MSACL 2025 Abstract

Molecular Mapping of Amyloid Pathology Across Organs: Integration of Imaging Mass Spectrometry, Single Cell Spatial Transcriptomics, and Microscopy

Angela R. S. Kruse (1), Lukasz G. Migas (2), Roy Lardenoije (2), Claire F. Scott (1), Cody Marshall (1), Morad C. Malek (1), Adel Eskaros (3), Thai Pham (1), Madeline Colley (1), Kristie I. Aamodt (3), Melissa A. Farrow (1), Raf Van de Plas (2), Joana P. Gonçalves (2), Matthew Schrag (1), Alvin C. Powers (3,4), Jeffrey M. Spraggins (1)
(1) Vanderbilt University, Nashville, TN, (2) Delft University of Technology, Delft, Netherlands, (3) Vanderbilt University Medical Center, Nashville, TN, (4) Veteran Affairs Tennessee Valley Healthcare System, Nashville, TN

Angela Kruse, PhD (Presenter) Ohio State University

Presenter Bio: Angela Kruse is a Research Assistant Professor in the department of Cell and Developmental Biology and the Mass Spectrometry Research Center at Vanderbilt University. Her research integrates imaging mass spectrometry, proteomics, spatial transcriptomics, biochemistry, and microscopy to understand how diabetes affects the molecular environment in the pancreas, kidney, and eye. She received her Ph.D. in Plant Pathology with a focus in Biochemistry from Cornell University prior to conducting her postdoctoral studies under the guidance of Drs. Richard Caprioli and Jeff Spraggins at Vanderbilt University. She plans to spend her career applying and integrating cutting edge technologies to address important challenges in human health and the environment.

Relevant Financial Disclosures (within past 24 months, reported on Mar 13, 2026) No relevant financial relationship(s) to disclose.

INTRODUCTION:
Alzheimer’s disease (AD) is a neurodegenerative disorder often associated with amyloid deposits within the brain. Amyloid aggregation is also observed in other organs and disease systems. For example, amyloid plaques composed of islet amyloid polypeptide can be found within the pancreatic islet in type 2 diabetes (T2D). These individuals have an increased risk for AD, but the cellular and molecular commonalities between these diseases are not fully understood. Here, to compare the molecular environment of amyloid plaques in brain and pancreas, we integrate multiple imaging and molecular techniques. These include Xenium spatial transcriptomics, amyloid staining, highly multiplexed immunofluorescence, and matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS), enabling comprehensive characterization of AD brain and T2D pancreatic human tissues.

METHOD:
We employ a multimodal approach combining microscopy, IMS, and spatial transcriptomics to delineate spatial molecular changes in brain and pancreas tissue samples from donors with AD and T2D, respectively. First, Xenium spatial transcriptomics is used to spatially profile hundreds of genes in each sample. We then remove the Xenium flow cell from the sample and perform CODEX multiplexed immunofluorescence, followed by amyloid staining using thiazine red and thioflavin S for the brain and pancreas, respectively. On a serial section, we use MALDI IMS to measure lipid species at 10-µm pixel size with a timsTOF fleX mass spectrometer (Bruker Daltonics). Autofluorescence microscopy is collected before and after IMS, for image registration between the sections.

PRELIMINARY DATA:
By integrating microscopy, spatial transcriptomics, and MALDI IMS, we established a detailed molecular map of AD brain and T2D pancreas tissues at an unprecedented molecular and spatial scale. Using autofluorescence microscopy as a common modality, we co-registered each dataset using in-house developed software. Combining multiple modalities on a single tissue section allowed for cross-modality mining from the same cells. Xenium and CODEX data were first used to define the identity and spatial location of cells within each sample as binary masks. These cell-type masks were then used to mine average mass spectra from the IMS data. Lipids such as SHexCer 42:1;3O were detected in key cell types such as oligodendrocytes in the brain and alpha cells in the pancreas. Unsupervised clustering was used to establish spatial regions with unique lipid and transcriptional profiles. This allowed for the identification of transcripts and lipids with shared spatial localizations. For example, the gene TM4SF4 was spatially correlated with a ceramide-1-phosphate at m/z 642.48 and was localized to alpha cells based on CODEX data. Finally, masks corresponding to amyloid deposits were used to mine the IMS data for amyloid-associated lipid profiles. Interpretable machine learning and differential expression analysis were used to identify candidate lipid and gene biomarkers associated with amyloid. Notably, sphingolipids, including SM d34:1, were localized to amyloid plaques in both the brain and pancreas. Additionally, 16 genes, including SOX2 and SNCG, were differentially expressed in proximity to amyloid deposits in both organs. Current efforts include the use of CODEX microscopy to inform cellular boundaries, analysis of additional donor samples, and global analyses of gene-lipid relationships. This multimodal approach allowed us to discover potential biomarkers for amyloid pathology and presents a technical and analytical workflow that could be valuable when applied to other organs and disease systems.