Modular automated bottom-up proteomic sample preparation for high-throughput applications.

Manual proteomic sample preparation methods limit sample throughput and often lead to poor data quality when thousands of samples must be analyzed. Automated liquid handler systems are increasingly used to overcome these issues for many of the sample preparation steps. Here, we detail a step-by-step protocol to prepare samples for bottom-up proteomic analysis for Gram-negative bacterial and fungal cells. The full modular protocol consists of three optimized protocols to: (A) lyse Gram-negative bacteria and fungal cells; (B) quantify the amount of protein extracted; and (C) normalize the amount of protein and set up tryptic digestion. These protocols have been developed to facilitate rapid, low variance sample preparation of hundreds of samples, be easily implemented on widely-available Beckman-Coulter Biomek automated liquid handlers, and allow flexibility for future protocol development. By using this workflow 50 micrograms of protein from 96 samples can be prepared for tryptic digestion in under an hour. We validate these protocols by analyzing 47 Pseudomonas putida and Rhodosporidium toruloides samples and show that this modular workflow provides robust, reproducible proteomic samples for high-throughput applications. The expected results from these protocols are 94 peptide samples from Gram-negative bacterial and fungal cells prepared for bottom-up quantitative proteomic analysis without the need for desalting column cleanup and with protein relative quantity variance (CV%) below 15%.

Introduction

Proteomic sample preparation protocols consist of many liquid transfer steps that are well suited for automation with liquid handler systems. As the number of proteomic samples for biotechnological and clinical applications increases, automated solutions will be required to minimize human error, save time and resources, and improve the data quality. There have been a number of automated sample preparation protocols developed for both mammalian and bacterial cells that reduce processing time, variability, and overall cost [1-12]. Most of these methods automate the sample cleanup and tryptic digestion portions of the workflow whereas a few automate the entire workflow from cell lysis to digestion [5, 6, 11]. These automation methods show significant improvement in variability and time-savings over manual sample preparation methods. Additionally, high-quality, low variance results can be achieved by researchers without extensive experience in proteomic sample preparation. While automation methods for the full workflow are powerful and convenient they are not as flexible, consequently, when proteomic research projects incorporate new organisms, different amounts of cells, or other variations the entire automated process must be modified. The three protocols described here separate the steps of the fully automated protocol described in Chen et al. [6] to enable flexibility for changing research directions and needs. The modular protocols are much simpler to operate, enable flexible methods development, and process samples in half the time (<1 hour) of the fully-automated protocol due to manual intervention at various steps, such as centrifugation and protein resuspension. Furthermore, the modular automation protocols offer greater flexibility and adaptability without highly-specialized liquid handler systems.

These protocols detail three optimized step-by-step methods to: (A) lyse Gram-negative bacteria and fungal cells; (B) quantify the amount of protein extracted; and (C) normalize the amount of protein and set up tryptic digestion. Importantly, samples prepared through these protocols do not include salts that must be removed prior to LC-MS analysis, thus minimizing sample handling and the associated variance. These protocols have been developed to facilitate rapid, low variance sample preparation of hundreds of samples, be easily implemented on widely-available Beckman-Coulter Biomek automated liquid handlers that use disposable pipet tips, and allow flexibility for future protocol development. By using this modular workflow 96 samples can be prepared for tryptic digestion in under an hour. The tryptic digestion step can be optimized for the given application with many high-throughput digestion protocols such as microwave, elevated temperature, and ultrasonic methods [13, 14] or traditional overnight digestion.

Materials and methods

The protocol described in this peer-reviewed article is published on protocols.io (dx.doi.org/10.17504/protocols.io.b3gxqjxn) and is included for printing as S1 File with this article.” The individual protocols are published on protocols.io (Cell lysis: dx.doi.org/10.17504/protocols.io.b3gsqjwe, Protein quantification: dx.doi.org/10.17504/protocols.io.b3grqjv6, Protein normalization: dx.doi.org/10.17504/protocols.io.b3gtqjwn) and are included for printing as S2–S4 Files with this article.

