Patent Description:
Peptidomics targets and analyses the peptides (their length, distribution, protein origin, or de-novo sequencing). Peptides are protein fragments with various functions. As they regulate multiple physiological and pathological processes, alterations in peptidome may reflect both beneficial or deleterious phenomena. Thus, peptidome analysis in body fluids such as serum samples is a promising approach for identifying peptide biomarkers of various conditions and monitoring patient response to the applied treatment. The peptidome [<NUM>] is considered as low molecular-weight peptides (less than <NUM> kDa). Endogenous peptides primarily act as a messenger (cytokines, hormones, growth factors, etc.) and indicate various biological processes. Hence, these peptides can reflect the health or disease state of the person. In addition, the endogenous peptides also provide insight into proteolytic activity, degradation, and degeneration and allow the study of disease microenvironments.

Even though peptidomics has been introduced since the decade, the serum peptidome analysis has consistently been restricted for various reasons; a) enormous complexity because serum contain elements of all proteins produced in the body, b) physiologically status of high abundant proteins such as serum albumin, immunoglobulins are almost <NUM>% of total serum protein by weight, hence these abundant proteins mask the detection of other proteins, especially low abundance proteins or peptides, c) simultaneously limit detection of low abundant peptides with diagnostic potential, d) unequal concentrations of low and high abundant proteins (around <NUM>% of high abundant proteins and less than <NUM>% presence of low abundant) in the total serum protein concentrations, e) instability of low abundant proteins or peptides (hormones or small secreted proteins or possible diagnostic or prognostic biomarkers), f) depletion steps, fractionation and precipitation methods for the removal of high abundant proteins are overwhelming that compromise the number, yield of diagnostic peptides identified and important biological information can be lost in the background noise. Hence, all previously described methods for serum peptide analysis were characterized by low numbers of identified peptides, and no quantitative methods for serum peptidome analysis were introduced.

<CIT> discloses a sample preparation method for peptides from serum to be used in a liquid chromatography tandem mass spectrometry (LC-MS/MS) application; using acidic buffer to dissociate peptides from blood proteins like albumin, protection from acidic proteases by mesoporous particles (PG1); detection by densitometry of around <NUM>,<NUM> total peptides with <NUM> pore particles and glycine buffer; pH <NUM> citrate-phosphate buffer resulting in more release of peptides around <NUM> kDa compared to glycine buffer.

The invention includes the unique sample preparation steps for serum peptidomics qualitative and quantitative analysis as defined in the claims.

Using serum for early screening and diagnosis is vital to develop an effective therapy for many diseases. Data from previous literature showed that the serum peptidomics approach had been used for the diagnosis of various range of diseases such as lung cancer study included <NUM> patients, identified only <NUM> potential peptide biomarkers to distinguish lung cancer patients and healthy controls [<NUM>], reproductive disorders, study included <NUM> patients after statistical analysis, <NUM> peaks/peptides showed significantly different peak intensities in the pregnancy-induced hypertension (PIH) patients compared with the controls at an average [<NUM>], Gestational Diabetes Mellitus study included <NUM> patients total of <NUM> identified peptides, were significantly differentially expressed in the gestational diabetes mellitus (GDM) group compared with control [<NUM>], (did not perform any quantification) Rheumatoid Arthritis study included <NUM> patients, trial showed <NUM> peaks that discriminated rheumatoid arthritis (RA) from OA (primary osteoarthritis) and <NUM> peaks that discriminated RA from healthy controls (HC) [<NUM>], ulcerative colitis study included <NUM> patients <NUM> peptides were successfully identified by this method [<NUM>], etc. However, the ultimate success of these previously published methods is severely limited due to the low number of identified markers. There is a need to develop a simple and effective serum sample preparation method that should allow the quantification of serum peptidomics data. There are known methods for low identification number of peptides or quantification of few peptides (less than <NUM> peptides). Complex sample preparation that includes depletion of high abundant proteins step, precipitation step, lysis, protein labeling by fluorescence dye, multistep peptide purification and desalting step, require additional set up of chromatography, etc. Whereas in our approach we bypass all these time and cost-consuming steps.

In the past, several patents were published with methods, devices, and kits aiming to tackle biomarker discovery with different approaches such as protein/peptide levels and immunoassay [<NUM>] [<NUM>] [<NUM>] [<NUM>] [<NUM>], plasma enzyme levels [<NUM>], etc. Some patent [<NUM>] reported one or more peptide biomarkers for diagnosing cardiovascular disease (CVD) by mass spectrometry (MS) analysis such as Matrix-Assisted Laser Desorption/Ionization (MALDI), [<NUM>] Time-of-Flight (TOF). Protein quantification data was missing [<NUM>] [<NUM>] in these report. Samples from [<NUM>] total <NUM> patients serum samples were analyzed for free and bound peptides on total serum proteins. Moreover, considering a single biomarker such as glycogen phosphorylase- BB (GPBB) [<NUM>] or protein alpha- <NUM> antitrypsin [<NUM>], L-gultamine hydroxylamine glutamyl transferase (L-GHGT) and gamma glutamyl hydroxamate synthetase (GGHS) activity [<NUM>] for stroke might be too risky to rely on accurate diagnosis. On the contrary, our approach is high throughput and comprehensive, identifying <NUM>-<NUM> and successfully quantifying <NUM>-<NUM> serum peptides from <NUM> patients.

