Jun 23, 2025
Protein Extraction, Mass Spectrometry, and Data Analysis
- Sierra Palumbos1,
- Erika Holzbaur1
- 1University of Pennsylvania
Protocol Citation: Sierra Palumbos, Erika Holzbaur 2025. Protein Extraction, Mass Spectrometry, and Data Analysis. protocols.io https://dx.doi.org/10.17504/protocols.io.5jyl8qo16l2w/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: May 09, 2025
Last Modified: June 23, 2025
Protocol Integer ID: 218004
Keywords: chop penn proteomics core, protocol of protein extraction, protein extraction, mass spectrometry, data analysis protocol, extraction, steps of the protocol, protein
Funders Acknowledgements:
National Institute of Neurological Disorders and Stroke
Grant ID: R01-NS060698
National Institute of Neurological Disorders and Stroke
Grant ID: F32-NS129586
Michael J. Fox Foundation for Parkinson's Research (MJFF)
Grant ID: MJFF-021130
Michael J. Fox Foundation for Parkinson's Research (MJFF)
Grant ID: MJFF-15100
Michael J. Fox Foundation for Parkinson's Research (MJFF)
Grant ID: MJFF-019411
Abstract
Protocol describing protocol of protein extraction, mass spectrometry, and subsequent data analysis for Palumbos et al., 2025. All steps of the protocol were performed by CHOP PENN Proteomics core.
Protein Extraction
Solubilize EV pellets in 50 µL of extraction buffer containing 5% sodium dodecyl sulfate (SDS, Affymetrix), 50mM TEAB (pH 8.5, Sigma), and protease inhibitor cocktail (Roche cOmplete, EDTA free).
Shear DNA and ensure complete solubilization
Sonicate samples for 10 minutes at 20°C in a Covaris R230 focused-ultrasonicator with the following settings: Dithering: Y=3.0, Speed=20.0, PIP: 360.0, DF: 30, CPB: 200.
Centrifuge samples at 3000g for 10 minutes to clarify lysate.
Determine protein concentration by intrinsic tryptophan fluorescence excited at 280nm and read at 350nm, against an in-house generated E.Coli lysate standard curve on a Synergy H1 microplate reader (BioTek).
In-Solution Digestion
Digest 10 µg of each sample per the S-Trap Micro (Protifi) manufacturer’s protocol
Proteins were reduced in 5mM TCEP (Thermo), alkylated in 20mM iodoacetamide (Sigma), then acidified with phosphoric acid (Aldrich) to a final concentration of 1.2%
Dilute samples with 90% methanol (Fisher) in 100 mM TEAB
Load diluted samples onto an S-trap column
Wash three times with 90% methanol in 100 mM TEAB
Add a 1:10 ratio (enzyme: protein) of Trypsin (Promega) and LysC (Wako) suspended in 20 µL 50mM TEAB and allow samples to digested for 18 hours at 37°C in a humidity chamber.
After incubation, elute peptides with an additional 40 µL of 50 mM TEAB, followed by 40 µL of 0.1% trifluoroacetic acid (TFA) (Pierce) in water, and finally 40 µL of 50/50 acetonitrile:water (Fisher) in 0.1% TFA.
Combine eluates and organic solvent then dry via vacuum centrifugation.
Desalt samples using an Oasis HLB µElution plate (30 µm, Waters).
Wells should be conditioned two times with 200 µL of acetonitrile and equilibrated three times with 200 µL of 0.1% TFA.
Apply samples, wash three times with 200 µL 0.1% TFA, and elute directly into autosampler vials in three increments of 65 µL of 50:50 acetonitrile:water.
Dry eluates by vacuum centrifugation and reconstitute in 0.1% TFA containing iRT peptides (Biognosys, Schlieren, Switzerland).
Determine peptide concentration at OD280 using a Synergy H1 microplate reader (BioTek), and adjust samples to 400 ng/µL for injection.
