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Improving Label-Free Quanti cation of Plasma and Serum Proteins Using a High-Resolution Hybrid Orbitrap Mass Spectrometer
Overview
Assessing the differences between MS1- and MS2-based label-free relative
quantification in a complex plasma matrix using a novel real-time, intelligent acquisition
strategy for high-resolution, accurate-mass (HR/AM) global targeted quantification.
Introduction
Label-free mass spectrometry (MS) is an increasingly preferred method for biomarker
discovery workflows applied to serum and plasma samples. Given the right conditions,
label-free relative quantification is cleaner, simpler, and higher throughput. Resulting
differential analysis from these label-free discovery experiments often leads to targeted
analyses for verification. High resolution and mass accuracy are critical components to
successfully identifying and quantifying peptides in a label-free experiment. Here we
present a real-time intelligent acquisition strategy for HR/AM targeted quantification
and compare it to relative quantification from MS1 full scan spectra, and introduce a
strategy that enables higher confidence in both qualitative and quantitative results in
the label-free discovery runs. We propose using HR/AM MS and MS/MS schemes in
conjunction with validated spectral libraries for automated method building, data
acquisition, verification, and quantification in real-time using novel acquisition
schemes.
Methods
Sample Preparation
A protein mixture consisting of eight proteins — cytochrome c (horse),
α
-lactalbumin
(bovine), serum albumin (bovine), carbonic anhydrase (bovine), ovalbumin (chicken),
α
-S1-casein (bovine),
α
-S2-casein (bovine),
β
-casein (bovine) — was prepared at
equimolar ratios. The eight non-human proteins were analyzed at 100 fmol on column
in a “neat” background as well as 100 fmol on column spiked into a human plasma
matrix of 1ug on column. The eight proteins were also investigated in the human
plasma matrix at varying amounts ranging from 0.5 to 500 fmol each protein on
column.
MS Data Acquisition and Analysis
All samples were digested with trypsin and analyzed on a Thermo Scientific™
Q Exactive™ mass spectrometer equipped with a Thermo Scientific™ Nanospray Flex
Ion Source . Data was acquired in two steps to simulate traditional workflows. Initial
experiments employed unbiased data-dependent MS/MS acquisition resulting in
peptide/protein identification as well as building of a spectral library. These initial data-
dependent runs were run on both the “neat” conditions of the eight protein mix (without
the plasma matrix), and then on a 100 fmol level (each protein) on column in a plasma
matrix of (1 µg plasma on column). These initial data-dependent runs were searched
against a modified human database containing the eight additional proteins. The
combined results from the discovery experiments were used to build a local spectral
library consisting of precursor and product ion
m/z
values and relative abundance
distribution as well as relative retention time values. A highly multiplexed, targeted
protein list was created from the spectral library and used for automated data
acquisition and processing real-time to facilitate changes to the acquisition scheme.
For full description of acquisition method and scheme, please visit poster 131 on
Tuesday, by Prakash
et. al
.
1
Thermo Scientific™ Proteome Discoverer™ version 1.3 and Thermo Scientific™
Pinpoint™ version 1.3 software packages were used to analyze both the qualitative
and quantitative data. The spectral library resulting from initial runs was used to create
a targeted inclusion list and reference information to perform qual/quan determination
in real time. Data were acquired and peptide coverage and relative quantification were
measured for each of the eight standard proteins. All samples were run in triplicate.
Results
Intelligent Real-Time Data Acquisition
The discovery experiments were performed in an unbiased data-dependent acquisition
for the eight protein mixtures in “neat” conditions as well as in a complex plasma
matrix. From these initial results, 170 target peptides from the eight proteins were used
to build the spectral database, Figure 1. These 170 targets were built into a spectral
library look-up table that was used in real-time state modeled acquisition. The look-up
table includes the precursor
m/z
values for the defined charge state as well as the
expected retention time window, which are used to initiate product ion spectral
acquisition based on the presence of multiple precursor isotopes during the expected
elution window (Figure 2).
FIGURE 2. Pictorial represent
targeted peptide quantificatio
elution identification, and real
precursor and product ion sp
selectivity of data acquisition
0
20
40
60
80
100
120
140
160
180
0.5
1
Number of targeted peptides per mass load
fmol level of each non-
FIGURE 1. Histogram showin
MS2 peak area quantification
represent the number of conf
targets on the spectral library
*
*
Measured Ion Intensity
Start time for “watch l
Triggering
Threshold
1.
Theoretical
Isotope
Experimental
HR/AM MS
Spectrum