key: cord-1015677-txvvajba authors: Banerjee, Arghya; Gayathri J Pai, Medha; Singh, Avinash; Nissa, Mehar Un; Srivastava, Sanjeeva title: Mass spectrometry and proteome analysis to identify SARS-CoV-2 protein from COVID-19 patient swab samples date: 2022-02-02 journal: STAR Protoc DOI: 10.1016/j.xpro.2022.101177 sha: 5a1d4ac7a28f75a6fb0e0529db347d14a4a8d25b doc_id: 1015677 cord_uid: txvvajba With emerging SARS-CoV-2 new strains and their increased pathogenicity, diagnosis has become more challenging. Molecular diagnosis often involves the use of nasopharyngeal swab and subsequent real-time PCR-based test. While this test is the gold standard, it has several limitations and more complementary assays are required. This protocol describes how to identify SARS-CoV-2 protein from patients' nasopharyngeal swab samples. We first introduce the approach of label-free quantitative proteomics. We then detail target verification by triple quadrupole mass spectrometry (M.S.)-based targeted proteomics. i. Collect nasopharyngeal Swab from COVID-19 suspected patients following complete biosafety 25 protocol as per World Health Organization (WHO) and Indian Council of Medical Research 26 (ICMR) guidelines. 27 Note: This step to be performed by trained medical practitioners. 28 For this, insert a sterile cotton swab is into nostrils parallel to the palate, allowing it to absorb 29 secretions and remove while slowly rotating. 30 Note: Virus settled at the mucosal lining of the nasopharynx is thus picked-up on the swab while 31 removing. 32 Immediately immerse into sterile VTM (Viral Transport Media) (CDC, 2020 ii. Dissolve the protein pellets in freshly prepared lysis buffer by vortexing. 95 iii. Pool the proteins extracted from solvents (i.e., Ethanol, Acetone and Isopropanol). Further, it 96 can be stored at -80 °C or processed immediately by in-solution digestion. 97 Note: Urea buffer with Tris is used for lysis as Urea is a denaturant that helps to open the three-98 dimensional structure of proteins, which helps in digestion. This lysis buffer should be freshly prepared 99 each time you begin your experiment. The pool of protein from Ethanol, Acetone and Isopropanol 100 solvent extraction was made becuase the combination of these three yielded more SARS-CoV-2 unique 101 peptides after in-solution digestion followed by mass spectrometry analysis (Table S1 and Figure S1 ). is good enough to take for digestion followed by Mass Spectrometric analysis, otherwise it will be 112 detrimental for both columns and instrument. 113 Optional: Samples should be loaded in duplicate. Use 10-15 µg of protein for Q.C. check. 114 Place 30 µg of swab lysate in a fresh 1.5 mL Axygen tube for enzymatic digestion (make the 115 final volume to 20 µL). Tris pH 8.0, 1 mM CaCl2) so that the final concentration of Urea is less than 1 M. This is 127 required for optimal activity of trypsin. ii. To prepare desalting tips, use the backside of a 10 µL tip to measure the amount of material 142 (PK20 Empore Octadecyl C18 47 mm) required. DO NOT use material lower than this as it 143 may not be sufficient to sit inside the tip well, resulting in loose packing and leakage. 144 iii. Pack the tip with the material properly by making the surface even from all sides. If this is not 145 done, the material might not get activated evenly. 146 iv. Insert this tip into a 1.5 mL Eppendorf tube. house C18 tip (tip containing C18 material which is freshly prepared for peptide desalting from 158 step (2.b). Centrifuge at 1500 x g for 1 min (i.e., 40 µL, 1 min, 1500 x g) to pass the solution 159 through the tips. ii. Discard the remaining solvent with a syringe and insert the tip into a 1.5 mL sample tube. Reconstitute the peptide sample in an appropriate volume (preferably 40 µL) of Milli Q water 171 with 0.1% F.A. and add to the activated tip, which is passed through the tip by centrifugation 172 for 1 minute at 1000 x g. The eluted peptide solution is again passed through the same in-house 173 C18 tip (by centrifugation) at least 6 times ( Figure S3 ). 174 Note: Here, the eluted peptide was reapplied and repeatedly passed through C18 material of the in-175 house C18 tip to maximize the binding of peptides with the C18 material 176 f. In-house C18 tips desalting protocol: Sample cleaning 177 i. Pass Milli Q water with 0.1% F.A.; 40 µL, 1 minute, 1500 x g. Repeat this step 3 times. 