Mass Spectrometry Data Acquisition
Randomize and analyze samples on an Exploris 480 mass spectrometer (Thermofisher Scientific San Jose, CA) coupled with an Ultimate 3000 nano UPLC system and an EasySpray source.
Load 5 µL of sample onto an Acclaim PepMap 100 75 µm x 2 cm trap column (Thermo) at 5 µL/min, and separated by reverse phase (RP)-HPLC on a nanocapillary column, 75 µm id × 50 cm 2 µm PepMap RSLC C18 column (Thermo). Mobile phase A consisted of 0.1% formic acid and mobile phase B of 0.1% formic acid/acetonitrile.
Elute peptides into the mass spectrometer at 300 nL/min with each RP-LC run comprising a 105-minute gradient from 3% B to 45% B.
Data independent acquisition (DIA) mass spectrometer settings were as follows: one full MS scan at 120,000 resolution, with a scan range of 350-1200 m/z and normalized automatic gain control (AGC) target of 300%, and automatic maximum inject time. This was followed by variable (DIA) isolation windows, MS2 scans at 30,000 resolution, a normalized AGC target of 1000%, and automatic injection time. The default charge state was 3, the first mass was fixed at 250 m/z, and the normalized collision energy for each window was set at 27.
Mass Spectrometry QZ/QC and System Suitability
The suitability of the instrumentation should be monitored using QuiC software (Biognosys; Schlieren, Switzerland) for the analysis of the spiked-in iRT peptides.
As a measure for quality control, inject standard E. coli protein digest in between samples and collecting the data in data dependent acquisition (DDA) mode.
Analyze the collected DDA data in MaxQuant and the output can subsequently be visualized using the PTXQC package to track the quality of the instrumentation.
Database Searching
Process DIA raw files using Spectronaut 18.7 in direct DIA mode.
We utilized a mouse (mus musculus, 25,508 entries) database comprising canonical and reviewed isoforms from Uniprot, supplemented with a list of 245 common protein contaminants and iRT peptides.
Set enzyme specificityto trypsin with allowance for two potential missed cleavages.
Specify fixed modification as carbamidomethyl of cysteine, while protein N-terminal acetylation and oxidation of methionine were considered variable modifications.
To ensure high confidence, a false discovery rate limit of 1% was applied for precursors, peptides, and proteins identification, while the remaining search parameters were maintained at their default settings.
Bioinformatics Analysis
Conduct proteomics data processing and statistical analysis were conducted in R
Utilize the MS2 intensity values generated by Spectronaut for analyzing the entire proteome dataset
The data underwent log2 transformation and normalization by subtracting the median value for each sample
To ensure data integrity, filter to retain only proteins with complete values in at least one cohort
To compare proteomics data across groups, employ a Limma t-test to identify proteins with differential abundance, and visualize the impact of these differences through volcano plots
Generate lists of differentially abundant proteins based on criteria of adjusted P.Value <0.05, resulting in a prioritized list for subsequent bioinformatics analysis
Protocol references
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2.Bruderer R, Bernhardt OM, Gandhi T, Miladinović SM, Cheng LY, Messner S, Ehrenberger T, Zanotelli V, Butscheid Y, Escher C, Vitek O, Rinner O, Reiter L. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol Cell Proteomics. 2015 May;14(5):1400-10. doi: 10.1074/mcp.M114.044305. Epub 2015 Feb 27. PMID: 25724911; PMCID: PMC4424408.
3. Tyanova S, Temu T, Cox J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc. 2016 Dec;11(12):2301-2319. doi: 10.1038/nprot.2016.136. Epub 2016 Oct 27. PMID: 27809316.
4. Bielow C, Mastrobuoni G, Kempa S. Proteomics Quality Control: Quality Control Software for MaxQuant Results. J Proteome Res. 2016 Mar 4;15(3):777-87. doi: 10.1021/acs.jproteome.5b00780. Epub 2015 Dec 28. PMID: 26653327.