
==== Front
Mol Cell Proteomics
Mol Cell Proteomics
Molecular & Cellular Proteomics : MCP
1535-9476
1535-9484
American Society for Biochemistry and Molecular Biology

S1535-9476(24)00102-6
10.1016/j.mcpro.2024.100812
100812
Research
A Carrier-Based Quantitative Proteomics Method Applied to Biomarker Discovery in Pericardial Fluid
Campbell Amanda J. 12
Cakar Samir 12
Palstrøm Nicolai B. 123
Riber Lars P. 34
Rasmussen Lars M. 1235
Beck Hans C. hans.christian.beck@rsyd.dk
1235∗
1 Department of Clinical Biochemistry, Odense University Hospital, Odense, Denmark
2 Center for Clinical Proteomics (CCP), Odense University Hospital, Odense, Denmark
3 Department of Clinical Research, Faculty of Health Science, University of Southern Denmark, Odense, Denmark
4 Department of Cardiac, Thoracic and Vascular Surgery, Odense University Hospital, Odense, Denmark
5 Center for Individualized Medicine in Arterial Diseases (CIMA), Odense University Hospital, Odense, Denmark
∗ For correspondence: Hans Christian Beck hans.christian.beck@rsyd.dk
14 7 2024
8 2024
14 7 2024
23 8 10081220 12 2023
3 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Data-dependent liquid chromatography tandem mass spectrometry is challenged by the large concentration range of proteins in plasma and related fluids. We adapted the SCoPE method from single-cell proteomics to pericardial fluid, where a myocardial tissue carrier was used to aid protein quantification. The carrier proteome and patient samples were labeled with distinct isobaric labels, which allowed separate quantification. Undepleted pericardial fluid from patients with type 2 diabetes mellitus and/or heart failure undergoing heart surgery was analyzed with either a traditional liquid chromatography tandem mass spectrometry method or with the carrier proteome. In total, 1398 proteins were quantified with a carrier, compared to 265 without, and a higher proportion of these proteins were of myocardial origin. The number of differentially expressed proteins also increased nearly four-fold. For patients with both heart failure and type 2 diabetes mellitus, pathway analysis of upregulated proteins demonstrated the enrichment of immune activation, blood coagulation, and stress pathways. Overall, our work demonstrates the applicability of a carrier for enhanced protein quantification in challenging biological matrices such as pericardial fluid, with potential applications for biomarker discovery. Mass spectrometry data are available via ProteomeXchange with identifier PXD053450.

Graphical Abstract

Highlights

• Low-abundant protein detection in biofluids is challenged by a large dynamic range.

• We adapted single-cell proteomics by mass spectrometry to pericardial fluid.

• Using a myocardial carrier improved proteome coverage without compromising accuracy.

• Workflow can attenuate and boost biomarker discovery in other biofluids.

In Brief

Identification of novel, low-abundant protein biomarkers in biological fluids is challenging due to the large dynamic concentration range. We adapted single cell proteomics by mass spectrometry for pericardial fluid. Initially, different ratios of carrier to samples were tested to increase proteome coverage while maintaining quantitative accuracy. Next, we analyzed 80 pericardial fluid samples alongside a myocardial carrier using data-dependent LC-MS/MS. The carrier increased myocardial and overall proteome coverage, and differentially expressed proteins increased from 41 to 156.

Keywords

single-cell proteomics
carrier proteome
diabetic cardiomyopathy
biomarker discovery
Abbreviations

CV coefficient of variation

DDA data-dependent acquisition

DM diabetes mellitus

FDR false discovery rate

FFPE formalin-fixed paraffin-embedded

GO gene ontology

HCD higher-energy collisional dissociation

HF heart failure

LC-MS/MS liquid chromatography tandem mass spectrometry

PSM peptide spectrum match

SCoPE-MS single-cell proteomics by mass spectrometry

SNR signal-to-noise ratio

TEAB triethylammonium bicarbonate

TMT tandem mass tag
==== Body
pmcMass spectrometry–based proteomics allows the untargeted analysis of thousands of proteins in a complex mixture and can therefore be utilized to analyze biological fluids and tissues in great detail. Recent improvements in this technology include the analysis of protein expression at the single-cell level using an isobaric tandem mass tag (TMT)-based multiplexing approach, also denoted single-cell proteomics by mass spectrometry (1) (SCoPE-MS). With this approach, the issue of insufficient peptide ion signals of the reporter channels tagged with proteins from a single cell is addressed by including a mass tag channel tagged with a carrier proteome alongside the single-cell samples. The carrier proteome allows the selection of peptides that would otherwise be below the level of detection for the mass spectrometer, and the increased number of peptide fragments results in higher quality MS/MS scans, thus enabling efficient peptide identification. In SCoPE-MS, single-cell samples are run alongside a carrier typically consisting of 100 to 200 cells or more (1, 2).

As demonstrated by several recent studies, the depth of peptide identification should be balanced against the accuracy and precision of quantification (3, 4, 5). When fewer ions are sampled from the “single cell” tag, quantification becomes less precise. Although high levels of a carrier proteome lead to a higher number of protein identifications, this comes at the expense of quantitative precision, where variation in single-cell tags becomes less apparent due to high signal in the carrier tag, thus also affecting biological conclusions. Moreover, increasing Orbitrap instrumental settings, such as normalized collision energy, Orbitrap filling times, or the number of target ions in MS/MS scans, also improve quantification, but at the expense of the number of proteins identified (3, 6). In contrast, the employment of the real-time search feature of the Tribrid Orbitrap MS instrument with an additional MS3 scan used alongside a carrier to characterize single cells demonstrated improved quantitative accuracy (7). Despite these improvements in mass spectrometry–based proteomics, important analytical challenges still remain, especially regarding analysis of plasma and other body fluids. Human plasma proteins exhibit clinical relevance regarding the body’s physiological and pathological states and may constitute novel biomarkers that allow early diagnosis of disease events, making detailed analysis of plasma a compelling goal. Unfortunately, discovery of biomarkers in plasma faces a distinct technical challenge during analysis. The dynamic concentration range of plasma proteins spans more than 10 orders of magnitude, and high-abundant proteins such as albumin, immunoglobulins, and complement system proteins dominate data-dependent acquisition (DDA) workflows, making detection of low-abundant proteins challenging (8). Efficient detection and quantification of low-abundant proteins is of biological relevance, since they may stem from tissue leakage into blood due to damage or disease, thereby constituting novel biomarkers (9). Other types of body fluids such as urine, cerebrospinal, or pericardial fluid are comparable to plasma in their protein composition with the addition of local proteins specific to the adjacent tissue, thus presenting the same challenges as plasma with respect to the detection of tissue-related biomarkers present in low concentrations (10, 11).

