Advantages of a Two-Pass Workflow for Biomarker Discovery in Plasma or Serum Samples for Clinical Research
Maryann S Vogelsang1, Bryan Krastins1, David A Sarracino1, Michael Athanas2, Amol Prakash1, Alejandra Garces1, Gouri Vadali1,2, Janin Schulte3, Gaiané Demirdjian3,
Jennifer N. Sutton1, Joachim Struck3, Bruno Darbouret3, Mary F Lopez1
Fisher BRIMS, Cambridge, MA, USA 2 VAST Scientific, Cambridge, MA, USA 3 Thermo Scientific Biomarkers, Hennigsdorf, Germany
Purpose: Sample preparations relying on fractionation to simplify the complexity and
large dynamic range of plasma or serum samples do so at a cost that can result in
inaccurate or unreliable abundance measurements. Here we describe a simplified
approach to biomarker discovery using a Two-Pass workflow that reduces the need for
physical sample fractionation. The workflow covers robust, reproducible sample
preparation, chromatography and strong informatics-driven data analysis.
for the top marker candidates determined by their ROC Area-Under-the-Curve (AUC).
An inclusion list was created for the best candidates based upon various criteria
including ROC AUC, low ratios, high ratios, high abundance, and low abundance. This
inclusion list was used for MS/MS acquisition in Pass 2.
Results: Our workflow reduces the number of replicates needed. In our hands, the
Two-Pass Workflow also provides 20-50% more quantitatively-associated protein
identifications than a single pass experiment and at shorter times (2-5X or less)..
Proteomic-based biomarker discovery approaches have primarily focused on directly
profiling serum or plasma for abundance changes that can discriminate between
populations of patients. Due the complexity and high dynamic range of serum and
plasma, clinical samples are often processed by depletion and/or fractionation in order
to reduce the complexity of the sample. However, these manipulations can result in
inaccurate or unreliable abundance measurements. Here we describe a simplified
approach to biomarker discovery using a Two-Pass workflow that reduces the need for
physical sample fractionation. The workflow covers robust, reproducible sample
preparation, chromatography and strong informatics-driven data analysis.
Previously, we introduced a two-pass workflow exploiting the mass spectrometer’s
accurate mass and broad dynamic range capabilities, by investigating the
uncompromised quantitative data in Pass 1 and targeting differentially expressed MS
features in Pass 2 1,2. In Pass 1 we take advantage of the speed of the hybrid mass
spectrometer, and measure quantitative MS1 frames while concurrently measuring and
identifying top 10 data-dependent MS2. In Pass 2 we identify additional differentially
expressed peptides from our inclusion list built from Pass 1 data analysis.
Pass 2. Inclusion list masses were used exclusively for Pass-2 analysis using Top-10
from list configuration. This ensured that the instrument acquired MS2 only of the
inclusion list (not the highest intensity) masses. A larger sample load was used in Pass
2 runs (630 ng) allowing for higher quality MS2 spectra. Since these full scan spectra
would not be used for quantification, peak shape and intensity reproducibility were not
crucial. All fragmentation analysis was done in the Orbitrap, using both HCD and CID.
Fragmentation scans from Pass 2 were analyzed for identification using SEQUEST
and FDR analysis. SIEVE was used again to combine the fragmentation search results
from Pass 2 with the quantitative information from Pass 1. Fragmentation scan
information was assigned to SIEVE frames based upon the precursor MZ and
retention time
Condition/ Filters
Patient samples – Full Scan + Top 10
FIGURE 2. Pass 1 Acquisition Cycle. Method for assessing systematic errors
without sample technical replicates. Systematic errors are assessed from triplicate
acquisitions of standardized peptide samples. Internal standards are spiked in all
samples. This approach eliminates the need for clinical sample replicates and
conserves valuable specimens. All patient samples are acquired in high resolution full
scan and Top-10 data dependent mode, on the Velos-Orbitrap. Biological variance
and outliers are assessed with CV, PCA and other statistical methods.
