 blood matrices, see Eq. (6). This threshold (half of the projects) is a compromise between sensitivity and specificity—with presence in a lesser number of projects, we could measure more study-specific proteins, but at the cost of not measuring them in some other projects. This requirement was complemented by the requirement for the presence of the peptide in at least two sample matrix types. To avoid all peptides coming from those top abundant proteins in plasma and to allow covering a larger dynamic concentration range of proteins, no more than top 3 peptides are selected for each protein. Eventually, this selection process identified 382 consistently quantified peptides, approximately 5% of considered proteotypic precursors. Of them, 161 (42%) were detected in all eight studies with an average presence of 97% and a relative CV of 0.5. For 49 peptides detected in only four studies average peptide presence was 95%, and average RelCV ~ 0.5. All other peptides, detected in 5—7 studies, had an average presence of 96% and an average RelCV ~ 0.5.

Further selection based on physical-chemical-and analytical properties
The chemical properties of each peptide were calculated using the R package “Peptides v2.4.6”. The hydrophobicity of each peptide was calculated using the function “hydrophobicity_kyte”69, the hydrophobicity scales run from −2 to 2, where 94 peptides are hydrophobic (>0) and 117 are hydrophilic (<0); net charge is calculated with function “charge”; high missed cleavage is considered and excluded when “KK|KR|RR|RK|KP|RP” appears in the peptide sequence, with the exception of peptide “ANRPFLVFIR” (SERPINC1) which we previously found to be of interest and with good performance across large numbers of samples31. Peptides containing cysteine and N terminal glutamine that are easily modified are excluded, except “IC(Carboxymethylated)LDLQAPLYK” which is the only selected peptide for protein “PF4”. An additional 24 peptides (30 peptides, 6 of which are also selected from previously mentioned study pools selection) from the previous MRM panel31 were included in the list. For checking likelihood of successful synthesis of peptides, the Peptide Synthesis and Proteotypic Peptide Analyzing software tool (Thermo, [34]) was used with synthesis. 600pg/µl of each and housed in 50% acetonitrile. For matrix performance tests, the single-conc. Std was in 100pg/µl as diluted in 10% acetonitrile.

Clinical study design and participants
Patients samples were collected as part of an observational cohort38,39,41. The study protocol, including patient characteristics, treatment, and clinical outcomes, has been described previously37,70. Briefly, all hospitalized patients with PCR-confirmed SARS-CoV-2 infection treated at Charité—Universitätsmedizin Berlin, a tertiary care center, were eligible for inclusion regardless of age, sex, or disease severity, following written informed consent. To reduce selection bias toward less severe cases, patients requiring invasive ventilation were also included under a deferred consent procedure. The study size was determined by patient availability and logistical feasibility, and all enrolled patients were included in the analyses. Study date cutoffs were the 1st to 26th of March 2020. The study is registered in the German and the WHO international registry for clinical studies (DRKS00021688). The study was approved by the ethics committee of Charite´—Universitatsmedizin Berlin (EA2/066/20). The cohorts are summarized in Supplementary Data 4—metadata for study cohort.

Ethics
The COVID-19 cohort is a subcohort of the Pa-COVID-19 study conducted at Charité—Universitätsmedizin Berlin, Germany37,71, and the matrix test cohort is part of prospective observational cohort study. Both studies were carried out in accordance with the Declaration of Helsinki and the principles of Good Clinical Practice (ICH 1996), where applicable, and were approved by the ethics committee of Charité—Universitätsmedizin Berlin (EA2/066/20, EA4/245/20). Written informed consent was obtained from all participants or their legal guardians before initiation of study procedures. All data have been de-identified and presented in aggregate, with age reported as ranges to prevent identification, while maintaining sex/gender reporting in accordance with journal policy.

Blood sample acquisition
Blood collection tubes (EDTA, citrate, heparin, and serum, respectively) were centrifuged at 2000G, 4°C for 15mins, and plasma or serum, respectively, was collected and frozen at -80°C for future analysis.

Sample preparation
Plasma samples and BSA. Samples were prepared with minor modifications as described previously41. Briefly, plasma/serum samples were stored at −80°C for 24–36 months prior to preparation, and clinical samples and calibration series were prepared as follows: 5µl of citrate plasma were added to 55µl of denaturation buffer, composed of 50µl 8M Urea, 100mM ammonium bicarbonate, 5µl 50mM dithiothreitol (DTT) and an internal standard mix. The samples were incubated for 1h at room temperature (RT) before the addition of 5µl of 100mM iodoacetamide (IAA). After a 30min incubation at RT, the samples were diluted with 340µl of 100mM ammonium bicarbonate and digested overnight with 22.5µl of 0.1µg/µl trypsin (ca. 1:150 (m/m) trypsin: substrate ratio) at 37°C. The digestion was quenched by adding 50µl of 10% v/v formic acid. The resulting tryptic peptides were purified on a 96-well C18-based solid phase extraction (SPE) plate (BioPure SPE Macro 96-well, 100mg PROT C18, The Nest Group). The purified samples were resuspended in 120µl of 0.1% formic acid. 1µl of OSPP was spiked to 1.5µg of digested plasma and injected on LC-MS/MS platforms (ZenoTOF 7600, timsTOF, Exploris 480) at customized volumes.

Calibration curves. We introduce an 8-point calibration curve with BSA as a surrogate matrix. For the seven non-zero calibration samples, 10µl of the OSPP mixture (the same as what the samples are used for) was mixed with 10µl of a dilution series of the native peptide standard pool ranging from 1000 to 0.064pg/µl; 20µl of BSA tryptic digest was then added as a surrogate matrix. The last sample of the calibration series used 10% (v/v) acetonitrile buffer instead of the light peptide standard (see details in Supplementary Data 3 -Preparation of Calibration curves).

Liquid chromatography mass spectrometry
Micro-flow-rate (µflow) LC attached ZenoTOF 7600 (Zeno SWATH DIA, Zeno MRM-HR). All samples were acquired on an ACQUITY UPLC M-Class system (Waters) coupled to a ZenoTOF 7600 mass spectrometer with an Optiflow source (SCIEX). Prior to MS analysis, 250ng samples were loaded onto LC and chromatographically separated with a 20min gradient (time, % of mobile phase B: 0min, 3%; 0.86min, 7.1%; 2.42min, 11.2%; 5.53min 15.3%; 9.38min, 19.4%; 13.02min, 23.6%; 15.48min, 27.7%; 17.27min, 31.8%; 19min, 40%; 20min, 80% followed by re-equilibration for 10min before the next injection) on a HSS T3 column (300µm × 150mm, 1.8µm, Waters) heated to 35°C, using a flow rate of 5µl/min where mobile phases A and B are 0.1% formic acid in water and 0.1% formic acid in acetonitrile, respectively. To avoid introducing technical variance due to differences in injection volumes, we always injected a constant volume of the plasma sample or calibration series samples.

Zeno SWATH DIA. A Zeno SWATH DIA acquisition scheme with 85 variable-sized windows and 11ms MS2 accumulation time was used. Ion source gases 1 and 2 were set to 12 and 60 psi, respectively. Curtain gas was at 25 psi, CAD gas at 7 psi, and source temperature was set to 300°C; spray voltage was set to 4500 V.

Multiple reaction monitoring—high resolution (Zeno MRM-HR). A scheduled Zeno MRM-HR method with identical instrument setting parameters as for Zeno SWATH was developed and used. The choice of precursor and selection of retention time was adopted based on triplicate injections of EDTA plasma sample on the microflow attached Zeno SWATH DIA. The Zeno threshold was set to 20,000 cps and for all peptides, the TOF MS2-scan range was from 200 to 1500 m/z, respectively. MS2 accumulation time was set to 13ms. Retention time tolerance was set as +/- 20 seconds. Collision energies were defined based on the following formula: CE = slope * m/z + intercept, Supplementary Table 1).

Analytical flow-rate system LC attached ZenoTOF 7600 (Zeno SWATH DIA MS, ZT Scan DIA). Samples were acquired on a 1290 Infinity II UHPLC system (Agilent) coupled to a ZenoTOF 7600 mass spectrometer with a DuoSpray TurboV source (SCIEX). Prior to MS analysis, samples were chromatographically separated on an Agilent InfinityLab Poroshell 120 EC-C18 1.9µm, 2.1mm × 50mm column heated to 50°C. A gradient was applied that ramps from 3 to 36% buffer B in 3min (buffer A: 1% acetonitrile and 0.1% formic acid; buffer B: acetonitrile and 0.1% formic acid) with a flow rate of 800µl/min. For washing the column, the flow rate was increased to 1.2ml/min, and the organic solvent was increased to 80% buffer B in 0.1min and was maintained for 1.4min at this composition before reverting to 3% buffer B in 0.1min. 1.5µg of the plasma sample or calibration series sample was loaded prior to cohort samples entering MS.

Zeno SWATH DIA acquisition scheme with 60 variable-sized windows and 13ms MS2 accumulation time was used. Ion source gas 1 (nebulizer gas), ion source gas 2 (heater gas), and curtain gas were set to 60, 65, and 55 psi, respectively; CAD gas was set to 7 psi, source temperature to 600°C, and spray voltage to 4000 V.

The ZT Scan DIA72 method used the same instrumental source setup parameters as Zeno SWATH DIA. The method consisted of an MS1 scan from m/z 100 to m/z 1000 and 25 MS2 scans (25ms accumulation time) with variable precursor isolation width covering the mass range from m/z 400 to m/z 910. Q1 mass width is set as 2.5 Da with a scan speed of 750 Da/s. The applied collision energies were as for Zeno SWATH (derived from a linear equation, see above).

Analytical flow-rate system LC attached timsTOF HT. Samples were analyzed on a Bruker timsTOF HT mass spectrometer coupled to a 1290 Infinity II LC system (Agilent). Before MS detection, 5µg of the sample were chromatographically separated on a Phenomenex Luna® Omega column (1.6 μm C18 100A, 30×2.1mm) heated to 50°C, using a flow rate of 0.5ml/min where mobile phase A & B were 0.1% formic acid in water and 0.1% formic acid in acetonitrile, respectively. The LC gradient ran as follows: 1% to 36% B in 5min, increase to 80% B at 0.8mL over 0.5min, which was maintained for 0.2min and followed by equilibration with starting conditions for 2min.

For diaPASEF MS acquisition, the electrospray source (Bruker VIP-HESI, Bruker Daltonics) was operated at 3000 V of capillary voltage, 10.0 l/min of drying gas, and 240°C drying temperature. The diaPASEF windows scheme was as follows: we sampled an ion mobility range from 1/K0 = 1.30 to 0.7 Vs/cm2 using ion accumulation times of 100ms and ramp times of 133ms in the dual TIMS analyzer, each cycle times of 1.25s. The collision energy was lowered as a function of increasing ion mobility from 59 eV at 1/K0 = 1.6 Vs/cm2 to 20 eV at 1/K0 = 0.6 Vs/cm2.

For all experiments, TIMS elution voltages were calibrated linearly to obtain the reduced ion mobility coefficients (1/K0) using three Agilent ESI-L Tuning Mix ions (m/z, 1/K0: 622.0289, 0.9848 Vs/cm2; 922.0097, 1.1895 Vs/cm2; and 1221.9906, 1.3820 Vs/cm2).

Nanoflow rate LC attached Exploris 480 (Thermo Scientific). Samples were analyzed on an Exploris 480 (Thermo Scientific) coupled to a Vanquish Neo UHPLC-System (Thermo Scientific) utilizing a 22min gradient in nanoflow (0.25µl/min). For LC separation, the attached column was an in-house packed 20cm long 1.9µm column. A shortened gradient time was used with the published acquisition method74 where mobile phases A & B were 0.1% formic acid plus 3% acetonitrile in water and 0.1% formic acid in 90% acetonitrile, respectively. The LC gradient ran as follows: increased from 2% buffer B to 30% buffer B over the course of the first 14.5min and increased to 60% buffer B within the next 1.5min. Finally, buffer B concentration increased to 90% for one min and was held for 5min to flush the column.

