Here, we present an automated plasma protein digestion method for mass spectrometry-based quantitative proteomic analysis. In this protocol, the liquid transferring and incubation steps for protein denaturation, reduction, alkylation, and trypsin digestion reactions are streamlined and automated. It takes approximately five hours to prepare a 96-well plate with desired precision.
Sample preparation for mass spectrometry analysis in proteomics requires enzymatic cleavage of proteins into a peptide mixture. This process involves numerous incubation and liquid transfer steps in order to achieve denaturation, reduction, alkylation, and cleavage. Adapting this workflow onto an automated workstation can increase efficiency and reduce coefficients of variance, thereby providing more reliable data for statistical comparisons between sample types. We previously described an automated proteomic sample preparation workflow1. Here, we report the development of a more efficient and better controlled workflow with the following advantages: 1) The number of liquid transfer steps is reduced from nine to six by combining reagents; 2) Pipetting time is reduced by selective tip pipetting using a 96-position pipetting head with multiple channels; 3) Potential throughput is increased by the availability of up to 45 deck positions; 4) Complete enclosure of the system provides improved temperature and environmental control and reduces the potential for contamination of samples or reagents; and 5) The addition of stable isotope labeled peptides, as well as β-galactosidase protein, to each sample makes monitoring and quality control possible throughout the entire process. These hardware and process improvements provide good reproducibility and improve intra-assay and inter-assay precision (CV of less than 20%) for LC-MS based protein and peptide quantification. The entire workflow for digesting 96 samples in a 96-well plate can be completed in approximately 5 hours.
Mass spectrometry (MS) -based protein and peptide quantification is increasingly being applied as a bioanalytical tool for plasma analysis in basic research and clinical laboratories2,3. The requisite instrumentation and informatics have advanced rapidly as MS has become the method of choice for quantifying proteins or modified proteins by targeting specific peptide sequences due to its ability to quantify hundreds of peptides in a single MS run4. Sample preparation serves as the foundation for any proteomic analysis. Prior to MS analysis, the proteins in a biological sample are typically denatured, reduced, alkylated, digested into tryptic peptides, and desalted5. Alkylation blocks cysteines to prevent uncontrolled modifications and ensure all cysteines have the same mass. Next, trypsin is added to digest proteins into peptides. Each of these steps requires optimization, and the entire process is traditionally performed manually, allowing for the introduction of analytical errors.
The traditional biomarker development pipeline consists of two main processes: shotgun proteomics for global protein discovery to create an in-depth protein inventory6 and targeted proteomics for verification and validation aimed at high-precision and high-throughput protein quantitation7. Regardless of the MS approach, sample preparation is the same and centers on enzymatic cleavage of proteins into a peptide mixture. As proposed by Van Eyk and Sobhani8, having a method that allows for accurate, precise, and reproducible analysis for both discovery and targeted assays is desired to effectively move discovery biomarkers to clinical implemented assays. To do this, automation of sample preparation will be helpful and provide the ability to increase the efficiency to high-throughput analysis. Manual methods typically introduce analytical errors beyond acceptable limits specified by the Food and Drug Administration (FDA) in the revised Bioanalytical Method Validation Guidance, which includes biomarkers and diagnostics2,9. The development of a fast, highly accurate, and hands-free MS protein sample preparation workflow will be necessary to facilitate biomarker research, which requires preparing and analyzing thousands of biological samples. MS sample preparation has many steps where errors can be introduced, and the process is tedious and time-consuming.
In our improved method, an automated workstation was programed to perform all necessary plasma sample preparation procedures in a total of 6 steps (Figure 1) to ensure: 1) accurate liquid transfers; 2) the reaction is initiated and stopped at a consistent time; 3) the reaction is performed at a controlled temperature (i.e., incubator); and 4) the reaction has uniform mixing for all reactions. We also implemented adding exogenous quality control proteins and internal standards (stable isotope-labeled peptide standards) to ensure a quality and reproducible throughput of LC-MS-based protein and peptide quantification in a 96-well format. Including reagent preparation, hours of work time and a total of 1,000,000 pipetting steps would be required to process 96 samples in individual tubes. Automation reduces the hands-on time and the number of human interactions involved.
