Systematic evaluation of repeatability of IR-MALDESI-MS and normalization strategies for correcting the analytical variation and improving image quality
Mass spectrometry imaging is a powerful tool widely used in biological, clinical, and forensic research, but its often poor repeatability limits its application for quantitative and large-scale analysis. A systematic evaluation of infrared matrix-assisted laser desorption electrospray ionization mass spectrometry (IR-MALDESI-MS) repeatability in absolute ion abundances during short- and long-term experiments was carried out on liver slices from the same rat with minimal biological variability to be expected. Results of median %RSDs ranging from 14 to 45, pooled %RMADs ranging from 11 to 33, and Pearson correlation coefficients ranging from 0.83 to 1.00 demonstrated an acceptable repeatability of IR-MALDESI-MS. Normalization is commonly applied for the purpose of accounting for analytical variability of spectra generated from different runs so as to reveal real biological differences. Nine data normalization strategies were performed on the rat liver data sets to examine their effects on reducing analytical variation, and further on a hen ovary data set containing more morphological features for the investigation of their impact on ion images. Results demonstrated that the majority of normalization approaches benefit data quality to some extent, and local normalization methods significantly outperform their global counterparts, resulting in a reduction of median %RSD up to 22. Local median normalization was found to be promisingly robust for both homogeneous and heterogeneous samples.
KeywordsIR-MALDESI Mass spectrometry imaging Repeatability Normalization
All mass spectrometry measurements were carried out in the Molecular Education, Technology, and Research Innovation Center (METRIC) at NC State University. The authors gratefully acknowledge the financial support received from the National Institutes of Health (R01GM087964) and North Carolina State University.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
Use of research animals
This study utilized tissues sourced from animals managed in accordance with the Institute for Laboratory Animal Research Guide. All husbandry practices were approved by North Carolina State University Institutional Animal Care and Use Committee (IACUC).
- 4.Sampson JS, Hawkridge AM, Muddiman DC. Generation and detection of multiply-charged peptides and proteins by matrix-assisted laser desorption electrospray ionization (MALDESI) Fourier transform ion cyclotron resonance mass spectrometry. J Am Soc Mass Spectrom. 2006;17:1712–6.CrossRefPubMedGoogle Scholar
- 6.Rosen EP, Thompson CG, Bokhart MT, Prince HMA, Sykes C, Muddiman DC, et al. Analysis of antiretrovirals in single hair strands for evaluation of drug adherence with infrared-matrix-assisted laser desorption electrospray ionization mass spectrometry imaging. Anal Chem. 2016;88:1336–44.CrossRefPubMedGoogle Scholar
- 7.Nazari M, Bokhart MT, Muddiman DC. Whole-body mass spectrometry imaging by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI). J Vis Exp. 2016;109:e53942.Google Scholar
- 12.Semmes OJ, Feng Z, Adam BL, Banez LL, Bigbee WL, Campos D, et al. Evaluation of serum protein profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry for the detection of prostate cancer: I. Assessment of platform reproducibility. Clin Chem. 2005;51:102–12.CrossRefPubMedGoogle Scholar
- 20.Nazari M, Bokhart MT, Loziuk PL, Muddiman D. Quantitative mass spectrometry imaging of glutathione in healthy and cancerous hen ovarian tissue sections by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI). Analyst. 2018;143:654–61.CrossRefPubMedPubMedCentralGoogle Scholar
- 34.Abbassi-Ghadi N, Jones EA, Veselkov KA, Huang J, Kumar S, Strittmatter N, et al. Repeatability and reproducibility of desorption electrospray ionization-mass spectrometry (DESI-MS) for the imaging analysis of human cancer tissue: a gateway for clinical applications. Anal Methods. 2015;7:71–80.CrossRefGoogle Scholar