The invention differs from those available approaches; it created a serum peptidomics (based on amino acid sequences) approach. It is purely based on sequencing natural peptides of amino sequences; however, there are significant differences concerning existing methods such as in samples preparation, time, and cost-wise with the proteomics/peptidomics approach [<NUM>] [<NUM>] [<NUM>] for diagnostic and prognostic cardiovascular disease. Our novel peptidomics approach is unique in many stages;.

The invention aimed to develop a sample preparation and comprehensive method for qualitative, quantitative analysis of serum peptidome samples. Moreover, this novel approach can be used routinely to discover multi-biomarkers for diagnosing, prognosis, monitoring, and predicting various diseases. This approach facilitates to performed of both qualitative and quantitative analysis of serum peptidomics, mainly enables to DDA (data-dependent acquisition) to identify a total of <NUM>-<NUM> serum peptides (iProphet iprob value > <NUM>%, <NUM> samples). At the same time, <NUM>-<NUM> serum peptides were successfully quantified by DIA (data-independent acquisition) <NUM> run LC-MS/MS (Liquid Chromatography with tandem mass spectrometry) analysis of <NUM> clinical samples. It required very low volume of serum sample. Therefore, it is feasible to perform peptidomics analysis from <NUM>µl of the volume of each sample.

Novel method for analysis of serum peptidomics involved the following steps:
According to the invention is characterized in that the methods involves the following steps:.

The invention facilitates the crucial feature of serum peptidomics by screening a wide range of peptide lengths. These peptides are not generated by trypsin digestion, where the trypsin enzyme cleaves the proteins into every K (lysine) or R (arginine) site. On the contrary, the novel approach allows screening the natural and without any enzymatically digested peptides. Furthermore, the peptide length distribution is wide at <NUM>-<NUM> amino acid residues. Therefore, it can be easily tunable to <NUM>-<NUM> kilo Dalton in a range of natural serum peptides from the stroke samples.

Essence of the invention in details and the study description - approach leading to provision of the invention.

Serum peptidomics samples preparation - novel step is use acidic buffer (pH <NUM> -<NUM>) for peptides enrichment and another novel single step to deplete/remove the highly abundant protein and purification of serum peptides i.e. removing of proteins equal or bigger than <NUM> kDa from peptide enriched and simultaneously purification of peptides using hydrophilic-lipophilic balanced columns to obtain peptides smaller than <NUM> kDa), One step, <NUM>. is sequencing of serum peptides with tandem mass spectrometry, while <NUM>. Qualitative and quantitative data analysis (- the novel combination steps from <NUM> to <NUM> for comprehensive qualitative and quantitative serum peptidomics.

The invention differs from those available approaches; it created a serum peptidomics (based on amino acid sequences) approach. It is purely based on sequencing natural peptides of amino sequences and it includes the following steps.

The novel qualitative and quantitative serum peptidomics methodology applies to human and non-human serum samples. The methodology is not limited to stroke or healthy donor serum samples. To nullify the contingency of the sample batch, fifteen clinical samples; mainly, five samples of each group considered;.

The invention is described more deeply in examples and shown in figures where in <FIG>. the method of peptidomics sample preparation is shown, in <FIG>. diagram of qualitative analysis comparing identified peptides, in <FIG>. checked the performance of developed DIA (data-independent acquisition) method to classical DDA (data-dependent acquisition), in <FIG>. venn diagram comparing serum peptide hits between three groups (Healthy volunteers, acute ischemic stroke (AIS) and intracranial hemorrhage (ICH) patient samples),.

peptide length distribution of serum peptidomics, in <FIG>. shows the DDA (data-dependent acquisition)-mass spectrometry (MS) <NUM> individual run correlation heatmap, in <FIG>. shows the peptide heatmap and unsupervised hierarchical clustering between three groups (Healthy donor, acute ischemic stroke (AIS) and intracranial hemorrhage (ICH) patient samples), <FIG>. shown the peptide volcano plot of dysregulated serum peptides between three groups (Healthy donor, acute ischemic stroke (AIS) and intracranial hemorrhage (ICH) patient samples), in <FIG>. (A, B) shown string analyses for acute ischemic stroke (AIS) and intracranial hemorrhage (ICH) stroke samples determined from their quantitative comparison. The novel and high throughput serum peptidomics approach were employed to prove the concept of serum peptidomics analyses. The samples were collected, serum isolated within one hour, and stored at -<NUM> until the sample preparation.