Expected results

The modular bottom-up proteomic sample preparation automation protocol (S1 File) is composed of three protocols that detail: (A) cell lysis, protein extraction, protein precipitation; (B) protein quantification; and (C) protein normalization and tryptic digestion. Using the chloroform-methanol protein extraction protocol (S2 File) described, we obtained median amounts of over 115 μg and 50 μg of protein from one OD*mLs (~1 x 109 cells) of P. putida and two OD*mLs R. toruloides, respectively (Fig 1). To demonstrate the inter-day variability of the protocol, a single overnight cell culture of P. putida and R. toruloides was harvested and distribute into two 96 deep well plates and the protocol was repeated on two separate days (Day 1 and Day 7) to demonstrate the reproducibility of the method. The protocol takes 20 minutes to process one 96-well plate, including centrifugation steps. The amount of protein scaled with the starting amount of biomass which provides flexibility for the desired application. This amount of protein is sufficient for typical nano- and standard-flow LC-MS data acquisition methods and can easily be adjusted for applications requiring larger amounts of protein. The upper limit on the amount of biomass that can be processed with this protocol is limited by the amount of chloroform and methanol that can be added to the PCR plate (~125 μL). For applications that require larger amounts of protein, such as multi-dimensional chromatography, the protocol can easily be adapted to extractions in 96 deep-well plates with more chloroform-methanol. The protocol can also be scaled down to lower cell amounts, but the amount of protein extracted becomes increasingly variable as the amount of cells decreases, so increasing the number of replicates would be advisable. Sample types other than microbial cell pellets, such as tissues and complex biofluids, haven’t been tested with this protocol and may need additional preparation steps. Proteins resulting from these samples however are readily suitable for the following two protocols in the workflow.

Fig 1

Violin plots with data points showing the total protein extracted by using the modular automated protocol on P. putida and R. toruloides from different amounts of biomass (n = 47).

D1 and D7 samples correspond to repeat analysis of a single culture of each organism seven days apart to demonstrate the inter-day variability of the protocol.

The protein quantification protocol (S3 File) takes 15 minutes and produces concentration data for two replicates of the samples in a 96-well plate by using the DC protein assay (Bio-Rad), a modified Lowry protein quantification method [15]. The protocol uses a total of 3 μL of each sample and requires aliquoting known concentrations of a BSA standard in a separate plate for calibration curve generation. Duplicate protein quantification was chosen based on previous experience as a balance between sample consumption and accurate concentration measurement. When protein samples are processed by the protein extraction protocol above, the concentration measured by this method falls within a calibration range of 0.125 to 2 μg/μL. For larger or smaller amounts of cells, the concentration may fall outside the calibration range described here, consequently, the dilution factor may need to be adjusted. Once the concentrations of the samples are known the third protocol (S4 File) described here is used to normalize the amount of protein for tryptic digestion and subsequent LC-MS analysis. This protocol takes 20 minutes on the Biomek NX-S8 liquid handler system because the concentration of each well must be adjusted individually. Trypsin, iodoacetic acid, and tris(2-carboxyethyl)phosphine (TCEP) are then added via the Biomek NX-S8 or multi-channel pipette. These protocols are being used for proteomic analysis of metabolically engineered bacteria and fungi. The expected quantitative proteomic results from samples prepared by the modular protocol is demonstrated in Figs 2 and 3 by using an Agilent 1290 UHPLC coupled to a Thermo Orbitrap Exploris 480 system operating in data-dependent acquisition (DDA) mode [16]. The LC-MS/MS method (15 minute total run time) identified over 900 proteins (>6000 peptides) from 14 μg load of P. putida protein digest and over 1000 proteins (>4500 peptides) from 10 μg load of R. toruloides protein digest. To demonstrate the inter-day variability of the protocol, a single overnight cell culture of P. putida and R. toruloides was harvested and distribute into two 96 deep well plates at a total cell amount of 1 OD* mL and 2 OD*mL per well, respectively, and subsequently processed via the modular automation workflow on different days. We used the MS1 ion intensity method with Skyline [17] to quantify over 900 proteins from P. putida and over 1000 proteins from R. toruloides samples. The median protein variance for the samples were between 18 and 22% CV on two separate days from automated sample preparation protocol (Figs 2 and 3). The high-throughput modular automated protocol enables one researcher to prepare thousands of bottom-up proteomic samples per week. Supporting publications and other organisms are under development.

Fig 2

Reproducibility of the modular automated sample preparation workflow as measured by label-free LC-MS/MS shotgun proteomics analysis.

(A) Violin plots showing the coefficient of variation of MS1 ion intensity quantification for over 900 and 1000 proteins from P. putida and R. toruloides, respectively (n = 47). The violin plots display the kernel density estimation of the CV and inside each violin plot is a box plot summarizing ranges (IQR, whiskers, outlier points) and individual medians (solid lines). The LCMS analysis raw data have been deposited to the ProteomeXchange Consortium data depository at http://www.proteomexchange.org/. They are publicly accessible with the dataset identifier PXD029122 and 10.6019/PXD029122.

