Name: shotgun proteomics sample processing automated by an open-source lab robot
Uploaded: 2021-10-28T14:00:00
Description: Watch this Scientific Journal Video about Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot at JoVE.com

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&#160;1, Figure 2, Figure 3 are created with the aid of a web-based science illustration tool, BioRender.com.

The developed Python scripts have been deposited on GitHub at: https://github.com/MaggieLam-Lab/StandardDigestion-Opentrons.&#160;A copy of the scripts is given in Supplementary File 1. Please refer to the GitHub repository for the latest versions.
1. Experimental preparations
<ol>
	<li>Check the required hardware before starting the protocol. 
	&#8203;NOTE: The following hardware components are required: OT-2 pipettes, pipette tips, 4-in-1 tube rack se...

<ol>
	<li>Geyer, P. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Revisiting+biomarker+discovery+by+plasma+proteomics.">Revisiting biomarker discovery by plasma proteomics.</a> Molecular Systems Biology. 13 (9), 942 (2017).</li><li>Coscia, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+streamlined+mass+spectrometry-based+proteomics+workflow+for+large-scale+FFPE+tissue+analysis.">A streamlined mass spectrometry-based proteomics workflow for large-scale FFPE tissue analysis.</a> The Journal of Pathology. 251 (1), 100-112 (2020).</li><li>Yeung, Y. -. G., Neives, E., Angeletti, R., Stanley, E. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Removal+of+detergents+from+protein+digests+for+mass+spectrometry+analysis.">Removal of detergents from protein digests for mass spectrometry analysis.</a> Analytical Biochemistry. 382 (2), 135-137 (2008).</li><li>Addona, T. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-site+assessment+of+the+precision+and+reproducibility+of+multiple+reaction+monitoring-based+measurements+of+proteins+in+plasma.">Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.</a> Nature Biotechnology. 27 (7), 633-641 (2009).</li><li>Lowenthal, M. S., Liang, Y., Phinney, K. W., Stein, S. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+bottom-up+proteomics+depends+on+digestion+conditions.">Quantitative bottom-up proteomics depends on digestion conditions.</a> Analytical Chemistry. 86 (1), 551-558 (2014).</li><li>Elliott, K. C., Resnik, D. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scientific+reproducibility,+human+error,+and+public+policy.">Scientific reproducibility, human error, and public policy.</a> Bioscience. 65 (1), 5-6 (2015).</li><li>Brown, A. W., Kaiser, K. A., Allison, D. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Issues+with+data+and+analyses:+Errors,+underlying+themes,+and+potential+solutions.">Issues with data and analyses: Errors, underlying themes, and potential solutions.</a> Proceedings of the National Academy of Sciences of the United States of America. 115 (11), 2563-2570 (2018).</li><li>van den Broek, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+multiplex+LC-MS/MS+assay+for+quantifying+serum+apolipoproteins+A-I,+B,+C-I,+C-II,+C-III,+and+E+with+qualitative+apolipoprotein+E+phenotypic.">Automated multiplex LC-MS/MS assay for quantifying serum apolipoproteins A-I, B, C-I, C-II, C-III, and E with qualitative apolipoprotein E phenotypic.</a> Clinical Chemistry. 62 (1), 188-197 (2016).</li><li>M&#252;ller, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+sample+preparation+with+SP3+for+low-input+clinical+proteomics.">Automated sample preparation with SP3 for low-input clinical proteomics.</a> Molecular Systems Biology. 16 (1), 9111 (2020).</li><li>Fu, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+reproducible+automated+proteomics+sample+preparation+workflow+for+quantitative+mass+spectrometry.">Highly reproducible automated proteomics sample preparation workflow for quantitative mass spectrometry.</a> Journal of Proteome Research. 17 (1), 420-428 (2018).</li><li>Liu, X., Gygi, S. P., Paulo, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+semiautomated+paramagnetic+bead-based+platform+for+isobaric+tag+sample+preparation.">A semiautomated paramagnetic bead-based platform for isobaric tag sample preparation.</a> Journal of the American Society for Mass Spectrometry. 32 (6), 1519-1529 (2021).</li><li>Poulsen, K. M., Pho, T., Champion, J. A., Payne, C. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automation+and+low-cost+proteomics+for+characterization+of+the+protein+corona:+experimental+methods+for+big+data.">Automation and low-cost proteomics for characterization of the protein corona: experimental methods for big data.</a> Analytical and Bioanalytical Chemistry. 412 (24), 6543-6551 (2020).</li><li>Liang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fully+automated+sample+processing+and+analysis+workflow+for+low-input+proteome+profiling.">Fully automated sample processing and analysis workflow for low-input proteome profiling.</a> Analytical Chemistry. 93 (3), 1658-1666 (2021).</li><li>. Web URL Available from: <a target="_blank" href="https://opentrons.com/ot-app/">https://opentrons.com/ot-app/</a> (2021)</li><li>. Web URL Available from: <a target="_blank" href="https://docs.opentrons.com/v2/">https://docs.opentrons.com/v2/</a> (2021)</li><li>. Web URL Available from: <a target="_blank" href="https://www.cytivalifesciences.com/en/us/solutions/genomics/knowledge-center/cleanup-for-mass-spectrometry">https://www.cytivalifesciences.com/en/us/solutions/genomics/knowledge-center/cleanup-for-mass-spectrometry</a> (2021)</li><li>. Web URL Available from: <a target="_blank" href="https://www.thermofisher.com/order/catalog/product/23275#/23275">https://www.thermofisher.com/order/catalog/product/23275#/23275</a> (2021)</li><li>Han, Y., Wright, J. M., Lau, E., Lam, M. P. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determining+alternative+protein+isoform+expression+using+RNA+sequencing+and+mass+spectrometry.">Determining alternative protein isoform expression using RNA sequencing and mass spectrometry.</a> STAR Protocols. 1 (3), 100138 (2020).</li></ol>

