MS Imaging
AMASS: algorithm for MSI analysis by semi-supervised segmentation
Mass Spectrometric Imaging (MSI) is a molecular imaging technique that allows the generation of 2D ion density maps for a large complement of the active molecules present in cells and sectioned tissues. Automatic segmentation of such maps according to patterns of co-expression of individual molecules can be used for discovery of novel molecular signatures (molecules that are specifically expressed in particular spatial regions). However, current segmentation techniques are biased toward the discovery of higher abundance molecules and large segments; they allow limited opportunity for user interaction, and validation is usually performed by similarity to known anatomical features. We describe here a novel method, AMASS (Algorithm for MSI Analysis by Semi-supervised Segmentation). AMASS relies on the discriminating power of a molecular signal instead of its intensity as a key feature, uses an internal consistency measure for validation, and allows significant user interaction and supervision as options. An automated segmentation of entire leech embryo data images resulted in segmentation domains congruent with many known organs, including heart, CNS ganglia, nephridia, nephridiopores, and lateral and ventral regions, each with a distinct molecular signature. Likewise, segmentation of a rat brain MSI slice data set yielded known brain features and provided interesting examples of co-expression between distinct brain regions. AMASS represents a new approach for the discovery of peptide masses with distinct spatial features of expression.
Bruand, JPR 2013
Automated Querying and Identification of Novel Peptides using MALDI Mass Spectrometric Imaging
MSI is a molecular imaging technique that allows for the generation of topographic 2D maps for various endogenous and some exogenous molecules without prior specification of the molecule. In this paper, we start with the premise that a region of interest (ROI) is given to us based on preselected morphological criteria. Given an ROI, we develop a pipeline, first to determine mass values with distinct expression signatures, localized to the ROI, and second to identify the peptides corresponding to these mass values. To identify spatially differentiated masses, we implement a statistic that allows us to estimate, for each spectral peak, the probability that it is over- or under-expressed within the ROI versus outside. To identify peptides corresponding to these masses, we apply LC−MS/MS to fragment endogenous (nonprotease digested) peptides. A novel pipeline based on constructing sequence tags de novo from both original and decharged spectra and a subsequent database search is used to identify peptides. As the MSI signal and the identified peptide are only related by a single mass value, we isolate the corresponding transcript and perform a second validation via in situ hybridization of the transcript. We tested our approach, MSI-Query, on a number of ROIs in the medicinal leech, Hirudo medicinalis, including the central nervous system (CNS). The Hirudo CNS is capable of regenerating itself after injury, thus forming an important model system for neuropeptide identification. The pipeline helps identify a number of novel peptides. Specifically, we identify a gene that we name HmIF4, which is a member of the intermediate filament family involved in neural development and a second novel, uncharacterized peptide. A third peptide, derived from the histone H2B, is also identified, in agreement with the previously suggested role of histone H2B in axon targeting.
Bruand, JPR 2011