The method is novel, simple and it has only three steps in sample preparation (Figure.

The blood samples are collected directly after the stroke patients have been admitted to the hospital by specialized medical staff Additionally, as a control, blood samples from healthy donors samples. All research procedures were performed per the principles of the World Medical Association Declaration of Helsinki. Thus, peptidomics research involved three different groups of patient samples: <NUM>. healthy volunteers (<NUM> donors) <NUM>. patients were suffering from an acute ischemic stroke (AIS) (<NUM> Patients) <NUM>. patients from intracranial hemorrhage (ICH) stroke patients (<NUM> Patients). Furthermore, the clotted sample was centrifuged at <NUM> xg for <NUM> at <NUM>. The serum (supernatant) was stored at -<NUM> immediately until analysis.

First step was to prepare all the necessary buffers on the same day of peptidomics samples preparation.

1st buffer: citrate-phosphate buffer at pH <NUM> to <NUM> - (<NUM> citric acid/<NUM> Na2HP04, NaCl <NUM>) and adjust the pH <NUM> to <NUM> with <NUM> NaOH prepared in LC-MS (Liquid Chromatography with mass spectrometry) grade water.

2nd solution: <NUM>% formic acid/methanol volume per volume (v/v) was prepared (final proportion of formic acid in methanol was <NUM>%).

3rd solution: <NUM>% formic acid/water volume per volume (v/v) was prepared (final proportion of formic acid in water was <NUM>%).

4th wash buffer: water/<NUM>% methanol/<NUM>% formic acid volume per volume (v/v) was prepared (final proportion of methanol and formic acid in water was <NUM>% and <NUM>%, respectively).

5th elution buffer: water/<NUM>% methanol/<NUM>% formic acid volume per volume (v/v) was prepared (final proportion of methanol and formic acid in water was <NUM>% and <NUM>%, respectively).

<NUM>µl serum was taken from each sample and incubated with <NUM> of citrate-phosphate buffer- at pH <NUM>-<NUM>. <NUM> (with optimal results for pH <NUM>) for <NUM>-<NUM> minutes (gentle and manual vertexing necessary to dissociate protein-peptide interactions) in the temperature below <NUM> to <NUM> e.g. on the ice. All the above mixtures were mixed e.g. centrifuged at <NUM> xg for <NUM> minutes at <NUM>. The supernatant was collected and diluted to <NUM>:<NUM> with <NUM> <NUM>% FA in LC-MS (Liquid Chromatography with mass spectrometry) water volume per volume (v/v).

For the purification of peptides, all the diluted samples (~<NUM> each) were subjected to the Oasis cartridges (hydrophilic-lipophilic balanced columns, <NUM>; Waters), and all the detailed steps are as follows.

The serum peptides sample obtained after peptides purification were spun through <NUM> kilo Dalton/kDa molecular weight cutoff (MWCO) ultrafiltration devices (AmiconUltra2Centrifugal Filters, Ultracel-<NUM>, <NUM>: Millipore). Briefly, Amicon-Ultracel-<NUM> kilo Dalton/kDa centrifugal filter was rinsed before use (the 3kDa ultrafiltration device was rinsed with <NUM>% methanol with LC-MS (Liquid Chromatography with mass spectrometry) grade water volume per volume (v/v), <NUM> water/column, and centrifuged at <NUM> x g, <NUM>, <NUM>). Next, <NUM> kDa ultrafiltration device was inserted into the new collecting tube. Lastly, eluted and diluted peptide solution (~<NUM>) was pipetted into the <NUM> kDa ultrafiltration device, taking care not to touch the membrane with the pipette tip. Then the <NUM> kDa ultrafiltration device was span at <NUM> x g, <NUM>-<NUM>, <NUM>. The filtrate (peptides) was collected and then lyophilized to dryness. All dried peptide samples were stored at -<NUM> until the mass spectrometry analysis.

(hydrophilic-lipophilic balanced) to eliminate the abundant proteins and simultaneously elute the serum peptides further separated with an ultrafiltration approach using <NUM> kilo Dalton (3kDa) Molecular weight cutoff (MWCO) filters. The third step is the MS (Mass Spectrometry) measurement and data analysis, resulting serum peptides of each patient were measured three times, one by DDA (data-dependent acquisition) and two DIA-MS (data-independent acquisition)-(Mass Spectrometry) runs.