1. Programing for the automated liquid handler
2. Prepare specimen, labware, and reagents
3. Operating procedure
4. LC-MSMS
The automated proteomics sample preparation workflow on the automated workstation was adapted from our previous automated protocol with a robust LC-SRM-MS acquisition method1 for albumin, the selected plasma protein, and β-galactosidase (β-gal), an exogenous protein used for quality control. After processing, the samples were run on a triple quadrupole LC-MS in an SRM assay targeting serum albumin, β-galactosidase. The coefficient of variation (CV) of SRM signal for each transition was used to monitor the reproducibility of automated digestion protocol.
We automated the reagent addition and mixing steps for a digestion of 5 μL plasma samples with a 2-hour on-deck-incubator trypsin incubation. To determining the reproducibility, 5 μL of a plasma pool was pipetted into multiple wells of a reaction plate with a multichannel head (96 pin). To monitor for consistency, we added β-gal protein before the reduction and alkylation reactions. The automated proteomics sample preparation workflow was tested with a robust LC-SRM-MS acquisition method including albumin, the highest abundance plasma protein, and β-gal protein, used for quality control. Three β-gal peptides and two albumin peptides were monitored from processed plasma albumin proteins and spiked β-gal protein (Table 4).
In an effort to save time and simplify the procedure, we reduced the liquid transferring steps of adding/mixing/incubation of reagent and reaction mixture with the workstation from a nine-step workflow to a six-step workflow (Figure 1). The total proteomic workflow was comprised of two experimental components: automated sample preparation and LC-MS/MS. Firstly, we evaluated the precision of LC-MS/MS SRM data acquisition by eight consecutive injections from the same digestion well of the autosampler plate. The precision of the automated sample preparation workflow was calculated by the percent of coefficient of variance (%CV) of total proteomic SRM workflow minus%CV of the LC-MS/MS (Figure 9B). With the streamlined plasma digestion procedure on the automated workstation, the experimental precision of automated samples preparation was less than 11.4% for exogenous spiked β-gal proteins (10.0% as average), and less than 14.9% for the most abundant human serum albumin (9.9% as an average) (Figure 9). Good signal intensities were observed for both human serum albumin and β-gal proteins as expected (Figure 9C).
For each pipetting and liquid transferring step, techniques were specifically optimized. To monitor the precision of liquid transferring steps, we spiked stable isotope-labeled (SIL) peptide standards for endogenous human serum albumin protein and exogenous β–gal protein in three independent reagent transferring steps: Reaction Mix 1, Cysteine Blocker, and Reaction Mix 2 (Figure 10). MRM signals from these five SIL peptides were acquired to monitor the precision of automated liquid transferring steps. The average %CV for peptides, DDNPNLPR^, and GDFQFNISR^ (^ represents the N15 labeled amino acid) from Reaction Mix 1 step ranged from 1.8% to 11.2%. The average %CV of one peptide (IDPNAWVER^) from the Cysteine Blocker step ranged from 6.6 to 8.8%. The average %CV of two peptides (WVGYGQDSR^ and LVNEVTEFAK^) ranged from 6.2% to 11.9% (Figure 10).
To validate the automated proteomics sample preparation workflow, we evaluated reproducibility across multiple proteins and multiple days for human serum albumin, exogenous β–gal, and 40 additional plasma proteins. We processed 21 replicate samples (pooled normal human plasma), well location shown in Figure 11A, on three different days. Intra-day CVs were calculated from 21 wells prepared on the same day. The mean intra-day %CVs for 40 proteins ranged from 4% – 20% (Figure 11B). To evaluate the edge effect of the plate based automated workflow, %CV was calculated from specific wells within designated columns and rows (Figure 12A for column and row map). MRM signals intensities were similar in all column and row configurations with %CV ranging from 3% – 22% (Figure 12B).
In summary, the optimized automated workflow yields 96 uniformly-processed samples in less than five hours with excellent experimental precision. For compatibility with an automated workflow, we selected reagents that have negligible non-specific side reactions, are stable in ambient light, are LC-MS/MS friendly, and can be stored as frozen aliquots.
