Untangling Alzheimer’s disease with spatial multi-omics: a brief review

Marshall, Clayton B.; Farrow, Melissa A.; Spraggins, Jeffrey M.

doi:10.3389/fnagi.2023.1150512

Cited by 4 publications

(1 citation statement)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By then, it was the first database dedicated to managing public AD-related sc/snRNA-Seq data from human and mouse brain tissue. As the sequencing technology and scientific inquiry rapidly evolved, more studies are discerning the spatial information of differentially expressed genes (DEGs) associated with AD pathology, the interconnectedness of DEGs related to AD biomarkers, DEGs enriched in specific cell subtypes, cell-cell communications, and regional and cellular vulnerability in AD 4 . Spatial transcriptomics (ST) revolutionized our understanding of neurobiology and AD pathogenesis by enabling the visualization of gene expression patterns within their spatial context.…”

Section: Introductionmentioning

confidence: 99%

A Single-cell and Spatial RNA-seq Database for Alzheimer’s Disease (ssREAD)

Wang,

Acosta,

McNutt

et al. 2023

Preprint

View full text Add to dashboard Cite

Alzheimer’s Disease (AD) is a neurodegenerative malady predominantly affecting the elderly and exhibits its debilitating effects on a dementia-prone population. Recently, the advent of innovative technologies, such as single-cell and single-nucleus RNA-sequencing (scRNA-seq & snRNA-seq) and spatial transcriptomics (ST), has reformed our investigative approaches toward comprehending AD’s neuropathological intricacies and underpinning regulatory mechanisms, encompassing sub-cellular, cellular, and spatial dimensions. In light of the overwhelming proliferation of single-cell and ST data associated with AD, the imperative for a comprehensive, user-friendly database that addresses the scientific community’s analytical demands has never been more paramount. Introduced initially in 2020, scREAD presented itself as a pioneering repository that systematized publicly available scRNA-seq and snRNA-seq datasets derived from post-mortem human brain tissues and mouse models mirroring AD pathology. Here, we introduce ssREAD, a substantial upgrade over scREAD, enriching the platform with a broader spectrum of datasets, an optimized analytical pipeline, and enhanced usability and visibility. Specifically, ssREAD amalgamates an impressive portfolio of over 189 datasets extracted from 35 distinct AD-related scRNA-seq and snRNA-seq studies, encompassing a staggering 2,572,355 cells. In addition, we have diligently curated and archived 300 ST datasets, originating from 12 human and mouse brain studies, which include two focused on AD and ten control studies. Every dataset within our repository is meticulously annotated, bearing critical identifiers including species, gender, brain region, disease/control status, age, and AD stages. Besides the collection of above datasets in ssREAD, it delivers an exhaustive analysis suite offering cell clustering and annotation, inference of differentially expressed and spatially variable genes, identification of cell-type-specific marker genes and regulons, and spot deconvolution for integrative analysis of ST and scRNA-seq & snRNA-seq data from public domains. All these resources are freely accessible through a user-friendly, consolidated web portal available athttps://bmblx.bmi.osumc.edu/ssread/.

show abstract

Section: Introductionmentioning

confidence: 99%

A Single-cell and Spatial RNA-seq Database for Alzheimer’s Disease (ssREAD)

Wang,

Acosta,

McNutt

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section

Vandergrift,

Veličković,

Day

et al. 2024

Anal. Chem.

View full text Add to dashboard Cite

An increasing number of spatial multiomic workflows have recently been developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform the region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a new multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. This workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a poly(ethylene naphthalate)-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed the downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 μm × 200 μm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial-and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.

show abstract

A Streamlined Workflow for Microscopy-Driven MALDI Imaging Mass Spectrometry Data Collection

Esselman,

Ward,

Marshall

et al. 2024

J. Am. Soc. Mass Spectrom.

View full text Add to dashboard Cite

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a rapidly advancing technology for biomedical research. As spatial resolution increases, however, so do acquisition time, file size, and experimental cost, which increases the need to perform precise sampling of targeted tissue regions to optimize the biological information gleaned from an experiment and minimize wasted resources. The ability to define instrument measurement regions based on key tissue features and automatically sample these specific regions of interest (ROIs) addresses this challenge. Herein, we demonstrate a workflow using standard software that allows for direct sampling of microscopy-defined regions by MALDI IMS. Three case studies are included, highlighting different methods for defining features from common sample types�manual annotation of vasculature in human brain tissue, automated segmentation of renal functional tissue units across whole slide images using custom segmentation algorithms, and automated segmentation of dispersed HeLa cells using open-source software. Each case minimizes data acquisition from unnecessary sample regions and dramatically increases throughput while uncovering molecular heterogeneity within targeted ROIs. This workflow provides an approachable method for spatially targeted MALDI IMS driven by microscopy as part of multimodal molecular imaging studies.

show abstract

Untangling Alzheimer’s disease with spatial multi-omics: a brief review

Cited by 4 publications

References 94 publications

A Single-cell and Spatial RNA-seq Database for Alzheimer’s Disease (ssREAD)

A Single-cell and Spatial RNA-seq Database for Alzheimer’s Disease (ssREAD)

Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section

A Streamlined Workflow for Microscopy-Driven MALDI Imaging Mass Spectrometry Data Collection

Contact Info

Product

Resources

About