Extended Data Fig. 8: Metabolic heterogeneity within tumors as determined by MALDI-MSI.
From: A unique subset of glycolytic tumour-propagating cells drives squamous cell carcinoma
Extended Data Fig. 8 (related to Fig. 3). a, tSNE analysis of all the metabolites analyzed by MALDI-MSI in each sample. Rainbow spectrum represents how close each pixel to the others is within each sample. There was no input of histological information of tissue to perform this dimensionality reduction. Data are always reproducible every time the code was run. The key point for this stability is the random seed point in the t-SNE algorithm is set to zero and that maintained reproducibility. The t-SNE analysis was done on MATLAB 2018a that was installed on a workstation operating with Windows 10. b, H&E staining in the same section as MALDI-MSI. Images were taken at 40x and stitched with the software in Zeiss microscope. Scale bars indicate 1mm. c,e, MALDI-MSI data of glucose-6-phosphate and citrate in DMBA/TPA-treated tumors and adjacent skin Scale bars indicate 1mm. Data are from two biologically independent tumour samples, consisting of more than a thousand of pixel datapoints per sample. d,f, Quantification of MALDI-MSI signal intensity of each metabolite from CD34+ and CD34- areas. The non-parametric Wilcoxon rank-sum test (two-sided and 95% significance level) was used after checking normality distribution using Kolmogorov–Smirnov test. Details of the box plots are listed in the Source Data file 4. g, Co-registration images (the left two) of G-6-P and citrate on top of immunofluorescence stained image in tumor 1. Overlay image (the very right) of G-6-P and citrate with a correlation value of distribution between two metabolites. Scale bars indicate 300µm. Data are from two biologically independent tumour samples, consisting of more than hundred thousands of pixel data points per sample. h, tSNE analysis of CD34+ area in tumour 1. Data are always reproducible every time the code was run. The key point for this stability is the random seed point in the t-SNE algorithm is set to zero and that maintained reproducibility. The t-SNE analysis was done on MATLAB 2018a that was installed on a workstation operating with Windows 10.