For Orbitrap acquisition, full scans were acquired between 350–1650 m/z with a resolution of 120,000. For MS2 scans, the maximum injection time was set to 54ms, and scans were made over 40 variable-sized isolation windows.

Generation of OSPP-specific human spectral library. A comprehensive spectral library for human stable isotope labeling was constructed through a multi-step process using DIA-NN and a custom R script. For all the experiments, we used a project-independent public spectral library, DiOGenes29 reannotated by Human UniProt75 (UniProt Consortium, 2019) isoform sequence database (3AUP000005640, [27 March 2023]). The library was first automatically refined based on the dataset at 0.01 global q-value (using the “Generate spectral library” option in DIA-NN). DIA-NN was employed with specific commands to enhance the library’s accuracy and utility and label all arginines and lysines in the existing spectral library: --fixed-mod SILAC,0.0,KR,label --lib-fixed-mod SILAC --channels SILAC,L,KR,0:0; SILAC,H,KR,8.014199:10.008269 --peak-translation --original-mods --matrix-ch-qvalue0.01.

This set of commands facilitated the automatic segregation of the spectral library into multiple channels, particularly for precursors associated with the Lysine and Arginine label group modification. To improve precision and accuracy during quantification, this heavily labeled spectral library was further refined. This refinement involved only keeping the label for peptides from OSPP with only the C-terminal lysine or arginine labeled, and for quantification accuracy, all b-ions were excluded from quantification by labeling b-ions as “T” in the “ExcludeFromAssay” category.

Raw data processing. All raw data from the ZenoTOF 7600 system were acquired by SCIEX OS (v. 3.0). All raw data from timsTOF HT were acquired with timsControl (v. 5.1.8) and HyStar (v. 6.3.1.8). All raw data from Exploris 480 (Thermo) were acquired using Xcalibur. Discovery proteomics. The raw proteomics data from all DIA methods were processed using DIA-NN, 1.8.1, available on GitHub (DIA-NN GitHub repository76). The MS2 and MS1 mass accuracies were set to 20 and 12ppm (ZenoTOF 7600 data) or 15 and 15 ppm (timsTOF and Exploris 480 data), and the scan window to 7. The aforementioned OSPP-specific Human Spectral Library is used for data processing with additional commands: --fixed-mod SILAC,0.0,KR,label --channels SILAC,L,KR,0:0; SILAC,H,KR,8.014199:10.008269 --peak-translation --original-mods --matrix-ch-qvalue0.01 --restrict-fr --report-lib-info. Specifically, following a two-step MBR approach28, an in silico spectral library is first generated by DIA-NN from the FASTA file(s); this library is then refined based on the DIA dataset and subsequently used to reanalyze the dataset to obtain the final results.

The data were filtered in the following way. First, a 1% run-specific q-value filter per isotope channel was automatically applied at the precursor level by DIA-NN (--matrix-ch-qvalue0.01). We note that in any experiment processed using the MBR mode in DIA-NN, 1% global precursor q-value filtering is also applied automatically28.

For quantification, we used the “Precursor.Translated” value as quantities for each precursor in MS2 quantification. For Exploris 480 data, since orbitraps are sensitive in MS1, we also used “Ms1.Translated” was used.

Targeted proteomics. Zeno MRM-HR data were processed using Skyline (64-bit, v. 23.1.0.268). No blinding was performed during peak integration. The relative quantity of each peptide is calculated by the summation of peak areas of each selected fragments of a peptide (fragments used in DIA calculation extracted from DIA-NN output; the list of fragments used for quantification in Supplement Data 5).

Calibration curve, LOD, LOQs. The calibration curve (fixed amount of OSPP mixed with serial dilutions of native peptide standards in BSA (4ng/µl), covering a concentration range of~ 2×105 (details on the preparation of the calibration curve is in Supplementary Data 3 —Preparation of Calibration curves) was measured in technical triplicates on each of the LC-MS/MS systems for each of the 211 peptides.

For limit of detection (LOD) calculation, blanks showed negligible signal; therefore, the standard deviation of the y-intercepts from replicate calibration curves was used to estimate LOD. The LOD was calculated as: LOD = 3.3×σ/S and the calculated LOQ as LOQ = 10×σ/S, where σ is the standard deviation of the y-intercepts across replicates, and S is the mean slope of the corresponding calibration curves. This approach ensures that the LOD reflects assay sensitivity even in the absence of measurable blank noise. Calculated LODs for peptides, including CRP-derived peptides, were below the reported reference range for healthy adults, indicating that the assay is sensitive enough to detect physiologically relevant concentrations. for the detectability reported in the manuscript, “detectable” refer to a detected peak with S/N>3 by manual inspection.

The upper and lower limit of quantification (-LLOQ and –ULOQ) was determined based on the accuracy of replicated injections (n=3) on the same LC-MS platform (CV<=20% or expanded to CV<=40% if less than 4 calibration point are present in previous criteria). Peptide concentration (expressed in pg/µl) was determined from calibration curves constructed with native and isotopically labeled peptide standards in the surrogate matrix (4ng/µl BSA) and manually inspected and validated. Peptide values below the lowest or above the highest detected calibrant concentration across all samples were removed from the analysis. Linear regression analysis of each calibration curve in each acquisition platform was performed using custom R code (with 1/x weighting).

Data analysis. All quantitative data presented and used for statistical analysis are in linear scale and were not log-transformed at any stage of the analysis.

Data completeness. The completeness of data for each peptide was evaluated based on its frequency of detection across all biological samples. Peptides were considered if they were detected in more than 66.7% (⅔) of the samples. We calculated the percentage of each peptide measured on each LC-MS platform/method and used only the peptides with a completeness value exceeding 66.7% for subsequent analysis.

Data normalization. Two normalizing strategies to evaluate the quantification consistency were applied. The first approach was a normalization by median division of all endogenous peptide quantities in the study pools (except in the Thermo instrument, replicate 04 is excluded due to acquisition failure) measured on each platform. All peptides in each platform were applied with this factor, referred to as “norm_light”.

median(MS) = median(light(MS), na.rm=T) or calculated as
norm_light = light/median(MS)

In addition, with the spiked OSPP mixture, we use the heavy isotope-labeled spiked peptide standard in each sample to normalize the corresponding endogenous peptide levels in the sample (endogenous peptide quantity (light) / quantity of correspondent heavy labeled peptide (SIS)), termed as “ratio”.

ratio = light/SIS

Precursor selection. As several precursors (charge state of +1 to +4) from the same peptide are quantified on different platforms, several criteria should be fulfilled to choose the best precursor used for follow-up quantification and cross-method/cross-platform comparison: a) Due to the difference in analyte ionization ability on various MS platforms, different precursors from the same peptide will show various abundances; the most abundant one shall be the charge state with the best ionization efficiency. b) Moreover, the abundance will also affect the reproducibility of the performance of isotopically labeled peptide standards. To replicate injections of study pool samples on each platform, we filtered for precursors with CV less than 40% to guarantee reproducibility. c) Additionally, for precursors of different peptides from the same protein, we checked the behavior of isotopically labeled peptide standards throughout all study samples and only chose the precursor that showed the same trend. The precursors used for quantification on different MS platforms are listed in Supplementary Data 7—Precurs used for quantification.

Statistical analysis
Wilcoxon signed-rank test. To assess whether the relative abundance rankings of peptides differ across blood matrices, we performed pairwise Wilcoxon signed-rank tests for each peptide. For each comparison, peptide intensity values from matched samples (same donor measured across matrices) were tested using the wilcox.test() function in R (v 4.2.2) with paired = TRUE. All six pairwise matrix combinations were tested independently. To account for multiple hypothesis testing, p-values were adjusted using the Benjamini-Hochberg (BH) procedure with the p.adjust(method = “BH”) function. The resulting adjusted p-values were compiled into a long-format data table with peptide identifiers and matrix comparison labels. Peptides showing significant rank shifts (adjusted p < 0.05) were flagged for further interpretation.

Coefficient of variation (CV). To assess intra-group variability of peptide precursors, we calculated the coefficient of variation (CV) using two methods. The primary method employed a robust measure, defined as the median absolute deviation (MAD) divided by the median of intensity values, expressed as a percentage (CV=MAD/Median × 100). This approach was implemented using the mad() function from the R stats package (v 4.2.2), offering robustness to outliers and suitability for skewed or heteroscedastic data commonly observed in proteomics. For comparison, we also computed CV using the conventional formula based on standard deviation divided by the mean (CV=SD/Mean×100). Both measures are reported where relevant to provide complementary insights into variability.

Kendall’s tau (KT) trend test. Significance testing of the trend between peptide quantities (normalized endogenous quantity or ratio) and the ordinal classification as provided by the WHO disease severity (levels as indicated) was performed using Kendall’s tau (KT) statistics as implemented in the “EnvStats v2.8.1” R package “kendallTrendTest” function. For the clinical cohort, the KT statistics were calculated as the trend of absolute peptide concentrations against the following WHO groups: 0, 3, 4, 5, 6, and 7:

EnvStats::kendallTrendTest(value ~ as.numeric(I.WHO), data=data)

or calculated as trend of absolute peptide concentrations with respect to disease severity (treated as an ordinal factor with the levels healthy<mild<severe<critical, which was converted to numeric ranks prior to analysis):

Severity_num = as.numeric(factor(F.Severity, levels=c(“healthy”, “mild”, “severe”, “critical”), ordered=TRUE))
EnvStats::kendallTrendTest(value~Severity_num, data=data)

Selected peptides in each comparison were used for data analysis, without imputation. Kendall’s rank correlation (Kendall’s τ) was used to assess monotonic trends in peptide intensities across disease-severity groups. False discovery rates (FDR) were calculated using the Benjamini–Hochberg procedure. Because each mass-spectrometry (MS) platform represented an independent experiment, FDR correction was performed within each MS dataset (and type) separately rather than across all datasets combined. Multiple testing correction was performed by controlling for false discovery rate using the Benjamini-Hochberg procedure1 as provided by the R package “stats v4.2.2”—“p.adjust” function. A full summary of these statistical test results is provided in the respective supplementary Data. (Adjusted) p-values were considered significant when p < 0.05.

Intraclass correlation coefficient (ICC) analysis. Inter-platform reproducibility was assessed using intraclass correlation coefficients (ICC). For each precursor–fragment–label group, ICC(3,k) was computed with the psych package (version 2.5.6) using a two-way mixed-effects model to evaluate the reliability of the average measurement across MS platforms while allowing missing values. Pairwise platform agreement was additionally quantified using ICC(2,1), calculated for every platform pair with the irr package (version 0.84.1), and the resulting ICC values were averaged to summarize pairwise reproducibility.

Variance partitioning analysis. To assess the contribution of technical and biological factors to peptide-level variability, we used the variancePartition R package. Variance partitioning was applied at the peptide level, which estimates the proportion of variance in peptide intensity explained by each variable (e.g., disease severity, MS batch). The resulting variance fractions were summarized across all peptides to assess the overall contribution of biological versus technical sources of variation. As we only focus on the variance brought by different LC-MS platforms, a linear mixed-effects model was only applied to both MS platform (MS) and disease severity (Severity) as random effects, no other biological variance (e.g. age, sex, BMI) was included in the analysis, the formula used is shown as:

expression ~ (1|MS) + (1|Severity)

Missing values in the peptide intensity matrix were imputed using k-nearest neighbor (KNN) imputation (k=10) with the impute (v 1.80.0) R package. Prior to imputation, the expression matrix was transposed so that samples were treated as observations and peptides as variables, and subsequently transposed back to the original orientation after imputation.

