Nondestructive Spatial Lipidomics for Glioma Classification

Abstract

Mapping the molecular composition of tissues using spatial biology provides high-content information for molecular diagnostics. However, spatial biology approaches require invasive procedures to collect samples and destroy the investigated tissue, limiting the extent of analysis, particularly for highly functional tissues such as those of the brain. To address these limitations, we developed a workflow to harvest biomolecules from brain tissues using nanoneedles and characterise the distribution of lipids using desorption electrospray ionization mass spectrometry imaging. The nanoneedles preserved the original tissue while harvesting a reliable molecular profile and retaining the original lipid distribution for mouse and human brain samples, accurately outlining the morphology of key regions within the brain and tumour lesions. The deep neural network analysis of a cohort containing 23 human glioma biopsies showed that nanoneedle samples maintain the molecular signatures required to accurately classify disease state. Thus, nanoneedles provide a route for tissue-preserving spatial lipidomic and molecular diagnostics.

Multimodal bioimaging across disciplines and scales: challenges, opportunities and breaking down barriers

“…A key challenge in applying AI to the domain of multimodal bioimaging is the limited size of the datasets in this domain that hamper the potential of learning complex patterns in high dimensions from the (inaccessible) true distributions of these data. However, while the number of samples (e.g., patient cohort size) is limited, each sample contains a wealth of biologically relevant spatial information that enable effective application of AI, for example for single modality disease state classification, at the spatial scale of pixels 43 , cells 44 , or localized regions 45 . Pooling multiple localized regions where sample numbers are limited has already shown promise for analysis of multimodal bioimaging 35,[40][41][42] .…”