Fig 3

Scatter plot display of the CV% for each protein (y-axis) vs the mean MS1 ion intensity detected for each protein (x-axis).

Modular automated bottom-up proteomic sample preparation for high-throughput applications.

Also available on protocols.io.

(PDF)

Click here for additional data file.

Automated chloroform-methanol protein extraction on the Biomek-FX liquid handler system.

Also available on protocols.io.

(PDF)

Click here for additional data file.

Automated protein quantification with the Biomek-FX liquid handler system.

Also available on protocols.io.

(PDF)

Click here for additional data file.

Automated protein normalization and tryptic digestion on a Biomek-NX liquid handler system.

Also available on protocols.io.

(PDF)

Click here for additional data file.

29 Dec 2021

13 Jan 2022

Response to Reviewer Comments:

Reviewer #1: The authors present an optimized workflow to prepare samples for bottom-up proteomics using automated liquid handling devices. The expected result is less than 15% cv of relative protein abundance.

We thank the reviewer for your comments and suggestions. We have responded to your comments below and edited the corresponding text in our manuscript and protocols accordingly.

Specific comments:

1. Data availability statement needs to be edited to reflect accurately where the data has been made available.

Response: We clarified the text describing where to access the LCMS data in the Data Availability Statement at the end of the manuscript and included it in the caption for Figure 2. The text reads, “The LCMS analysis raw data have been deposited to the ProteomeXchange Consortium data depository at http://www.proteomexchange.org/. They are publicly accessible with the dataset identifier PXD029122 and 10.6019/PXD029122.”

2. The authors suggest the method to be a widely applicable proteomics sample preparation workflow, and demonstrate the utility with gram-negative and fungal cell culture. However, many proteomics applications utilize tissue and complex biofluids (serum, lavage fluid, etc) that will likely require significant additional sample preparation steps. The authors should comment on the broader applicability of the method, or revise language to describe suitable sample types

Response: We clarified the sample types that were used to test the protein extraction protocol and commented on the broader applicability of the protocols established in the workflow in the expected result section. We added text to the Abstract that now reads (new text in bold), “Here, we detail a step-by-step protocol to prepare samples for bottom-up proteomic analysis for Gram-negative bacterial and fungal cells.”

In the Expected Results section, the text now reads, “Sample types other than microbial cell pellets, such as tissues and complex biofluids, haven’t been tested with this protocol and may need additional preparation steps. Proteins resulting from these samples however are readily suitable for the following two protocols in the workflow.”

3. The document(s) should be carefully proofread to eliminate typographical errors, specifically with respect to subscripts in chemical formulas.

Response: Thanks for your comment. We went through all documents and corrected any typo errors we could find.

Reviewer #2: The manuscript by Chen et al. describes a protocol for “automated” sample processing of fungal and bacterial cells for proteomic analysis. Specifically, the authors report 3 separate protocols that are carried out in a commonly used Beckman liquid handler - protein extraction from cells, normalization of protein content, and then trypsin digestion. There is some value in the described approach that could be of interest to a targeted audience; however, there are several issues that need to be addressed prior to publication consideration:

Response: We appreciate your detailed review comments and suggestions for improving our manuscript. We have responded to your comments below and edited the corresponding texts in our manuscript and protocols accordingly.

1) The reported protocol seems to be an extension of a previously published research article from their lab, which is within the guidelines of this type of article. However, the authors need to further detail the processing/automation that was mentioned in the first paper but also distinguish any differences. The protocol itself could be more detailed.

Response: Our manuscript describes a collection of three detailed automation procedures that modularized the full automated proteomic sample preparation process we published previously. The fully automated method requires customized scripts that are difficult to adapt at other labs and requires a highly specialized hybrid liquid handling system with an integrated centrifuge and plate reader which further complicates adoption of the protocol by others. The strengths of the protocols described here are their robustness and application on commonly available liquid handling systems and non-integrated lab equipment. We clarified the text to emphasize the differences between the two methods.

The text now reads, “While automation methods for the full workflow are powerful and convenient, they are not as flexible, consequently, when proteomic research projects incorporate new organisms, different amounts of cells, or other variations the entire automated process must be modified. The three protocols described here separate the steps of the fully automated workflow described in Chen et al. [6] to enable flexibility for changing research directions and needs. The modular protocols are much simpler to operate, enable flexible methods development, and process samples in half the time (<1 hour) of the fully-automated protocol due to manual intervention at various steps, such as centrifugation and protein resuspension. Furthermore, the modular automation protocols offer greater flexibility and adaptability without highly-specialized liquid handler systems. ”

2) The authors state that this protocol was very similar to the previously published work but that the updated version was more flexible since it could “operated independently”. This point should be described more clearly and highlighted further in the protocol section if applicable.

