Detailed protocol and three Python scripts are provided for operating an open-source robotic liquid handling system to perform semi-automated protein sample preparation for mass spectrometry experiments, covering detergent removal, protein digestion, and peptide desalting steps.
Mass spectrometry-based shotgun proteomics experiments require multiple sample preparation steps, including enzymatic protein digestion and clean-up, which can take up significant person-hours of bench labor and present a source of batch-to-batch variability. Lab automation with pipetting robots can reduce manual work, maximize throughput, and increase research reproducibility. Still, the steep starting prices of standard automation stations make them unaffordable for many academic laboratories. This article describes a proteomics sample preparation workflow using an affordable, open-source automation system (The Opentrons OT-2), including instructions for setting up semi-automated protein reduction, alkylation, digestion, and clean-up steps; as well as accompanying open-source Python scripts to program the OT-2 system through its application programming interface.
Mass spectrometry-based shotgun proteomics is a powerful tool to measure the abundance of many proteins in biological samples simultaneously. Proteomics experiments with bioinformatics analysis are routinely employed to identify biomarkers and discover associated biological complexes and pathways underpinning pathological mechanisms. With its high analyte specificity and potential quantitative accuracy, shotgun proteomics also has excellent potential to be adopted by research facilities and diagnostic laboratories for clinical sample analysis without the need to rely on antibodies1,2.
To prepare protein samples for shotgun proteomics analysis, proteins extracted from biological samples (e.g., cells and tissues) typically first need to be processed using lengthy protocols, including measuring the sample protein concentration, protein reduction, and alkylation, and enzymatic digestion into peptides. Moreover, proteins extracted in common lysis buffers containing detergents often require additional steps of buffer exchange or detergent removal before analysis because detergent can interfere with trypsin digestion and significantly degrade the performance of downstream liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis3. Peptides are typically further desalted, dried, and reconstituted in LC-MS/MS compatible solvents following enzymatic digestion. These protein biochemistry procedures can be labor-intensive and time-consuming. Thus, they continue to limit the throughput of proteomics workflows and contribute to the variability of acquired data4,5. Human errors and biases have been recognized as crucial factors affecting data variance and reproducibility6,7. To minimize human errors in mass spectrometry sample preparation workflows, automated pipetting robotic systems have been utilized to improve the throughput and reproducibility of protein identification and quantification from shotgun proteomics and targeted mass spectrometry analysis, where such advances have been hailed as instrumental for continuing the push for widespread adoption of proteomics technologies in critical research and clinical settings8,9,10,11,12,13. However, most existing protocols utilize robotic liquid handling platforms that require substantial investment and training, limiting their utility in many laboratories in the academic environment or otherwise with a limited budget.
This article describes a protocol that utilizes a low-cost, open-source robotic liquid handling system, the OT-2, to semi-automate a typical shotgun proteomics sample preparation workflow. The OT-2 has a lower cost than many other robotic liquid handling systems, and at the time of writing, costs approximately $5,000 US dollars. When factoring in the prices of different modules and labware, the total cost to set up experiments in this protocol at the time of writing is around $10,000, which renders it more affordable to a considerably broader set of laboratories over more expensive options. The OT-2 is compatible with open-source programming through Python scripts and offers great flexibilities in user-defined DIY protocol design. Using three in-house developed scripts, the protocols below cover executing a typical shotgun proteomics sample preparation workflow on the OT-2 station with an archetypical protein standard (bovine serum albumin; BSA) and a complex protein sample of a normal human heart lysate (Figure 1). The procedures for processing (1) a BSA sample and (2) a complex cardiac lysate sample are detailed in Protocol sections 1, 2, 5, 6 and 3, 4, 5, 6, respectively. Sera-Mag carboxylate-modified magnetic beads are utilized in single-pot solid-phase-enhanced sample preparation (SP3) to remove detergents and salts in the protein and peptide samples. Tryptic digests from bovine serum albumin and human heart proteins are further cleaned by SP3 beads and submitted for LC-MS/MS analysis. Mass spectra are then analyzed using the MaxQuant software for peptide and protein identification. Representative results performed by us show that the protocol achieves excellent technical coefficients of variation (CV) while saving bench time and is non-inferior to hand digest.
The developed Python scripts have been deposited on GitHub at: https://github.com/MaggieLam-Lab/StandardDigestion-Opentrons. A copy of the scripts is given in Supplementary File 1. Please refer to the GitHub repository for the latest versions.