To address the aforementioned limitation, several methods have been developed to reduce sample complexity of plasma and related fluids. These include immunodepletion (12), affinity enrichment methods (13), and/or chromatographic prefractionation methods orthogonal to conventional C18-chromatography such as high-pH chromatography (14) or hydrophilic interaction chromatography (15). Despite these efforts, mass spectrometry–based proteomics on plasma and other body fluids still lags behind tissue and cellular proteomics in terms of throughput, suggesting these methods do not sufficiently reduce sample complexity. Although the concentration of high-abundant proteins can be lowered, the concentration of low-abundant proteins is likely affected as well, thus the large concentration range remains. These methods are additionally rendered impractical for the analysis of thousands of samples in larger clinical studies due to increased cost and time associated with their use (16). Therefore, investigating the proteomes of plasma and other related fluids warrants new strategies for low-abundant biomarker discovery.

In the current study, we present proof-of-principle of the utility of SCoPE-MS for increasing proteome coverage of tissue-specific proteins in body fluids by the utilization of a tissue-specific carrier aiding protein discovery with liquid chromatography tandem mass spectrometry (LC-MS/MS). The carrier increased protein throughput while still allowing precise quantification of differences in protein levels in clinical samples. More specifically, we used myocardial tissue as a protein carrier to characterize the undepleted pericardial fluid proteome in patients with heart failure (HF) and type 2 diabetes mellitus (DM).

Experimental Procedures

Experimental Design and Statistical Rationale

First, an optimization experiment was performed where five different carrier amounts were tested (Table 1) to ascertain the level of carrier that would increase proteome coverage without compromising quantitative precision. For each carrier amount, technical triplicates were analyzed, where each replicate consisted of 10 identical samples of pooled pericardial fluid (TMT labels 126–131N) and the carrier (131C). Next, the main analysis was performed where the effect of the carrier proteome was investigated in a clinically relevant setting. This was termed the patient experiment, where pericardial fluid was collected from 80 patients and analyzed twice, once with no carrier and once with 3.3× carrier amount, generating two datasets. Patient characteristics and experimental groups are described below. Pericardial fluid is not routinely accessible; therefore the number of patients and the amount of pericardial fluid utilized was limited. To determine differential protein expression in the patient experiment, data was initially filtered to ensure at least five quantified values for each protein per experimental group; hereafter p-values were calculated using a two-sided Student's t test assuming equal variance comparing each experimental group to the control group. Protein expression levels were calculated as abundance ratios scaled to the control, dividing protein expression in each sample by the control. Abundance ratios were normalized and log2-transformed as described below. Fold changes were calculated as the mean protein expression for the experimental group divided by the mean protein expression in the control group. p-values <0.05 were considered significant. Data were analyzed using R, version 4.2.1 (17). UpSetR package was used for visualization of intersecting sets of differentially expressed proteins (18). InteractiVenn was used for Venn diagrams (19).Table 1 Tested ratios to determine the appropriate ratio of tissue-derived carrier proteome to patient-derived pericardial fluid samples

Ratio of carrier compared to samples
Carrier: Samples (n = 10)	Relative amount of carrier compared to one sample	Calculated carrier amount
(ng)	Calculated sample amount
(ng, n = 10)	
1:0	-	500	-	
7:1	70×	437.5	62.5	
3:1	30×	125	375	
1:1	10×	250	250	
1:3	3.3×	375	125	
1:7	1.4×	62.5	437.5	
0:1	-	-	500	
For these experiments, a pool of pericardial fluid samples (n = 10) was tagged with mass tags 126-131N, while the carrier proteome was tagged with 131C. Each MS analysis consisted of 500 ng total protein, split between the carrier and samples. Each ratio was tested in triplicate. TMT, tandem mass tag.

Patients

The study cohort consisted of 80 patients who underwent cardiovascular surgery (aortic valve replacement and/or coronary by-pass) at the Department of Cardiac, Thoracic and Vascular Surgery, Odense University Hospital. Patients were divided into four experimental groups, with 20 patients per group: HF, DM, both HF and DM, or neither HF nor DM. HF was defined as left ventricular ejection fraction less than or equal to 45%. DM was acquired from medical history. For statistical analysis, the HF, DM, and HF + DM groups were considered experimental groups. The non-HF, non-DM group was regarded as the control group and hereafter will be referred to as such, with the discernment that this group was used as a control for statistical testing and not as ‘healthy controls.’ Sixty-two patients (78%) were male, the median age of the cohort was 68 years (range 33–86), and the median body mass index was 28 kg/m2 (range 18–40) (Supplemental Table S1). Formalin-fixed paraffin-embedded sections of myocardial tissue were available from four patients.

A written informed content was retrieved from each patient prior to enrollment. The study was conducted in accordance with the Declaration of Helsinki and approved by the Regional Ethics Committee (S-20100044).

Sample Processing

Pericardial Fluid

Pericardial fluid samples were collected from 2013 to 2017 and were drawn promptly following median sternotomy into K2EDTA/aprotinin-coated tubes (5 ml, BD Vacutainer) and centrifuged at 3220 g for 10 min at 4 °C. The resulting supernatant was allocated in 1 ml fractions (1.8 ml, Nunc cryogenic tube, Thermo Fisher Scientific) and stored at −80 °C until analysis. Protein concentration of each sample was measured using bicinchoninic acid assay. Sample equivalent to 20 μg protein was transferred, concentrated by vacuum centrifugation, and redissolved in 20 μl 0.2 M triethylammonium bicarbonate (TEAB). Redissolved proteins were reduced by the addition of 2.2 μl 50 mM DTT and incubated at 50 °C for 30 min. Then, 2.5 μl 150 mM iodoacetamide was added to each sample and left to incubate at room temperature for 30 min. Finally, 1 μg of trypsin was added to each sample in a 20:1 ratio and incubated over night at 37 °C.

Myocardial Tissue

Deparaffinization of formalin-fixed paraffin-embedded tissue section was performed by incubating the slices in xylene at room temperature for 5 min. To each tissue slice, 1 μl 10% sodium dodecyl sulfate was added prior to adding 5 μl extraction buffer (2% sodium dodecyl sulfate, 200 mM DTT, 50 mM TEAB). Each tissue slice was then transferred to separate sample tubes and incubated at 99 °C for 20 min prior to incubation at 80 °C for 120 min. Then, 120 μl 200 mM iodoacetamide was added to each sample and left to incubate at room temperature for 30 min prior to acetone precipitation at −20 °C for 1 h followed by centrifugation at 10,000 g for 5 min. Acetone was then removed and the pellet was left to air dry before redissolving proteins in 50 μl 0.2 M TEAB followed by a second round of acetone precipitation by the addition of 250 μl acetone followed by incubation, centrifugation, and removal of acetone as mentioned above. Proteins were then redissolved with 5 μl 8M urea and incubated at room temperature for 60 min prior to protein digestion with 0.5 μg Lys-C at 30 °C for 5 h after which 35 μl 200 mM TEAB and 1 μg trypsin was added to each tube and incubated at 30 °C overnight.

Isobaric Labeling of Tryptic Peptides

Digested peptides were chemically labeled with 11-plex TMTs (Thermo Fisher Scientific) according to vendor instructions. Briefly, 5 μg of tryptic digest from each sample was randomly labeled with either of the TMT tags 127N, 127C, 128N, 128C, 129N, 129C, 130N, 130C, and 131N mixed in equal ratios with a peptide pool of all samples tagged with mass tag 126. Ion signals from this mass tag were used for data normalization across TMT datasets and for the calculation of relative protein abundances. Trypsin digest of the myocardial extract was labeled with the 131C TMT tag and pooled with remaining samples in varying proportions relative to the pericardial samples, as described in detail in the Results section.