LC-MS/MS (Liquid Chromatography with tandem mass spectrometry) analysis; the eluted and dried serum peptide samples were resuspended in <NUM>µl of loading buffer composed of <NUM>% trifluoroacetic acid (TFA) in water with <NUM>% acetonitrile (ACN). indexed retention time (iRT) peptides (Biognosys) were added according to the manufacturer's guidelines. Then <NUM>µl of the dissolved sample were injected into UltiMate™ <NUM> RSLCnano liquid chromatograph (Thermo Scientific) online coupled with Orbitrap Exploris <NUM> mass spectrometer (MS) (Thermo Scientific,). µ-precolumn C18 trap cartridges (<NUM> i. ) and <NUM> length packed with C18 PepMap <NUM> sorbent with PepMap <NUM> sorbent (P/N: <NUM>, Thermo Fisher Scientific) were used to concentrate and desalt serum peptides using a <NUM>µl/min flow of loading buffer. Peptides were then eluted on <NUM> ID and <NUM> length fused-silica analytical column packed with PepMap <NUM> sorbent (PIN: <NUM>, Thermo Scientific). Analytical peptide separation was performed by a non-linear increase of a mobile phase B (<NUM> % formic acid (FA) in ACN) in a mobile phase A (<NUM> % FA in water). A non-linear gradient started at <NUM>% B linearly increasing up to <NUM>% B in <NUM>, followed by a linear increase up to <NUM>% B in the next <NUM> with a flow rate of <NUM> nl/minute. Serum peptides eluting from the column were ionized in a nano-electrospray ion source (NSI) and were introduced to Exploris <NUM> (Thermo Scientific).

The data-dependent acquisition method was prepared in our laboratory. Briefly, Exploris <NUM> acquired the data in data-dependent peptide mode. The full scan was operated in profile mode with <NUM> resolution, scanning the precursor range from m/z <NUM> Th to m/z <NUM> Th. Normalized AGC (Automatic Gain Control) target was set to <NUM>% with <NUM> msec maximum injection time, and each full scan was followed by fragmentation of the top <NUM> the most intense precursor ions and acquisition if their MS/MS spectra. The dynamic mass exclusion was set to <NUM> sec after the first precursor ion fragmentation. Precursor isotopologues were excluded, and mass tolerance was set to <NUM><NUM> parts per million (ppm). Minimum precursor ion intensity was set to <NUM>. 0e3 , and only precursor charge states of +<NUM> to +<NUM> were included in the experiment. The precursor isolation window was set to <NUM> Th. Normalized collision energy type with fixed collision energy mode was selected. The collision energy was set to <NUM>%. Orbitrap resolution was set to <NUM>. Normalized AGC (Automatic Gain Control) target was set to <NUM>% with an automatic setting of maximum injection time, and data type was centroid.

LC (Liquid Chromatography) separation parameters were kept identical to DDA (data-dependent acquisition) acquisition. Orbitrap Exploris <NUM> mass spectrometer operated in positive polarity data-independent mode (DIA) accompanied by a full-scan in profile mode with <NUM> resolution. The full-scan range was set from m/z <NUM> Th up to m/z <NUM> Th, and the normalized AGC (Automatic Gain Control) target was set to <NUM>% with <NUM> msec maximum injection time. Each DIA (data-independent acquisition) cycle was accompanied by the acquisition of <NUM> precursor windows/scan events. DIA (data-independent acquisition) precursor range was set from m/z <NUM> Th up to m/z <NUM> Th with <NUM> Th window width and <NUM> Th window overlap. Normalized collision energy type with fixed collision energy mode was selected to fragment precursors included within each isolation window. The collision energy was set to <NUM>% and Orbitrap resolution in DIA (data-independent acquisition) mode was set to <NUM>. Normalized AGC (Automatic Gain Control) target was set to <NUM>% with automatic setting of maximum injection time while data type was profile.

Initially, compared DIA (data-independent acquisition) and DDA (data-dependent acquisition) based on qualitative results (Venn DIA (data-independent acquisition) vs. DDA (data-dependent acquisition) <FIG>) to justify that it has appropriate mass serum peptide isolation protocol and mass spectrometry-based peptidomics research methods. Venn diagram allows us to see the benefit of using any method while combining all patient identifications (IDs) between both approaches. Most of the identifications (IDs) are nicely overlapping; however, there are also unique hits for both DIA (data-independent acquisition) and DDA (data-dependent acquisition).