1. Experimental preparations
2. Mass spectrometry (MS) sample preparation with a single protein bovine serum albumin (BSA)
3. Peptide clean-up using SP3 paramagnetic beads
4. MS sample preparation with protein lysate of the human heart (5 mg/mL) with SP3 paramagnetic beads
5. Peptide clean-up using SP3 paramagnetic beads
6. Liquid chromatography and mass spectrometry
Three Python scripts are provided here that are compatible with the OT-2 robot, and that perform sample preparation for mass spectrometry proteomics with a single protein standard bovine serum albumin (technical replicates n = 5 digestions) and a detergent-containing human heart lysate sample (n = 5 digestions). Each digest product is partitioned into two peptide clean-up reactions. The number of identified peptide-spectrum matches (PSMs), peptides, and proteins in each run of the BSA and heart samples are shown in Figure 4 and Figure 5. A median of 728 PSMs and 65 peptides were identified with the BSA sample, with 5.2% and 3.2% coefficients of variation (CV), respectively. With the complex heart sample, a median of 9,526 PSMs, 7,558 peptides, and 1,336 proteins was identified in 10 runs with 7.6%, 5.9%, and 3.6% coefficient of variation. A total of 1,935 proteins were identified from 10 runs of the heart sample, and among those, 1,677 proteins were identified in two or more runs. To determine the variability in peptide quantification, the CV of the extracted ion chromatogram (XIC) intensities were calculated for 10 peptides that mapped to a unique protein (Table 2). The variabilities of human (hand-pipetted) vs. robot experimental results on measuring protein concentration were further compared using three protein standard samples with the BCA assay. The average CV (7.57%) of robot BCA assay was found to be lower than the human manual BCA assay (9.22%) (Supplementary Table 1).
The described protocol showed consistent performance over time when the BSA digestion protocol was performed 2 months apart and produced comparable results. The median number of unique PSMs and peptides in Figure 2 are 728 and 65, respectively. The same experiments performed on the OT-2 system 2 months before generated an average of 647 PSMs and 54 peptides (n = 2) (Supplementary Table 2). Longer-term stability may be estimated similarly.
The manual bench processing time (incubation time not included) is calculated between the robot protocol and human processing18 per sample preparation. With the digestion protocol without detergent removal followed by peptide desalting, the manual processing time is 41 min with the robotic system vs. 61 min by hand. With detergent removal, digestion, and peptide desalting protocol, the manual processing time is 54 min with the robotic system vs. 79 min by hand. Therefore, the semi-automated protocol reduces about 20-25 min of hands-on bench processing time per sample. This time reduction becomes considerable when many samples are processed and may be further improved when multiple OT-2 robots are used in parallel.
Figure 1: Schematic workflow. Proteins extracted with detergent aid will require processing with an extra step of detergent removal before digestion. Protein samples are digested, and peptides are desalted on the OT-2 robotic system. The peptide digests are injected into a Q-Exactive HF mass spectrometer coupled with a nano-LC. MS spectra are searched against a protein database for protein identification. Please click here to view a larger version of this figure.
Figure 2: Robot deck set up for protein digestion. The specified positions of tip racks, samples, trash, temperature module, and magnetic module are shown. Asterisks denote labware and reagents that are only required for the digestion protocol with detergent removal steps. Boxes with numbers denote unoccupied deck positions. Please click here to view a larger version of this figure.
Figure 3: Robot deck set up for the peptide clean-up script. The specified positions of tip racks, samples, trash, and magnetic module are shown. Boxes with numbers denote unoccupied deck positions. Please click here to view a larger version of this figure.
Figure 4: Number of peptide-spectrum matches (PSMs) and peptides detected in the digestions of BSA protein (n = 5). Each digest was split into two for technical replicate peptide clean-ups (R1 and R2). Coefficient of variations: 5.2% for PSMs; 3.2% for peptides. Please click here to view a larger version of this figure.
Figure 5: Number of PSMs, peptides, and proteins identified from a human heart lysate. Five digestions were performed with SP3 detergent removal. Each digest was split into two for peptide clean-ups (R1 and R2). Coefficients of variation: 7.6% for PSMs; 5.9% for peptides; 3.6% for proteins. Please click here to view a larger version of this figure.
Digestion enzyme | Trypsin/P |
Maximal missed cleavage | 2 |
Fixed modification | Carbamidomethylation of cysteine |
Variable modification | N-terminal protein acetylation; oxidation of methionine |
Peptide length range | 7 – 25 aa |
Precursor mass tolerance | ± 4.5 ppm |
MS/MS ions mass tolerance | ± 20 ppm |
Label-free quantification | LFQ |
False discovery rate (FDR) for peptide-spectrum match (PSM) | 0.01 |
Table 1: The peptide database (MaxQuant) search parameters.