178 ii. Discard the remaining solvent with a syringe and insert the tip into a fresh 1.5 mL tube. 179 J o u r n a l P r e -p r o o f Note: Here, 0.1% F.A. was passed with the C18 material of the in-house C18 tip to remove and wash 180 the unwanted salts present in the peptide solution. 181 g. In-house C18 tips desalting protocol: Sample elution 182 i. Pass 50% ACN in 0.1% FA; 60 µL, 1 minute, 1000 x g. Repeat this step 3 times. Fresh ACN 183 aliquot is used each time and the elute was collectively pooled. 184 ii. Pass 70% ACN in 0.1% F.A.; 60 µL, 3 min, 4000 x g and pool the eluent with the eluent 185 obtained from step (2. g.i). 186 iii. After pooled eluent is obtained from passing 50% ACN and 70% ACN elution, dry the eluent 187 with Savant speed-vac (ISS 110) at low-temperature mode. 188 Note: Once you have passed the peptide solution 6 times and washed the in-house C18 tip with 0.1% 189 F.A. to remove the debris, pass 50% and 70% ACN in 0.1% F.A., sequentially to elute the peptides 190 binding to the C18 material of the in-house C18 tip ( Figure S3 ). ( iii. Take 1 µg of peptide forward for the mass spectrometry analysis. (50%), methanol (25%) and acetic acid (1%) -as per manufacturer's instructions (Catalog, 2015) . 213 214 b. Nanospray columns 215 Note: The column outline from the L.C. is connected to the precolumn/trap column using Viper union, 216 the trap column is connected to the Analytical column using Mixing / Venting Tee (Fisher Scientific 217 Inc, 2013). The analytical column is then placed near to sweep cone and Ion transfer capillary with the 218 HESI probe ( Figure S4) . 219 For the above mentioned setup, use precolumn Acclaim PepMap TM 100, 100 µm*2 cm, 220 nanoviper C18, 5 µm,100 Å. 221 Note: Precolumn is mainly involved in conditioning the mobile phase before it reaches to the analytical 222 column of nanoLC. 223 ii. For Keep the peptide samples obtained from the in-solution digestion of the swab protein pool (i.e., 240 Ethanol, Acetone, and Isopropanol) in the nano-LC autosampler maintained at 8 °C and analyze 241 the sample by using LC-MS setup as per parameters mentioned in Table S2 . 242 ii. Inject one µg (0.25 µg/µL) of the peptide for the 120 min analysis on is subjected Orbitrap 243 Fusion™ in ddMS² OT HCD mode (Table S2) for spectral acquisition (Figure 2) . 244 Note: Technical replicates for Mass spectrometry runs of individual samples were not performed. 245 However, the individual samples in each cohort were considered as biological replicates. Samples were 246 randomly run for discovery analysis and for targeted verification study. Blanks were run after each 247 sample to ensure that there was no carryover from one sample to another. 248 Save the mass spectrometric raw files (.raw) generated after mass spectrometry in a folder named DD- ii. In MaxQuant, use Label-Free-Quantification (LFQ) parameters and set the label-type setting 266 as "standard" with a multiplicity of 1. Keep maximum missing cleavage of 2 for both human 267 and COVID-19 analysis, Carbamido-methylation at Cysteine (+57.021464 Da) as the fixed 268 modification and oxidation at Methionine (+15.994915 Da) as the variable modification (Table 269 S3). 270 iii. Set the False-Discovery-Rate (FDR) to 1% to ensure high protein and peptide detection 271 reliability. 272 iv. Set the decoy mode to "revert" and the type of identified peptide to "unique+razor". format. Open the proteingroups.txt file in an excel sheet and remove the contaminants, 288 Rev_proteins (Revere Decoy database) and proteins with a q-value greater than 0.05. 289 ii. To understand the quality of the datasets, investigate the chromatogram of the samples and 290 correlation coefficient (Spearman Rank Correlation) between the samples by using 291 Metaboanalyst software (Xia et al., 2015) . Reader can go through the given link for detailed 292 tutorials and guidelines (https://www.metaboanalyst.ca/docs/Tutorials.xhtml). 293 Critical: This step is very crucial before going for the statistical analysis. Few samples having poor 294 chromatograms and quality issues can be excluded for downstream analysis. For day-to-day variability 295 and reproducibility of the instrument, user can run all human sample pools and BSA for quality control 296 and observe the correlation coefficient between the sample pools and individual pools as described in 297 Bankar et al., 2021. 298 Now transform the data into Log2 value and calculate the fold change by comparing the median 299 intensities of the two sample groups and perform the Welch's t-test using Microsoft excel. 