A Vision-Guided Pipetting Robot for the Automated Preparation of Labeled N-Glycans

The GlycoWorks N-glycan analysis kit with RapiFluor-MS (RFMS) (Figure 1) labelling provides sensitive MS detection. Compared to reductive animation labelling methods, for example 2-AB, it can also provide significant time savings.

Structure of RFMS.

Figure 1: Structure of RFMS.

Automation of this kit, using an automated pipetting robot, promises further productivity gains for any laboratory setting. As demonstrated here, automation of the GlycoWorks with RFMS kit provides a reliable, robust and easy solution to released N-glycan analysis.

The N-glycan labelling protocol was automated using an Andrew Alliance pipetting robot (Figure 2). Currently, this robotic platform is limited by an inability to move reaction plates or tubes, while the RFMS protocol requires the different temperatures for protein denaturation and de-N-glycosylation with PNGase F.

The workbench requirements of this automation protocol. Unique features include a heating/cooling feature, SPE cleanup component, released N-glycan kit component and vacuum manifold. Each block of the automation kit is modular allowing for adaptation to any protocol.

Figure 2: The workbench requirements of this automation protocol. Unique features include a heating/cooling feature, SPE cleanup component, released N-glycan kit component and vacuum manifold. Each block of the automation kit is modular allowing for adaptation to any protocol.

A custom built component that allows the heating and cooling steps to be automated was therefore constructed. Consequently, the heating and cooling steps for de-N-glycosylation and denaturation needed to be optimized. To establish the optimal temperature ranges, several different protein samples, each with unique characteristics, were used. This optimization, and the measurements of reliability of the automated process will be presented.

Methods

Protocol Optimization

The Andrew Lab software was used to automate each step in the GlycoWorks with RFMS kit (Figure 3). Steps were optimized for maximum time efficiency.

The top panel shows the GlycoWorks with RFMS workflow. The bottom panel shows the Andrew Lab protocol development soft-ware.

Figure 3: The top panel shows the GlycoWorks with RFMS workflow. The bottom panel shows the Andrew Lab protocol development soft-ware.

Comparison to Manual Procedure

In order to evaluate the reproducibility and accuracy of the robot, both manually and robot prepared samples were weighed following each step liquid transfer. Sample concentration normalization can be automated easily, and this was not included for evaluation as part of this study.

Temperature Optimization

Temperatures for de-N-glycosylation and denaturation were analyzed systematically in order to achieve the most robust N-glycan release ranges:

  • Denaturation temperatures between 60 oC and 90 oC were tested.
Gradient
Time
(min)
Flow Rate
(mL/min)
%A %B Curve
0 0.4 25 75 6
35 0.4 46 54 6
36.5 0.2 100 0 6
39.5 0.2 100 0 6
43.1 0.2 25 75 6
47.6 0.4 25 75 6
55 0.4 25 75 6

 

Column: ACQUITY UPLC Glycan BEH Amide, 130 Å, 1.7 µm, 2.1 x 150 mm
Column Temperature: 60 °C
Sample Temperature: 10 °C
Mobile Phase A: 50 nM Ammonium Formate @ pH 4.4 in LC-MS-grade Water
Mobile Phase B: 100% LC-MS-grade Acetonitrile
FLR Conditions: EX 265 nm, EM 425 nm, Sampling @ 2 Hz

Results and Discussion

The liquid transfer capacity of the automated protocol versus the manual protocol was a main area of concern (Figure 4). As demonstrated by gravimetric results, the performance of the automated protocol is comparable to that of the manual protocol. The single deviation in the procedure was during the transfer for the fully labeled and quenched sample onto the SPE plate from the reaction tubes.

Reproducibility test comparing the robot’s ability to per-form the protocol versus a manual user performing the same proto-col side-by-side.

Figure 4: Reproducibility test comparing the robot’s ability to per-form the protocol versus a manual user performing the same proto-col side-by-side.

A manual user can ensure the complete transfer of the sample by manipulating the reaction tube. However, the robot cannot. Therefore, to improve the quantitative transfer, an additional step was added. In this step, acetonitrile is added to the reaction tube to dilute the sample that remains  (~ 10 µL). This is then added to the SPE plate.

Each protein chosen for the temperature optimization tests provided unique characteristics and analytical challenges (Figure 5). Fetuin is made of highly sialylated glycans, RNase B contains high mannose structures, Murine IgG1 is a control mAb with common N-glycosylation and cetuximab is a complex mAb with a unique N-glycan site on the Fab domain.