The model was fitted using fitExtractVarPartModel() from the variancePartition v1.36.2 R package, and the proportion of variance explained by each factor was computed for each peptide. The results were summarized to quantify the overall impact of technical and biological variance in the dataset.

Bland–Altman analysis. To assess the agreement between different mass spectrometry acquisition methods, we performed Bland–Altman analyses at the peptide level (using ratios in two datasets). For each experimental condition, peptides were filtered to exclude infinite values and missing data. For every peptide detected under each condition, the mean and difference of the ratios from two datasets (e.g., hZsSWATH dataset and the composite dataset (not including any data from hZsSWATH dataset to avoid data leakage)) were calculated. The bias (mean difference), standard deviation (SD), and limits of agreement (LoA, calculated as bias ± 1.96 × SD) were determined. Confidence intervals (95%) for the bias and LoA were estimated using Student’s t-distribution and the standard errors derived from the sample variance and size. Bland–Altman plots were generated for each peptide, visualizing the agreement between methods along with the calculated LoA and confidence intervals. The corresponding summary statistics were compiled into Supplementary Data 7—Bland-Altman output. All calculations and visualizations were performed in R using custom scripts based on the tidyverse (v 2.0.0) package for data handling and base R graphics functions for plotting.

Data visualization. To visualize the overlap of peptides (or peptide groups) across different experimental conditions or statistical criteria, set visualizations were generated using both UpSet and Venn diagram approaches. UpSet plots were created using the upset() function from the UpSetR v1.4.0 R package. For comparisons involving two to four sets, Venn diagrams were generated using the ggvenn() function from the ggvenn v0.1.10 R package. All other visualization is performed using “ggplot2 v.3.5.1”.