Figure 1: Schema of sample preparation workflow. The main 6 liquid transferring steps are listed. Please click here to view a larger version of this figure.
Figure 2: Tip loading Script. The VB Script details are shown in here. The script specifies tip loading conditions. Please click here to view a larger version of this figure.
Figure 3: Automated workstation Deck layout. The deck consists of 1×1 ALPs, Tip Loading ALPs, Trash, Tip wash, Peltier and an incubator. Please click here to view a larger version of this figure.
Figure 4: Properties of the reagent plate. Shown are properties needed for the Reagent Plate labware when accessed using the Guided Labware Setup. Select the corresponding column and type in the variables as indicated in the figure. Please click here to view a larger version of this figure.
Figure 5: Tip counting script. This script helps to keep track of number of tips on the deck. Please click here to view a larger version of this figure.
Figure 6: Overview of the method for digesting and aliquoting plasma samples. Steps for reagent calculations, labware setup, and liquid manipulations in the liquid handler’s method. Please click here to view a larger version of this figure.
Figure 7: Layout of the reagent plate. Shown are the chemical reagents needed for plasma digestion and autosampler preparation and distributed across the reagent plate labware. This figure has been modified from a technical note10. Please click here to view a larger version of this figure.
Figure 8: Layout for the labware on the deck of the automated workstation. Shown is the deck layout for the plasma digestion method for the liquid handler. This figure has been modified from a technical note10. Please click here to view a larger version of this figure.
Figure 9: The precision of total proteomic workflow is comprised of workstation CV and targeted LC MS/MS CV.
Five peptides from β-gal and albumin were monitored, the chromatograms and peptide retention time for each peptide is shown (A), Precision was determined from 30 wells/samples processing representative experiment, CVs% for total proteomic workflow, LC MS/MS analysis and automated sample processing were calculated (B). Overall, the digested peptides showed good signals ranged from 1×105 up to 1×108. Please click here to view a larger version of this figure.
Figure 10: Determination of precision for liquid transferring steps. Synthetic peptides were spiked in step specific reagents, and automated liquid transferring was determined by total%CV. From 30 wells/samples experiment minus% CV LC MSMS (determined by 8 repeat LC MSMS injections. Please click here to view a larger version of this figure.
Figure 11: Multi-days reproducibility of automated proteomic sample preparation workflow with 42 protein MRM analysis. (A) Reaction plate map for each of three days is show here, 5 µL plasma was added to each of 21 wells. 3 wells received 5 ul water were used as negative/blank controls. (B) The average intensities of 190 transitions comprise of 75 peptides and 42 proteins (left) and%CV for each MRM transition was calculated from 21 wells digestion for each day (right). Please click here to view a larger version of this figure.
Figure 12: Reproducibility of specific locations of wells (position of columns and rows). (A) Columns and Rows location with a plate map are shown here. (B) Column and row location specific MRM signals of average intensities from specified wells (left) and cv% (right) from a single plate digestion. Please click here to view a larger version of this figure.
Figure 13: Screen shot of technique editor. For each Pipetting template, define the properties of liquid level sensing, Clot detection, Piercing, Liquid Type, General, Aspirate, Dispense, Mix and Calibration. The template and techniques used in this protocol are shown in Supplemental Table 2). Please click here to view a larger version of this figure.