Several labeled N-glycans were chosen to monitor the percent area count of each protein in relation to the others (Figure 5). A change in these relative areas represents incomplete de-glycosylation and/or denaturation for that specific temperature test.

Representative chromatograms for each of the 4 mAbs tested with monitored N-glycans labeled.

Figure 5: Representative chromatograms for each of the 4 mAbs tested with monitored N-glycans labeled.

Response surfaces are used to visualize the highest total recovery of labeled N-glycans over all temperature tests (Figure 6). These show that the optimal temperatures for denaturation and de-glycosylation (PNGase F has an active temperature at around 50 oC1) are 75 oC – 80 oC and 55 oC – 60 oC, respectively. Due to the gradual temperature changes of the Peltier block, these temperatures vary from the manual protocol.

Surface plots showing the optimal denaturation and de-glycosylation temperatures for each mAb tested. The optimal range is from 75 °C-80 °C for denaturation and from 50 °C-60 °C for de-glycosylation.

Surface plots showing the optimal denaturation and de-glycosylation temperatures for each mAb tested. The optimal range is from 75 °C-80 °C for denaturation and from 50 °C-60 °C for de-glycosylation.

Figure 6: Surface plots showing the optimal denaturation and de-glycosylation temperatures for each mAb tested. The optimal range is from 75 °C-80 °C for denaturation and from 50 °C-60 °C for de-glycosylation.

Another key factor is the minimization of the time between N-glycan release and labeling. This is important because under these conditions, the N-glycosylamine converts spontaneously to a reducing-end glycan. Overall, the relative areas were highly stable over most temperature tests. This indicates robust de-glycosylation and denaturation (Figure 7). In all cases, deviation was low. This indicates that, once the optimal temperature ranges are found, the quality of results will not be impacted by minor temperature variations.

% areas of each glycan used to monitor relative area during tempera-ture optimization of the procedure. Legend is the format denaturation temperature (°C)/de-glycosylation temperature (°C).

Figure 7: % areas of each glycan used to monitor relative area during tempera-ture optimization of the procedure. Legend is the format denaturation temperature (°C)/de-glycosylation temperature (°C).

Conclusion

  • The GlycoWorks with RapiFluor-MS N-glycan rapid labeling procedure can be successfully and reproducibly performed by the Andrew Alliance automated pipetting robot.
  • Relative areas of released N-glycan remain constant the optimal de-glycosylation and denaturation temperatures.
  • The optimal de-glycosylation temperature range is between 55 oC – 60 oC, and this takes between 5 - 12 minutes to heat and cool from room temperature.
  • The optimal denaturation temperature ranges from 75 oC – 80 oC, which takes between 20 - 24 minutes to heat and cool from room temperature.
  • Although automating the protocol increases preparation time approximately 2-fold, it frees the analyst from the arduous task of pipetting. This effectively allows them to perform two jobs simultaneously.

Future Directions

Optimization and adaptation of this automated protocol is ongoing. The intent is to offer an alternative version for QC laboratories, and to further reduce the need for user interventions throughout the protocol. These improvements include:

  1. A quality control process is in development which aims to remove small volume pipetting steps. This will reduce the number of times that the robot has to adjust the pipetting volumes during the protocol and so minimize sample preparation time.
  2. An automate vacuum pump control will allow the robot to turn on the pump during SPE prep, sample clean-up and elution steps. At the moment, these steps require user input.
  3. For those steps that still need user actions, email alerts will soon be available to notify the user that they are required.

References

[1] New England BioLabs. www.neb.com/products/p0710-rapid-pngase-f#tabselect0

About Andrew Alliance S.A.

Andrew Alliance is an independent, privately financed company, based in Geneva, Boston and Paris. The company was created in March 2011.

Andrew Alliance is dedicated to advance science by working with scientists to create a new class of easy-to-use robots and connected devices that take repeatability, performance, and efficiency of laboratory experiments to the level required by 21st-century biology.

Start with meeting customer needs, end with customer feedback.

Andrew Alliance delivers solutions that are focused on customer needs, both today and in the future. Our products are manufactured to the highest standards, using a range of carefully selected, proven, and sustainable technologies, that ensure both high performance and reliability. We actively seek continuous customer feedback, in order to guarantee the best possible design outcomes.


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Last updated: Jul 22, 2019 at 8:01 AM

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