300 Note: In this study, no missing value imputation is done to avoid any discrepancy in the data. Also, the 301 datasets do not require normalization because a Gaussian distribution of features across the samples 302 was observed, as represented in Figure 3 . Calculate the significance level of the proteins based on the t-test independent samples with 314 Bonferroni correction and use the Log2 transformed data for the violin plot. 315 Note: In this study, proteins passing the t-test and having the p-value less than 0.05 are considered as 316 significant (p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00; *: 1.00e-02 < p <= 5.00e-02; **: 317 1.00e-03 < p <= 1.00e-02; ***: 1.00e-04 < p <= 1.00e-03; ****: p <= 1.00e-04). 318 Plot the heatmap by using Metaboanalyst. Set the Distance Measure parameter to Euclidean 319 and clustering algorithm to Ward clustering. features for different types of data apart from metabolomics. 324 In this step, all the significant proteins are used for the pathway and network analysis. The verification step involves the targeted proteomics approaches as Selected Reaction Monitoring 345 (SRM) or MRM, which has grown in popularity as a method for detecting proteins of interest with high 346 sensitivity, quantitative precision, and repeatability in mass spectrometry-based protein quantification. 347 The study's verification phase tries to corroborate the differential abundances of selected proteins 348 between experimental conditions, such as disease and control groups or severe verses non severe 349 conditions. 350 Timing ( iii. Always take replicate measurements in any quantitative study. 367 Note: Replicates help to determine the precision with which a particular analyte can be quantified. 368 These replicate measurements can be used to determine an estimate of the coefficient of variation (CV), 369 which further define the repeatability and reproducibility of the experiment. assay that has been rigorously tested and satisfies specific regulations so that it can be used in routine 380 clinical applications. In the current study, we have used the MRM technique for a qualitative verification 381 of the shotgun results only. MRM assay that can be used for clinical applications needs to be first 382 rigorously optimized in terms of response, selectivity, accuracy, precision and stability of the selected 383 targets for the assay. Once basic parameters like repeatability and reproducibility are established, further 384 parameters can be assessed. The response of this target peptide should not be affected by interference 385 from the sample matrix. For this, an SIL peptide with the same sequence (as that of the peptide in 386 question) should be spiked into the sample and response to be compared with the endogenous peptide. 387 For absolute quantification, the LOD and LOQ of this peptide should be determined. It is also advised 388 to check the stability of this peptide in different storage conditions and several biological replicates. ii. Go to the Uniprot website (https://www.uniprot.org). 400 iii. In the search box, type "Homo sapiens" and press "enter". Filter by "Reviewed" only. 401 iv. Click on download. Ensure that the options box looks as shown below and then press "Go". 402 This ensures the entire reviewed Human proteome is downloaded in FASTA format ( Figure 403 4) . 404 The following setup describes how to set up the mass spectrometer before data acquisition using the 406 Thermo iii. HPLC Column: Hypersil GOLD analytical column (100 x 2 mm, C18, Thermo Fisher 417 Scientific) was used. 418 419 iv. Set up the sample module as given in Table 3 (Temperature= 4°C). 420 421 in as given below and click OK (Figure 7) . Note: There is a limitation to the number of transitions that can be monitored in a single method or list 477 depending on the instrument capabilities. For a TSQ Altis, usually, 450-500 transitions can be taken in 478 an unscheduled method. If the total number of transitions in your Skyline document is more than this 479 limit, ensure that you export them as "Multiple methods". 480 481 g. Another dialog box pops up as shown. Click YES (Figure 8) . 482 h. The file will be saved in a .csv format (e.g., SRM_final.csv). 