Automating MALDI Sample Plate Loading Tomas Andersson,†,§ Mattias Johansson,†,§ Gunnar Bolmsjo1 ,† and Peter James*,‡ Divisions of Robotics and Protein Technology, Lund University, Lund, Sweden Received July 20, 2006
Abstract: We describe the design and implementation of a generic robotic solution to automate the loading of MALDI sample plates into a mass spectrometer. The soft- and hardware aspects are described together with the various safety issues that need to be addressed. The automation increases thoughput by a factor of between 5- and 80fold. Keywords: Automation • data acquisition • high throughput • robotics
Introduction Recently, a lot of attention has been focused on the use of proteomics as a clinical tool to aid in diagnostics and prognostics (1). The demands on proteomics techniques in a clinical setting are quite different from those in the academic laboratory. Ease of use and reproducibility are major demands as is throughput. The approach we have decided to take is to develop a “high” throughput academic environment to define protein markers for various diseases and states thereof that can then be used to define content for antibody-based protein arrays for use in a clinical setting. The proteins should be expressed at a fairly high level since we wish to be able to detect their leakage out into the blood using the protein chips. After initial evaluations, we decided that the approach best suited to this type of study is 2D gel electrophoresis. One smallscale preliminary study has involved profiling of 80 histologically and pathologically well-defined ovarian tumors. Using the DIGE (2) approach, we ran each sample in duplicate with a control generating 240 gel images. We developed a robot handling station to automate gel image acquisition to eliminate this bottleneck. After image analysis, the gels can be placed in a robotic gel handling station, the Spot Handling Workstation from Amersham, which allows 12 gels to be loaded in a batch and 1500 spots to be cut, destained, digested, and spotted onto a MALDI target in a 24-h period. The sixteen 96-sample position MALDI plates must then be manually loaded one by one into the Waters M@LDI TOF mass spectrometer for unattended protein fingerprinting which takes ca. 4 days. This has become the major bottleneck in our system. The remaining unidentified proteins can be scheduled for automated MS/MS using an * To whom correspondence should be addressed: Dr. Peter James, Protein Technology, BMC D13, Lund University, SE-221 84 Lund, Sweden; Email:
[email protected]; Fax: +46 46222 1496. † Division of Robotics. ‡ Division of Protein Technology. § These authors contributed equally to the study.
894
Journal of Proteome Research 2007, 6, 894-896
Published on Web 12/20/2006
Advion Nanomate robot interfaced to a Waters QTOF Ultima. This allows 300 samples to be analyzed (1500 MS/MS) in an 8-h period. We therefore decided to design and construct a generic MALDI plate loader and exchanger with a sample holding area and appropriate software to allow unattended operation of the instrument in a fully automated mode.
Workflow Definition The first stage in automating a process is to define a preliminary workflow. The number of MALDI plates to be analyzed per batch was set at 20 per day (corresponding to 2000 digests). The logistics involved the following: plate storage during a batch, how to move the plates, simulation of the robot’s movements, manufacture of the parts required, assembly of the work cell, and finally the definition of the communications between the various devices and the programming of the environment and interface. The simulation of the robot’s movements (also called offline programming) was done using UltraArc, a program developed by Delmia (http:// www.delmia.com) for arc welding. The idea is that it is easier to work without the presence of physical devices and that changes can be easily made. The need for simulation was mainly for the design of the robot cell layout and the workflow therein as well as the robot motion planning.
Hardware Implementation After we selected the three robots that were most suitable for this application, we started to perform computer simulations. The simulation is necessary to find which robots are capable of carrying out the task. The simulation is especially important for the five-axis robots because they cannot reach every point in the area in every orientation. An important point to consider was the angle of the inlet housing of 2° where the MALDI plates are loaded into the mass spectrometer. This angle can be a problem for a five-axis robot. The 2° inclination in the inlet housing of the Waters MALDI makes it difficult to pick up and place in plates from a vertical rack. To solve this problem, we could develop a low rack that stretches out horizontally, allowing the robot to pick the plates from vertically above. This however requires a large amount of bench space for storage and does not allow for later expansion of the system. An alternative would to use a second automatic plate handler that moves the plate holder into the same space for pickup. However, there is a big disadvantage to this solution, due to the complexity of such a device and the high price. Instead, we tested the idea to reorient the plate on a re-grip station. With this solution we would have a vertical 10.1021/pr0603607 CCC: $37.00
2007 American Chemical Society
technical notes
Andersson et al.
effectively if holding the plate horizontally (Figure 1c). The only real disadvantage is the slightly longer time it takes to load the MS, but this is almost negligible in relation to the time it takes to analyze all the samples on one plate. After evaluation of the robots commercially available, we chose the ThermoCRS: Catalyst 5. The main reason is one can develop programs in Microsoft Visual Basic or in other programming languages for Microsoft Windows, with Active Robot on a PC independent of the robot. This makes it easy to develop applications that are easy for the handling personnel to operate. It also is going to make it easier to modify and move the program if it is going to be used on another robot in the future. It also has the benefit of it being easier to test the communication with the serial cable that is connected with the MALDI.