Proteome Analysis

Offline Fractionation

The resulting TMT sets were fractionated into seven fractions by high-pH fractionation virtually as previously described (20). Briefly, samples were loaded onto an ACQUITY UPLC M-Class CSH C18 column (130 Å, 1.7 μm bead size, 300 μm id × 100 mm length) using a 25 min linear gradient from 10% solvent B (20 mM ammonium formate in 80% acetonitrile, pH 9.3) to 55% solvent B at 6 μl/min flowrate on a Dionex Ultimate 3000 RSLnano system inline coupled to a Dionex 3000 Ultimate UV detector (210 nm) and a Dionex Ultimate 3000 autosampler configured as a fraction collector (Thermo Fisher Scientific).

Orbitrap Mass Spectrometry

Mass spectrometry analysis of the fractionated samples was conducted on an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher Scientific) equipped with a nano-HPLC interface (Dionex UltiMate 3000 nano-HPLC, Thermo Fisher Scientific). The samples (5 μl) were loaded onto a custom-made fused capillary precolumn (2 cm length, 360 μm OD, 75 μm ID packed with ReproSil Pur C18 5 μm resin [Dr Maish, GmbH]) with a flow rate of 4.5 μl/min for 6 min. The trapped peptides were separated on a custom-made fused capillary column (25 cm length, 360 μm OD, 75 μm ID, packed with ReproSil Pur C13 1.9 μm resin) using a linear gradient ranging from 90 to 73% solution A (0.1% formic acid, Fluka) and 10 to 27% B (80% acetonitrile (J.0020T. Baker) in 0.1% formic acid) over 45 min followed by 2 min at 45% B, 5 min at 95% B, and 5 min at 95% A at a flow rate of 250 nl per minute. Mass spectra were acquired in positive ion mode using DDA in a 2 s duty cycle starting with a high resolution MS1 scan in the Orbitrap with a mass range of 350 to 1400 m/z at a resolution of 60,000 at m/z 200 with automatic maximum injection time and 100% acquisition gain control target. MS/MS scans were acquired using a 0.7 m/z isolation window (higher energy collisional dissociation [HCD], normalized collision energy: 35%) with an acquisition gain control target of 200%, automatic maximum injection time, and at a resolution of 45,000 at m/z 200. Selected ions were dynamically excluded for 60 s.

Raw Data Processing

All raw data files were processed using the Proteome Discoverer software (v. 2.4.0.305 or 2.4.1.15, Thermo Fischer Scientific) and searched with the Sequest HT search algorithm. Data from myocardial tissue and the patient experiment were searched with Sequest HT combined with MSPepSearch using the NIST spectral library (Orbitrap HCD 20160923 v.1 [1127970 spectra]). Sequest HT search parameters were set to default except for MS accuracy of 8 ppm, MS/MS accuracy of 0.05 Da for HCD data, and trypsin digestion with two missed cleavages allowed. Fixed modifications were set to carbamidomethylation at cysteine residues, TMT 6-plex N-terminal, and TMT 6-plex on lysine residues. Variable modifications were set to methionine oxidation, N-terminal carbamylation, and deamidation of asparagine and glutamine residues. Raw data files were searched against the UniProt database (downloaded 17th June 2020, 42,369 entries). Peptide identifications and peptide-spectrum matches (PSMs) were filtered to a 1% false discovery rate (FDR) using the Peptide and Protein Filter node. For quantification, a minimum average signal-to-noise ratio (SNR) of 10 and a maximum co-isolation of 50% for reporter ions was required at the PSM level. For the optimization experiment only, an additional threshold was applied at the protein level requiring average SNR to be greater than 10 for reporter ions from sample channels. Proteins identified with at least one unique peptide and with a high confidence (FDR <1%) were permitted in the final dataset. Reporter ion SNRs, which are directly proportional to the number of ions in the Orbitrap, were used for the calculation of raw and relative protein abundances. Reporter ion SNRs from all PSMs that passed the quantification filters were summed to infer raw protein abundances. Raw protein abundances were scaled to the control sample (mass tag 126) by multiplying by a protein-specific scaling factor (100/control abundance). Protein abundance ratios were calculated by dividing protein expression in each sample by expression in the control sample. The scaling procedure and abundance ratio calculations were implemented through Proteome Discoverer.

Data Postprocessing

Data normalization was performed in Excel 2016 by dividing protein abundance ratios by the mean protein expression for each sample, thereby compelling mean protein expression in each sample to one. Subsequently, log2-transformation was applied. Proteins with less than five values in any of the sample groups were removed from the data. To calculate average sample SNR during optimization of carrier proteome amount, raw abundances corresponding to reporter ion SNRs for PSMs were exported from Proteome Discoverer and the average SNR of sample channels (126–131N) was calculated in Excel 2016. The number of proteins with an average sample SNR >10 was determined by filtering PSMs and using the resulting UniProt accession numbers to identify proteins.

Pathway Analysis

PANTHER (21, 22) (version 17.0) was used to perform enrichment analysis on data annotated with GO Biological Process terms (23, 24) (released 2023-03-06). Upregulated and downregulated proteins were submitted for analysis separately and compared to a background of all proteins quantified in the data with at least five values in each experimental group (1398 proteins when using the carrier proteome, 265 proteins without a carrier). Fisher’s exact test was used to determine significance with Benjamini–Hochberg correction at 5% FDR.

Results

Principle of Using a Tissue-Specific Carrier to Aid in Biomarker Discovery

We adapted the SCoPE-MS multiplexing principle to boost sensitivity for detecting tissue-derived low-abundant protein biomarkers in body fluids. We demonstrated the method via the search for heart tissue–specific protein biomarkers in pericardial fluid from patients with or without HF and with or without DM using myocardial protein extracts as a carrier proteome. A tissue-based carrier proteome was chosen rather than depleted pericardial fluid since the absolute abundances of low-abundant peptides are not increased when high-abundant proteins are depleted. Myocardial tissue was utilized to intentionally bias the analysis by increasing the intensity of cardiac-derived proteins. On the other hand, the carrier cannot aid in the detection of proteins which are not present in the pericardial fluid samples, thus the carrier and sample proteomes must overlap.

In principle, peptides measured in MS1 and selected for MS/MS analysis may have three origins (Fig. 1). Firstly, peptides can be carrier-derived (mass tag 131C in present study), where no signal is detected across the pericardial fluid samples (mass tags 127N-131N) or the internal control sample (mass tag 126) (red peptide, Fig. 1). Second, no carrier-derived peptide triggers the MS/MS scan, indicating low-abundant or no heart tissue–derived carrier peptide present (blue peptide, Fig. 1). Lastly, the measured peptide can originate from a protein present in both the pericardial fluid samples and in the heart tissue carrier (yellow peptide, Fig. 1). Here, the reporter ion signals are present in all channels with intensities that reflect the measured protein in each sample.Fig. 1 Principle of adapting the SCoPE-MS method to measure tissue-derived low-abundant protein biomarkers in body fluids, where a tissue-derived carrier channel (mass tag 131C) is added to patient-derived body fluid samples (mass tags 127N-131N) in addition to an internal control sample pool (mass tag 126). Three possible origins for peptides selected for MS/MS analysis are presented: carrier-derived peptide with no signal detected in patient samples (red peptide), sample-derived peptide with no signal in the carrier channel (blue peptide), or when the measured peptide originates from both the tissue-derived carrier and samples (yellow peptide), allowing quantification in all channels.