483 i. Also, save this Skyline document in a preferred folder by clicking on File Save As and 484 browse to the folder. This file will be used to view and analyze the acquired results. Note: If the sample amount is limited and the number of transitions for selected targets are too high so 487 pools can be used. Equal parts of samples belonging to a specific subgroup are pooled together and run 488 as representatives for that group. Pools can be run to optimize unrefined transition lists. Along with the use of pools, SRMAtlas (http://www.srmatlas.org/) can also be helpful in this situation 490 (for Human targets). Many human proteins are huge and have a large number of unique peptides. They 491 can be narrowed down using SRMAtlas that gives the most frequently and consistently observed unique 492 peptides for a protein in SRM experiments all around the world. In this study, abundance change of a 493 few host proteins was verified using the MRM approach. Intensities of the peptide for proteins 494 interleukin-6 (IL-6), L-lactate dehydrogenase A chain (LDH-A) and aspartate aminotransferase, and 495 cytoplasmic (GOT1) were observed to be statistically significant between COVID-19-positive and 496 COVID-19-negative patient samples (p < 0.05; Fold change > 1.5 at a confidence interval of 99%). 497 However, if one aims to monitor and quantify a low-concentration analyte (peptide) in a sample targeted 498 proteomics, stable isotope labelled (SIL) spike-in peptides (of the same sequence as that of target 499 analyte) can be utilized for achieving the quantification. Firstly, serial dilutions of heavy SIL spike-in 500 peptide samples can be run to determine a concentration at which a sufficient signal is obtained for the 501 SIL spike-in peptide. This is followed by a spike-in of an equal amount of both heavy and light SIL 502 peptides into the sample. The signal intensity of heavy and light peptides should give the same intensity 503 if there is no endogenous peptide in the sample. In this way, the amount of endogenous peptide can then 504 be quantified by monitoring the difference between the intensity of heavy and light peptide. 505 506 11. Day 4: Preparation of the method file using transition list on QQQ mass spectrometer, 507 Timing (3 Hour) 508 a. Under Scan parameters of QQQ, go to import option. 509 b. Browse to the folder where the transition list was saved and select the list (e.g., SRM_final.csv). 510 c. The selected list should have several columns, as shown in the table below. 511 d. After importing the list, go to filesave as option to save the prepared method (e.g., 512 SRM_final.method). Refer Table S7 for a representative illustration of transition list. least once a day. The concentration to be injected and transitions to be monitored are standardized by 519 each lab and reproducibility is measured in teams of the uniformness in peak areas of the response. 520 Filename: Customized file name can be given to annotate the data file. 521 iii. Path: It is the location of the drive or folder where the acquired data has to be saved. 522 iv. Instrument Method: Here, the method is browsed and selected from the folder where it was 523 saved (This is the same method file that had been created using the transition list, e.g., 524 SRM_final.method) 525 v. Position: It represents the position of the sample in the autosampler (e.g., G: A1 is the A1 526 position in the Green tray). 527 vi. Injection Volume: It tells the volume of injection specified for a run. 528 vii. For all the samples, the same is repeated. 529 viii. Once the acquisition queue is complete, save it in a particular folder and start the run by 530 clicking on the run sample icon (if only a single sample has to be run) or run sequence icon 531 (for multiple samples run). 532 Note: While the acquisition queue is running, the row for the running sample cannot be edited. 533 b. Import and overview of acquired data in Skyline 534 i. Open the same Skyline document which was used for exporting the transition list. 535 ii. Go to File Import Results 536 iii. Browse to the folder where acquired data is saved. Select the raw files and open them. 537 iv. Observe the peaks in which the x-axis represents the retention time and the y-axis represents 538 the intensity. 539 v. Annotate the peak area considering the coelution of all peptide transitions (library can be used 540 to identify the correct peak). 541 Note: Check if all the peaks are properly annotated by the software else; annotate each peak manually 542 by dragging the cursor and selecting the peak. 543 To view the spectra for all the samples at once for a particular peptide, go to View Arrange 544 graphs Tiled. 