Mar 26, 2026
Label Free Quantitative (LFQ) Mass Spectrometry Analysis
- Le Zhang1,
- Tukiet Lam1
- 1Yale University
Protocol Citation: Le Zhang, Tukiet Lam 2026. Label Free Quantitative (LFQ) Mass Spectrometry Analysis. protocols.io https://dx.doi.org/10.17504/protocols.io.14egn1y5pv5d/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: February 28, 2026
Last Modified: March 26, 2026
Protocol Integer ID: 244199
Keywords: ASAPCRN, mass spectrometry analysis label free quantitative, mass spectrometry analysis, label free quantitative, lfq
Funders Acknowledgements:
ASAP
Grant ID: ASAP-000529
Abstract
Label Free Quantitative (LFQ) Mass Spectrometry Analysis
Guidelines
LFQ sample preparation was carried out similar to that described in Henderson et al., 2015 (58), but with slight updated modification. Brain tissue of control and PD samples were first dissolved in 400μL RIPA buffer containing protease with Phosphatase inhibitor cocktail and sonicated at 10% amplitude for 15 seconds with one second burst. Sonicated samples were then centrifuged at 4 °C for 10 minutes at 14.6K RPM on a bench top centrifuge. 100μL of supernatant was transferred to a new tube for protein extraction with Chloroform:Methanol:Water method. Protein pellet was air dried, dissolved in 80-μl 8 M urea containing 0.4 M ammonium bicarbonate. Protein amount was determined by NanoDrop and 50μg total protein amount was aliquoted and brought up to 80ul with 8 M urea containing 0.4 M ammonium bicarbonate. Samples were then reduced with DTT for 10 minutes at 37 °C, and Cysteine were alkylated with iodoacetamide at room temperature for 30 minutes in the dark. 10 μL of 0.1 μg/μL LysC was then added for enzymatic digestion overnight at 37 °C; followed by that addition of 2 μL of 0.5 μg/μL trypsin with incubation at 37 °C for 6 hours. The digestion was quenched with 0.1% trifluoroacetic acid (TFA) then desalted utilizing a C18 UltraMicro Spin column (The Nest Group). The effluents from the de-salting step were dried and re-dissolved in 5 μL 70 % FA and 35 μL 0.1 % TFA. An aliquot was taken to obtain total digested protein amount. A 1:10 dilution of Pierce Retention Time CalibrationMixture (Cat# 88321) was added to each sample prior to injecting onto the UPLC coupled Orbitrap Fusion mass spectrometer system for normalization of the LFQ data.
Data Dependent Acquisition (DDA) LC MS/MS data collection for LFQ was performed on a Thermo Scientific Orbitrap Fusion connected to a Waters nanoACQUITY UPLC system equipped with a Waters Symmetry® C18 180 μm × 20 mm trap column and a 1.7-μm, 75 μm × 250 mm nanoACQUITY UPLC column (35 °C). The digests were diluted to 0.05 μg/μL with 0.1 % TFA prior to injecting 5 μL of each triplicate analysis in block randomized order. To ensure accurate identification and quantitation integrity, a resolution of 120,000 and 30,000 was utilized for MS and MS/MS data collection, respectively. MS and MS/MS (from Higher-energy C-Trap Dissociation (HCD)) spectra were acquired using a three second cycle time with Dynamic Exclusion on. All MS (Profile) and MS/MS (centroid) peaks were detected in the Orbitrap. Trapping was carried out for 3 min at 5 μl/min in 99 % Buffer A (0.1 % FA in water) and 1 % Buffer B [(0.075 % FA in acetonitrile (ACN)] prior to eluting with linear gradients that reach 25 % B at 150 min, 50 % B at 170 min, and 85 % B at 175 min; then back down to 3% at 182 min.. Two blanks (1st 100 % ACN, 2nd Buffer A) followed each injection to ensure there was no sample carry over. The LC–MS/MS LFQ data was processed with Progenesis QI software (Nonlinear Dynamics, version.4.2) with protein identification carried out using the Mascot search algorithm (Matrix Science, v. 2.7). The Progenesis QI software performs feature/peptide extraction, chromatographic/spectral alignment (one run was chosen as a reference for alignment), data filtering, and quantitation of peptides and proteins. A normalization factor for each run was calculated to account for differences in sample load between injections as well as differences in ionization. The normalization factor was determined by comparing the abundance of the spike in Pierce Retention Time Calibration mixture among all the samples. The experimental design was set up to group multiple injections from each run. The algorithm then tabulated raw and normalized abundances, and maximum fold change for each feature in the data set. The combined MS/MS spectra were exported as .mgf (Mascot generic files) for database searching. The Mascot search results were exported as .xml files using a significance cutoff of p c 0.05 and FDR of 1 % and then imported into the Progenesis QI software, where search hits were assigned to corresponding aligned spectral features. Relative protein fold changes were calculated from the sum of all unique and non-conflicting, normalized peptide ion abundances for each protein on each run. Additional downstream biostatistical analyses were conducted utilizing a custom R script. An uncorrected nominal P-value was used to determine significance in the proteomic analysis, as the differentially expressed protein analysis was conducted on individual-level data and we only have 12 subjects. To deal with the potential low-power issue, only raw p-values without any correction were used to identify differentially expressed proteins, which represents a disease trend.
