Omic studies traditionally focused on analyzing metabolites from homogenized samples, resulting in the loss of morphological information. However, new techniques like mass spectrometry imaging (MSI)1, useful for proteomics and metabolomics studies, and spatial transcriptomics2 (ST) have emerged, allowing for the acquisition of images while retaining morphological information. MSI generates an image containing a mass spectrum in each pixel whereas ST allows for the identification of gene expression information and its location at the image. These techniques provide both structural information that characterizes and identifies the compounds or genes found on the surface of the samples, and morphological information that represents where these compounds are located. The latest developments involve the fusion of multiomic images, which can be achieved by generating transcriptomic, proteomic, and/or metabolomic images from consecutive tissue sections3. Targeted studies analyze pre-defined molecules, while untargeted methods consider the entire dataset, using multivariate analysis to reveal most of the biological information and generate new hypotheses.

Peer reviewed