545 vii. For getting the stacked bar plot for the peak area of all the samples (replicates), go to View 546 Peak area Replicate comparison. 547 viii. Refine the unrefined data by selecting the peptides which show consistent results across the 548 samples. Delete the peptides or transitions which are not up to the mark. 549 ix. Perform statistical analysis as in the following steps. 550 Note: Before moving to the next step, it should be ensured that the obtained results have sufficient 551 data points for each spectral peak. An optimal number of data points is required for quantitative 552 analysis, which is usually preferred as 10-15. If there are very few data points, such as only 3, the 553 peak shape will be sharp and distorted, while the peak would be noisy with too many data points. 554 This can be optimized by taking care of cycle time and dwell time. Dwell time is the amount of 555 time for monitoring one transition and cycle time represents the total time for monitoring all the 556 transitions once (one cycle). So, for getting 10 data points, the same cycle should repeat 10 times 557 in the given window (peak width). The cycle time or dwell time can be finalized during optimization 558 based on the peak width and number of data points required. Suppose there are 300 transitions to 559 be monitored and we set the dwell time as 5 ms (milliseconds), then cycle time would be 1.5 s 560 (300*5). With these settings, if the peak width is 20 sec, we may get 13 data points (20/1.5). In this 561 study, the cycle time was set as 2sec. 562 This section shows the steps for comparing the data obtained across the groups. 564 a. Document settings options 565 i. Go to Settings Document settings Annotations Add. A box appears for defining 566 annotation. 567 ii. In the Name option, write the name for a particular annotation (e.g., in this study, we have 568 created four annotations; Subject ID, BioReplicate, Condition (+/-), Conditions), define 569 the values in the provided box (e.g., for Conditions it could be Severe, Non-severe). For 570 applying to the option, select replicates and then select OK. 571 iii. Values for all the annotations can be defined (as in the skyline document). 572 iv. In the annotation tab, all the annotations will appear. 573 v. Each annotation can be edited by going to Annotations Edit List Select a particular 574 annotation Edit. Further information and requests for resources and reagents should be directed to the lead contact Mumbai 400 076; India: Email-sanjeeva@iitb.ac This study did not generate any new unique reagents and/or materials Data and code availability All the proteomic data associated with the main study is available in (Bankar et al., 2021) Data and 704 code availability. The LFQ raw files and search output files for proteomics data sets Proteome Xchange Consortium via the PRIDE partner repository is "PRIDE: PXD020580" and 706 The targeted proteomicsdata is deposited 708 in the Panorama Public and can be accessed through this link The present research did not use any new codes (Bankar 713 et The study was supported through Science and Engineering Research Board (SERB), Department of 716 Minis Government of India 717 (SB/S1/Covid-2/2020) and a special COVID-19 seed grant (R.D./0520-IRCCHC0-006) from IRCC, 718 Department of Biotechnology We would like to thank Prof Bioengineering to fabricate U.V. transport device for sample transport and Prof. Anirban Banerjee for 721 the BSL-2 biosafety aspects is gratefully acknowledged. The authors thank Kasturba Hospital for 722 sample collection and sharing sample related information. We would like to thank Renuka Bankar Akanksha Salkar from the Department of Biosciences & Bioengineering 724 involved in sample collection. We also acknowledge Saicharan Ghantasala, and Kruthi Suvarna 725 Department of Biosciences & Bioengineering involved in sample preparation Biswas for helping in data analysis and pathway study. A.B. is supported by CSIR fellowship, India for 727 PhD conceived and designed the project. A.B. was involved in sample preparation and 731 global proteomics study. A.V. performed LFQ data and pathway analysis. M.G. performed the Targeted 732 proteomic study The authors declare no competing interests.The authors have filed an Indian patent related to this work 736 ''Protein markers and method for prognosis of COVID-19 in individuals''. (Application 737 number:202021034688), ''Proteomics-based