Variable | Variable | Value | Description |
Autosampler | Boolean | TRUE | Use autosampler |
Betagal | Integer | 5 | Betagal volume |
BG1 | Integer | 0.8 | BG1 volume |
BG2 | Integer | 0.8 | BG2 volume |
BG3 | Integer | 0.8 | BG3 volume |
CysteinBlocker | Integer | 1.25 | Cysteine blocker volume |
CysteineBuffer | Integer | 0.45 | Cysteine buffer volume |
Denaturant | Integer | 5 | Denaturant volume |
DigestTransfer | Integer | 10 | Digest transfer volume |
First Buffer | Integer | 25.9 | First buffer volume |
First Column | Integer | 1 | First Column |
HSA1 | Integer | 0.8 | HSA1 volume |
HSA2 | Integer | 0.8 | HSA2 volume |
lastcolumn | Integer | 12 | Last Column |
MobilePhase | Integer | 90 | Mobile phase volume |
Quench | Integer | 10 | Quench column |
ReducingAgent | Integer | 5 | Reducing agent column |
Sample | Integer | 5 | Sample column |
SamplePlate | Boolean | TRUE | Use sample plate |
SecondBuffer | Integer | 58.4 | Second buffer volume |
Trypsin | Integer | 10 | Trypsin column |
Table 1: Start step variables
Value | Variable |
=FirstBuffer+Denaturant+ReducingAgent+Betagal+BG1+HSA1 | FirstMix |
=CysteineBlocker+CysteineBuffer+BG2 | CysteineMix |
=SecondBuffer+BG3+HSA2 | SecondMix |
=(FirstMix*Columns)+30 | FirstMixWell |
= (CysteineMix*Columns)+20 | CysteineWell |
=(SecondMix*Columns)+10 | SecondMixWell |
=(Trypsin*Columns)+10 | TrypsinWell |
=(Quench*Columns)+10 | QuenchWell |
=(FirstMixWell*8)+100 | FirstMixStock |
=CysteineWell*8+20 | CysteineMixStock |
=(SecondMixWell*8)+100 | SecondMixStock |
Table 2: Volume mix variables
Tipo | Name | Posizione | Depth | Properties | Use? |
BCDeep96Round | Reagent Plate | P5 | 1(top) | # | =not SamplePlate |
BCDeep96Round | Reagent Plate | P5 | 1(top) | # | =SamplePlate |
BCDeep96Round | Reaction Plate | P11 | 1(top) | TRUE | |
Bio_RadPCR96* | Samples | P9 | 1(top) | =SamplePlate | |
Bio_RadPCR96* | Autosampler Plate | P10 | 1(top) | =Autosampler | |
BC90 | Empty | 1(top) | TRUE | ||
BC90 | 1(top) | TRUE | |||
BC90 | 1(top) | TRUE | |||
BC230 | 1(top) | =Autosampler | |||
BC90 | 1(top) | =Columns>1 | |||
BC90 | 1(top) | =Columns>3 | |||
BC90 | 1(top) | =Columns>5 | |||
BC90 | 1(top) | =Columns>7 | |||
BC90 | 1(top) | =Columns>9 | |||
Note: *: Corresponds to the 96 well Bio-Rad plate. #: Click properties and then enter the volume variables as indicated in Figure 6. |
Table 3: Setting up the guided setup
Protein ID | Peptide sequence | Q1 Mass (Da) | Q3 Mass (Da) | Time (min) | Fragment Ion | Declustering Potential | Collision energy | Collision Cell Exit Potential |
sp|P00722|BGAL_ELOCI | GDFQFNISR | 542.3 | 262.1 | 19.7 | +2y2 | 61 | 21 | 8 |
542.3 | 636 | 19.7 | +2y5 | 61 | 25 | 12 | ||
542.3 | 764.2 | 19.7 | +2y6 | 61 | 25 | 18 | ||
IDPNAWVER | 550.3 | 436.1 | 18.1 | +2y7+2 | 61 | 23 | 8 | |
550.3 | 871.2 | 18.1 | +2y7 | 61 | 25 | 18 | ||
550.3 | 774.2 | 18.1 | +2y6 | 61 | 33 | 8 | ||
WVGYGQDSR | 534.3 | 782.1 | 12.1 | +2y7 | 51 | 25 | 6 | |
534.3 | 562.1 | 12.1 | +2y5 | 51 | 27 | 6 | ||
534.2 | 505.2 | 12.1 | +2y4 | 90 | 25 | 8 | ||
sp|P02768|ALBU_HUMAN | DDNPNLPR | 470.8 | 596.2 | 9.2 | +2y5 | 61 | 27 | 16 |
470.8 | 499.3 | 9.2 | +2y4 | 61 | 27 | 18 | ||
470.8 | 710.4 | 9.2 | +2y6 | 61 | 27 | 18 | ||
LVNEVTEFAK | 575.3 | 694.4 | 18.2 | +2y6 | 73.1 | 29.6 | 18 | |
575.3 | 937.5 | 18.2 | +2y8 | 73.1 | 29.6 | 18 | ||
575.3 | 823.4 | 18.2 | +2y7 | 73.1 | 29.6 | 18 |
Table 4: MRM parameters
Supplemental Table 1: Reagent plate Please click here to download this table.