First, multi-search engine strategy has been applied for comprehensive qualitative analysis of serum peptidome. DDA (data-dependent acquisition) data were centroided and converted to mzML and mzXML format using MSconvert. Converted MS data were searched using MSFragger <NUM>. <NUM> embedded in Fragpipe suite and Comet embedded in Trans-Proteomic Pipeline (TPP) <NUM>. <NUM> against Homo sapiens SwissProt+TrEMBL reference database concatenated with indexed retention time (iRT) peptide sequence (Biognosys), decoy reverse target sequences and contaminants. The search database was constructed in Fragpipe (v. The following search settings were used for MSFragger: precursor mass tolerance was set to +/-<NUM> parts per million (ppm) and fragment mass tolerance to <NUM> parts per million (ppm). Enzyme digestion was set to non-specific and peptide length was set from <NUM> to <NUM> amino acids. Peptide mass range was set from <NUM> to <NUM> Dalton. Variable modifications were set to: methionine oxidation, protein N-term acetylation, and cysteinylation of cysteine. Data were mass recalibrated, and automatic parameter optimization setting was used to tune fragment mass tolerance. Output file format was set to pep. The following search settings were used for Comet: precursor mass tolerance was set to <NUM> parts per million (ppm) and fragment mass tolerance to <NUM> parts per million (ppm). The rest of the settings were identical to MSFragger. The search results (pep. XML files) were processed with PeptideProphet and iProphet as part of the Trans-Proteomic Pipeline (TPP) <NUM>. Resulting recalculated pep. XML files with peptide iprobabilities (iPROB values) were further processed in Skyline-daily (<NUM>-bit, <NUM>.

A spectral library was built from recalculated pep. XML files in Skyline-daily (<NUM>-bit, <NUM>. In "Peptide settings" a "Library" tab was selected. An option of "Build" was selected and in the newly popped-up window the "Cut-off score" was set to <NUM> (considers only peptides with false discovery rate (FDR) < <NUM> or <NUM> < iPROB value) and as an indexed retention time standard peptides were selected "Biognosys- <NUM> (iRT-C18)". All recalculated pep. XML files were loaded and a process of building-up the spectral library was initiated. Next, in the "Digestion" tab in "Background proteome" section the "add" option was selected. In the newly popped up window the background proteome was created from the. FASTA file that was previously used as a reference search library (SwissProt+TrEMBL reference protein database concatenated with indexed retention time peptide sequence (Biognosys), decoy reverse target sequences and contaminants). Next, in the "Library" tab, "Libraries" section the newly built spectral library was selected and the "Explore" button was clicked to explore the library. In the library window an "Associate proteins" option was checked and the "Add all" button was clicked. All peptides including non-unique were added to the document. Skyline automatically generated a retention time calculator based on the indexed retention time peptide retention time values observed in recalculated pep. The Skyline file was saved on the PC hard disc drive along with newly built spectral library (. blib format).

Each patient serum sample (a total of <NUM> patients) were measured using the optimized DDA (data-dependent acquisition)/DIA/SWATH (Sequential Window Acquisition of all Theoretical Mass Spectra) method. Each serum samples were run in one DDA (data-dependent acquisition) and two SWATH (Sequential Window Acquisition of all Theoretical Mass Spectra)/DIA (data-independent acquisition) technical replicates to assure the highest LC-MS/MS (Liquid Chromatography with tandem mass spectrometry) assay quality. DDA (data-dependent acquisition) data were used to generate the spectral library that is not only used for qualitative serum peptidomics but also later necessary to extract quantitative data from DIA/SWATH (Sequential Window Acquisition of all Theoretical Mass Spectra) files. Bar graph diagrams (<FIG>) of the identified peptides over (<NUM> DDA (data-dependent acquisition) vs. <NUM> DIA/one group) represents all serum peptidomes analyzed in three patient groups with our multi-search engine approach described in 'methods' section. Error bars represent a variation of serum peptides no among patients (patient wise, no run wise). Shades of grey color (<FIG>) represent number of identified peptides in two data types, namely DDA (data-dependent acquisition) and DIA (data-independent acquisition) each searched with both Comet and MSfragger. Comet search engine performs best searching DIA (data-independent acquisition) data while MSFragger performs perfect DDA (data-dependent acquisition) data search. Performance of search engines highly depends on the search settings for the purpose of the comparison to set the optimal but equal settings for both search engines. The number of peptides identified in stroke patients is significantly lower than that of healthy controls (<FIG>). The invention considered only quantified serum peptides in the patients and controls samples in further analysis. It have shown (<FIG>) the identification reproducibility. Moreover, the relatively high standard error (STDEV > <NUM>) in a number of peptide identifications within the patient group could be addressed to interpatient heterogeneity, which is further visible from venn plot showing the variability among the patient groups presented as a serum peptide identifications (IDs) overlap within patient group s (<FIG>). This observation will be present in any dataset and impact the results; however, later show that our approach could deal with this issue.

Another aspect of the invention provides the comprehensive identification of serum peptides from the patient samples. Our DDA (data-dependent acquisition) analysis showed (<FIG>) identified a total of <NUM> to <NUM> serum peptides with high confidence of statistical filtrations (iProphet iprob value > <NUM>%, <NUM> samples acute ischemic stroke/AIS, intracranial hemorrhage stroke/ICH, and Control/CON).