Critical steps within the protocol 
For the best performance, Opentrons-verified labware, modules, and consumables&#160;compatible with OT-2 should be used. Custom labware can be created following Opentrons&#39; 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&#39; manufacturer to prepare beads for peptide and protein clean-up. Notably, during the bead ...

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.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>300 &#181;L pipette tips</td>
			<td>Opentrons</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>4-in-1 tube rack set</td>
			<td>Opentrons</td>
			<td></td>
			<td>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.</td>
		</tr>
		<tr>
			<td>Acclaim PepMap 100 C18 HPLC Column</td>
			<td>Thermo Scientific</td>
			<td>#164568</td>
			<td>3 &#956;m particle; 100 &#197; pore; 75 &#956;m x 150 mm</td>
		</tr>
		<tr>
			<td>Acetonitrile LC-MS grade</td>
			<td>VWR</td>
			<td>#JT9829</td>
			<td></td>
		</tr>
		<tr>
			<td>Aluminum block set</td>
			<td>Opentrons</td>
			<td></td>
			<td>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.</td>
		</tr>
		<tr>
			<td>Ammonium Bicarbonate</td>
			<td>Sigma-Aldrich</td>
			<td># A6141</td>
			<td></td>
		</tr>
		<tr>
			<td>Bovine Serum Albumin Standard, 2 mg/mL</td>
			<td>Thermo Scientific</td>
			<td>#23210</td>
			<td></td>
		</tr>
		<tr>
			<td>Dimethylsulfoxide (DMSO) LC-MS grade</td>
			<td>Thermo Scientific</td>
			<td>#85190</td>
			<td></td>
		</tr>
		<tr>
			<td>Dithiothreitol</td>
			<td>Sigma-Aldrich</td>
			<td>#D5545</td>
			<td></td>
		</tr>
		<tr>
			<td>EASY-Spray HPLC Columns</td>
			<td>Thermo Scientific</td>
			<td>#ES800A</td>
			<td></td>
		</tr>
		<tr>
			<td>EasynLC 1200 Nano LC</td>
			<td>Thermo Scientific</td>
			<td>#LC140</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethanol Proof 195-200</td>
			<td>Fisher</td>
			<td>#04-355-720</td>
			<td></td>
		</tr>
		<tr>
			<td>Formic Acid LC-MS grade</td>
			<td>Thermo Scientific</td>
			<td>#85178</td>
			<td></td>
		</tr>
		<tr>
			<td>Human heart lysate</td>
			<td>Novus Biologicals</td>
			<td>NB820-59217</td>
			<td></td>
		</tr>
		<tr>
			<td>Iodoacetamide</td>
			<td>Sigma-Aldrich</td>
			<td>#I1149</td>
			<td></td>
		</tr>
		<tr>
			<td>Magnetic tube rack</td>
			<td>Thermo Scientific</td>
			<td>#MR02</td>
			<td></td>
		</tr>
		<tr>
			<td>MAXQuant v.1.6.10.43</td>
			<td></td>
			<td></td>
			<td>Tyanova et al., 2016 (https://www.maxquant.org/)</td>
		</tr>
		<tr>
			<td>mySPIN 6 Mini Centrifuge</td>
			<td>Thermo Scientific</td>
			<td>#75004061</td>
			<td>benchtop mini centrifuge for quick spin</td>
		</tr>
		<tr>
			<td>NEST 2 mL 96-Well Deep Well Plate, V Bottom</td>
			<td>Opentrons</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>OT-2 magnetic module</td>
			<td>Opentrons</td>
			<td></td>
			<td>GEN1</td>
		</tr>
		<tr>
			<td>OT-2 P300 single channel pipette</td>
			<td>Opentrons</td>
			<td></td>
			<td>GEN1</td>
		</tr>
		<tr>
			<td>OT-2 P50 single channel pipette</td>
			<td>Opentrons</td>
			<td></td>
			<td>GEN1</td>
		</tr>
		<tr>
			<td>OT-2 robot pipetting robot</td>
			<td>Opentrons</td>
			<td>OT-2</td>
			<td></td>
		</tr>
		<tr>
			<td>OT-2 temperature module</td>
			<td>Opentrons</td>
			<td></td>
			<td>GEN1</td>
		</tr>
		<tr>
			<td>Pierce Quantitative Colorimetric Peptide Assay</td>
			<td>Thermo Scientific</td>
			<td>#23275</td>
			<td></td>
		</tr>
		<tr>
			<td>Protein LoBind tubes 2.0 mL</td>
			<td>Eppendorf</td>
			<td>#022431102</td>
			<td></td>
		</tr>
		<tr>
			<td>Protein Sequence Database</td>
			<td>UniProt/SwissProt</td>
			<td></td>
			<td>https://www.uniprot.org/uniprot/?query=proteome:UP000005640% 
			20reviewed:yes</td>
		</tr>
		<tr>
			<td>Sera-Mag SpeedBead Carboxylate-Modified Magnetic Particles, Hydrophobic</td>
			<td>Cytiva</td>
			<td>#65152105050250</td>
			<td></td>
		</tr>
		<tr>
			<td>Sera-Mag SpeedBead Carboxylate-Modified Magnetic Particles, Hydrophylic</td>
			<td>Cytiva</td>
			<td>#45152105050250</td>
			<td></td>
		</tr>
		<tr>
			<td>SpeedVac</td>
			<td>Thermo Scientific</td>
			<td></td>
			<td>Vacuum evaporator</td>
		</tr>
		<tr>
			<td>Thermo Q Exactive HF Mass Spectrometer</td>
			<td>Thermo Scientific</td>
			<td>#IQLAAEGAAPFALGMBFZ</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypsin MS Grade</td>
			<td>Thermo Scientific</td>
			<td>#90057</td>
			<td></td>
		</tr>
		<tr>
			<td>Water LC-MS grade</td>
			<td>VWR</td>
			<td>#BDH83645.400</td>
			<td></td>
		</tr>
	</tbody>
</table>

