Biology subjects

Prentice, B. M.

Publications and source records attributed to Prentice, B. M..

3 recordsLinked to original sources

bioRxiv2024

Examination of Lipid Distributions in Hydrogel-Expanded Mouse Brain Tissue Using Imaging Mass Spectrometry

Imaging is an essential tool in biological research, and imaging mass spectrometry uniquely provides a label-free approach with high molecular specificity. However, imaging mass spectrometry is limited in spatial resolution, which in turn limits the biological structures and processes that can be studied at small dimensions. Custom lens setups and altered optical paths have been used to shrink the diameter of the incident laser beam probe in matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry to achieve high spatial resolutions (< 5 m). However, these research-grade instruments are complex and expensive, making high spatial resolution imaging experiments unrealistic for the broader community. An alternative method for improving spatial resolution is through physical magnification of the substrate, which has been well established in the subfield of expansion microscopy (ExM). ExM leverages superabsorbent hydrogels for isotropic expansion of tissues and retention of analytes containing fluorescent tags. While typical ExM involves covalently anchoring the analyte of interest to the hydrogel network, lipid retention without anchoring has been recently demonstrated for imaging mass spectrometry. Herein, we demonstrate expansion imaging mass spectrometry (ExIMS) of expanded, whole brain tissue and examine lipid distributions in both positive and negative ion mode across multiple brain structures. A linear expansion factor of 4.5-fold is achieved and used to obtain high spatial resolution images of mouse brain cerebellum. Approximately 95% of lipids in both positive and negative ion mode are retained in expanded tissue compared to unexpanded tissue. Additionally, the majority of lipid distributions across the brain are maintained post-expansion. Alterations to the hydrogel formulation (e.g., crosslinker density) can significantly affect the ability of ExIMS to maintain accurate lipid distributions in expanded tissue.

biochemistry↗

bioRxiv2024

A multi-modal image fusion workflow incorporating MALDI imaging mass spectrometry and microscopy for the study of small pharmaceutical compounds

Multi-modal imaging analyses of dosed tissue samples can provide more comprehensive insight into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multi-modal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry, but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity co-registration with higher resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high spatial resolution microscopy image. As a proof of concept, our multi-modal workflow was applied to brain tissue extracted from a Sprague Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models including linear regression, partial least squares regression (PLS), random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 m spatial resolutions, a significant improvement compared to the original images acquired at 25 m spatial resolution. The predicted mass spectrometry images were then co-registered with an H&E image and IHC fluorescence image of the - opioid receptor to assess co-localization of corynantheidine with brain cells. Our study also provides insight into the different evaluation parameters to consider when utilizing image fusion for biological applications.

biochemistry↗

bioRxiv2023

Spatial Metabolome Lipidome and Glycome from a Single brain Section

Metabolites, lipids, and glycans are fundamental biomolecules involved in complex biological systems. They are metabolically channeled through a myriad of pathways and molecular processes that define the physiology and pathology of an organism. Here, we present a blueprint for the simultaneous analysis of spatial metabolome, lipidome, and glycome from a single tissue section using mass spectrometry imaging. Complimenting an original experimental protocol, our workflow includes a computational framework called Spatial Augmented Multiomics Interface (Sami) that offers multiomics integration, high dimensionality clustering, spatial anatomical mapping with matched multiomics features, and metabolic pathway enrichment to providing unprecedented insights into the spatial distribution and interaction of these biomolecules in mammalian tissue biology.

systems biology↗