Peptide | Protein ID | PEP | Median XIC Intensity | CV | |
LSTSQIPQSQIR | Q92523 | 7.72E-08 | 1.96E+07 | 6.70% | |
SEDFSLPAYMDR | P13073 | 9.64E-17 | 8.05E+08 | 7.30% | |
YLQEIYNSNNQK | P02679 | 2.76E-23 | 9.69E+08 | 7.60% | |
TDDCHPWVLPVVK | P17174 | 4.51E-14 | 4.60E+08 | 8.60% | |
VIVVGNPANTNCLTASK | P40925 | 7.90E-29 | 1.17E+09 | 8.70% | |
DYIWNTLNSGR | O75390 | 1.63E-15 | 1.38E+08 | 8.80% | |
VSVPTHPEAVGDASLTVVK | P13611 | 1.86E-09 | 6.77E+07 | 9.10% | |
QVAEQFLNMR | P22695 | 3.25E-08 | 1.09E+08 | 9.30% | |
NTFWDVDGSMVPPEWHR | Q9UI09 | 2.05E-11 | 4.00E+07 | 9.60% | |
SASDLTWDNLK | P02787 | 5.29E-11 | 1.92E+09 | 9.80% |
Table 2: Extracted ion chromatogram (XIC) intensity quantification of 10 peptides.
Supplementary Table 1: Comparison of manual and automated BCA assays. Please click here to download this Table.
Supplementary Table 2: BSA digestion performed 2 months apart from the samples processed in Figure 4. Please click here to download this Table.
Supplementary File 1: A copy of the developed Python scripts. Please click here to download this File.
Supplementary File 2: Method parameters for the liquid chromatography program for LC-MS/MS analysis. Please click here to download this File.
Supplementary File 3: Method parameters for acquiring shotgun proteomics data using a mass spectrometer. Please click here to download this File.
Critical steps within the protocol
For the best performance, Opentrons-verified labware, modules, and consumables compatible with OT-2 should be used. Custom labware can be created following Opentrons' instruction at Reference14. Make sure to calibrate the OT-2 deck, pipettes, and labware when used for the first time. It is also critical to follow guidelines from SP3 beads' manufacturer to prepare beads for peptide and protein clean-up. Notably, during the bead and peptide binding reaction, the volume of acetonitrile in the binding reaction needs to be ≥95%, and the bead concentration needs to be ≥0.1 µg/µL. Keep the peptide concentration in the range of 10 µg/mL-5 mg/mL. With the optimized parameters in the peptide clean-up script here, the acetonitrile volume ratio is 95%, the bead concentration is 0.37 µg/µL, and the peptide concentration is in the range of 14-37 µg/mL. The peptides mass is estimated to be 40-100 µg in 100 µL of digestion reaction from our experience. For SP3 protein clean-up, 5-10 µg of beads to 1 µg of protein was used and ensured that the minimal beads concentration is 0.5 µg/µL during protein binding. The recommended protein concentration is in the range of 10 µg/mL-5 mg/mL. With the default parameter in the provided script, 1 mg of beads is used for 100 µg protein, and the bead concentration during binding is 3.75 µg/µL, whereas the protein concentration is 0.35 mg/mL.
Modifications and troubleshooting
The default variables in the Python scripts are optimized for standard workflows in our laboratory. Users need to adjust the variables to make the scripts compatible with their applications if required. If low MS intensity is observed, check for protein or peptide loss after each significant protocol section with the protein BCA assay and quantitative peptide assay. When using the protocol on OT-2 for the first time, observe robot handling for each step to ensure the robot performs procedures as expected. At the time of writing, the P50 electronic pipette is no longer available in the Opentrons store. The current script has been modified to indicate where the P20 pipette may be used in its place. Users may refer to the Opentrons API to modify the scripts to use other pipettes, if necessary.
Limitations of the technique
Despite the advantages of using a robotic liquid handling system, caution should be exercised at the performance of fluid transfer between technical replicates. Monitoring the robot during the initial setup and liquid handling steps is highly recommended. After the robotic liquid transfer, manual recovery of residual volumes may be required to avoid sample loss and reduce variabilities.
Significance with respect to existing methods
This protocol describes a semi-automated mass spectrometry-based sample preparation method using the low-cost and open-source OT-2 liquid handling robot. Very recently, other works have also begun to use OT-2 toward proteomics applications11. Compared to existing methods, distinguishing features of this protocol include the use of a relatively low-cost, Python-programmable robot; the incorporation of semi-automated SP3 beads in two steps in the sample preparation protocols, namely, the protein sample detergent removal step and the peptide desalting/clean-up step; as well as the availability of open-source Python scripts to support further development. SP3 Paramagnetic beads bind proteins and peptides efficiently and have been coupled with automated liquid handling systems toward applications in protein clean-up/detergent removal before enzymatic digestion in MS sample preparation11,13.