Troubleshooting
Label Free Quantitative (LFQ) Mass Spectrometry Analysis.
Dissolve brain tissue of control and PD samples in 400μL RIPA buffer containing protease with Phosphatase inhibitor cocktail.
Sonicate at 10% amplitude for 15 seconds with one second burst.
Centrifuge sonicated samples at 4 °C for 10 minutes at 14.6K RPM on a bench top centrifuge.
Transfer 100μL of supernatant to a new tube for protein extraction with Chloroform:Methanol:Water method.
Air dry protein pellet, dissolve in 80-μl 8 M urea containing 0.4 M ammonium bicarbonate.
Determine protein amount by NanoDrop and aliquot 50μg total protein amount, bringing up to 80μL with 8 M urea containing 0.4 M ammonium bicarbonate.
Reduce samples with DTT for 10 minutes at 37 °C.
Alkylate Cysteine with iodoacetamide at room temperature for 30 minutes in the dark.
Add 10 μL of 0.1 μg/μL LysC for enzymatic digestion overnight at 37 °C.
Add 2 μL of 0.5 μg/μL trypsin with incubation at 37 °C for 6 hours.
Quench digestion with 0.1% trifluoroacetic acid (TFA) then desalting utilizing a C18 UltraMicro Spin column (The Nest Group).
Dry effluents from the de-salting step and re-dissolve in 5 μL 70 % FA and 35 μL 0.1 % TFA.
Take an aliquot to obtain total digested protein amount.
Add a 1:10 dilution of Pierce Retention Time Calibration Mixture (Cat# 88321) to each sample prior to injecting onto the UPLC coupled Orbitrap Fusion mass spectrometer system for normalization of the LFQ data.
Data Dependent Acquisition (DDA) LC MS/MS data collection for LFQ
Dilute digests to 0.05 μg/μL with 0.1 % TFA prior to injecting 5 μL of each triplicate analysis in block randomized order.
Ensure accurate identification and quantitation integrity with a resolution of 120,000 and 30,000 for MS and MS/MS data collection, respectively.
Acquire MS and MS/MS (from Higher-energy C-Trap Dissociation (HCD)) spectra using a three second cycle time with Dynamic Exclusion on.
Detect all MS (Profile) and MS/MS (centroid) peaks in the Orbitrap.
Carry out trapping for 3 min at 5 μl/min in 99 % Buffer A (0.1 % FA in water) and 1 % Buffer B [(0.075 % FA in acetonitrile (ACN)] prior to eluting with linear gradients that reach 25 % B at 150 min, 50 % B at 170 min, and 85 % B at 175 min; then back down to 3% at 182 min.
Follow each injection with two blanks (1st 100 % ACN, 2nd Buffer A) to ensure there was no sample carry over.
LC–MS/MS LFQ data processing
Process the LC–MS/MS LFQ data with Progenesis QI software (Nonlinear Dynamics, version.4.2) with protein identification carried out using the Mascot search algorithm (Matrix Science, v. 2.7).
Perform feature/peptide extraction, chromatographic/spectral alignment (one run was chosen as a reference for alignment), data filtering, and quantitation of peptides and proteins.
Calculate a normalization factor for each run to account for differences in sample load between injections as well as differences in ionization.
Determine the normalization factor by comparing the abundance of the spike in Pierce Retention Time Calibration mixture among all the samples.
Set up the experimental design to group multiple injections from each run.
Tabulate raw and normalized abundances, and maximum fold change for each feature in the data set.
Export combined MS/MS spectra as .mgf (Mascot generic files) for database searching.
Export Mascot search results as .xml files using a significance cutoff of p c 0.05 and FDR of 1 % and then import into the Progenesis QI software, where search hits were assigned to corresponding aligned spectral features.
Calculate relative protein fold changes from the sum of all unique and non-conflicting, normalized peptide ion abundances for each protein on each run.
Conduct additional downstream biostatistical analyses utilizing a custom R script.
Use an uncorrected nominal P-value to determine significance in the proteomic analysis, as the differentially expressed protein analysis was conducted on individual-level data with only 12 subjects.
Identify differentially expressed proteins using only raw p-values without any correction to address potential low-power issues, representing a disease trend.