Optimization Experiment: Optimal Ratio of Carrier to Samples Based on Reporter Ion SNRs

Akin to SCoPE-MS, it is important to optimize the amount of carrier relative to the samples to ensure preservation of quantitative precision, while maximizing the number of the tissue-specific proteins analyzed. To determine the optimal amount of proteome carrier to add to samples, different ratios of the carrier, consisting of myocardial tissue, and pooled samples, consisting of pericardial fluid, were analyzed by LC-MS/MS by DDA as described in “Experimental Procedures”. Each ratio was analyzed in three technical replicates, with each replicate consisting of 10 pooled samples labeled with separate mass tags. For the five ratios tested (Table 1), the signal intensities from pericardial fluid samples comprise the signals from 10 samples relative to the carrier. For instance, when the ratio of carrier to samples was 3:1, the ratio of carrier to each sample channel constituted 30:1. In this case, the signal of each individual reporter ion channel tagged with a single pericardial fluid sample was 30 times lower than the signal for the carrier reporter ion channel, also referred to as ‘30× carrier.’ This calculation of carrier levels was used for all ratios implemented in the experiment (Table 1). Two parameters were used to assess quantitative precision at different carrier-to-sample ratios. The SNR for sample channels (mass tags 126–131N, identical pooled pericardial fluid samples) was used to evaluate reporter ion abundance. For each PSM, the average sample SNR was calculated and the coefficient of variation (CV), a measure of analytical variability, was calculated by dividing the SD of sample channels by the mean of sample channels, then multiplying by 100.

The number of PSMs was increased by adding a carrier until 70× carrier, although a gradual deterioration of quantitative precision was observed (Fig. 2A). Average sample SNR and CV were plotted to provide further insight into the effect of the carrier on the data (Fig. 2, B–G). Without a carrier, 98 ± 0.1% of PSMs had an average sample SNR greater than 10 (Fig. 2B). Based on this, SNR greater than 10 was chosen as a reasonable threshold for subsequent investigations to exclude PSMs only containing signal from the carrier channel and to avoid imprecise relative quantification. A CV threshold of 20% was also chosen to divide PSMs into lower and higher variance. As a reference, for data acquired with no carrier across three replicates, 71 ± 0.2% of the PSMs were in the lower right quadrant, with an SNR greater than 10 and a CV lower than 20% (Fig. 2B). When 1.4× or 3.3× carrier was added, the number of PSMs observed in this quadrant increased, although the proportion of total PSMs decreased (Fig. 2, C and D). The number of PSMs with average sample SNR greater than 10, regardless of CV, was also increased at these carrier levels. Although this includes PSMs with a CV greater than 20%, data with some variance can still provide important biological information, especially in the context of biomarker discovery. However, at higher carrier amounts, the number of PSMs with an SNR greater than 10 and a CV lower than 20% fell substantially (Fig. 2, E–G), indicating significant noise in the data caused by excess carrier signal. At 30× and 70× carrier, data below the SNR threshold were distinctly shaped (Fig. 2, F and G). Here, sample signals were very low, often originating only from a few reporter ions, while CV varied widely, since the relative differences in signal were large. This suggests additional optimization of the MS acquisition method is necessary for these carrier levels to be informative, since most PSMs were below the threshold of reliable quantification. A negative exponential relationship also emerged between CV and average sample SNR, resulting in straight lines in the lower left quadrant. This artifact resulted from PSMs where only two reporter ions were quantified, always with abundances within 0.1 of each other. Due to the very low reporter ion abundances, these PSMs were removed by the quantification filter. Pairwise correlations of raw protein abundances were inversely correlated to the level of carrier, reflecting the increased variation and decreased sample signal seen at the PSM level. Adjacent carrier levels were more highly correlated than distant carrier levels (Supplemental Fig. S1).Fig. 2 Effect of amount of carrier proteome on SNR and CV for PSMs.A, the effect of different amounts of carrier proteome on average sample SNR and CV for PSMs in the optimization experiment. B–G, scatterplots of the effect of different amounts of carrier proteome on average sample SNR and CV for PSMs. Dashed red lines indicate either 20% CV or average sample SNR equal to 10, while the number of PSMs in each quadrant was indicated in the corners of each plot. For simplicity, only data from the first of three replicates were displayed. All replicates with the same carrier level were similar in appearance. Ratios were indicated by the amount of carrier added compared to a single TMT tag. CV, coefficient of variation; PSM, peptide-spectrum match; SNR, signal-to-noise ratio; TMT, tandem mass tag.

Evaluation of unique peptide abundances confirmed that increased carrier amounts were linked to increased variance (Fig. 3). Controls were scaled to an abundance of 100 and other samples were adjusted in turn; however, this simple normalization could not overcome the variance introduced by the carrier. Since the same pool was used in all sample tags, the average peptide abundance should be close to 100 after scaling, while average abundances higher or lower than 100 correlated with higher variance. The proportion of peptides with a CV less than 20% was 69 ± 0.8% without a carrier, decreasing steadily as carrier was added, until finally at 70× carrier, only 8 ± 0.2% of peptides had a CV less than 20%.Fig. 3 Scatterplots of the effect of different amounts of carrier proteome on average sample peptide abundances and CV in the optimization experiment.A, results with no carrier added. B–F, from 1.4X to 70X carrier amount as indicated to the left of the graph. Raw abundances were defined as SNR for unique peptide groups, control abundances were arbitrarily set to 100, and the remaining sample abundances were scaled accordingly. The dashed red line indicates 20% CV, while the number of peptides above or below this threshold was indicated on each plot. For simplicity, only data from the first of three replicates were displayed. All replicates with the same carrier level were similar in appearance. Ratios were indicated by the amount of carrier added compared to a single TMT tag. CV, coefficient of variation; SNR, signal-to-noise ratio; TMT, tandem mass tag.