Three open-source Python scripts are provided along with this protocol to researchers. The scripts are customizable for individual experimental conditions (e.g., sample number, replicate number, incubation temperature and time, etc.) and allow further development for modified workflows. The protocols afforded an excellent 3%-6% technical CVs in the number of peptides and/or protein identification between MS runs in our laboratory, comparable with previous work on other liquid handling systems (<20%)9,11.
Future applications
This protocol demonstrates the utility of a low-cost programmable liquid handling system in conjunction with SP3 beads for semi-automated proteomics sample preparation, which can be potentially applicable to mass spectrometry laboratories and core facilities to improve the efficiency of sample processing.
The authors have nothing to disclose.
This work was supported in part by NIH awards F32-HL149191 to YH; R00-HL144829 to EL; R21-HL150456, R00-HL127302, R01-HL141278 to MPL. Figure 1, Figure 2, Figure 3 are created with the aid of a web-based science illustration tool, BioRender.com.
300 µL pipette tips | Opentrons | ||
4-in-1 tube rack set | Opentrons | Each set includes 2 base stands and 4 tube holder tops 1.5mL, 2mL, 15mL + 50mL, 15mL, and 50mL. We use 2mL and 15 mL + 50 mL tops in this study. | |
Acclaim PepMap 100 C18 HPLC Column | Thermo Scientific | #164568 | 3 μm particle; 100 Å pore; 75 μm x 150 mm |
Acetonitrile LC-MS grade | VWR | #JT9829 | |
Aluminum block set | Opentrons | This block set includes 3 tops that are compatible with 96-well, 2.0 mL tubes and a PCR strip to use with the OT-2 temperature module. We use the 2.0mL tube holder in this manuscript. | |
Ammonium Bicarbonate | Sigma-Aldrich | # A6141 | |
Bovine Serum Albumin Standard, 2 mg/mL | Thermo Scientific | #23210 | |
Dimethylsulfoxide (DMSO) LC-MS grade | Thermo Scientific | #85190 | |
Dithiothreitol | Sigma-Aldrich | #D5545 | |
EASY-Spray HPLC Columns | Thermo Scientific | #ES800A | |
EasynLC 1200 Nano LC | Thermo Scientific | #LC140 | |
Ethanol Proof 195-200 | Fisher | #04-355-720 | |
Formic Acid LC-MS grade | Thermo Scientific | #85178 | |
Human heart lysate | Novus Biologicals | NB820-59217 | |
Iodoacetamide | Sigma-Aldrich | #I1149 | |
Magnetic tube rack | Thermo Scientific | #MR02 | |
MAXQuant v.1.6.10.43 | Tyanova et al., 2016 (https://www.maxquant.org/) | ||
mySPIN 6 Mini Centrifuge | Thermo Scientific | #75004061 | benchtop mini centrifuge for quick spin |
NEST 2 mL 96-Well Deep Well Plate, V Bottom | Opentrons | ||
OT-2 magnetic module | Opentrons | GEN1 | |
OT-2 P300 single channel pipette | Opentrons | GEN1 | |
OT-2 P50 single channel pipette | Opentrons | GEN1 | |
OT-2 robot pipetting robot | Opentrons | OT-2 | |
OT-2 temperature module | Opentrons | GEN1 | |
Pierce Quantitative Colorimetric Peptide Assay | Thermo Scientific | #23275 | |
Protein LoBind tubes 2.0 mL | Eppendorf | #022431102 | |
Protein Sequence Database | UniProt/SwissProt | https://www.uniprot.org/uniprot/?query=proteome:UP000005640% 20reviewed:yes |
|
Sera-Mag SpeedBead Carboxylate-Modified Magnetic Particles, Hydrophobic | Cytiva | #65152105050250 | |
Sera-Mag SpeedBead Carboxylate-Modified Magnetic Particles, Hydrophylic | Cytiva | #45152105050250 | |
SpeedVac | Thermo Scientific | Vacuum evaporator | |
Thermo Q Exactive HF Mass Spectrometer | Thermo Scientific | #IQLAAEGAAPFALGMBFZ | |
Trypsin MS Grade | Thermo Scientific | #90057 | |
Water LC-MS grade | VWR | #BDH83645.400 |