<FIG> shows a total of ~<NUM>, ~<NUM>, ~<NUM>, serum peptide sequences were identified in Control/CON, acute ischemic stroke/AIS, and intracranial hemorrhage/ICH stroke samples, respectively, with ~<NUM> (<NUM>%) stripped peptides commonly identified. The potential of our novel approach to employ on a large set of clinical sets shows that (<FIG>) the total peptides from <NUM> experimental groups have a high degree of overlap.

The invention highlights other crucial features of serum peptidomics analysis in our DDA (data-dependent acquisition) screens are peptide length distribution and range of molecular weight (<FIG>). The peptide length distribution depends on interest; it can easily tune and enrich the short (less than <NUM> kilo Dalton) and long (less than <NUM>, <NUM> kilo Dalton) by using <NUM>, <NUM>, <NUM> kilo Dalton ultrafiltration cutoff membranes, respectively.

In the current analysis, during peptides purification step employed a <NUM> kilo Dalton (kDa) filter, and hence the distribution was <NUM>-<NUM> amino acid residues, with the <NUM> amino acid residues as the median value (<FIG>). Therefore, the molecular weight range of serum peptides enriched showed that mass distribution ranges between <NUM>-<NUM> kilo Dalton (kDa).

The further application is wholly based on serum peptide quantitation. In a quantitative experiment, first used DDA (data-dependent acquisition) data to generate an appropriate spectral library not losing many important serum peptides at the same time not introducing too many low-confidence serum peptides. Then, used exclusively DIA (data-independent acquisition) data to extract quantitative information about serum peptides included in the spectral library. Furthermore, consider DIA (data-independent acquisition) data as an only choice to properly quantitate serum peptides due to its perfect reproducibility multiplexing. Moreover, as already suggested DIA (data-independent acquisition) data could be used for both identification and precise quantitation of serum peptides. Several methods with distinct window width were developed to accurately quantify stroke-specific markers. The peptides precursor ions distribution was initially screened in the optimized DDA (data-dependent acquisition) method from serum samples. The inventions determined the most populated part of precursor range which covers the most of the peptide masses observed. The rest of the DIA (data-independent acquisition) methods parameters such as normalized AGC (Automatic Gain Control) target, injection time, window overlap were optimized to keep the cycle time up to <NUM>. Such method cycle In combination with described chromatographic conditions (in method section) will yield at least <NUM> datapoints pep peak as is a minimum recommendation by FDA (The United States Food and Drug Administration) at the same time keeping sufficient selectivity and sensitivity of the method.