Supplemental Table 2: Protocol template Please click here to download this table.
Sample processing for mass spectrometry requires protein denaturation, reduction and alkylation to block cysteines, and trypsin digestion to cleave proteins into peptides. Each chemical or enzymatic reaction needs to be initiated at a designated time and performed at a controlled temperature, and every step in the process involves multiple liquid transfer steps where experimental variation can be introduced. Automated sample processing provides a solution to this dilemma. Presently available liquid handling systems have the capability to transfer reagents into 96-well plates with an accuracy and precision of less than 5%, depending on the head and tip type used, and to incubate samples, with shaking if desired, under controlled temperatures ranging from 14 °C to 70 °C. We used an automated liquid handler to process plasma for SRM assays in a 96-well format.
There are many thousands of protease cleavage sites within the complex mixtures of proteins in serum, plasma, and other biological samples; and each of these proteins has unique properties affecting cleavage site accessibility and the stability of the resulting peptides. It is, therefore, impossible to design a sample processing procedure that is optimal for every protein. The best alternative is to be as consistent as possible.
To achieve consistency, we optimized each pipetting step performed by the automated liquid handler. We first considered the volume required and constraints imposed by the liquid’s type (plasma, aqueous, or organic) and corresponding properties (viscosity, cohesion, and volatility), hardware (workstation pipetting-head and plate-grabbing arms), and labware. We then varied the speed and trailing air gap for aspiration, the speed and blowout volume for dispensing, and the force and duration of mixing, while incorporating a tip touch after aspiration and/or dispensing, if needed, to eliminate liquid adhering to the outside of the tips (Figure 13, Supplemental Table 1). A unique SIL peptide, pre-screened for stability, was spiked into each reagent to make it possible to monitor the precision of each liquid handling step. After optimization, the process CVs for the majority of the transitions were less than 10%, demonstrating good reproducibility with the automated workstation (Figure 9, Figure 10, Figure 11 and Figure 12).
The automated workflow presented here provides for consistent enzymatic digestion with improved reproducibility and throughput compared to manual methods (Figure 9, Figure 10). This approach promises to improve the accuracy and reliability of biomarker discovery and validation by mass spectrometry.
The authors have nothing to disclose.
None.
A Pooled healthy human plasma | Bioreclamation Inc. | human plasma tested in the manuscript | |
Acetonitrile, HPLC Grade | Thermo Fisher Scientific | A998SK1 | LC MS/MS solvent |
B-Galactosidase Recombinant from E.Coli | Sigma-Aldrich | G3153-5MG | exogenous control proteins. |
Biomek i7 Automated Workstation | Beckman Coulter, Inc. | The proteomic sample preparation workstation: Biomek automated workstations are not intended or validated for use in the diagnosis of disease or other conditions | |
Biomek i-Series Tips 230µL Non-Sterile | Beckman Coulter, Inc. | B85903 | Tips, i7 consumable |
Biomek i-Series Tips 90µL Non-Sterile | Beckman Coulter, Inc. | B85881 | Tips, i7 consumable |
FG, Kit Trypsin TPCK | Sciex | 4445250 | Trypsin used in digestion |
Hard-Shell 96-Well PCR Plates, low profile, thin wall, skirted, green/clear | Bio-Rad | #hsp9641 | Autosampler plate |
Octyl B-D-Glucopyranoside | Sigma-Aldrich | O9882-5G | detergent used in digestion |
Polypropylene, 96-Round Deep Well Plates Sterile | Beckman Coulter, Inc. | 267007 | Reagent and digestion plate |
Prominence UFLCXR HPLC system | Shimadzu, Japan | High flow LC sytem | |
QTRAP 6500 | SCIEX | Mass spectrometer | |
Water, HPLC Grade | Thermo Fisher Scientific | W54 | LC MS/MS solvent |
Xbridge BEH30 C18 2.1mm x 100mm | Waters | 186003564 | LC MSMS column |