Correlative and multimodal molecular imaging of biological tissues with high sensitivity and cellular spatial resolution

About This Grant

Spatially-resolved analysis of multiple classes of biomolecules in animal and human tissues is critically important to understanding the structure and function of different tissue types in health and disease. Despite considerable progress in spatial omics, the inherent complexity of this analytical task drives the development of novel technologies aimed at achieving high chemical specificity, sensitivity, and subcellular spatial resolution. Among these technologies, mass spectrometry imaging (MSI) has emerged as an ideal tool for label-free simultaneous mapping of multiple classes of biomolecules in biological systems with high specificity and sensitivity. Nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI MSI) is an ambient ionization technique that enables highly sensitive quantitative imaging of hundreds of biomolecules in biological tissues. This technique has achieved a spatial resolution approaching that of the traditional histology imaging, sub-femtomole sensitivity, and circumvents the need for specialized sample pretreatment. This project is focused on the development of a highly sensitive platform for correlative and multimodal imaging of biomolecules in biological tissues. This platform will provide cell-specific spatially-resolved molecular maps of biological systems with unprecedented specificity and cellular spatial resolution. This will be achieved by developing new strategies for enhancing the extraction and ionization efficiency of important low-abundance and poorly ionizable analytes and establishing robust experimental and computational approaches for correlative and multimodal imaging of tissues. The proof-of-concept studies demonstrating and validating the performance of this enabling technology will be performed by examining the inflammatory response in streptozotocin-induced mouse model of type 1 diabetes mellitus (T1DM). This model is of a broad interest to understanding T1DM and presents analytical challenges that will be addressed in the proposed research.