Continuing with the Skyline file generated in the previous step is highly recommended to set the extraction settings properly. Quantitative values reflecting the peptide quantity in sample (peptide peak areas) were extracted from DIA (data-independent acquisition) data in Skyline-daily (<NUM>-bit, <NUM>. <NUM>) based on the peptide-product ion pairs (transitions) listed in the spectral library from previous step. In the "Peptide settings" tab, the "Max. missed cleavages" was set to <NUM>. Next, in the "Filter" sub-tab a maximum peptide length was set from <NUM> to <NUM> amino acids. The "Exclude N terminal AAs" option was set to zero. A function of the "Auto-select all matching peptides" was selected. In the "Modifications" sub-tab, no modifications were selected. In the "Library" sub-tab, in the "Libraries" window, a spectral library that was created in the previous step was selected. Other settings in the tab were left default. In "Transition settings" tab, the "Prediction" sub-tab was left default. In the "Filter" sub-tab +<NUM>, +<NUM>, +<NUM>, +<NUM>, +<NUM> peptide precursor charges were specified. Ion charges were set to +<NUM> and +<NUM>. Ion type was set to y and b. Product ion selection was set as follows: in the "From:" window the "ion <NUM>" and in the "To:" window the "last ion" options were selected. The "Auto select all matching transition" option was selected at the bottom of the sub-tab and in the " Special ions:" menu the "N-terminal to Proline" option was selected. In the "Library" sub-tab, the "Ion match tolerance" window was set to <NUM>/z and only peptides that have at least <NUM> product ions were kept in analysis. In addition, if more product ions were available per peptide, then the <NUM> most intense were picked-up from the filtered product ions. A function of the "From filtered ion charges and types" was chosen. In the "Instrument" sub-tab included product ions from m/z <NUM> up to m/z <NUM>. The "Method match tolerance m/z" window was set to <NUM>/z mass tolerance. mass spectrometry (MS) <NUM> filtering was set to "none" in the full scan subtab and in tandem mass spectrometry (MS/MS) filtering sub-section, the "Acquisition method" window the "DIA (data-independent acquisition)" option was selected and in the "Product mass analyzer" the "Orbitrap" option was selected. In the "Isolation scheme: " the "Add" option was selected and in the "Edit isolation scheme" pop-up window an option of the "Prespecified isolation windows" was selected. The "Import" isolation windows from DIA (data-independent acquisition). raw file option was then selected and the isolation scheme was read from a DIA (data-independent acquisition) raw file. Back in the "Full-Scan" sub-tab in the "Resolving power:" section the resolving power was set to <NUM> at <NUM>/z. In the retention time filtering sub-section "Use only scans within <NUM> minutes of tandem mass spectrometry (MS/MS) identifications (IDs)" was specified. Under the "Refine" sub-tab, the "Document" section of the "Advanced" window the "Min transitions per precursor" option was set to <NUM>. Empty proteins were removed from the document. An equal number of reverse sequence decoys via the "Add Decoys" function in the "Refine" sub-tab was added. In the "Add Decoy Peptides" pop-up window a reverse sequence decoy generation method to create decoy peptides was selected. Following, DIA (data-independent acquisition) ". raw files" were imported. mProphet model to reintegrate peptide product ion peak boundaries in product ion chromatograms was trained as follows: in the "Refine" sub -tab the "Reintegrate" function was selected. In the "Reintegrate" pop-up window in the "Peak scoring model: " the "Add" option was selected. Following, in the "Edit Peak Scoring Model" mProphet model was activated. mProphet model was trained using targets and decoys. The "Score rows" with negative "Weight" and/or "Percentage Contribution" (red highlighted) were removed, and the mProphet model was then re-trained. New mProphet peak scoring model was then selected in the "Edit peak scoring Model" window and applied for reintegration of new peak boundaries. "Export report" function to export a summary of all dependencies required for MSstats <NUM>. was used to generate quantitative report for downstream analysis. DIA (data-independent acquisition) statistical data analysis in MS stats R-module; Statistical analysis of Skyline extracted quantitative DIA (data-independent acquisition) data was performed in R (version <NUM>. <NUM>) package MSstats <NUM>. The protein column was combined with the peptide sequence column to preserve analysis at peptide level, this formatting prevents summing peptide intensities originating from a single protein in MS stats. Extracted peakgroups were reduced by filtering on mProphet q-value < <NUM> cut-off "SkylinetoMSstatsFormat" function was set to keep proteins with one feature and to transform Skyline output to MSstats compatible input. Further, peptide intensities were log2 transformed and quantile normalized. Differential serum peptide quantitation across intracranial hemorrhage stroke (ICH) and acute ischemic stroke (AIS) conditions was performed pairwise via mixed-effect models implemented in MSstats "groupComparison" function. p-values were adjusted using the Benjamini-Hochberg method and output result matrix was exported for downstream analyses. Log2 transformed quantile normalised data matrix was exported to ProBatch <NUM>. <NUM> running under R (version <NUM>. <NUM>) to generate sample correlation heatmaps, dendrograms, analyses. Heatmaps were generated in Heatplus <NUM>. package, volcano plots in eulerr <NUM>. package, bar graphs in plyr <NUM>. and ggplot2 <NUM>. all running under R (version <NUM>.

With our novel approach, ~<NUM>-<NUM> serum peptides (The exact method was described i n the "method" section) were successfully quantified by DIA (data-independent acquisition) <NUM> run LC-MS/MS (Liquid Chromatography with tandem mass spectrometry) analysis of <NUM> clinical samples. Some peptides are quantitated across all patient groups (acute ischemic stroke /AIS, intracranial hemorrhage stroke/ICH, and Control/CON), showing the wide range (<NUM>-<NUM> fold change/FCH) of fold changes across compared patient groups, particularly, signal intensity of the studied peptide of patient groups is at least <NUM> fold higher (=><NUM>) or at least <NUM> fold lower (=<-<NUM>) than the signal intensity of the control samples. Our result shows a peptides that are completely missing in one of the conditions. This phenomenon provides an infinite changes and p values with zero value. On the comparison with matched control samples identification (qualitative analysis) of at least <NUM> serum peptides and quantification (quantitative analysis) of at least <NUM> serum peptides can be performed. Followed our quantitative serum peptidomics pipeline to acquire and extract quantitative information about serum peptides from DIA (data-independent acquisition) data. then plotted a MS (Mass Spectrometry) run correlation heat map (<FIG>) to compare and correlate <NUM> individual DIA (data-independent acquisition)-MS (Mass Spectrometry) runs of serum peptidomes. As expected, we see the most correlation between technical replicates (squares in diagonal), proving the excellent performance of the LC-MS/MS (Liquid Chromatography with tandem mass spectrometry) system (R > <NUM>). Further, the MS (Mass Spectrometry) run correlation heatmap suggests that healthy donor serum peptidomes are more correlated than stroke patients serum peptidomes (left top square). However, there is a clear difference stratifying between stroke patients and healthy donor serum peptidomes. This discrimination showed that our novel approach fulfills the significant accomplishment of the peptidomics platform. <FIG>, the bottom right part of the heatmap suggests a relatively good correlation (R~<NUM>) within intracranial hemorrhage stroke (ICH) patient group. On the other hand, acute ischemic stroke (AIS) patients display the highest peptidome heterogeneity. Sample correlation heatmap suggests only slight serum peptidome differences among intracranial hemorrhage stroke (ICH) and acute ischemic stroke (AIS); however, at the same time, it indicates that even minute differences are visible in serum with our peptidomics platform. Overall, a relatively lower correlation coefficient mainly in acute ischemic stroke (AIS) between studied subjects could be addressed to interpatient heterogeneity.

