Response Surface Methodology As a New Approach for Finding Optimal MALDI Matrix Spraying Parameters for Mass Spectrometry Imaging

Journal Article · Thu Feb 06 00:00:00 UTC 2020 · Journal of the American Society for Mass Spectrometry

DOI: https://doi.org/10.1021/jasms.9b00074 · OSTI ID:1605416

Velickovic, Dusan ^[1]; Zhang, Guanshi ^[2]; Bezbradica, Dejan ^[3]; Bhattacharjee, Arunima ^[1]; Pasa Tolic, Ljiljana ^[1]; Sharma, Kumar ^[2]; Alexandrov, Theodore ^[4]; Anderton, Christopher R.  ^[5]

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1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Environmental Molecular Sciences Lab. (EMSL)
2. Univ. of Texas Health-San Antonio, TX (United States). Center for Renal Precision Medicine
3. Univ. of Belgrade (Serbia)
4. European Molecular Biology Lab., Heidelberg (Germany); Univ. of California, San Diego, La Jolla, CA (United States)
5. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Environmental Molecular Sciences Lab. (EMSL); Univ. of Texas Health-San Antonio, TX (United States). Center for Renal Precision Medicine

Automated spraying devices have become ubiquitous in laboratories employing matrixassisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), in part because they permit control of a number of matrix application parameters that can easily be reproduced for intra- and interlaboratory studies. Determining the optimal parameters for MALDI matrix application, such as temperature, flow rate, spraying velocity, number of spraying cycles, and solvent composition for matrix application, is critical for obtaining high-quality MALDI-MSI data. However, there are no established approaches for optimizing these multiple parameters simultaneously. Instead optimization is performed iteratively (i.e., one parameter at a time), which is time consuming and can lead to overall non-optimal settings. In this report, we demonstrate the use a novel experimental design and the response surface methodology to optimize five parameters of MALDI matrix application using a robotic sprayer. Thirtytwo combinations of MALDI matrix spraying conditions were tested, which allowed us to elucidate relationships between each of the application parameters as determined by MALDI-MS (specifically, using a 15 Tesla Fourier transform ion cyclotron resonance mass spectrometer). As such, we were able to determine the optimal automated spraying parameters that minimized signal de-localization and enabled high MALDI sensitivity. We envision this optimization strategy can be utilized for other MALDI-MSI applications, tissue types, and matrix application approaches.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States). Environmental Molecular Sciences Laboratory (EMSL)
• Sponsoring Organization: European Research Council (ERC); European Union’s Horizon 2020; National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK); National Institutes of Health (NIH); USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
• Contributing Organization: KPMP Consortium
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 1605416
• Report Number(s): PNNL-SA--147292
• Journal Information: Journal of the American Society for Mass Spectrometry, Journal Name: Journal of the American Society for Mass Spectrometry Journal Issue: 3 Vol. 31; ISSN 1044-0305
• Publisher: American Society for Mass SpectrometryCopyright Statement
• Country of Publication: United States
• Language: English