Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping
Authors: Richard M Caprioli
Junhai Yang
Jeffrey Spraggins
Raf Van de Plas
Journal: Nature Methods (2015)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: predictive imaging modality; imaging mass spectrometry; microscopy; molecular maps; chemical information
Affiliations: 1] Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA. [2] Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA. [3] Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands., 1] Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA. [2] Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA., 1] Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA. [2] Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA. [3] Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA. [4] Department of Pharmacology, Vanderbilt University, Nashville, Tennessee, USA. [5] Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA.

Abstract:  We describe a predictive imaging modality created by 'fusing' two distinct technologies: imaging mass spectrometry (IMS) and microscopy. IMS-generated molecular maps, rich in chemical information but having coarse spatial resolution, are combined with optical microscopy maps, which have relatively low chemical specificity but high spatial information. The resulting images combine the advantages of both technologies, enabling prediction of a molecular distribution both at high spatial resolution and with high chemical specificity. Multivariate regression is used to model variables in one technology, using variables from the other technology. We demonstrate the potential of image fusion through several applications: (i) 'sharpening' of IMS images, which uses microscopy measurements to predict ion distributions at a spatial resolution that exceeds that of measured ion images by ten times or more; (ii) prediction of ion distributions in tissue areas that were not measured by IMS; and (iii) enrichment of biological signals and attenuation of instrumental artifacts, revealing insights not easily extracted from either microscopy or IMS individually. 

Retracted: No

PubPeer comments:
 In this article, Van De Plas et al. develop a method to extrapolate high spatial resolution MS signal from the combination of MALDI-MSI and optical images (H E stained tissue). The presented method assumes that the correlation between the MSI signal and the H E pixel intensity is sufficient for the extrapolation. No demonstration of why H E signal should correlate with molecular signatures is given, and also the generality of the method is questionable. Defects in the H E introduce artefacts in the high-resolution MSI data. In Fig. S4a, a clear artefact is introduced between the left and right hemispheres that was completely absent in the original data: ![file] The method used to evaluate the quality of the extrapolation is questionable. The authors reduce the high-resolution to the original resolution and then compare with the original data. In this process, all the errors due to the extrapolation are averaged out, determining an unfaithful quantification of the real error. The extrapolation of the MSI data in unseen regions is questionable. This is again based on the assumption of a correlation between the H E and the MSI data. This assumption is not guaranteed in regions with unseen MSI data. , 
 Same problem in Fig. S14 ![file] ,