Principal component analysis was performed and visualized using the fviz_pca_biplot function from the factoextra v1.0.7 R package. Missing values in the peptide intensity matrix were imputed using k-nearest neighbor (KNN) imputation (k=10) with the impute (v 1.80.0) R package. Prior to imputation, the expression matrix was transposed so that samples were treated as observations and peptides as variables, and subsequently transposed back to the original orientation after imputation. Biplots display both individual samples (observations) and peptides (variables) in the principal component space, allowing assessment of sample separation according to severity group and identification of key peptides contributing to principal component loadings. Points were colored by clinical severity category (e.g., healthy, mild, moderate, critical) or acquisition platforms, and variable arrows represent the contribution and direction of individual peptides in the PCA space. Ellipses representing the 95% confidence interval (ellipse.level=0.95) around each acquisition platform.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary:\nWang et al. present an interesting work, where they developed the Charite Open Peptide Standard for Plasma Proteomics\n(OSPP), a panel of 211 peptides that can be used for both targeted and untargeted proteomics. This is indeed a quite\nlaborious work of plasma proteomics method development. The authors further demonstrate to some degree a potential for\nclinical utility in a small cohort of COVID-19 patients, where they demonstrate a continuous trend in differential levels of\nplasma proteins associated with COVID-19 severity.\nThe authors’ efforts in providing an open resource that includes good quality peptides are much appreciated. However, there\nmight be some biases related to the peptide selection that the authors should clarify. From a proteomic perspective, the\nmanuscript misses to explain what the OSPP adds to the field of targeted proteomics that previous efforts have not.\nThe claims regarding the OSPP’s demonstrated clinical utility in patient classification and biomarker identification are too\nstrong and not supported by the evidence presented in the manuscript. The authors should be more precise in their\nlanguage regarding the clinical potential of the panel. Considering the proteins included in the panel, it is not quite clear if\nthe panel would have a broader clinical potential. The panel might have applications in acute, systemic inflammatory\nconditions but even for this case the authors need to argue further what would be the added value in comparison to other\npanels and assays, some of which are widely used at the clinic.\nThere are also some concerns in data analysis and the presentation of methodology and findings, which the authors must\naddress. There were many typos and lack of clarity in some parts of the manuscript, with the latter limiting the understanding\nof some parts in the results and methods section. Under specific remarks, I point out some examples. The authors should\ncarefully correct the language, clarify their statements, and mind the tense when reporting results.\nSpecific comments:\n1. Page 4, Lines 14-28 and Page 22, Lines 5-15: Please recheck the mathematical annotations of the selection algorithm.\nSome elements are not well defined, and some expressions appear to be written in programmatic language (e.g. ==). It is not\nclear in which units were the ranks expressed and how were the relative ranks calculated. Was the (cid:1)(cid:2)((cid:3),(cid:4)) calculated on log-\ntransformed or raw values?\n2. It would be useful to the reader to specify whether during selection there was a condition that some project characteristics\nhad to be considered or PresInNProjects(p) ≥ 4 could mean that all four projects are EDTA-plasma. Would be useful to the\nreader to report which peptides were present in which project, to infer whether some are specific to a sample type. Apologies\nif I missed that information elsewhere, but I couldn’t find it in Supplementary Table 1.\n3. How did the sample type bias the ranks and relative ranks? Can this be estimated? This could be relevant for the\ncoagulation factors or other proteins that are being captured in the coagulum. It would be relevant to explain this in the\nmanuscript. It would be relevant to further explain the setting of blood sample collection and the processing that led to\nobtaining the plasma/serum samples.\n4. How did the weighing approach bias the selection? E.g. if the maximum presence of a peptide was let’s say 50% in a\nstudy and the same peptide had very low CV of 1%, the weight would have been the same as for a peptide that had 100%\npresence and 2% CV. Both CVs would have been low, but the latter peptide would have been detected in twice more\nsamples. Would these two peptides be equally relevant for downstream selection, i.e. what is the rationale that the presence\nand CV should have equal importance in the weighing?\n5. Why was the lower cut-off of the relative rank average set to be 0.6?\n6. It would be important to specify in Figure 1 the stepwise exclusion of peptides at each step. The authors should specify\nthe total number of peptides per project, followed by the number of peptides that are excluded at each step when a criterion\nis not met (e.g. how many peptides were excluded because they were not proteotypic or did not meet the chemical\nproperties criteria, etc.)\n7. Are some proteins represented by a single peptide? How has this affected the accuracy and precision in quantifying the\ndifferent proteins? It would be useful to present the effect of the selection-related factors on precision in supplementary.\n8. Figure 2b – from where were the annotations obtained in the second, third and fourth panel from above?\n9. How was the limit of detection (LOD) determined? For proteins that have low abundance in non-diseased blood samples\n(such as CRP) – did the assay measure concentrations with the LOD below the reference values cut-off for that protein in a\nhealthy population (where those are known) or was the LOD above the reference cut-off? This is important, since if the assay\nis to be clinically relevant, it should be able to measure concentrations that would include reference values as well.\n10. Page 8, Lines 35-38: the COVID-19 cohort is not well balanced. It has a different number of patients with different levels\nof severity. I assume effective size is a typo referring to effect size. What does effect (or effective, if not a typo) size refer to in\nthis sentence?\n11. It is unclear how the design allows for “identification of peptides that are only abundant in either healthy or diseased\nsamples, avoiding missing values due to low abundance”. Was there a pattern of missing values for some proteins that was\nrelated to the COVID-19 status? E.g. CRP levels would be elevated in a good portion of COVID-19 patients but not in some\nvery mild cases or in healthy individuals. Shouldn’t the latter two groups have very low-abundant CRP levels that might be in\nsome cases undetectable? Or was it undetectable regardless of the COVID-19 status? Same can be said for many of the\nother proteins for which there is strong evidence that they are elevated in COVID-19, like ORM1 and ORM2. The authors\nshould explore if the missingness in the data is related to COVID-19 status.\n12. There is a lack of clarity/consistency in the terminologies regarding the WHO severity scales for COVID-19. Seems that\nthe WHO ordinal scale used in the study refers to the scale from reference 41 and not reference 42. The WHO ordinal scale\nhas 8 orders (8 = death) – should be corrected in the manuscript. The reference 42 refers to the WHO clinical progression\nscale. In Figure 2, the authors write “WHO treatment escalation score”. It is important to clarify which scale exactly was used,\nto use the right terminology consistently, and to cite it properly.\n13. Although the focus of the study is on plasma proteomics method development, the cohort of COVID-19 patients needs\nfurther description – at least what is the distribution of age and sex because these factors can affect the plasma protein\nlevels. How have age or sex biased the analysis of the association between protein levels and COVID-19 severity, since it is\nwell known that male sex and older age are associated with severity and worse outcomes in\nCOVID-19 patients?\n14. Figure 3b: PCA is not clustering. What do the ellipses represent? Information on these analyses are missing in the\nmethods section too.\n15. Page 11, Line 34: I assume the authors refer to Figure 3c. Why were the changes in protein levels normalised to WHO\ngrade 3 patients? Wouldn’t it be a more intuitive comparison to normalise the fold changes to healthy controls?\n16. Figures 3c and 3d nicely present a gradual change related to COVID-19 severity. However, the authors should further\nexplain why C3 and CST3 are presented in Figure 3d, since the published evidence for their elevated levels in COVID-19\npatients is variable. Wouldn’t it be more relevant to prioritise other proteins for presentation? The authors should include\nmore references related to the claims on Page 11, Lines 34-45.\n17. Supplementary Table 5. Would it be valid to use the Kendall´s Tau trend test for an analysis of trend where the orders\nare not proportional? In the analyses, how were the patient groups treated numerically – as orders of 0, 3, 4, 5, 6, and 7, or\nfrom 0 to 5, or as grouped based on the severity group as 0 (healthy), 1 (mild), 2 (moderate), and 3 (critical)? The authors\nshould explain the rationale for their analytical decision and how a different categorisation of the order could have affected\nthe analysis.\n18. The boxplots show data related to the groups described in Figure 3a. They should be presented in the same manner the\norders are defined in the trend analysis if the authors justify the validity of the method. It can be helpful to the reader to add p\nvalues for the comparison in the boxplots. How have age and sex affected the association between the plasma proteins and\nCOVID-19? The protein and gene names that correspond to the peptides should be included in the supplementary tables in\nall sheets that show peptide information.\n19. How consistent is the association between A2M and COVID-19 based on published evidence?\n20. Page 16, Lines 35-40: The % related to the variance explained differs from the one shown in Figure 5c. Please recheck.\nIn the format it is presented, it is not clear from the PCA that the primary source of variance is related to biology.\n21. Page 17: the use of the term “composite cohort” is misleading. Based on the information presented in the manuscript, the\ncohort is not a separate cohort nor a separate MS run but a computationally generated dataset for the purpose of comparing\nthe agreement in quantifications of the OSPP between a single platform and a hypothetical scenario where the sample could\nhave been analysed on any of the platforms. If that is correct, the authors should explain this clearer and avoid expressions\nlike “two cohorts”. The authors should also compare the agreement between each of the single platforms and the agreement\nbetween the randomly sampled dataset and the remaining platforms, to demonstrate that the high agreement is reproducible\nwhen choosing any of the other single platform runs. When generating the randomly sampled dataset, the authors should\nconsider excluding the single platform they intend to compare the randomly sampled dataset to, since sampling from the\ndataset of the comparing platform would imply a leakage of information, overestimating the agreement.\n22. Page 17, Lines 19-25: The Bland-Altman plot analysis is unclear. Is the quantification consistency measured in two\ncohorts or in two different runs on the same samples from the same cohort on another platform (see comment above)? Figure\n5f is presented in relation to the claim that the quantification between the two measurements agree nicely but it represents a\nsingle peptide (from A2M) and not the agreement in all peptides. Lines 23-25: This segment refers to the agreement in\nquantifications of all the peptides and should be presented as a main figure instead. The bias close to absolute values of a\ndifference of 1 in some proteins is not negligible.\n23. The claim on page 19, Lines 29-31 is not supported by the presented evidence. The OSPP is not validated for patient\nstratification and outcome prediction. The authors have demonstrated that some proteins have a gradual increase or\ndecrease in levels at the time of a given COVID-19 severity. OSPP is not built into a model that provides patient stratification\nand outcome prediction.\n24. Were the DIA data log-transformed prior to normalisation?\n25. Why was the CV calculated based on median and median absolute deviation and not mean and standard deviation? Did\nthis approach underestimate the CVs?\n26. Some analyses were performed but were not described under statistical analysis and visualisation.\n27. Some examples of typos that the authors should correct are provided below. There were more – the authors should\ncarefully recheck the manuscript.\n- Page 3, Lines 31-33: maybe these two sentences were supposed to be connected by a comma?\n- Page 4, Line 7: there is an additional “from” in front of prepared.\n- Page 8, Line 1: “in combination” with is missing?\n- Page 8, Line 20: sentence unclear.\n- Page 11, Line 30: a closing bracket is missing. And the subsequent sentence is unclear and long.\n- Discussion: the second sentence is long and unclear, with some typos.", "reviewer_2": "The manuscript by Ralser and coworkers presents the development, validation, and application of the Charité Open Peptide\nStandard for Plasma Proteomics (OSPP). This is an open-source and cost-effective stable isotope-labeled internal standard\ndesigned to improve precision, reproducibility, and cross-platform comparability in plasma proteomics workflows. The\npeptide selection process is methodical, combining empirical data from thousands of plasma proteomes and control\ninjections. The use of a relative rank metric ensures the selection of analytically robust peptides with low technical\nvariability. The validation of the OSPP across different platforms (e.g., ZenoTOF 7600, timsTOF, and Orbitrap Exploris 480)\ndemonstrates its versatility and robustness. The authors demonstrate the utility of OSPP for both targeted and untargeted\nproteomics across multiple LC-MS platforms in a case study involving a COVID-19 patient cohort. The manuscript is easily\nreadable and highlights an affordable technique that could help standardize plasma proteomics. However, I have a number\nof comments and concerns that need to be addressed before I can recommend publication.\nSpecific comments to address:\nI assume that all the proteotypic peptides selected for the OSPP standard are tryptic peptides but this is not specified in the\nmanuscript text. Were the selected peptides derived from other proteases than trypsin or are they all fully tryptic peptide\nsequences? This should be clearly explained in the manuscript text.\nThe peptide/protein relationship is an issue that should be more directly addressed. The authors are very open that they are\nonly evaluating peptide quantification but the abstract and the title reflects that they are also looking at protein quantification\neven though this is not assessed in the manuscript. This should be addressed. Preferably the potential on protein level\nquantification should be evaluated and if not the abstract should reflect this and the text should discuss the connection.\nOrthogonal measurements (eg. ELISA or RIA) of at least some proteins would also be beneficial.\nWhile the OSPP includes 211 peptides from 131 plasma proteins, this represents only a small subset of the plasma\nproteome. Key biomarkers or protein isoforms relevant to specific diseases may be missing, limiting its applicability in\ncertain contexts. To demonstrate the advantages and also the limitations of the OSPP standard, the authors should\nbenchmark the performance of the OSPP standard against the state-of-the-art peptide standards for plasma proteomics, i.e.\nthe PQ500 standard from Biognosys. This benchmark should be performed evaluating plasma proteomics from a clinical\ncohort in terms of biomarker coverage and peptide and protein quantitation.