shotgun proteomics sample processing automated by an open-source lab robot

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.

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 <stro...

Watch this Scientific Journal Video about Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot at JoVE.com

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot

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, ...

Research

JoVE Journal

Biochemistry

Automated Acoustic Dispensing for the Serial Dilution of Peptide Agonists in Potency Determination Assays

As with small molecule drug discovery, screening for peptide agonists requires the serial dilution of peptides to produce concentration-response curves. ...

LERLIC-MS/MS for In-depth Characterization and Quantification of Glutamine and Asparagine Deamidation in Shotgun Proteomics

Characterization of protein deamidation is imperative to decipher the role(s) and potentialities of this protein posttranslational modification (PTM) in ...

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

Number and brightness is a calibration-free fluorescence fluctuation spectroscopy (FFS) technique for detecting protein homo-oligomerization. It can be ...

Preparation of Chloroplast Sub-compartments from Arabidopsis for the Analysis of Protein Localization by Immunoblotting or Proteomics

Chloroplasts are major components of plant cells. Such plastids fulfill many crucial functions, such as assimilation of carbon, sulfur and nitrogen as ...

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ...

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

In order to visualize the relationship between bacterial phylogeny and specialized metabolite production of bacterial colonies growing on nutrient agar, ...

An All-in-one Sample Holder for Macromolecular X-ray Crystallography with Minimal Background Scattering

Macromolecular X-ray crystallography (MX) is the most prominent method to obtain high-resolution three-dimensional knowledge of biological macromolecules. ...

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Sample preparation for mass spectrometry analysis in proteomics requires enzymatic cleavage of proteins into a peptide mixture. This process involves ...

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view ...

Making Precise and Accurate Single-Molecule FRET Measurements using the Open-Source smfBox

The smfBox is a recently developed cost-effective, open-source instrument for single-molecule F&#246;rster Resonance Energy Transfer (smFRET), which makes ...