Two-Pass Workflow
Classic Fractionation
10 fractions
50 samples
500 samples
100 hrs
1000 hrs
The strategy for the Two-Pass workflow consists of the separate optimization of MS
parameters and configuration for protein quantification and identification.
Liquid Chromatography & High-resolution Mass Spectrometry
Proteomics data analysis was performed using SIEVE v2.0 chromatographic
alignment, framing, differential ROC or ratio analysis 3. Both Top-10 data-dependent
scans and full scan data were analyzed with SIEVE v2.0 software (Thermo Scientific)
by chromatographic alignment followed by feature extraction using unsupervised
statistical techniques including isotope deconvolution. ROC curves were constructed
FIGURE 3. SIEVE gel view of 9 LC-MS runs from clinical patient samples. In this
example experiment, we have 20,000 potentially useful frames in gel view. A frame
represents a potentially interesting feature (peptide) found in a collective data set.
Using frame filters we can separate the differentially expressed frames between the
two groups (see Table 2).
Time Saving Two-Pass Workflow
A small cohort of IRB approved patient plasma samples collected from emergency
room cardiac patients were analyzed . The preliminary results in this report are
intended to demonstrate the two-pass workflow, not for biomarker discovery.
Figure 1. Two-Pass Discovery Workflow using SIEVE and Orbitrap Velos. Given
our robust LC-MS/MS methods and the power of SIEVE, we are able to identify
differentially expressed proteins in unlabeled clinical samples.
On average, we were able to obtain 82% success rate in MS2 acquisitions from our
inclusion list. Frame parameter pending, we have had even higher success rates in
other experiments.
Peptide Calibration Standards
Sample Preparation
The following data are representative of a proteomics differential case study with ROC
Analysis from clinical patient plasma samples.
Pass 1
Acquisition Cycle
Blank run
The described Two-Pass workflow was applied to 60 clinical plasma samples Figures
1 and 2. Our previous findings have demonstrated that the two-pass workflow can
accurately detect, quantify and identify unlabeled differentially expressed proteins
within clinical plasma samples 1,2,4,5. Given the robust chromatography, high-resolution
mass spectrometers and computing power of SIEVE, our workflow allows for
identification of differentially expressed proteins in a single pass experiment at shorter
time periods at (2-5x or greater), Table 1.
Pass 2 Results
SIEVE v2.0 Analysis
Pass 1. Plasma samples (500 ng, digested with trypsin) were injected onto an Easy
nLC system configured with a 10cmx100um trap column and a 25cm x 100um ID
resolving column (Thermo Scientific). We optimized the sample load for optimum
quantification, ie full scan data. Buffer A was 98% water 2% methanol 0.2% formic
acid, Buffer B was 10 % water, 10% isopropanol, 80% acetonitrile, 0.2% formic acid.
Samples were loaded at 4uL/min for 10 min, and a gradient from 0-45% B at 375nl/min
was run over 130min, for a total run time of 150min (including regeneration, and
sample loading). Velos-Orbitrap (Thermo Scientific) was run in a standard Top-10
data dependent configuration except with a higher trigger threshold (20K) to ensure
that the MS2 did not interfere with the full scan duty cycle. This ensured optimal full
scan data for quantification. MS2 fragmentation and analysis was performed in the ion
Data Analysis
Methods: Two-Pass discovery workflow using high-resolution LC/MS-MS coupled to
ROC and differential expression analyses from stratified patient cohorts.
Two-Pass Workflow
Classic Fractionation6
Total Protein IDs
16 hrs
432 hrs
TABLE 1. Advantages of Two-Pass workflow over conventional discovery
pipelines. (a) Throughput: At the sample preparation and mass spectrometer
acquisition steps the two-pass workflow already has at maximum, a 10-fold time
advantage over conventional fractionation preparations. This allows for stronger
computational/statistical numbers. In comparable time frame we are able to investigate
10-times more patient samples allowing for stronger statistics. (b) Proteomic results:
Equivalent number of protein IDs were obtained using the Two-Pass workflow as with
classic fractionation, in significantly less time. Proteins identified covered
approximately seven logs of abundance in both methods.