\nIt is great that the authors evaluated different plasma/serum material and different MS setups. Additionally different protein\ndigestion protocols or different centers would also be very relevant to test the true potential for standardizing. It should at\nleast be discussed as a limitation.\nThe OSPP was primarily validated in the context of a COVID-19 cohort. While this is a relevant use case, additional studies\nin other diseases (e.g., cancer, cardiovascular diseases) would strengthen the generalizability of the standard for broader\nclinical applications.\nPage 4, line 17: It is a good strategy to select peptides based on previous knowledge of detection and reliable quantification\nwith LC-MS. The strategy will be biased by the samples included in the datasets. And if the criteria deselect peptides with\nlow CVs in a database using non-healthy and healthy samples, then peptides with biological dynamics in disease states\ncould to filtered out even though they might be well quantified. Similarly, the resulting list includes several IgG-related\ngenes that might be of questionable value from a biomarker perspective. Is there a rationale for including so many IgG peptides?\nPage 8, line 1-14: The robustness test across different plasma/serum material is very relevant. There is no figure to illustrate\nthese data. Should be included – at least supplementary. Information regarding the material handling (centrifugation steps)\nis lacking.\nPage 14, line 12: Why is A2M chosen as example? It would be nice to see examples of differential abundance. E.g. a\npeptides from each “concentration bin”.\nPage 15, figure 5c: The PCA plot is central to the result section, and I do not think the technical vs. biological accuracy is\nvery clearly shown.\nPage 19, line 4: “Indeed, the use of an internal standard can also be helpful to improve the 5 quantification of peptides that\nare not covered by the standard”. This is potentially a strength of the standard but is not evaluated in the text nor is there any\nreferences to back up the claim. The text should reflect this or find references.\nPage 19, line 8: “simplifying data analysis is an underestimated benefit of SIS panels”. It does not appear that the overall\ndata analysis will be more simplified with SIS. It requires specific software and there are problems regarding peptide\ndetection etc., so it could in some cases also make it more complex.", "reviewer_3": "This paper logically lays out the selection of 211 tryptic peptides that arise from plasma proteins and the development of\nthese peptdies into a standard “kit” that can be used across both targeted and untargeted (DIA) mass spectrometry-based\nplasma studies. The authors provide an example of its application in a small COVID-19 patient study. Finally, they provide\nthe information needed for the inclusion of all or some of these peptides in the spectral libraries that would be needed from\ntransferability.\nThis is a very well-designed approach, and it has been exceptionally well-executed. The ability to have a set of SIL peptides\nin which the performance has been well-characterized and that reach the preclinical requirements will allow, if adopted,\ncross-comparison between mass spectrometry platforms and workflows, different laboratories and, importantly, act to allow\nnormalization between cohorts.\nImportant considerations.\n1. It would be very helpful if the authors included a dynamic range figure that shows the concentration range of each\nendogenous peptide and the concentration of the corresponding SIL peptide.\n2. The authors need to include the retention times and the retention time variability for each peptide, at least across the\nCovid study. If possible, also in the study using the Exploris 480 instead of the ZenoTOF 7600. This is important as this will\nimpact whether these peptides can be used as retention time standards (replace IRT in DIA studies) as well as quantitative\ninternal standards.\n3. The authors discuss potential expansion of the multiplex but also need to state (speculate) the minimum number of\npeptides needed for the SIL reproducible/cross valuation kit. This can be part of a “next stage/limitation section,” which could\ninclude deployment across more instruments and laboratories. The authors may also want to address how this could be\nused for comparison to other proteomics studies that measure protein/proteoform using capture (or other) reagents.\nMinor but important.\n1. The paper should not include cost-effectiveness as there is no cost associated with the kit yet, and this may not be the\ncase. Please remove this from the manuscript.\n2. The protein full name is cardiac troponin T (not just troponin T, as there are several isoforms) and should also include\ncardiac troponin I, which is equally used clinically.\n3. Figure 5C is hard to read and should go in the online supplement, where it can be enlarged.\n4. There are a few typos that need to be fixed."}, "author_response": "We thank the Reviewer for their detailed report and the constructive comments. We have addressed all specific comments below. To address the point about clinical utility, we\napologise that our attempts to demonstrate difffernt levels of application of a broad internal\nstandard in plasma proteomics created confusion. The OSPP is designed to be an internal\nstandard, and the result of an untargeted, disease-agnostic selection of peptides with ideal\nanalytical properties. The principal application of such a panel is to improve the quantitative\naccuracy and to achieve cross-platform comparability of neat plasma proteome\nmeasurements.\nIndeed, we are excited that we could show evidence for direct clinical utility of this SIL panel,\nwithout the need to expand the standard into a panel for disease-specific biomarkers, as we\ndemonstrated for two diseases (COVID-19 and Loa Loa). However, this situation is rather a\nconsequence of the selection of the analytically best peptides, rather than by design. Because\nwe have focused on the identification of the most suitable peptides, we ended up covering a\nset of highly important and physiologically central plasma proteins, which are of medical,\npharmacological, and diagnostic relevance. The proteins covered by the SIL panel include\nApolipoproteins, proteins of the immune system, and coagulation factors, including known\nbiomarkers such as C-reactive protein, Alpha-1-Acid Glycoproteins, Serum Amyloid A\nproteins, or complement factors C2, C3, and C9, to name a few. As such, the plasma proteome\ncaptured by the OSPP responds to a wide range of diseases and captures FDA-approved drug\ntargets. We have reworked the language of our manuscript substantially to make this clearer.\nSpecific comments:\n1. Page 4, Lines 14-28 and Page 22, Lines 5-15: Please recheck the mathematical annotations\nof the selection algorithm. Some elements are not well defined, and some expressions appear\nto be written in programmatic language (e.g. ==). It is not clear in which units were the ranks\nexpressed and how were the relative ranks calculated. Was the 𝐶𝑉(𝑝,𝑛) calculated on log-\ntransformed or raw values?\nWe thank the reviewer for the comment.\nWe have rechecked all the mathematical annotations for peptide selection in the manuscript.\nEvery introduced variable is accompanied by its notation and description. Specifically, to avoid\nmisunderstanding, functions like max or mean are supplied with an index indicating the\nvariable over which the function is applied.\nRe: programmatic language: The == refers to relational operators; as in the applied\nprogramming convention, one would distinguish between the assignment (“=”) and equality\noperator (‘==’). Nonetheless, if this is confusing, we have replaced all equality operators “==”\nby an “=”.\nWe thank the reviewer for bringing up the point about the rank-based statistics. Here, we\nwould like to point out that rank is unitless, and in our case, it changes from 1 (the lowest\nrank) to the number of precursors in a study (Np ~ 5E3) (the highest rank). We have included\nthe above information in our manuscript ( Page 24,25, Methods).\nAs suggested we also provide more information about the calculations of the coefficient of\nvariation (CVs.) Measurements in mass spectrometry are conducted on an intensity scale, and\ntherefore the CV of precursor p in study n is calculated on an intensity scale. (Page 24,25)\n2. It would be useful to the reader to specify whether during selection there was a condition\nthat some project characteristics had to be considered or PresInNProjects(p) ≥ 4 could mean\nthat all four projects are EDTA-plasma. Would be useful to the reader to report which peptides\nwere present in which project, to infer whether some are specific to a sample type. Apologies\nif I missed that information elsewhere, but I couldn’t find it in Supplementary Table 1.\nWe thank the reviewer for raising this point. Yes, we had accounted for the possibility\nmentioned, and our selection strategy included the condition that a peptide must be present\nin more than 2 blood matrices. We apologize that this was hard to find and have now explicitly\nstated this criterion in the main text and Methods:\n“We selected only proteotypic peptides quantified in at least half (𝑃𝑟𝑒𝑠𝐼𝑛𝑁𝑃𝑟𝑜𝑗𝑒𝑐𝑡𝑠(𝑝) ≥ 4) of the examined\nstudies and covering at least two blood matrices (𝑃𝑟𝑒𝑠𝐼𝑛𝑁𝑀𝑎𝑡𝑟𝑖𝑐𝑒𝑠(𝑝) ≥ 2). ”\nA peptide may be missing from the individual studies not only for the reason of its specific\nmatrix and the MS setup applied but also for biological reasons. For this reason, we tested\nthe detection of the selected peptides across the different matrices experimentally. In these\nexperiments, 147 of the 211 peptides are detected in all four matrices, 174 in three, and 190\npeptides were selected from studies in at least 2 matrices. This information has been included\nin Supplementary Table 1.\n3. How did the sample type bias the ranks and relative ranks? Can this be estimated? This\ncould be relevant for the coagulation factors or other proteins that are being captured in the\ncoagulum. It would be relevant to explain this in the manuscript. It would be relevant to\nfurther explain the setting of blood sample collection and the processing that led to\nobtaining the plasma/serum samples.\nWe thank the reviewer for this comment. The reviewer raised three questions here:\n1) How does sample/matrix type bias peptide ranks and relative ranks? Can they be\nestimated?\n2) Are coagulation factors affected by matrix type?\n3) How were blood samples collected and processed (since this may influence peptide\ndetectability)?\nRegarding question 1), yes, as expected, the matrix composition is an important factor for the\npeptide signals. In order to estimate this, we calculated rank variability using pairwise\nWilcoxon signed-rank tests across blood matrices for each peptide. While a subset of peptides\nexhibited stable ranks between matrices, others showed rank shifts between matrices\n(p.adjust < 0.05). We have included these results in Supplementary Table 1 and summarized\nthe matrix test result in a PCA plot in Supplementary Figure 2. We have also added more\ndetailed information on this matrix test in the manuscript:\n“Quantification of each selected peptide, as expected, was also affected by matrix type\n(Supplementary Table 1). To assess peptide-specific abundance differences across matrices, we\nperformed pairwise Wilcoxon signed-rank tests to evaluate rank shifts of the respective endogenous\npeptides between sample types. A subset of peptides maintained consistent relative abundance, with\nserum and heparin plasma showing the highest similarity—148 out of 188 peptides did not change\nrank significantly (p.adjust > 0.05). In contrast, substantial rank shifts were observed for the majority\nof endogenous peptides across other matrix comparisons (healthy cohort with 4 different matrices).\nThe largest difference was seen between serum and citrate plasma, where 143 out of 194 peptides\nshowed significant rank changes (p.adjust < 0.05) (Supplementary Table 1, Supplementary Figure 2b).“\nRegarding question 2), our internal standard includes peptides from 7 out of 13 coagulation\nfactors, including all three chains of Fibrinogen (Factor I), as detailed in the table below\n(adapted from Supplementary Table 1). While Factor IX (F9) was not detected in serum, at\nleast one Fibrinogen chain was consistently observed across all evaluated matrix types.\nSequence Genes Pres_in N Projects Pres in EDTA Pres in Heparin Pres in serum Pres in Citrate\nTGIVSGFGR F10 5 1 1 1 1\nVVGGLVALR F12 4 1 1 1 0\nEQPPSLTR F12 7 1 1 1 1\nELLESYIDGR F2 8 1 1 1 1\nVTGWGNLK F2 4 1 1 1 0\nSALVLQYLR F9 5 1 1 0 1\nGSESGIFTNTK FGA 5 1 1 0 1\nYQISVNK FGB 4 1 1 1 0\nYEASILTHDSSIR FGG 4 1 1 0 0\nVELEDWNGR FGG 4 1 1 0 0\nTable 1 in rebuttal letter, extracted from Supplementary Table 1 in the manuscript\nThe above table supports that for coagulation factors, there was no apparent bias against\ntheir selection. Furthermore, in our validation experiments, peptides from 5/7 included\ncoagulation factors that could be reliably detected across all four matrix types (F2 and FGA in\ntwo matrices) (Supplementary Table 1).\nRegarding question 3), Yes, absolutely, we agree that details on sample handling are\nimportant to interpret matrix-specific findings. We have now added a full description of blood\ncollection and processing procedures to the Methods-Blood Sample Acquisition. We also\nexpanded the discussion, now explicitly spelling out the importance of standardizing sample\ncollection and pre-analytics to achieve consistent results in plasma and serum proteomic\nstudies.\n4. How did the weighing approach bias the selection? E.g. if the maximum presence of a\npeptide was let’s say 50% in a study and the same peptide had very low CV of 1%, the weight\nwould have been the same as for a peptide that had 100%\npresence and 2% CV. Both CVs would have been low, but the latter peptide would have been detected in twice more\nsamples. Would these two peptides be equally relevant for downstream selection, i.e. what\nis the rationale that the presence and CV should have equal importance in the weighing?\nWe thank the reviewer for this thoughtful observation.\nIn practice, peptides with high variability or low presence were deprioritized by our weighting\nscheme. This explains why we did end up with highly stable peptides in the final selection for\nthe OSPP.\nIn more detail, while weight is calculated as the ratio of a precursor’s (%) presence to the\ncoefficient of variation (CV) (Weight = Presence × 1/CV), they are not equally influential in\nthe downstream selection. Peptides with low presence—even if they had excellent CV, they\nreceived lower weights and were filtered out in subsequent selection steps.\nSpecifically, peptides with a presence below 60% were not retained in the final standard\nselection, and only three peptides fall within the 60–70% range. Therefore, the scenario\ndescribed by the reviewer (e.g., a peptide with 50% presence being weighted equally to one\nwith 100% presence) did not occur in the selected set. Our rationale for this weighting\napproach was to ensure peptides were both consistently detectable and quantitatively robust\nwhile avoiding overly complex composite metrics. We have clarified this point in the\nmanuscript.\n5. Why was the lower cut-off of the relative rank average set to be 0.6?\nWe apologize for the typo. We actually used the threshold of 0.5. We have selected this\nthreshold to select precursors that, on average, have a CV below the median CV. We added\nexplanations into the Methods.\n“Finally, the precursor’s average (over considered studies) relative rank RelRank(p) was used to select the best\n‘global’ (i.e., non-project-specific) precursors for every protein, while we also required that the lower cutoff of\nthe relative rank be set as 0.5.”\n6. It would be important to specify in Figure 1 the stepwise exclusion of peptides at each step.\nThe authors should specify the total number of peptides per project, followed by the number\nof peptides that are excluded at each step when a criterion is not met (e.g. how many peptides\nwere excluded because they were not proteotypic or did not meet the chemical properties\ncriteria, etc.)\nWe thank the reviewer for their comment.