Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed t-SNE of Mass Spectrometry Imaging Data

Abstract

Mass spectrometry imaging (MSI), which quantifies the underlying chemistry with molecular spatial information in tissue, represents an emerging tool for the functional exploration of pathological progression. Unsupervised machine learning of MSI datasets usually gives an overall interpretation of the metabolic features derived from the abundant ions. However, the features related to the latent lesions are always concealed by the abundant ion features, which hinders precise delineation of the lesions. Herein, we report a data-driven MSI data segmentation approach for recognizing the hidden lesions in the heterogeneous tissue without prior knowledge, which utilizes one-step prediction for feature selection to generate function-specific segmentation maps of the tissue. The performance and robustness of this approach are demonstrated on the MSI datasets of the ischemic rat brain tissues and the human glioma tissue, both possessing different structural complexity and metabolic heterogeneity. Application of the approach to the MSI datasets of the ischemic rat brain tissues reveals the location of the ischemic penumbra, a hidden zone between the ischemic core and the healthy tissue, and instantly discovers the metabolic signatures related to the penumbra. In view of the precise demarcation of latent lesions and the screening of lesion-specific metabolic signatures in tissues, this approach has great potential for in-depth exploration of the metabolic organization of complex tissue.

Nanoprojectile Secondary Ion Mass Spectrometry Enables Multiplexed Analysis of Individual Hepatic Extracellular Vesicles

“…This is a powerful data analysis tool used in mass spectrometry to aid in biomarker discovery and data interpretation. 52,53 Figure S9 highlights that t-SNE analysis of multiple biomarkers detected by NP-SIMS (see Figure 3A) differentiates EVs harvested from normal hepatocytes and HepG2 cells.…”

“…The analysis of NP-SIMS data can be extended to encompass the abundance and colocalization of multiple biomarkers using multivariate analysis tools such as t-distributed stochastic neighbor embedding, t-SNE. This is a powerful data analysis tool used in mass spectrometry to aid in biomarker discovery and data interpretation. , Figure S9 highlights that t-SNE analysis of multiple biomarkers detected by NP-SIMS (see Figure A) differentiates EVs harvested from normal hepatocytes and HepG2 cells.…”

Nanoprojectile Secondary Ion Mass Spectrometry Enables Multiplexed Analysis of Individual Hepatic Extracellular Vesicles

“…This is a powerful data analysis tool used in mass spectrometry to aid in biomarker discovery and data interpretation. 48,49 Figure 3D highlights that t-SNE analysis of multiple biomarkers detected by NP-SIMS (see Figure 3A) differentiates EVs harvested from normal hepatocytes and HepG2 cells.…”

Evaluation of Inlet Temperature with Three Sprayer Designs for Desorption Electrospray Ionization Mass Spectrometry Tissue Analysis

“…Nonlinear dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), are increasingly common approaches for evaluating and visualizing molecular heterogeneity in MSI. t-SNE, as the more established method, has been shown to provide segmentation of metabolic features in several applied and preclinical studies. − UMAP has been shown to differentiate tumor and normal tissues in a mouse patient-derived xenograft (PDX) model with a glioblastoma (GBM) tumor . However, within MSI, there has been little to no examination of the use of these methods in the context of metrology or parameter space optimization studies.…”