Software Implementation The initial program development was done with Microsoft Visual Basic with robot control from Active Robot. This was chosen since this is the most usual method and is integrated into the Active Robot software from CRS. (i) Code Layout. The code was split into independent modules. The relationships between the various code modules are shown schematically in Figure 2. . A graphical user interface was written in Microsoft Visual Basic to allow the user to change the basic variables easily and without danger of changing important safety settings. The second module is the serial communication between the robot and instrument. Finally, the robot controller is the part that controls all of the communication with the robot. It also handles unexpected breaks and allows the robot to restart from the same position as it stopped without recalibration. The robot controller is also divided into two subparts with parent-child relations: the controller that is executing the commands sent from the RS232 and the GUI. Additionally, the RobotMove subpart is a library of movement commands for the robot controller to use.
MALDI Loading
Figure 1. Computer animation shots of the design. (a) Robot picking up plate with horizontal grip from a vertical rack. (b) Robot changing from horizontal to vertical plate holding in the re-grip station. (c) Robot transferring plate to MALDI after regripping to allow the plate to be held vertically.
rack that would be relatively easy to make and that allows one to expand with further vertical racks later. The gripper picks up the vertically stacked plates using a horizontal side grip (Figure 1a). Since this is a five-axis robot, the plate must be placed onto a re-gripping station to allow the gripper to reposition itself to pick up the plate from vertically above (Figure 1b). This allows the gripper to place the plate in the tilted plate holder of the MALDI, which it could not do
We developed a solution based on the use of a metal plate storage rack. In future versions a plastic casting would be much quicker and cheaper to develop, but for prototyping the metal version is much more flexible. The initial design takes 30 plates but this can be expanded linearly simply by adding more racks horizontally or vertically. The only other consideration was the stability of the tables supporting the robot and the MALDI instrument. These have to be vibration-free, and although independent supports could be used, the tables had to be fixed together to ensure constant distances and to prevent any variation caused by the movements of the robot. Prior to the development of this automatic loader, we could process one 96-sample plate in approximately 1 h, including spectral accumulation, internal calibration, peak picking, and automated database searching. The plate had to be manually replaced every hour, limiting throughput to an average of 6 plates a day. Now with the automation, this has been increased to 24 plates per day, a 4-fold increase. This is even more important now considering the 5 year old instrument is equipped with a 10 Hz laser and now most manufacturers have moved to 200 Hz lasers. We would thus increase the throughput of a new instrument by 80-fold. We have run comparisons between manually loaded and robot-loaded plates on our instrument and have observed no difference in mass accuracy of the spectra in over 1000 samples. This is in part due to the Journal of Proteome Research • Vol. 6, No. 2, 2007 895
technical notes
Automating MALDI Plate Loading
Figure 2. Schematic diagram showing the relationship between the code modules.
laser alignment system employed in the Waters design that automatically corrects for misplacement of the plate. Now other manufacturers have compensatory mechanisms to allow for plate movement.
Safety Considerations Safety in and around a robot cell is an important issue. It is important to have a well-visualized safety system but even more important is to create a safety system that does not disturb production or deteriorate the quality of the work. The robot movements may seem irrational to untrained persons, which makes it hard to predict the next step for the robot. To avoid injuries, it is important to keep unauthorized people out of range of the robot and to train the user to give him or her a good comprehension and knowledge of the system. The instrumentation is located in a converted dark room with the computer terminal outside. Emergency safety power-off buttons are mounted both inside and outside the room, which is kept locked during operation of the robot.
Conclusions We have developed a simple, cheap, and generic solution to a very common proteomics laboratory problem. The robot used cost $22,000 and all the machining and parts required to construct the station cost a further $1000. An extra computer is not required since the software can be installed on the computer controlling the mass spectrometer. This is about onefifth to one-tenth the price of a commercial system. All the plans and software necessary to build this system as well as a
896
Journal of Proteome Research • Vol. 6, No. 2, 2007
user guide are available from the authors on request. A longer description of the development of the system is available upon request from Peter James. This setup can be easily modified to fit most of the popular MALDI MS systems currently on the market. The robotics system described above, using the same positioning of the sample hotel, re-gripping station, and robot arm can be used to load an ABI 4800 or a ThermoFinnigan vMALDI LTQ with minor changes needed only in the robot trajectories when approaching the instrument loading bays. This takes only a few days work to optimize. Abbreviations: DIGE, differential in gel electrophoresis; MALDI, matrix-assisted laser desorption and ionization.
Acknowledgment. The present investigation was supported by a grant from the Swedish Research Council (Vetenskapsrådet, Natur- och teknikvetenskap), as well as from the SSF CREATE Health center and by funding from Waters Corporation. A complete description of the programming and construction is available at http://www.robotics.lu.se. References (1) Krieg, R. C.; Paweletz, C. P.; Liotta, L. A.; Petricoin, E. F. Clinical proteomics for cancer biomarker discovery and therapeutic targeting. Technol. Cancer Res. Treat. 2002, 1, 263-272. (2) Unlu, M.; Morgan, M. E.; Minden, J. S. Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis 1997, 18, 2071-2077. (3) Quadroni, M.; James, P. Proteomics and automation. Electrophoresis 1999, 20, 664-677.
PR0603607