method for detection of Coronavirus in a sample'' 738 (Application number 202021034687) mentioned in main article Sequence-specific determination of protein and peptide 741 concentrations by absorbance at 205 nm Proteomic investigation reveals dominant alterations of neutrophil 743 degranulation and mRNA translation pathways in patients with COVID-19', iScience Quick Start TM Bradford Protein Assay Instruction Manual A Guide to Polyacrylamide Gel Electrophoresis and Detection BEGIN Quantitative proteomics to study mitogen-activated protein 749 kinases Calibration Tools for Mass Spectrometry', Thermo Fisher Scientific Preparation of Viral Transport Medium', Preparation Of Viral Transport Medium The STRING database in 2021: customizable protein-protein networks, and 757 functional characterization of user-uploaded gene/measurement sets Trans-Proteomic Pipeline, a standardized data processing pipeline for 760 large-scale reproducible proteomics informatics MS Amanda, a Universal Identification Algorithm Optimized for High 763 Accuracy Tandem Mass Spectra Reactome graph database: Efficient access to complex pathway data The Complete and Easy Guide to Configuring Your Specific Thermo 768 NanoLC for Mass Spec Analysis Quantitative Proteomics Workflow using 770 Multiple Reaction Monitoring Based Detection of Proteins from Human Brain Tissue An Integrated Quantitative Proteomics Workflow for 773 Cancer Biomarker Discovery and Validation in Plasma Skyline: An open source document editor for creating and analyzing 776 targeted proteomics experiments Using Skyline to Analyze Data-Containing Liquid Chromatography, Ion 779 Mobility Spectrometry, and Mass Spectrometry Dimensions Proteome Discoverer-A Community Enhanced Data Processing Suite for 782 Protein Informatics The Skyline ecosystem: Informatics for quantitative mass spectrometry 784 proteomics Measurement of protein by spectrophotometry at 205 nm p. pdb.rec087908 STRING v11: Protein-protein association networks with increased 791 coverage, supporting functional discovery in genome-wide experimental datasets Visualization of LC-MS/MS proteomics data in MaxQuant The MaxQuant computational platform for mass 796 spectrometry-based shotgun proteomics Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics 799 of Tissue Samples MetaboAnalyst 3.0-making metabolomics more meaningful Metascape provides a biologist-oriented resource for the analysis of systems-level 803 datasets This step shows the steps after completing the document settings 578 i.Go to View Document grid; a grid appears with all the replicates and annotations 579 which were selected in the document settings. 580ii.Against each replicate, annotations can be selected from the dropdown option in each cell 581 (e.g., for condition (+/-), three options appear in the dropdown; COVID-19 Positive, 582COVID-19 Negative and Non-COVID-19). 583iii.As in above two steps 13b (i) and (ii), complete the grid for other columns. Provide the 584 subject I.D. in each cell or all values can be copied from a Microsoft document and pasted 585in the grid. 586iv.Go to Tools Tools store Select MSstats Install (If not already installed). 587 588c This step shows the settings for Severe vs Non-Severe group comparison. 590 Go to Settings Document settings Group Comparison Add. A box appears. 591ii.Fill in the details for the group comparison. Provide the name (e.g., Severe vs Non-Severe), 592select the Control group annotation from the dropdown option (e.g., Conditions in this 593 case), select the control group value (e.g., Non-Severe), Value to compare against (e.g., 594Severe) 595iii. Select Subject ID against the option 'Identify annotation for technical replicates'. 596 iv.Set the confidence level to 95% and scope to peptide and select OK 597 Note: Confidence level can be increased and for protein wise comparison, protein option can be 598 selected against scope. 599 Under the group comparison tab, the name of the prepared group comparison appears. 600Please Similarly, the report can be prepared and saved for other comparisons (e.g., Positive vs 610Negative). 611x.Results can be visualized in Skyline itself by clicking on the Peak area graph, going to 612 group by  Select the particular condition (e.g., Condition (+/-) for Positive vs negative 613and Conditions for Severe vs Non-severe). 614xi.The analysis report can also be exported using the 'export' option in .csv format. 