For each carrier level tested, the observed ratio of carrier to samples was calculated to compare to the theoretical carrier amount. To calculate the observed ratios, the SNR of the carrier channel was divided by the average sample SNR for each PSM. Although the ratio between the total protein amount of the carrier and pericardial fluid samples was known, the observed ratios of carrier to samples varied widely at the peptide level in all experiments (Fig. 4). At carrier levels of 1.4× and 3.3×, the median-observed carrier ratio was similar to the theoretical carrier ratio (Fig. 4, A and B). At carrier levels of 10× or higher, the median-observed carrier ratio was notably higher than the theoretical carrier amount (Fig. 4, C–E). This could be caused by a high number of PSMs originating solely from carrier peptides, which resulted in very high observed ratios due to a small average sample SNR. At all carrier levels, the observed ratios close to zero indicate PSMs with very low signal in the carrier channel and high signal in sample channels, likely due to peptides exclusively present in pericardial fluid.Fig. 4 Histograms of the observed ratio of carrier to samples in the optimization experiment.A–E, 1.4X to 70X ratios of carrier to samples. Observed ratios were calculated by dividing the SNR in the carrier channel by the average sample SNR for each PSM. For each carrier amount, all three replicates are displayed. The theoretical ratio of carrier to samples was inset as a black dashed line and as a text annotation in each plot. The median observed ratio was inset as a red dashed line and as a text annotation in each plot. The x-axis was represented on a log10 scale. Med., median; PSM, peptide-spectrum match; SNR, signal-to-noise ratio.

Additionally, how different amounts of carrier affected the distribution of average sample SNR for PSMs was investigated (Fig. 5A). Average SNR for sample channels was inversely correlated with carrier amount, demonstrating that when high amounts of carrier were added, reporter ion signal in the sample channels was negatively affected. For experiments containing 10× carrier or less, the median of the average sample SNRs was greater than 10, indicating sufficient reporter ion signal in sample channels. Unsurprisingly, when the carrier proteome was run without the addition of sample tags, insufficient SNR was observed in the sample channels for essentially all PSMs. Scaled abundances for unique peptides also decreased slightly as the carrier level increased, although the effect was less pronounced since the control channel for all peptides, no matter their abundance, was scaled to 100 while remaining channels were scaled accordingly (Fig. 5B). The average sample peptide abundances at high carrier levels (30×, 70×) and empty sample channels showed high variation. Based on the above, a carrier-to-sample ratio of 10× or less was necessary to ensure low variation and sufficient sample reporter ion signals.Fig. 5 Effect of different amounts of carrier proteome on average sample SNR, peptide abundance, and proteome coverage.A, average sample SNR for PSMs and (B) average scaled sample abundances for unique peptide groups in the optimization experiment at different carrier levels. Dashed red line indicates average sample SNR equal to 10. Each ratio of carrier to samples was tested in triplicate. C–F, effect of different amounts of carrier proteome on the number of (C) protein identifications, (D) protein quantifications, (E) carrier tissue-specific protein identifications, and (F) carrier tissue-specific protein quantifications in sample channels in the optimization experiment. Error bars indicate ± SD of triplicates. A threshold of average sample SNR >10 was set for sufficient reporter ion signal in experimental samples. Ratios were indicated by the amount of carrier added compared to a single TMT tag. PSM, peptide-spectrum match; SNR, signal-to-noise ratio; TMT, tandem mass tag.

Optimization Experiment: Effect of the Carrier on Protein Identification and Quantification

Also important for the determination of the optimal carrier-to-samples ratio was the effect of the carrier proteome on protein identifications and quantifications. For each carrier-to-sample ratio, total identified and quantified proteins were considered as well as proteins with sufficient sample reporter ion signal, which was calculated by filtering PSMs for average sample SNR greater than 10 and determining the number of associated unique UniProt accession numbers (Fig. 5, C–F). Adding a threshold for sample reporter ion signal allowed the removal of proteins only present in the carrier. Identified proteins included all proteins identified by the database search at 1% FDR, while quantified proteins were restricted to proteins where reporter ions passed the quantification filters of minimum average SNR of 10 and maximum co-isolation threshold of 50%. Although the total number of identified proteins increased with increasing carrier amounts (Fig. 5C, red bars), the number of identified proteins with sufficient sample reporter ion signal initially rose but then decreased after 10× carrier (Fig. 5C, orange bars). A similar trend was seen for protein quantifications (Fig. 5D). Many proteins could be identified when only the carrier was added; however, few could be quantified, since there was only signal in the carrier channel. When considering proteins with sufficient sample reporter ion signal, carrier amounts of 3.3× and 10× resulted in an average of 345 and 364 identifications and 339 and 356 quantifications, respectively. Since identifications and quantifications with these two ratios produced similar outcomes, a carrier amount of 3.3× was favored and chosen for the following experiments due to the increased quantitative precision previously demonstrated. At 3.3× carrier, approximately 50% of PSMs and peptide abundances demonstrated a CV less than 20%, which was deemed acceptable considering the gain in protein throughput (Figs. 2D and 3C).

Optimization Experiment: Effect of the Carrier on Carrier-Specific Protein Identification and Quantification

Next, we aimed to investigate the effect of the carrier on identification and quantification of carrier proteome-specific proteins. As described in the methods section, highly fractionated carrier, consisting of myocardial tissue, was used to determine the carrier proteome in detail, identifying 3864 proteins in total (Supplemental Table S2). In pericardial fluid samples run with no carrier, 95 out of 147 identified proteins (65%) were also identified in highly fractionated carrier proteome. This indicated that a significant number of proteins were shared between the pericardial fluid samples and the myocardial carrier, which was not surprising considering their adjacent origins. The proteomes of the carrier and samples must be similar for the carrier to sufficiently boost low-abundant protein identifications (i.e. Fig. 1, yellow peptide). As was the case for total protein identifications, carrier-specific identifications were highest when the amount of carrier was high, although sample reporter ion signals decreased (Fig. 5E). Carrier-specific protein quantifications also mirrored total protein quantifications (Fig. 5F), suggesting agreement between the optimal ratio for total protein detection and the optimal ratio for carrier-specific protein detection. Therefore, whether considering either total proteins or carrier-specific proteins, using a carrier amount of 3.3× increased the number of protein identifications and quantifications while maintaining quantitative precision for the sample channels. At 3.3× carrier, circa 80% of proteins identified (280 of 345) or quantified (276 of 339) with sufficient sample signal were carrier-specific proteins.

Patient Experiment: Effect of the Carrier on Proteome Coverage in Pericardial Fluid Samples from Patients with Type 2 Diabetes Patients and/or HF

To demonstrate the ability of a carrier to promote relevant protein quantification in patient samples, the 3.3× carrier was subsequently used for proteome analysis of pericardial fluid from 80 patients (Supplemental Table S1). Patients were divided into four experimental groups: patients with HF or DM, patients with HF + DM, or controls, with 20 patients in each group. In total, 2035 proteins were quantified when a carrier proteome was added to the samples. Proteins were then filtered to ensure that in each of the four sample groups, at least five values were quantified for each protein, leaving 1398 proteins. For comparison, the same samples were run without the addition of a carrier. When no carrier was present, the total number of proteins quantified was 392, of which 265 remained after filtering. Therefore, total protein quantification was greatly increased when using a carrier, and 223 proteins were shared between the experiments (Fig. 6A), demonstrating that 84% of total proteins quantified with no carrier were also quantified with a carrier. This suggested that the addition of the carrier proteome increased protein quantification without obscuring proteins that would otherwise have been quantified.Fig. 6 Impact of a carrier proteome on protein quantification in pericardial fluid samples in the patient experiment.A, Venn diagram comparing proteins quantified in patient samples with a carrier proteome to proteins quantified when no carrier proteome was used. B and C, Venn diagram comparing the myocardial carrier proteome to (B) proteins quantified in pericardial fluid samples run with no carrier and (C) proteins quantified in pericardial fluid samples run with a carrier. D, stacked bar plot to demonstrate the impact of the carrier proteome on the acquired MS/MS spectra used for reporter ion quantification and PSMs. Sufficient signal was defined as SNR >10 and MS/MS spectra and PSMs with signal exclusively from the carrier or sample channels were counted. PSM, peptide-spectrum match; MS/MS, tandem mass spectrum; SNR, signal-to-noise ratio.