\nWe have included detailed selection information in Figure 1 (right panel) that gives the\npeptide numbers in each selection step and indicates the range in the text on Page 4:\n“Number of precursors was changing from study to study, min = 4,653 and max = 6,361 with mean = 5,390\n(Supplementary Table 1)”;\n“Eventually, this selection process identified 382 consistently quantified peptides.”\n“Eventually, this selection procedure converged on 187 new peptides, expanded by 24 peptides that\ndemonstrated excellent cross-platform performance as part of a previous SIL-IS panel designed to assess disease\nseverity in COVID-19 and MPox patients.”\nTo avoid further overloading of the already complex Figure 1, we added more details (e.g.,\nthe size of the peptidome and how many precursors were proteotypic in each selected study\ncohort) in Supplementary Table 1 and Methods on Page 25 .\n“The number of precursors was changing from study to study, with min = 4653 and max = 6361 (the full\npeptidome consisted of 10560 precursors, of which 7621 were proteotypic).”\n“Eventually, this selection process identified 382 consistently quantified peptides. This is almost exactly 5% from\nthe starting number of proteotypic precursors.”\nWe also indicated that the final set of peptides was about 5% from the starting peptidome.\n7. Are some proteins represented by a single peptide? How has this affected the accuracy and\nprecision in quantifying the different proteins? It would be useful to present the effect of the\nselection-related factors on precision in supplementary.\nIn our selected proteins, about 50% (71/211) are represented by a single internal standard.\nFor the remaining 60 proteins captured by our peptide standard, 44 proteins are represented\nby 2 standard peptides, 13 proteins by 3 internal standard peptides, 2 proteins by 4 internal\nstandard peptides, and 1 protein SERPINA3 by 5 internal standard peptides. We have added\nthis detail in the main text:\n“These 211 peptides are derived from 131 proteins, of which 71 proteins are represented by one internal\nstandard peptide, 44 proteins are represented by 2 internal standard peptides, 13 proteins by 3 internal\nstandard peptides, 2 proteins, APOA1 and APOB, represented by 4 internal standard peptides, and SERPINA3 is\nrepresented by 5 internal standard peptides (Supplementary Table 1, Supplementary Figure 1).”\nWe also added a barplot showing the distribution of the number of peptides per protein in\nSupplementary Figure 1.\nWhile quantification is precise for the internal standards, one cannot generally estimate the\nprecision at the protein level. The reason for this is that in the plasma proteome, proteins\nexist in multiple isoforms that share the same peptide; i.e., as in other SIL panels, the selected\npeptides are proteotypic but not isoform specific. However, because several of our proteins\nare represented by several internal standards, we indeed had the opportunity to get some\nestimates. We analyzed the correlation between peptides from the same protein model\nacross different LC-MS setups on the example of the COVID-19 cohort. 40% of peptide pairs\nthat come from the same protein model show a high correlation (R² > 0.7), but 60% of the\npeptides, despite being precisely quantified with the internal standard, show lower\nconcordance, indicating a contribution of different isoforms to the peptide abundance.\nResults : “Peptide-level analysis revealed that correlations between peptides mapping to the same protein\nshowed diverging overall correlations; in each platform, over 40% of same-protein peptide pairs had an R² above\n0.7 (Supplementary Table 7). It is worth speculating that the high isoform diversity in the human plasma is a\nmain contributor to the diverging quantities of peptides derived from the same protein model. ”\nDiscussion: “While OSPP-based internal standard workflows provide robust quantification at the peptide level,\ninferring accurate protein-level abundances remains a broader challenge in bottom-up proteomics, and\nspecifically in plasma, which is rich in protein isoforms.”\n8. Figure 2b – from where were the annotations obtained in the second, third and fourth\npanel from above?\nWe thank the reviewer for spotting this and apologize that we had not specified the source\nof these data.\nThe protein annotations for panels 1, 2, and 3 in Figure 2b were obtained and downloaded\nfrom the Human Protein Atlas (https://www.proteinatlas.org/search). We have added this\ninformation to the Figure legend.\nThe availability of clinical chemistry assays for the proteins covered by the OSPP has been\nsurveyed by the Institute of Laboratory Medicine, Ludwig Maximilian University, Munich,\nGermany. We have acknowledged Prof. Dr. Daniel Teupser for this contribution in the\nrespective section.\n9. How was the limit of detection (LOD) determined? For proteins that have low abundance\nin non-diseased blood samples (such as CRP) – did the assay measure concentrations with the\nLOD below the reference values cut-off for that protein in a healthy population (where those\nare known) or was the LOD above the reference cut-off? This is important, since if the assay\nis to be clinically relevant, it should be able to measure concentrations that would include\nreference values as well.\nWe thank the reviewer for the comment. LOD and LOQ were calculated based on replicates\nof the external calibration curve. LODs were determined using the standard formula\nLOD=3.3×σ/S, where σ is the standard deviation of replicate y-intercepts of regression lines\nof the calibration curve, and S is the slope of the calibration curve. LOQs were determined\nbased on triplicate injections of calibration standards, using a coefficient of variation (CV)\nthreshold of ≤ 0.2 (or ≤ 0.4 if fewer than three points were available). We have to mention\nthat both LOD and LOQs are instrument-related technical parameters (varying between\ninstrument setup, LC-MS methods, and also the status of the instrument when we measure)\nand not OSPP-specific parameters.\nRegarding the comparison to clinical reference values, our measurements focus on peptide-\nlevel concentrations rather than whole protein concentration and were performed in plasma\nrather than serum. Nevertheless, we compared our peptide LODs to reported CRP reference\nranges in healthy adults. For example, among the three proteotypic peptides from CRP in\nOSPP, two are not detected in healthy control groups; one CRP peptide (RQDNEILIFWSK) is\ndetected, and we calculated the LOD ranging from 2 to 12 pg/µL across different acquisition\nplatforms. When we consider the dilution factor during digestion, the molecular weight of\nthis peptide as MW ≈ 1764.925 g·mol⁻¹, our LC-MS measurement, we would have 0.136-0.816\npmol of this peptide, and when we assume no loss during digestion and SPE, we would also\nhave 0.136-0.816 pmol of CRP in plasma, which is roughly 3.4-20.4 ng of CRP in 5 µl of Plasma,\nwhich LOD is roughly 0.68-4.08 mg/L similar to the reported healthy CRP values (~0.8-3 mg/L)\n(Delongui et al. 2013; Erlandsen & Randers 2000) , demonstrating that our assay captures the\nphysiologically relevant concentrations. Also here, we would like to highlight that both LOD\nand LOQ are dependent on the acquisition method used; it is not the property of the SIL-IS\nwe introduced but rather a parameter of the analytical platform used.\nWe have updated the above information in Methods – Data Acquisition & Processing –\nCalibration Curve, and the calibration curve preparation is summarized in Supplementary\nTable 3.\n10. Page 8, Lines 35-38: the COVID-19 cohort is not well balanced. It has a different number\nof patients with different levels of severity. I assume effective size is a typo referring to effect\nsize. What does effect (or effective, if not a typo) size refer to in this sentence?\nWe thank the reviewer for the comment. The COVID-19 cohort includes the different\ntreatment escalation levels, with reasonable case numbers, considering the size of the cohort.\nWe apologize for having used the term, “Well balanced”, which indeed is subjective\nterminology. We would like to highlight that the cohort was not actively selected or stratified\nbased on disease severity. The only inclusion criterion was patient symptoms after a\nconfirmed COVID-19 diagnosis in the early start of the pandemic in 2020; therefore, the\ndistribution across severity levels (WHO 3 to WHO 7) occurred naturally among the\npopulation, and it is without intentional bias toward any particular group. We have removed\nthe mention of “effective size” during the revision process in which the paragraph was\nextensively reworked. The revised paragraph for introducing the clinical cohort reads:\n“To demonstrate the usability of our SIL-IS, we applied OSPP as internal standards in a clinical cohort study. We\nchose a well-characterized clinical cohort of severely ill COVID-19 patients (Kurth et al. 2020; Messner et al. 2021;\nDemichev et al. 2021; Wang et al. 2022) (n = 45, Figure 3a, Supplementary Table 4). This cohort, enrolled at\nCharité between March 1–26, 2020, included individuals across the WHO ordinal scale of treatment escalation,\nwhich serves as a proxy of disease severity(World Health Organization 2020). This reference cohort enrolled\npatients with WHO levels 0, 3, 4–5, and 6–7, ranging from uninfected to critically ill. ”\nWe have also added an additional section in Methods—Clinical Study Design and Participants\ndescribing in detail how this cohort is selected:\n“Patient samples were collected as part of an observational cohort (Demichev et al. 2021; Messner et al. 2020;\nMessner et al. 2021). The study protocol, including patient characteristics, treatment, and clinical outcomes, has\nbeen described previously (Kurth et al. 2020; Thibeault et al. 2021). Briefly, all hospitalized patients with PCR-\nconfirmed SARS-CoV-2 infection treated at Charité – Universitätsmedizin Berlin, a tertiary care center, were\neligible for inclusion regardless of age, gender, or disease severity, following written informed consent. To\nreduce selection bias toward less severe cases, patients requiring invasive ventilation were also included under\na deferred consent procedure. The study size was determined by patient availability and logistical feasibility, and\nall enrolled patients were included in the analyses. Study date cutoffs were the 1st to 26th of March 2020. The\nstudy is registered in the German and the WHO international registry for clinical studies (DRKS00021688). The\nstudy was approved by the ethics committee of Charite´ - Universitatsmedizin Berlin (EA2/066/20). The cohorts\nare summarized in Supplementary Table 4.”\n11. It is unclear how the design allows for “identification of peptides that are only abundant\nin either healthy or diseased samples, avoiding missing values due to low abundance”. Was\nthere a pattern of missing values for some proteins that was related to the COVID-19 status?\nE.g. CRP levels would be elevated in a good portion of COVID-19 patients but not in some\nvery mild cases or in healthy individuals. Shouldn’t the latter two groups have very low-abundant\nCRP levels that might be in some cases undetectable? Or was it undetectable regardless of\nthe COVID-19 status? Same can be said for many of the other proteins for which there is\nstrong evidence that they are elevated in COVID-19, like ORM1 and ORM2. The authors should\nexplore if the missingness in the data is related to COVID-19 status.\nWe thank the reviewer for raising this point; we apologize if our approach in this regard was\nnot explained in detail. It is expected that depending on the study and matrix and analytical\nsetup, not all of the peptides (selected in a disease-agnostic way) are detected, and this could\nalso be the case for specific disease groups. The way we dealt with missing peptide values in\nour COVID-19 use case is that we extracted the spectra of each peptide and checked\ndetectability, applied a completeness filtering, and considered only peptides that were\nquantified in two-thirds of the samples. Additionally, we have reported the number of\nsamples where each peptide is above LOD in Supplementary Table 7. This approach ensures\nthat peptides with excessive missingness—whether due to low abundance or technical\nissues—are excluded from downstream analysis. We examined the pattern of missing values\nacross WHO severity grades and did not observe peptides that were consistently missing from\nany specific severity group (Supplementary Table 7 - Detection of peptide in certain Severity\nGroup). This suggests that missingness was not systematically associated with COVID-19\nstatus in our dataset but rather reflects the expected variability and lower abundance of these\nproteins in some healthy or mildly affected individuals. We have reported the detection of\neach peptide in each severity group in Supplementary Table 7.\nWe also checked our healthy cohort with different matrices; certain peptides/respective\nproteins show very low abundance in healthy individuals (Supplementary Table 1), which\nindicates the missingness is just due to low abundance of peptides not related to COVID\nstatus. We have clarified our filtering criteria as follows:\n“Peptides that were detected in more than two-thirds of samples were included for subsequent\nanalysis, and this criterion was met by 202 of the 211 peptides.”\n12. There is a lack of clarity/consistency in the terminologies regarding the WHO severity\nscales for COVID-19. Seems that the WHO ordinal scale used in the study refers to the scale\nfrom reference 41 and not reference 42. The WHO ordinal scale has 8 orders (8 = death) –\nshould be corrected in the manuscript. The reference 42 refers to the WHO clinical\nprogression scale. In Figure 2, the authors write “WHO treatment escalation score”. It is\nimportant to clarify which scale exactly was used, to use the right terminology consistently,\nand to cite it properly.\nWe thank the reviewer for the comment, and we have revised the manuscript to achieve a\nfully consistent terminology throughout the manuscript.\nWe have cited the WHO treatment escalation score used to define disease severity, reference\n41 (now 43) to support our approach to grouping WHO grades based on clinical progression.\nWHO grade 8 was omitted from our figures, as we have not collected and not analyzed any\npostmortem samples. We have also adjusted the Figure legends accordingly.\n13. Although the focus of the study is on plasma proteomics method development, the cohort\nof COVID-19 patients needs further description – at least what is the distribution of age and\nsex because these factors can affect the plasma protein levels. How have age or sex biased\nthe analysis of the association between protein levels and COVID-19 severity, since it is well\nknown that male sex and older age are associated with severity and worse outcomes in\nCOVID-19 patients?\nWe thank the reviewer for the suggestion.\nIn the revised manuscript, we have expanded the description of the COVID-19 cohort to clarify\nits composition and added in Methods a section of “Clinical Study Design and Participants”.\n“Patient samples were collected as part of an observational cohort (Demichev et al. 2021; Messner et al. 2020;\nMessner et al. 2021). The study protocol, including patient characteristics, treatment, and clinical outcomes, has\nbeen described previously (Kurth et al. 2020; Thibeault et al. 2021). Briefly, all hospitalized patients with PCR-\nconfirmed SARS-CoV-2 infection treated at Charité – Universitätsmedizin Berlin, a tertiary care center, were\neligible for inclusion regardless of age, gender, or disease severity, following written informed consent.\nTo reduce selection bias toward less severe cases, patients requiring invasive ventilation were also included\nunder a deferred consent procedure. The study size was determined by patient availability and logistical\nfeasibility, and all enrolled patients were included in the analyses. Study date cutoffs were the 1st to 26th of\nMarch 2020. The study is registered in the German and the WHO international registry for clinical studies\n(DRKS00021688). The study was approved by the ethics committee of Charite´ - Universitatsmedizin Berlin\n(EA2/066/20). The cohorts are summarized in Supplementary Table 4.”\nThe reviewer asks reasonable questions about the role of age and sex in COVID-19 and their\nimpact on the proteome. These questions have been extensively addressed in previous\nstudies on this cohort (References 28, 33, 41, 42), and they confirmed that the observed\nproteomic differences are not primarily driven by demographic variables, including age or sex.