FIGURE 5. Example whisker plot of peptide coverage of a potential biomarker.
Pass 2 often results in an increased peptide coverage of the proteins identified in
Pass 1. With respect to roughly 10% of frames representing 1 peptide of a given
protein in Pass 1, we identified a second peptide for that corresponding protein in
Pass 2, ultimately strengthening the confidence in that identification and
Peptides (frames)
ALL frames
AUC > 0.7
Fold Change >1.5
BRIMS Two-Pass Workflow was successfully applied to multiple cohorts of
clinical plasma samples.
BRIMS Two-Pass Workflow allows for faster time to targeted assays and
validation of potential biomarkers.
BRIMS Two-Pass Workflow delivers increased protein biomarker confidence with
increased peptide coverage of differentially expressed peptides.
Identified in Pass 1
Unidentified in Pass 1
(for Pass-2 inclusion list )
TABLE 2. Number of proteins and peptides that meet filtering criteria in Pass-1,
within a clinical ROC analysis experiment. Our workflow allows for confident
identification of total protein as well as unlabeled differentially expressed peptides.
Initial steps in SIEVE, generate 20,000 frames (peptides). Using frame filters based
upon ROC AUC or ratios, we easily identified frames that separated the patient
groups. Given that our Pass 1 experiments are simultaneously collecting Top-10
data-dependent MS2 spectra, we can easily identify the abundant proteins. The
unidentified frames can then be exported to an inclusion list for Pass-2 analysis.
1. Athanas, M., MacCoss, MJ., Prakash, A., Kall, L., Tomazella, D., Maclean, B.,
Rezai, T., Krastins, B., Sarracino, D., Garces, A., Fortune, S., and Lopez, MF.
(2009) Label-free Differential Analysis: An Iterative Approach to Increased
Coverage, Improved Statistics and Results. Poster presentation at ASMS.
2. Athanas, M., Sarracino, D., Rezai, T., Prakash, A., Sutton, J., Krastins, B., Ning,
M., and Lopez, MF. (2010) A Two-pass Informatics-driven Label-free Workflow
For Discovery Of Neurovascular Mediators In PFO-Related Stroke. Poster
presentation at ASMS.
3. SIEVE Analysis Platform, ThermoFisher & VAST Scientific,
4. Lopez, MF., Kuppusamy, R., Sarracino, DA., Prakash, A., Athanas, M., Krastins,
B., Rezai, T., Sutton, JN., Peterman, S., and Nicolaides, K. Mass Spectrometric
Discovery and Selective Reaction Monitoring (SRM) of Putative Protein
Biomarker Candidates in First Trimester Trisomy 21 Maternal Serum. J Proteome
Res. 2011; 10(1):133-42.
5. Lopez, MF., Sarracino DA., Prakash A., Athanas M., Krastins, B., Rezai, T.,
Sutton JN., Peterman S., GvozdyakO., Chou S., Lo E., Buonanno F., and Ning
MM. Discrimination of ischemic and hemorrhagic strokes using a multiplexed,
mass spec-based assay for serum apolipoproteins coupled to multi-marker ROC
algorithm. JPR (in press)
FIGURE 4. Frequency histogram of scheduled fragment events per minute for
Pass-2 analysis (Pass-2 Inclusion List). After the given frame filter conditions
[AUC>0.7 and (NRatio>1.4 or NRatio<0.6) and PRElement<1 and goodid=0], 927
frames were exported as an inclusion list for Pass-2 analysis. NOTE: These frames
represent differentially expressed peptides.
6. Faca, V., Pitteri, SJ., Newcomb, L., Glukhova, V., Phanstiel, D., Krasnoselsky, A.,
Zhang, Q., Struthers, J., Wang, H., Eng, J., Fitzgibbon, M., McIntosh, M., and
Hanash, S. Contribution of Protein Fractionation to Depth of Analysis of the
Serum and Plasma Proteome. Journal of Proteome Research 2007, 6, 3558-65.
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