The 42 proteins only quantified when no carrier was added included extracellular proteins such as C-reactive protein and sex hormone–binding globulin, as well as proteins from the complement and coagulation cascades. The 1175 proteins unique for when a carrier was added were characterized by skeletal muscle proteins such as desmoplakin, myosin, and myosin-regulating proteins as well as metabolic proteins from pyruvate metabolism and the tricarboxylic acid cycle. Lastly, the 223 shared proteins included apolipoproteins as well as complement and coagulation cascade proteins, such as fibrinogen.

Next, we investigated the number of carrier-specific proteins quantified. As a baseline, proteins quantified in pericardial fluid samples where no carrier proteome was added were compared to the fractionated myocardial carrier proteome (Fig. 6B). This demonstrated that 161 out of 265 (61%) of the proteins quantified in pericardial fluid samples were also quantified in the carrier proteome. When pericardial fluid samples were run with the carrier proteome added, 1180 out of 1398 (84%) of proteins were also quantified in the isolated carrier proteome (Fig. 6C). In conclusion, using a carrier proteome increased the total number of proteins quantified in pericardial fluid samples and increased the proportion of proteins that were tissue-specific for the myocardial carrier.

To determine to what extent the carrier proteome was driving data acquisition, the number of MS/MS spectra used for quantification and PSMs with reporter ion signal exclusively from the carrier or sample channels was counted (Fig. 6D). Sufficient signal was defined as average SNR >10 for sample channels or carrier channel SNR >10. A similar number of total MS/MS spectra were acquired in the patient experiment both with and without the carrier. When a carrier was added, samples were no longer the driver of data acquisition, with most spectra (87%) containing signal from both carrier and samples and 12% of spectra with only carrier signal. A similar pattern was observed with PSMs, although the total number of PSMs was higher when a carrier was added.

Patient Experiment: The Carrier Proteome Increased the Number of Differentially Expressed Proteins

To determine if the myocardial carrier proteome improved the identification of biologically relevant proteins characterizing HF and/or DM, differentially expressed proteins were investigated as well. Protein expression in pericardial fluid samples from patients with HF, DM, or both was compared to protein expression in control samples using a Student’s t test. When the carrier proteome was added to samples, 156 proteins were differentially expressed in pericardial fluid among the three experimental groups (Supplemental Table S3). For comparison, when pericardial fluid samples were run with no carrier proteome, 41 proteins were differentially expressed among the three experimental groups (Supplemental Table S4). Therefore, as could be anticipated, the increased number of total proteins quantified when a carrier proteome was added led to increased detections of differentially expressed proteins compared to the same samples when no carrier was added. Differentially expressed proteins were visualized through volcano plots (Supplemental Fig. S2). Of note, no proteins retained statistical significance after Benjamini–Hochberg correction for multiple testing. This is likely a consequence of the low number of samples due to the inaccessibility of pericardial fluid as well as complex and overlapping pathologies. Although this limits the impact of the biological conclusions, the purpose of this exploratory study was to determine whether a carrier proteome could successfully attenuate protein detection in pericardial fluid samples; therefore, p-values were not corrected for multiple testing to allow proof-of-concept of the methodology.

The number of unique differentially expressed proteins associated with HF and/or DM was visualized with an UpSet plot (Fig. 7), where a bar graph indicated unique and shared proteins (intersection) of different combinations of experimental groups. The highest number of unique differentially expressed proteins was found when using a carrier for all experimental groups. For example, pericardial fluid samples from patients with DM or HF were characterized by 33 unique differentially expressed proteins each, while samples from patients with DM and HF were characterized by 51 unique proteins. In contrast, only 2, 3, or 17 unique differentially expressed proteins were found for samples from patients with HF, DM, or HF and DM, respectively, when no carrier was added. Therefore, using a carrier allowed characterization of the pericardial fluid proteome in greater detail. A considerable amount of information is gained when using a carrier (117 [33+33+51] unique proteins characterizing the three experimental groups), while little information is lost (22 [2+3+17] unique proteins, no carrier experiment).Fig. 7 UpSet plot demonstrating the impact of a carrier proteome on the number of differentially expressed proteins in pericardial fluid samples in the patient experiment. Each of the three experimental groups (HF, DM, HF+DM) was compared to the control group to determine differentially expressed proteins, and each of these comparisons was repeated when samples were run with and without a carrier proteome, resulting in six total sets (rows). The bar graph represents the shared proteins (intersection) of the different sets. A filled circle indicates that a set is included in the above intersection, with connecting lines between. A green background indicates rows where a carrier proteome was added to samples, while a red background indicates rows where no carrier proteome was added. p-values were not corrected for multiple testing. DE, differentially expressed; DM, type II diabetes mellitus; HF, heart failure; HF+DM, heart failure and type II diabetes mellitus.

Figure 7 also demonstrated that few differentially expressed proteins were shared between experimental groups. The distinct proteomes corroborate the knowledge that samples were from separate biological entities. Additionally, few differentially expressed proteins were shared between carrier and noncarrier experiments (Supplemental Table S5 lists proteins in Fig. 7). For 15 proteins which were differentially expressed both with and without the carrier, the direction of regulation was the same for all proteins, and the median Pearson’s squared correlation coefficient was 0.54 (Supplemental Fig. S3). In conclusion, the minimal overlap between differentially expressed proteins discovered when a carrier was added compared to when no carrier was added suggested that the carrier provided orthogonal information.

Patient Experiment: The Carrier Proteome Promoted Biologically Relevant Findings

To investigate the effect of the myocardial carrier proteome on the biological interpretation of results, enrichment analysis using the GO database was used to determine whether biological pathways were enriched or depleted in the data. For samples from patients with both HF and DM, upregulated or downregulated proteins were compared to all proteins quantified in the data.