\nWe have revised our paper so that the previous work on the cohort is more visible.\n“For example, the peptide quantities classified the patients according to the clinically observed disease\nseverity according to clinical severity (WHO ordinal scale), with prior studies confirming these patterns are independent\nof age and sex (Kurth et al. 2020; Messner et al. 2021; Demichev et al. 2021; Wang et al. 2022)”\n14. Figure 3b: PCA is not clustering. What do the ellipses represent? Information on these\nanalyses are missing in the methods section too.\nWe thank the reviewer for pointing this out.\nWe had included confidence ellipses at the 80% level as a tool to visualize the within-group\nvariance and the distribution of samples around the group centroid. We apologize if this may\nhave been confusing, and we noticed the ellipse is not needed in this plot; therefore, we have\nremoved the ellipse for better visualization and revised the legend of Figure 3b. We have also\nrefined the terminology in the main text and added details in the methods about this analysis.\n15. Page 11, Line 34: I assume the authors refer to Figure 3c. Why were the changes in protein\nlevels normalised to WHO grade 3 patients? Wouldn’t it be a more intuitive comparison to\nnormalise the fold changes to healthy controls?\nWe thank the reviewer for pointing out the typo. We were indeed referring to Fig 3c and have\ncorrected it. In this figure, we chose to normalize fold changes to the WHO grade 3 (mild\nCOVID-19) group primarily for illustration purposes and to ensure consistency with our\nprevious work (Wang et al. 2022). We have now clarified this rationale in the figure legend\nand main text\n16. Figures 3c and 3d nicely present a gradual change related to COVID-19 severity. However,\nthe authors should further explain why C3 and CST3 are presented in Figure 3d, since the\npublished evidence for their elevated levels in COVID-19 patients is variable. Wouldn’t it be\nmore relevant to prioritise other proteins for presentation? The authors should include\nmore references related to the claims on Page 11, Lines 34-45.\nWe thank the reviewer for the comment. C3 and CST3 might not have received much\nattention in the context of severe COVID; however, we have detected an increase in C3 and\nCST3 in severe COVID also previously (Wang et al. 2022) and by being connected to the\ncomplement to kidney function, they are part of biological processes of direct relevance to\nsevere COVID-19 disease. We thus concluded that highlighting understudied rather than well-\nknown COVID-19 proteins gives extra value to our study.\nFollowing the reviewer’s suggestion, we have added more references to our previous work,\nas well as relevant studies by others, to support that the peptides discussed behave\nconsistently with our prior observations. The text and additional references have been\nupdated accordingly to reflect this alignment.\n17. Supplementary Table 5. Would it be valid to use the Kendall´s Tau trend test for an analysis\nof trend where the orders are not proportional? In the analyses, how were the patient groups\ntreated numerically – as orders of 0, 3, 4, 5, 6, and 7, or from 0 to 5, or as grouped based on\nthe severity group as 0 (healthy), 1 (mild), 2 (moderate), and 3 (critical)? The authors should\nexplain the rationale for their analytical decision and how a different categorisation of the\norder could have affected the analysis.\nWe thank the reviewer for asking about the statistics. By being a non-parametric test that\nmeasures monotonic association between two variables Kendall's Tau (following WHO groups\n0, 3, 4, 5, 6, and 7) is applicable to our study. We consider it suitable in this context, as it works\non ranks rather than assumptions of equal group sizes or distributions. In order to give\nadditional confidence to the differences of the severity groups and match what was shown in\nboxplots, we added the statistics using ordinary logistic regression (following severity:\nhealthy, mild, moderate, and critical); we did not see an obvious change in whether the\npeptide is statistically significant in COVID-19.\nTo respond to the reviewer, we included in the statistics both Kendall's Tau trend test (based\non different WHO grades) and ordinal logistic regression (based on severity groups) and\nupdated the output in Supplementary Tables 5, 6, and 7 and the respective statistical\napproach in the Methods; in Figures with boxplots, we have updated the p-value using the\nstatistical method that matches the plot.\n18. The boxplots show data related to the groups described in Figure 3a. They should be\npresented in the same manner the orders are defined in the trend analysis if the authors\njustify the validity of the method. It can be helpful to the reader to add p values for the\ncomparison in the boxplots. How have age and sex affected the association between the\nplasma proteins and COVID-19? The protein and gene names that correspond to the peptides\nshould be included in the supplementary tables in all sheets that show peptide information.\nWe thank the reviewer for this observation.\nWe agree that our previous statistics of Kendall's Tau trend test follow WHO groups (WHO 0,\n3, 4, 5, 6, and 7). Although a valid statistical method, the box plots are shown based on\nseverity groupings, which caused some confusion. We have now changed the adjusted p-\nvalues derived from the ordinal logistic regression analysis (which is calculated based on\nseverity group to match what the boxplot shows) for boxplots in Figure 3d (as well as Figures\n4g, 5d, and 5e) and mentioned the statistical method in the figure legend.\nWe acknowledge that age and sex influence plasma protein profiles in COVID-19; however, as\nthis manuscript focuses on the development and application of the OSPP internal standard,\nand our previous studies have addressed the clinical aspects in detail, we chose not to expand\non this topic here to maintain a clear focus on OSPP.\nSupplementary Table 1 has summarized the peptide sequences along with their\ncorresponding gene and protein names. Since all quantification was performed at the peptide\nlevel, to avoid redundancy and to avoid causing confusion, we did not repeat protein names\nin supplementary tables where protein-level information was not required/reported.\n19. How consistent is the association between A2M and COVID-19 based on published\nevidence?\nA2M was also detected as a decline in severe COVID-19 in previous works (REF 49,50). We\nhave added this information to the manuscript.\n20. Page 16, Lines 35-40: The % related to the variance explained differs from the one shown\nin Figure 5c. Please recheck. In the format it is presented, it is not clear from the PCA that the\nprimary source of variance is related to biology.\nWe thank the reviewer for pointing this out. We have corrected the numerical inconsistency\nin the text.\nFor easier visual interpretation, we have split figure 5c into 2 subplots. The biplot on the left\npanel of Figure 5c in the revised manuscript indicates that the variation across disease severity\ngroups aligns with PC1, while platform-related differences align more with PC2. Since PC1\nexplains a larger proportion of variance, this indicates that biological variation contributes\nmore than technical variation.\nTo further support this, we added variance partitioning analysis across peptides. When\nnormalized by OSPP ratios, biological factors explained a median of 15.5% of variance\ncompared to 5.1% from the LC-MS platform. In contrast, using endogenous peptide\nnormalization (norm_light), biological variance was only 8.5%, while platform effects explain\n39.6%. These results confirm that OSPP normalization enhances biological signal over\ntechnical variation.\nWe have added the above analysis output to Supplementary Table 7 - variance partitioning\nacross peptides—and updated the manuscript and details of analysis in Methods. Now the\nmanuscript reads:\n“Variance partitioning analysis across peptides further confirmed the dominance of the biological signal versus\ntechnical differences between the platforms (Supplementary Table 7). Among all peptides, the treatment\nescalation level explained a median of 15.47% of the variance, while the technical differences between the\nacquisition platforms caused only 5.07% of the variance.”\n21. Page 17: the use of the term “composite cohort” is misleading. Based on the information\npresented in the manuscript, the cohort is not a separate cohort nor a separate MS run but a\ncomputationally generated dataset for the purpose of comparing the agreement in\nquantifications of the OSPP between a single platform and a hypothetical scenario where the\nsample could have been analyzed on any of the platforms. If that is correct, the authors should\nexplain this more clearly and avoid expressions like “two cohorts”. The authors should also\ncompare the agreement between each of the single platforms and the agreement between\nthe randomly sampled dataset and the remaining platforms to demonstrate that the high\nagreement is reproducible when choosing any of the other single platform runs. When\ngenerating the randomly sampled dataset, the authors should consider excluding the single\nplatform they intend to compare the randomly sampled dataset to, since sampling from the\ndataset of the comparing platform would imply a leakage of information, overestimating the\nagreement.\nWe thank the reviewer for the helpful clarification. We apologize that the term “composite\ncohort” was misleading and have revised it to “composite dataset” and ‘artificial dataset’\nthroughout the manuscript to reflect that this is a generated dataset that computationally\ncombines randomly selected data from existing datasets, not a separate biological cohort or\nMS run. We have revised the main text:\n“To further highlight the possibility for cross-platform data alignment using the OSPP internal standard, we\nevaluated whether data collected with different acquisition platforms can be mixed to produce comparable\nresults to those from a single platform. We created an artificial \"composite dataset\" with peptide quantities\nrandomly taken from datasets acquired from various platforms (OSPP normalized), reflecting the sample and\nseverity distribution of the original clinical cohort (Supplementary Table 7).”\nRegarding the reviewer’s suggestion for additional comparisons between single platforms and\nrandomly sampled datasets, we fully agree that such an analysis would provide a more\ncomprehensive assessment. However, conducting all possible randomizations and cross-\nplatform comparisons would represent a very large and complex study beyond the scope of\nthe present work. Instead, we designed the composite dataset comparison to illustrate one\npossible and practical approach, which we believe the current manuscript sufficiently\ndemonstrates the reproducibility and agreement across platforms.\nTo address the concern about information leakage, we have now updated our resampling\nprocedure to exclude data from the comparison platform (e.g., hZsSWATH) when generating\nthe composite dataset. This avoids circularity and ensures an unbiased comparison. The\nrespective statistical analyses are updated in Supplementary Table 7.\n22-1.. Page 17, Lines 19-25: The Bland-Altman plot analysis is unclear. Is the quantification\nconsistency measured in two cohorts or in two different runs on the same samples from the\nsame cohort on another platform (see comment above)?\nThe quantification consistency is measured for each peptide in two cohorts; one is from a\ndataset generated from a single platform, and the other is the composite dataset. In order to\nimprove clarity, we have revised both the manuscript and the figure legend and added in\ndetail of the Bland-Altman analysis in the methods. We revised the text; it now reads:\n“To assess quantification consistency between the composite dataset and individual dataset, we performed\nBland–Altman analysis for each peptide, using all corresponding sample measurements from both datasets\n(Supplementary Table 7).”\n22-2. Figure 5f is presented in relation to the claim that the quantification between the two\nmeasurements agree nicely but it represents a single peptide (from A2M) and not the\nagreement in all peptides.\nWe thank the reviewer for this valuable comment.\nIt is correct that Figure 5f displays a Bland–Altman plot for a single peptide (from A2M).\nApplying Bland–Altman analysis to multiple peptides simultaneously would not be correct, as\nit would mix distributions from different peptides. To address the suggestion of the reviewer\nto show more peptides, we have reported the low median bias and low limits-of-agreement\nrange across all peptides in the main text and provided an overall measure of agreement\nbetween the two datasets. Now the text reads:\n“Bland–Altman analysis across all peptides that were above the limit of quantification showed a median absolute\nbias of 0.037 between datasets, with an average limits-of-agreement range of 1.716 units (Supplementary Table\n7). Notably, 76.1% of peptides demonstrated differences within ±10%, indicating high concordance between\nquantification methods and 96.5% of the peptides showing bias between -1 and 1, signifying significant\nconcordance in peptide ratios between the two datasets (Supplementary Table 7);”\nAdditionally, for the remaining peptides that did not show bland-altman results as a plot, we\nhave included the individual Bland-Altman statistics for each peptide in Supplementary Table\n7 - bland-altman output.\n22-3. Lines 23-25: This segment refers to the agreement in quantifications of all the peptides\nand should be presented as a main figure instead. The bias close to absolute values of a\ndifference of 1 in some proteins is not negligible.\nWe thank the reviewer for the suggestion. Referring to the comment above, we have listed\nthe detailed information of all Bland-Altman statistics in Supplementary table 7/-sheet\n“bland-altman” and report the median of bias in the main text.\nFor the analysis, we calculated the mean of the absolute bias (using non–log-transformed\nratios) and agree that several peptides exhibit considerable bias. We acknowledge that\nproteome digests are highly complex, and in high-throughput analyses certain limitations\nremain, particularly for low-abundance peptides or under strong matrix effects. Our focused\nanalysis of peptides with absolute bias >1 suggests that discrepancies largely arise from\ndifferences in instrument sensitivity, which can push signals close to the limits of\nquantification, as well as variations in ionization across platforms, e.g. peptide,\nEGPYSISVLYGDEEVPRSPFK, from protein FLNA. We want to mention that these peptides\nrepresent only a minority and do not significantly affect the overall agreement, as\ndemonstrated by the near-zero median bias across all peptides. We keep those peptides with\nabsolute bias > 1 in the OSPP, as they may prove useful when detected with more sensitive\nmethods or at elevated levels.\n23. The claim on page 19, Lines 29-31 is not supported by the presented evidence. The OSPP\nis not validated for patient stratification and outcome prediction. The authors have\ndemonstrated that some proteins have a gradual increase or decrease in levels at the time of\na given COVID-19 severity. OSPP is not built into a model that provides patient stratification\nand outcome prediction.\nWe apologize if our wording caused confusion. Certainly, OSPP is a disease-agnostic internal\nstandard for MS proteomic experiments, and we have not conducted prospective clinical\nstudies using it as a marker panel herein. Having said that, we conducted such studies in the\npast using a biomarker panel, with several peptides also contained in the OSPP (Wang et al.\n2022). These studies confirmed the classification potential of highly abundant plasma\nproteins, including those captured by the OSPP, in acute COVID-19. We have revised the\nwording:"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Summary:\nThe authors have addressed most of the raised concerns and improved the text. I only have two important concerns\nremaining: a) the choice of some exemplary proteins to demonstrate the utility of the panels, particularly A2M; and b) the\nchoice and design of statistical models. I believe the authors are well equipped to address these. Another important\nsuggestion, though not one that should determine acceptance, concerns the supplementary tables – better descriptions\nand/or text references would benefit readers.