615 Mass spectrometry-based deep proteomic study helps efficiently analyze clinical samples by 617identifying unique peptides in a simple, rapid and high throughput process. Thus, a successful MS-618 based assay followed by target verification has high potential in developing clinically relevant 619 J o u r n a l P r e -p r o o f tests/assays, providing clinicians with therapeutic choice, diagnosis, and treatment. One of the crucial 620 steps in MS-based studies is identifying unique peptides and proteins depending on sample preparation 621 and protein precipitation. Three organic solvents (ethanol, acetone, and isopropanol) and their protein 622 precipitate pool were used for studying the swab samples. Discovery proteomic analysis resulted in the 623 maximum number of peptides identified from the protein precipitate pool from all the 3 solvents, 624followed by acetone precipitation ( Figure S1 ). In this study (Bankar et more patient samples are required to be studied, which will increase the predictive confidence of this 641 study. 642 Users may find the chromatogram pattern is not satisfactory and has low relative intensity during M.S. 645analysis of samples (step 3.d ). 646 One can optimize the sample preparation reduction and alkylation time. The mentioned time has been 648 optimized and works best in the present case. Buffer pH (pH 8) should be checked before trypsin 649 addition (Digestion with trypsin works best at optimum pH of 8). Obtaining clean peptides (without any 650 salt and urea) is a prerequisite; that is why one should focus on the peptide desalting step. To obtain a 651 good chromatogram, one needs to optimize the gradient time and buffer composition during gradient 652 optimization. Periodically the Ion transfer capillary should be cleaned weekly and the instrument should 653 be calibrated at least once in a month to obtain good fragmentation and maintaining mass accuracy. 654 Sometimes, it is observed that you may end up with low protein coverage across the samples (step 5.a). 656 J o u r n a l P r e -p r o o f To get maximum protein coverage from the clinical samples, one needs to optimize the solvent-based 658 protein extraction. We have used three solvents (ethanol, acetone, and isopropanol) for protein 659 precipitation of swab samples and found that the pool of all the protein precipitate works the best, 660 followed by acetone-based protein precipitation. One can also use TCA-Acetone and Trizol based 661 protein extraction, which is also most frequently used for LCMS-based analysis. One should keep in 662 mind the final peptide going into the LCMS should be free of any impurity, which can interfere with 663 instrument performance. For the LCMS setting, one can start with the default protocol provided in the 664 instrument, which can be optimized under the guidance of the application scientist. 665 For your targeted experiment, you may observe that the list of target proteins is very long while the 667 amount of sample available is limited (step 10). 668 Start the experiment with a pooled sample and optimize the list of transitions/peptides for the final run. The actual samples can be run against the optimized list only. 671 You have observed a drop in the ion signals over time in your Triple Quadrupole mass spectrometer 673(step 12.b ). 674 Firstly, verify the observation using quality control (Q.C.) sample. If the problem persists in the Q.C. 676sample as well, then perform the following troubleshooting steps for the instrument. Clean the source 677and ITC (Ion Transfer Capillary), prepare fresh buffer and perform column conditioning of the HPLC 678 system. 679Moreover, columns have a limited life and if the problem continues, switch the column in use with a 680 fresh one. 681 While analyzing the targeted data, you realize that multiple overlapping peaks appear in the results (step 683 12.b). 684 It is possible that such results are obtained when the run time is too short of eluting all the targeted 686peptides. In that case, the problem can be solved by slightly increasing the run time. 687 Your targeted data is showing very sharp or distorted peaks in the results (step 12.b ). 689 J o u r n a l P r e -p r o o f Such results are usually due to a less number of data points that are not sufficient to give a proper peak 691shape. Optimization of the cycle and dwell time to acquire around 8-12 data points per transition can 692 be pivotal in solving this problem. 693 Lead contact 695