Response: Thanks for your suggestion. We have added sentences in the introduction section to clarify the “operated independently” point and highlight its advantages in comparison to the fully automated procedure.

The text now reads, “The modular protocols are much simpler to operate, enable flexible methods development, and process samples in half the time (<1 hour) of the fully-automated protocol due to manual intervention at various steps, such as centrifugation and protein resuspension. Furthermore, the modular automation protocols offer greater flexibility and adaptability without highly-specialized liquid handler systems.”

3) Some points in the “expected results” don’t match up to the online protocol. For example, the online protocol instructs use of 5ul of sample for protein assay; however, in the results, authors state it uses 3ul of each sample.

Response: Thanks for pointing out the error. 5 ul was used in the proof-of-concept protocol version, and later was updated to 3 ul in the method. We updated the protocol to be consistent with the statement in the manuscript. In addition, we moved the Note containing additional information about the dilution to a more prominent place in the Step.

4) Description and/or assessment of lysis/lysis efficiency aren’t clear. The main concern is that there might not be adequate representation of the entire proteome (e.g., membrane proteins or other specific classes) or perhaps variability in proteome composition from sample to sample. The authors in the previous paper use MRM to assess specific targets and in this reported protocol show low-coverage, spectral counting data. I understand the potential time savings and benefit of automation, but the authors should expand on limitation of the approach in this regard if applicable.

Response: The Chloroform-methanol extraction of proteins is a well established method for protein extraction that disrupts lipid membrane and precipitate proteins very effectively without biases to certain classes of proteins. Likewise, the metabolite, protein, and lipid extraction (MPLEx) protocol, has been proven to be robust and applicable to a diverse set of sample types, including cell cultures, microbial communities, and tissues (DOI:https://doi.org/10.1128/mSystems.00043-16). It has proven useful for multi-omics profiling of microbial pathogens (DOI: 10.1039/c6an02486f). Chloroform-methanol extraction method was evaluated to be very effective for the study of membrane proteins of non-model plants (DOI:10.1007/s00425-010-1121-1). We also demonstrated the effectiveness of the chloroform-methanol method in over 50 publications, including using it to accurately quantify membrane electron transport chain proteins in E. coli (DOI: 10.1126/science.aat7925). Furthermore, here, we identified over 900 proteins from 14 μg load of P. putida protein digest and over 1000 proteins from 10 μg load of R. toruloides protein digest with a rapid 15 minutes total LC-MS/MS method, which represent proteins in a wide range of categories including multi-pass integral membrane proteins, cytosolic soluble proteins, lipoproteins, and others. As such, we don’t believe that this is a limitation of the method.

5) The original paper addresses getting the protein precipitate back in solution by performing “80 cycles of pipetting mixing at the maximum allowable aspiration and dispensing speed” but this is not addressed at all in newer protocol. Based on the extraction procedure used, this seems like a critical point that is important for subsequent processing steps.

Response: The extensive resuspension process in the fully-automated paper was one of the primary issues with process time and reproducibility that we overcame by switching to the modular protocols. We address this issue in the text by adding, “The modular protocols are much simpler to operate, enable flexible methods development, and process samples in half the time (<1 hour) of the fully-automated protocol due to manual intervention at various steps, such as centrifugation and protein resuspension.”

We highlight this aspect in the protein extraction protocol, step 22 states that the user could define mixing times (an adjustable variable) in the method to mix protein resuspension on deck. The variable mixing time provides users great flexibility to balance method time and efficient protein suspension. We also noted in the same step that users could implement manual pipetting after visual inspection of the resuspension to ensure that no visible protein aggregates in the samples.

6) There is concern of cross contamination between samples and reagents based on the deep well reagent reservoir used (there doesn’t seem to be any separation/ wells in the reservoir). Are there any washes (e.g., needle washes) in between steps?

Response: The BioMek liquid handling system uses disposable tips instead of fixed tips. New tips were used to address contamination concerns. We added the bold text, shown below, to convey that point.

“”These protocols have been developed to facilitate rapid, low variance sample preparation of hundreds of samples, be easily implemented on widely-available Beckman-Coulter Biomek automated liquid handlers that use disposable pipet tips, and allow flexibility for future protocol development.