Fifty-one proteins upregulated when a carrier proteome was added were found to have 38 overrepresented GO terms (Fig. 8 and Table S6). Several pathways were concerned with the innate or adaptive immune system, such as complement activation and humoral immunity. This is likely indicative of the increased activation of immune inflammatory cells in DM, such as macrophages and T lymphocytes (25). A total of 27 proteins annotated with “response to stress” (GO:0006950) were also enriched, confirming an increase in inflammation and oxidative stress, with detrimental effects on cardiac tissue as well as elsewhere. This GO term included proteins such as attractin, part of the immune inflammatory response, and N-myc downstream regulated 1, a protein upregulated in response to oxidative stress which regulates cell growth and differentiation. “Proteolysis” (GO: 0006508)) and “protein activation cascade” (GO:0072376) were both enriched, indicating an altered metabolic state. Interestingly, terms regarding negative regulation of wound healing, hemostasis, and coagulation were also overrepresented, suggesting an altered response to injury in these patients.Fig. 8 Bar plot of GO biological process enrichment analysis for 51 proteins upregulated in samples from patients with HF and DM when a carrier proteome was added to the patient experiment. The GO biological process column lists the name and GO term of the enriched pathway. The background frequency indicates the number of proteins from the reference proteome which are annotated with the GO term. All proteins identified in the data with at least five values quantified for each experimental group (1398 proteins) were used as a reference proteome. The sample frequency indicates the number of upregulated proteins annotated with the GO term. The expected frequency was calculated by multiplying the background frequency by the proportion of upregulated proteins in the reference proteome (50/1365, since not all proteins were in the PANTHER database). Whether the pathway was over- or under-represented was also indicated. Fold enrichment was calculated by dividing the sample frequency by the expected sample frequency. Fisher’s exact test was used to calculate p-values, and a 5% FDR with Benjamini-Hochberg correction was applied. GO terms were ranked by fold enrichment. The number of observed proteins associated with each GO term was indicated by the size of the gray bubble. The FDR-adjusted p-value was indicated by the color of each bar. DM, type II diabetes mellitus; FDR, false discovery rate; GO, gene ontology; HF, heart failure.

Proteins downregulated in samples from patients with both HF and DM did not contain perturbed pathways after FDR correction. For the same samples, when a carrier proteome was not added, no pathways were significantly enriched or depleted after FDR correction. Therefore, the addition of the myocardial carrier revealed dysregulated biological pathways which would not otherwise have been identified.

Discussion

In this study, we have demonstrated that the use of a biologically relevant carrier proteome can aid immensely in the detection and quantification of less abundant proteins not normally measured in body fluid samples while preserving quantitative precision of the TMT-based multiplex method applied. By utilizing a carrier, the MS1 intensities of carrier tissue-specific peptide ions are elevated, resulting in an improved detection limit for these peptides and increasing the likelihood that a DDA workflow will select the peptides for an MS/MS scan. As proof-of-principle, we aimed to identify heart-specific molecular signatures for patients with or without HF and with or without DM in pericardial fluids by using protein extracts from myocardial tissue as carrier. More specifically, peptides from myocardial tissue were labeled with TMT 131C and mixed with peptides from pericardial fluid samples and an internal control, which were labeled with the remaining TMT11plex isobaric labels. For comparison, the experiment was repeated without the addition of the carrier. Our results demonstrated that adding the carrier vastly increased the number of proteins quantified, from 265 to 1398 without sacrificing the analytical precision. The proportion of proteins with a myocardial origin was also higher. The number of differentially expressed proteins also increased, from 41 to 156 proteins. The sets of differentially expressed proteins were largely unique for our three experimental groups, namely patients with HF or DM, or both HF and DM.

The use of a carrier proteome for single-cell samples, pioneered as SCoPE-MS by Budnik et al. (1), has allowed major improvements in single-cell proteomics. However, the carrier channel can affect quantification results. Our study confirmed previous findings that increasing the amount of carrier increased proteome coverage but also data variability (26). In single-cell proteomics, carrier cells-to-single cell ratios reported are typically around 100 to 200, that is, the carrier channel contains protein extract from 100 to 200 cells versus 1 cell in each of the remaining channels (1, 6). We tested carrier amounts 70, 30, 10, 3.3, or 1.4 times higher than one sample channel and found that a carrier-to-sample ratio of 3.3 resulted in increased protein quantification while maintaining data precision (Figs. 2C, 3C and 4). A lower carrier-to-sample ratio resulted in slightly reduced data variation (Fig. 2B) and also a decrease in protein identification and improved precision (Fig. 4C). When making this choice, we considered the SNR of heart-specific proteins as well as overall proteins to ensure that sufficient sample reporter ion signals were obtained. Our ratio of carrier to samples was lower than for single-cell applications (1, 6). This divergence is due to the different challenges facing single cell and pericardial fluid experiments. Single cells have very low sample abundance and large amounts of carrier are needed to boost peptide intensities. In contrast, in pericardial fluid, peptides from high-abundant proteins mask peptides from low-abundant proteins. We expect that cardiac proteins which are shared between the carrier and samples (Fig. 1, yellow peptide) will have low abundance in pericardial fluid; therefore, the amount of carrier only needs to be high enough to increase the intensity of these low-abundant peptides relative to the high-abundant peptides. We also found that even small amounts of carrier were effective at increasing the detection of relevant proteins, since 1.4× carrier nearly doubled carrier-specific protein quantifications (Fig. 4F). Additionally, we extensively evaluated the effect of the carrier on variance and throughput. However, in future studies, it would be relevant to evaluate the accuracy of measured fold changes. Using tryptic HeLa digests, Cheung et al. found that the dynamic range of TMT isobaric labels was 100-300×, suggesting that higher carrier levels would affect the quantification of the single-cell samples (3). An equivalent experiment performed in a background matrix of pericardial fluid would provide an indication of the precision of different levels of fold changes when using SCoPE-MS in this context.

The increased data variability observed at high carrier levels is hypothesized to be due to reduced sampling of single-cell reporter ions and is termed the “carrier proteome effect” (3). Data quality can be improved by reducing the carrier-to-sample ratio or by optimizing MS acquisition settings. On the other hand, this may also decrease the number of quantified peptides and proteins, since longer sampling times result in fewer MS/MS events. Therefore, a balance between carrier level, the number of ions sampled, and allowed sampling time is required to optimize the number of quantifications (4, 26). In our study, after the ratio of carrier to sample had been adjusted, we did not extensively optimize MS acquisition settings and instead opted to use common tandem MS/MS settings for Orbitrap measurement, similar to the MS settings for single-cell proteomics as determined by Cheung et al. (3) and Specht et al. (27). Other studies have found that adjusting MS settings can improve data quality when high amounts of carrier are used. This included using a higher normalized collision energy for fragmentation (6), as well as increasing automatic gain control targets and maximum injection times (3). These changes ensured that more single-cell reporter ions were sampled by the mass spectrometer. We expect that increasing these parameters would have affected quantification results for this study as well, resulting in increased precision as more reporter ions from sample channels would have been sampled, albeit possibly at the cost of some protein identifications. The ideal acquisition parameters are specific to each experiment and its goal, and here we chose to demonstrate proof-of-principle of SCoPE-MS for pericardial fluid, instead of optimizing experiment-dependent parameters.

An alternative approach was demonstrated by Furtwängler et al, where real-time search was used to identify peptides based on an ion trap MS2 scan, target fragments were selected via synchronous precursor selection and subjected to another round of fragmentation prior to an MS3 Orbitrap scan with a maximum injection time of 750 ms to characterize single cells alongside a carrier. This improved quantitative precision and increased the number of true-positive differentially expressed proteins identified compared to an MS2 method (7). Theoretically, this strategy allows the use of higher injection times while avoiding the costs associated with longer acquisition cycles, since only reliably identified peptides are subject to the time-consuming MS3 scan and the method is therefore well-adapted to low-abundant single-cell samples. In the context of this study, adding an MS3 scan could have allowed higher amounts of carrier to be added without decreased quantitative precision.