Next, correlated quantitative serum peptidomics signatures with patient groups. Finally, performed unsupervised hierarchical clustering of <NUM> DIA (data-independent acquisition) LCMS/ MS (Liquid Chromatography with tandem mass spectrometry) runs. Presented peptide heatmap and unsupervised hierarchical clustering (<FIG>) show an overview of quantitative peptidomics data analysis among stroke patients and healthy donor serum peptidome DIA-MS runs (data-independent acquisition-Mass Spectrometry).

Surprisingly, similar log2 peptide intensity patterns were observed within compared groups, resulting in a perfect clustering of healthy donor and strokes (AIS, ICH) serum peptidomes. Interestingly, the hierarchical clustering function also reliably discriminates acute ischemic stroke (AIS) from intracranial hemorrhage stroke (ICH) serum peptidomes. Two outlier serum peptidomes clustered differently could result from interpatient heterogeneity mentioned before or different patient clinical history, which was not considered. These data are in excellent agreement with the sample correlation heatmap (<FIG>).

The invention next proceeded to provide peptide quantitation among compared patient groups. Significantly dysregulated peptides (adjusted P value (adj. pval) ≤ <NUM>, fold-change ≥ <NUM>) were visualized as volcano plots provided in <FIG>.

Volcano plots suggest that peptide quantitation provides a list of peptides significantly stratifying between compared patient groups. Hence, it successfully screened significant statistical serum peptides.

To further understand biological significance inferred from serum peptidome quantitative data in stroke patients, employed multi-bioinformatics approaches such as Gene Ontology (GO) enrichment analysis (molecular function, biological process, component analysis), Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Keyword enrichment (from Uniport) was carried out. Initially, generated interactome maps in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) to determine characteristic nodes enriched in intracranial hemorrhage stroke/ICH or acute ischemic stroke/ AIS compared respectively. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) aims to discriminate specifically between strokes (intracranial hemorrhage stroke/ICH and acute ischemic stroke/AIS). String analyses of significantly dysregulated protein identifiers i n acute ischemic stroke/AIS and intracranial hemorrhage (ICH) stroke. The filtered sub set of unique protein identifiers corresponding to significantly up-regulated peptides (adjusted P value (adj. pval) ≤ <NUM>, fold-change ≥ <NUM>) resulting from a comparison of acute ischemic stroke/AIS to intracranial hemorrhage stroke/ICH and intracranial hemorrhage stroke/ICH to acute ischemic stroke/AIS were converted to Uniport protein identifiers. Identifiers common for both up and down-regulated interaction maps were removed, and unique identifiers were subjected to Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) analysis. Analysis shows interesting Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) nodes characteristic for intracranial hemorrhage stroke/ICH serum <FIG> and <FIG> for acute ischemic stroke (AIS) serum determined from their quantitative comparison.

Claim 1:
A mass spectrometry method for qualitative and quantitative analysis of peptidome in serum based on amino acid sequencing with a tandem mass spectrometry (MS) tool with signaling intensities measurement of peptides, characterized in that the method comprises the following steps:
a) enrichment of peptides in the analyzed serum sample by incubation of a sample with a citrate-phosphate buffer with pH <NUM>-<NUM> in water with a purity ≥ <NUM>%, wherein the buffer and serum volume ratio is <NUM>:<NUM> and the buffer and serum are mixed at a temperature of range <NUM> to <NUM> to dissociate peptides from highly concentrated serum proteins;
b) removing of proteins equal or bigger than <NUM> kDa from peptide enriched and simultaneously purification of peptides using hydrophilic-lipophilic balanced columns to obtain peptides smaller than <NUM> kDa;
c) washing the hydrophilic-lipophilic balanced columns with high-grade water purity ≥ <NUM>% with <NUM>% formic acid (v/v) in order to remove the unbound and low-binding peptides from the columns;
d) eluting of serum peptides bound to the hydrophilic-lipophilic balanced columns by using water / <NUM>% methanol / <NUM>% formic acid (v/v);
e) filtrating of the sample through <NUM> kDa molecular weight filters to collect peptides smaller than <NUM> kDa;
f) providing lyophilization of the obtained peptides by drying the eluted peptides through lyophilization for tandem mass spectrometry analysis, and then sequencing the isolated peptides based on amino acid sequences with a tandem mass spectrometry followed by intensities signaling measurement in studied serum sample in comparison with the matched control serum samples.