\nSpecific comments :\n1. Figure 3b: Please note that PCA is not a method for clustering, but a method for dimensionality reduction. It should not be\nreferred to as unsupervised clustering, as done so in figure legends.\nEllipses are not confusing. Using ellipses with confidence intervals (CI) to show the spread and covariance structure of each\ngroup in a geometric space is excellent (95% CI preferred than 80%), but it’s important to explain which method was used to\ncalculate those areas. This information should be specified in the methods section.\n2. Figures 3c and 3d: The issue with C3 and CST3 is not because they are understudied. On the contrary, if the reviewers\nscreen the literature, they will find many studies that show inconsistent association with COVID-19 status. Shouldn’t in the\ncontext of this study, the utility of the panel be demonstrated with regards to well-known changes in plasma protein levels in\nCOVID-19 patients.\n3. Figure 3 and text. On several occasions the authors write that they have performed ordinal linear regression, whereas\nunder methods they describe ordinal logistic regression. These two methods are different and imply different assumptions.\nPlease recheck your code to ensure that you have used ordinal logistic regression and correct the text. Related to this –\ndoes ordinal logistic regression require proportionality in the plasma protein change linked to COVID-19 severity?\n4. To my previous question about how consistent the association between A2M and COVID-19 is based on published\nevidence, the authors respond that there was a decline in severe COVID-19 in previous works and refer to references 49\nand 50. Based on the text under results, I assume that the authors refer to references 48 and 49, since reference 50 is not\nrelated to the question. However, reference 48 is an old review from 2021 that concludes that “analysis of A2M protein levels\nin plasma samples from patients with COVID-19 revealed no significant differences or correlations with other disease\nparameters”. Refence 49 is a transcriptomics study that finds decrease in A2M transcript levels in peripheral blood\nmononuclear cells. Perhaps the question was not specific enough to imply that the evidence in question is related to plasma\nproteomics data. The authors should still explain why A2M is highlighted as an exemplary protein to demonstrate the panel’s\nutility for COVID-19 severity. I would further recommend evaluating the published literature on the relevance of this protein\nand reconsider highlighting more consistent proteins. In addition to the inconsistent published evidence, the authors\nhighlight that A2M, an (moderate) acute phase protein decreases in severe COVID-19 patients. It is counterintuitive in a\ndisease that has high systemic inflammation.\n5. Adding a variance partitioning analysis is very useful. However, the supplementary table was poorly described. The\nauthors need to clarify why the variance partitioning was performed per peptide and not on the entire dataset if they wanted to\nsummarise the effect of biological signal vs. technical bias. Furthermore, the model is too simplistic to include only\nseverity and MS as variables. The authors should consider including age, sex, site, sample type, and other potential\nconfounders, and/or explain why other factors have not been included in the model. The authors then write under methods\nthat a linear mixed-effects model was applied, but this makes the results unreliable since severity is on an ordinal scale, and\nnot a proportional one (see question above). The authors should reconsider their model choice and use a model that is fitting\nfor the used variables or explain how this model is applicable to the dataset. Last, the authors write that “missing values are\nimputed with zero” – please specify whether this refers to proteomics data or clinical data. In this setting the values should\nnot be imputed for clinical data. For proteomic data, imputing with zero will severely reduce the variance in the data and a\nmultiple imputation model should be considered.\n6. This also leads to me an additional point – the supplementary tables are not well organised and require improvement in\nstructure. There are many sheets under a single Supplementary Table, and these should be referenced separately. E.g. the\nvariance partitioning table was referenced as Supplementary Table 7, but it was on sheet 15 and the structure of the\nspreadsheets was not helpful in finding the right information.", "reviewer_2": "The authors have satisfactorily addressed by comments and concerns in the revised version of their manuscript. I have no\nmore comments.", "reviewer_3": "The authors have done a great job in addressing my concern. I particularly like the inclusion of the dynamic range figure. My\nonly extremely minor but extremely important point is that the authors need to change gender to sex. Sex is the correct term\nas gender includes other factors than biological sex, such as social status, which is not what is captured in this data. Please\nmake this change in Supplemental Figure 2 and throughout the manuscript. I missed this in my first review."}, "author_response": "We thank the reviewer for this helpful input.\ni) We apologize for the confusion about the description of the PCAs, and have revised the\nfigure 3b legend accordingly, it now reads\n“Principal component analysis (PCA) of OSPP-normalized precursor quantities across samples. Each\npoint represents a sample, colored by disease severity (as indicated in the legend), plotted along the\nfirst two principal components (PC1 and PC2), which explain 31.2% and 13.5% of the total variance,\nrespectively. The overlaid arrows represent loadings of selected peptides/proteins, indicating their\ncontribution to the principal components.”\nii) For Figure 3b, we had removed the 80% ellipses that were present in our original figure as\npart of the revision, based on the then-received reviewer feedback. We agree they might not\nbe confusing to all readers, but they are no longer necessary, as the separation between the\ngroups is now visualized with the loading plot.\niii) For Figure 5c, we agree with the reviewer that using a 95% confidence interval can\nprovide a clearer representation of the subtle differences between platforms. Accordingly, we\nhave updated the plot to display 95% CIs. We have also specified in the Methods section\nwhich package was used and how the ellipses were generated, as requested.\n2. Figures 3c and 3d: The issue with C3 and CST3 is not because they are understudied. On\nthe contrary, if the reviewers screen the literature, they will find many studies that show\ninconsistent association with COVID-19 status. Shouldn’t in the context of this study, the\nutility of the panel be demonstrated with regards to well-known changes in plasma protein\nlevels in COVID-19 patients.\nWe thank the reviewer for this comment. We agree that we can illustrate the usability of the\nOSPP with other proteins than C3 and CST3, as the reviewer correctly states, that these two\nproteins are not COVID19 severity responsive across all studies. In order to address this\ncomment, we now added additional examples of commonly reported COVID-19 response\nproteins which are covered by our standard panel, namely APOA2, ORM1 and SERPINA3\n(Supplementary Figure 4, Ref 41,48-52). We also highlight now specifically the situation of\ncohort-depency of the COVID-19 response of C3 and CST3 (Ref 61,62). Now the revised\nmanuscript reads:\n“Consistent with previous observations, most of the apolipoproteins classically associated with HDLs\nwere less abundant in COVID-19 HDL particles, including APOA-II 48. In contrast, well-known key\nacute-phase plasma proteins such as Orosomucoid 1 protein (ORM1) 49 and Serpin A3 Protein\n(SERPINA3) 50 were increased depend on the severity of COVID-19, in agreement with our previous\nstudies and other reports 41,51,52 (Supplementary Figure 4).”\n“Notably, some proteins such as C3 or CST3 are increasing in the presented cohort but not across all\nCOVID-19 studies 61,62. These differences likely reflect cohort-specific effects.”\n3. Figure 3 and text. On several occasions the authors write that they have performed\nordinal linear regression, whereas under methods they describe ordinal logistic regression.\nThese two methods are different and imply different assumptions.\nPlease recheck your code to ensure that you have used ordinal logistic regression and correct\nthe text. Related to this – does ordinal logistic regression require proportionality in the\nplasma protein change linked to COVID-19 severity?\nWe thank the reviewer for the helpful comment. We had used ordinal logistic regression. It is\ncorrect that the method assumes proportionality (across severity thresholds). On reflection,\none can’t be sure this assumption is always met. To avoid this assumption, we thus now use\nthe Kendall’s Tau trend test for both WHO grade and severity-based analyses. The\ncorresponding P values in boxplots have been updated accordingly, and the updated results\nand details are provided in the revised Methods, Supplementary Tables, and Figures.\nConclusions remain unchanged, but in this way we improve consistency in the use of\nstatistics, and avoid potential pitfalls of ordinal logistic regression.\n4. To my previous question about how consistent the association between A2M and\nCOVID-19 is based on published evidence, the authors respond that there was a decline in\nsevere COVID-19 in previous works and refer to references 49 and 50. Based on the text\nunder results, I assume that the authors refer to references 48 and 49, since reference 50 is\nnot related to the question. However, reference 48 is an old review from 2021 that concludes\nthat “analysis of A2M protein levels in plasma samples from patients with COVID-19\nrevealed no significant differences or correlations with other disease parameters”. Refence\n49 is a transcriptomics study that finds decrease in A2M transcript levels in peripheral blood\nmononuclear cells. Perhaps the question was not specific enough to imply that the evidence\nin question is related to plasma proteomics data. The authors should still explain why A2M is\nhighlighted as an exemplary protein to demonstrate the panel’s utility for COVID-19 severity.\nI would further recommend evaluating the published literature on the relevance of this protein\nand reconsider highlighting more consistent proteins. In addition to the inconsistent published\nevidence, the authors highlight that A2M, an (moderate) acute phase protein decreases in\nsevere COVID-19 patients. It is counterintuitive in a disease that has high systemic\ninflammation.\nWe agree that we can use other proteins than A2M to illustrate cross-platform normalization\nusing the OSPP, and apologize if the references we have cited for this protein created\nconfusion. To address the reviewer’s comment, we have now removed the A2M peptide data\nand now demonstrate cross-platform normalization using another peptide (SPELQAEAK\nfrom APOA2), which is a well-known dysregulated protein in COVID-19 (Ref 53-55). We\nhave also updated the references (previous Ref 48–50) to include recent studies reporting\naltered APOA2 levels in COVID-19 patients. Conclusions remain unchanged.\n5. Adding a variance partitioning analysis is very useful. However, the supplementary table\nwas poorly described. The authors need to clarify why the variance partitioning was\nperformed per peptide and not on the entire dataset if they wanted to summarise the effect\nof biological signal vs. technical bias. Furthermore, the model is too simplistic to include only\nseverity and MS as variables. The authors should consider including age, sex, site, sample\ntype, and other potential confounders, and/or explain why other factors have not been\nincluded in the model. The authors then write under methods that a linear mixed-effects\nmodel was applied, but this makes the results unreliable since severity is on an ordinal scale,\nand not a proportional one (see question above). The authors should reconsider their model\nchoice and use a model that is fitting for the used variables or explain how this model is\napplicable to the dataset. Last, the authors write that “missing values are imputed with\nzero” – please specify whether this refers to proteomics data or clinical data. In this setting the\nvalues should not be imputed for clinical data. For proteomic data, imputing with zero will\nseverely reduce the variance in the data and a multiple imputation model should be\nconsidered.\nWe thank the reviewer for the insightful comment. The variance partition analysis was\nconducted at the peptide level, and we highlight this now better in the text.\nWe agree that a variance partition analysis could be used to discriminate against a number\nof biological confounders, including age, sex, site, sample type in study cohorts; however our\nexemplary COVID-19 patient cohort is not large enough to do this in any biological\nmeaningful manner. Therefore, we revised the section, to highlight now better that our\nanalysis focuses on reducing cross-platform variation for LC-MS proteomics using the OSPP\nas an internal standard. In order to address this comment of the reviewer, we have clarified\nthis better in our text\n“In a principal components analysis (PCA) of OSPP-normalized ratios for 187 peptides quantified\nacross all platforms, the data showed strong cross-platform consistency. The first principal component\n(PC1, 22.7%) corresponded to the major variation associated with disease severities, while PC2\n(10.5%) was mostly driven by technical variance between platforms (Figure 5c, left panel).\nVisualization using 95% confidence ellipses highlighted the substantial overlap of data points across\nplatforms, indicating minimal technical bias and consistent peptide quantification between different\nLC-MS platforms after OSPP normalization (Figure 5c, right panel). Variance partitioning analysis\nacross peptides further confirmed that OSPP-based normalization substantially reduced technical\nvariance attributs to cross-platform differences, from a median of 39.6% among all peptide quantities\nusing median normalization down to 4.8% after OSPP normalization. (Supplementary Table 7 -\nvariance partitioning across peptides).”\nFor imputation, in accordance with the reviewer’s comment, we have replaced all zero\nimputation with k-nearest neighbor (KNN) imputation per peptide using the impute R\npackage (v1.80.0). Details have been added to the Methods section accordingly and the\nmodified result was updated in the respective supplementary table. Conclusions remain\nunchanged.\n6. This also leads to me an additional point – the supplementary tables are not well\norganised and require improvement in structure. There are many sheets under a single\nSupplementary Table, and these should be referenced separately. E.g. the variance\npartitioning table was referenced as Supplementary Table 7, but it was on sheet 15 and the\nstructure of the spreadsheets was not helpful in finding the right information.\nWe apologize if the numbering of our Supplementary material was confusing. We have\ncarefully reviewed our table References, and now include detailed Table References to the\nmain manuscript."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have thoroughly addressed all the suggestions and concerns. I thank them for their patience and look forward to\nsharing this interesting work.", "reviewer_2": "The authors have satisfactorily addressed by comments and concerns in the revised version of their manuscript. I have no\nmore comments.", "reviewer_3": "The authors have done a great job in addressing my concern. I particularly like the inclusion of the dynamic range figure. My\nonly extremely minor but extremely important point is that the authors need to change gender to sex. Sex is the correct term\nas gender includes other factors than biological sex, such as social status, which is not what is captured in this data. Please\nmake this change in Supplemental Figure 2 and throughout the manuscript. I missed this in my first review."}, "author_response": ""}]

Decision letter