Another important consideration besides data acquisition is data processing. Data processing is a general challenge for MS experiments but especially within the framework of SCoPE-MS, as commercial workflows are not adapted to this approach. Proteome Discoverer assigns equal weights to all TMT channels when assessing SNR, while ideally the software would only take into account the SNR of sample channels. In a more broad sense, many software suites currently lack flexibility for different data-processing approaches, which can be problematic as these workflows are constantly updated and improved as data interpretation techniques evolve.

To the best of our knowledge, one previous study has suggested the use of relevant tissue together with biological fluid samples to boost the intensities of less abundant peptides. Here, Russell et al. used a microglial murine cell line, termed as TMTcalibrator, to improve the detection of microglial proteins associated with Alzheimer’s disease in cerebrospinal fluid (28). In this experiment, four TMT channels were allocated different levels of the calibrator to allow for a calibration curve. However, a major drawback of this method was that only peptide intensities in the linear range of the calibrator peptide signals were taken into account, leading to the exclusion of more than 60% of the identified peptides. We took a different approach where optimization experiments were performed first to determine the carrier amount that balanced quantitative precision with increased proteome coverage. This allowed us to allocate only one TMT channel to the carrier and one reference channel to calculate relative protein abundances, leaving space for additional patient samples per experiment and multiple TMT sets. This is important in clinical proteomics, where hundreds of samples may be analyzed. Moreover, Russell et al. used synchronous precursor selection-MS3 acquisition to eliminate the risk of co-isolation of peptide fragments from the TMTcalibrator and patient samples. We have shown, however, that it is possible to use low amounts of carrier without comprising precision, thus demonstrating that more standard MS/MS methods can also be used with the carrier approach.

Using a myocardial carrier increased biological insight into protein expression profiles in pericardial fluid. Several differentially expressed proteins detected with a carrier and not detected with the conventional method were relevant to disease conditions in the study. For example, metalloproteinase inhibitor 1 (TIMP1) was downregulated in pericardial fluid samples from patients with HF, while cardiac troponin T was downregulated in samples from patients with both HF and DM. An increase in plasma TIMP1 is associated with fibrosis (29), while blood high-sensitivity troponin immunoassays are used to diagnose acute myocardial infarction. Elevated levels of troponin T are associated with chronic HF (30). These discrepancies likely result from differences in the method of detection and the biological fluid analyzed. Of note, cardiac troponin I and skeletal/cardiac troponin C, which are released along with cardiac troponin T in the case of myocardial cell death (31), were also detected but not differentially expressed in our study. Control groups also differ, since in our study, comparison to a healthy control group was not possible. We also found an upregulation of fibulin-1 in patients with both DM and HF, which correlated with Holmager et al., where a trend towards increased plasma fibulin-1 was found in patients with HF with reduced ejection fraction and DM compared to patients without DM. Fibulin-1 is an extracellular matrix glycoprotein considered as a potential biomarker of cardiomyopathy (32). We also found an upregulation of apolipoprotein C-I in patients with HF, correlating with results from Lemesle et al., who found that elevated plasma levels of this protein were associated with increased cardiac mortality (33). Other known biomarkers for HF or diabetic cardiomyopathy, including B-type natriuretic peptide, atrial natriuretic peptide, and galectin-3 (34), were unfortunately not detected in our study due to the stochasticity of DDA. In summary, while some proteins associated with HF or DM appear to show similar changes in pericardial fluid and blood, others do not. Pericardial fluid surrounds the heart and cardiac proteins are thought to enter this fluid through tissue leakage (9). Thus, characterization of pericardial fluid is relevant for conditions associated with heart dysfunction, such as HF and DM, and could lead to a better understanding of these diseases. However, pericardial fluid is not routinely accessible. Regarding putative prognostic or diagnostic markers associated with these diseases, ease of sampling is important and validation in blood is preferred.

Both diabetes and HF are broad entities spanning several subtypes. The HF patients in this study consisted of patients with reduced left ventricular ejection fraction, which constitutes approximately 50% of HF diagnoses and results in a lower percentage of blood pumped from the left ventricle (35), while the diabetes group consisted of patients with type 2 diabetes, which accounts for approximately 90% of diabetes diagnoses (34). An additional complication results from the fact that HF and DM are closely interconnected. DM patients have a higher prevalence of cardiovascular complications with higher overall and cardiovascular mortality rates than their counterparts without DM (34). The pathology of combined DM and HF can therefore differ from the pathology of either disease alone. There is a significant clinical need for improved risk stratification and early diagnosis for these patients as well as a more detailed biological understanding.

This study demonstrated that a carrier, a concept from single-cell proteomics, can be used to increase carrier-specific protein throughput when analyzing clinical body fluid samples. This allowed detailed characterization of the pericardial fluid proteome where samples were not enriched for specific protein subsets or depleted of high-abundant proteins. We expect that a carrier can be used to increase protein throughput in other undepleted body fluids as well, thus simplifying sample preparation. Another advantage is that the conscious choice of carrier material can be used to attenuate which proteins are identified. Myocardial tissue was chosen as a carrier in this study based on the hypothesis that pericardial fluid contains low-abundance cardiac proteins due to tissue leakage, thus the carrier could influence the analysis to allow detection of these proteins. This study has also demonstrated that optimization of carrier levels is necessary for application of the method to body fluid samples. Although increasing levels of carrier increases protein detection, this also affects data quality.

Conclusion

In this study, we have demonstrated proof-of-principle that the SCoPE-MS concept can be adapted to improve the coverage of proteins of interest in pericardial fluid samples, a biological fluid with a similar protein composition to plasma and comparable technical challenges. The myocardium-derived carrier proteome was able to increase protein throughput of cardiac proteins specifically, and the number of differentially expressed proteins was increased nearly four-fold compared to traditional LC-MS/MS, providing valuable biological information. The relatively unique protein expression profiles suggest that distinction of patients with HF, DM, or HF and DM based on protein expression in pericardial fluid—and blood, by extension—could be possible. We expect that a carrier proteome would have applications in the analysis of biological samples where DDA analysis is challenged by high abundant proteins.

Data availability

Mass spectrometry data (.raw) and database search files (.mztab and .pdResult) have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (36) with the dataset identifier PXD053450.

Supplemental Data

This article contains supplemental data.

Conflict of interest

The authors declare no competing interests.

Supplementary data

Supplemental data

Funding and additional information

This work was supported by a research grant from the 10.13039/501100004196 Odense University Hospital Research Fund to Lars Melholt Rasmussen.

Author contributions

A. J. C., N. B. P., L. P. R., L. M. R., and H. C. B. writing–review and editing; A. J. C. and H. C. B. writing–original draft; A. J. C., S. C., N. B. P., and H. C. B. data curation; A. J. C. and H. C. B. conceptualization; S. C. formal analysis; L. P. R. and L. M. R. resources; L. P. R. and H. C. B. investigation; L. M. R. and H. C. B. funding acquisition.
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