7) More specifics should be added in the step that describes mixing on deck “with user defined times”. The total step duration to add Methanol shown is 3 minutes – is this mixing only or include other aspects of this overall step?

Response: We specified the mixing step with the number of mixing cycles we used in the demonstration experiments, and clarified that the number of mixing cycles is a variable that users could define themselves as needed. The time shown at each step is an estimated duration of all the components of a step. Specifically, the 3 minutes in your commented step includes tip box moving, tip loading, liquid transfer, and mixing.

8) The authors state “In case of large protein pellets, mixing with a multichannel pipette may be necessary”. Is there a particular range you can provide to help the reader assess when that would be required? Related to this, while eliminating most manual pipetting, there are still some manual steps at critical points preventing this from being fully automated.

Response: We noted in the step that users need to visually inspect samples after method finishes in case chunks of protein may present in samples of large protein pellets. Our established modular proteomic sample preparation protocols are semi-automatic.

9) The authors should explain what day 1 and day 7 represent in Figure 1 (how long the organisms were left to grow before collection or days between processing?).

Response: We added text to explain how samples were collected for day 1 and day 7 batches. The text now reads, “To demonstrate the inter-day variability of the protocol, a single overnight cell culture of P. putida and R. toruloides was harvested and distribute into two 96 deep well plates and the protocol was repeated on two separate days (Day 1 and Day 7) to demonstrate the reproducibility of the method.”

We also added “D1 and D7 samples correspond to repeat analysis of a single culture of each organism seven days apart to demonstrate the inter-day variability of the protocol.” to the Figure 1 caption.

10) In Figure 2, the authors state UHPLC-MRM; however, they describe spectral counting as their quant approach. I believe MRM is copied over from the original paper where they used the MRM approach.

Response: Thanks for pointing out the error. We have changed the analysis method to “label-free LC-MS/MS shotgun proteomics analysis”

11) Although the mass spectrometry analysis is not necessarily part of the processing protocol, it was used to assess the protocol in terms of protein ID and quant (CV measurement across proteome). A short gradient was used, but the instrument employed should be able to identify more proteins than reported (especially if the protein loading amount on-column reported is correct). A more appropriate quant approach could be used (rather than spectral counting) to determine CVs across the dataset, which could address reproducibility in protein representation across the proteome if the LC-MS system is set up for precise LFQ-based quant. Measurement of the (high-abundance) top few hundred proteins with spectral counting is not really that informative.

Response: We agree with the reviewer that the Exploris 480 Orbitrap instrument can identify more proteins if longer gradients or nano-flow chromatography are used, but the purpose of the analysis was to demonstrate the use of the protocol for high-throughput applications. As such, the proteome coverage of about 1000 proteins in a microbial sample using a 15 minutes total DDA shotgun method at a standard flow LC-MS system is expected. Users could choose nano-LC systems, longer LC gradients, and DIA workflows to achieve deeper proteome coverage, however, those choices are dependent on the proteomics applications of interest and are separate from the sample preparation protocols detailed here. We chose to increase the number of replicates instead of the depth of proteome coverage to show the power of the sample preparation method for our application. Our data shows high reproducibility for a large number of replicates indicating that this protocol is useful for quantitative proteomic applications.

Regarding the quantification method, spectral count-based label free quantification (LFQ) is a well-accepted approach that has been applied widely and is discussed in a review by Bantscheff et al (DOI:10.1007/s00216-012-6203-4). And, the Scaffold software used to process the label free quantification is also benchmarked in the paper published in Journal proteomic research (DOI:10.1021/acs.jproteome.6b00645). We agree with the reviewer that other LFQ methods are available and each has its own advantages and disadvantages to be considered. To address the reviewer’s point, we analyzed the data by using the MS1 ion intensity LFQ method in Skyline and observed CVs comparable to that of the spectral count analysis for the data that we use for demonstrating the protocol (updated figures 2 and 3 in the manuscript). We quantified more proteins and still observed a median CV of ~20% for the samples. As such, we replaced Figures 2 and 3 with the results of the MS1 ion intensity quantification analysis. Our data shows that the sample preparation protocols detailed in the manuscript produce data with coefficients of variation that are consistent with good analytical results.

Submitted filename: Response to Reviewers.docx

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11 Feb 2022

Modular automated bottom-up proteomic sample preparation for high-throughput applications

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17 Feb 2022

PONE-D-21-32984R1

Modular automated bottom-up proteomic sample preparation for high-throughput applications

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