SfN2022

Transcript

1. Contact me: Neuropathological lesions are hypothesized to trigger molecular and cellular processes that disturb homeostasis in the local tissue microenvironment of individuals affected with neurodegenerative disorders. Alzheimer’s disease (AD) is a neurodegenerative disorder with senile plaques of β-amyloid (Aβ) and neurofibrillary alterations of phosphorylated tau (pTau). Accumulating evidence suggests that Aβ and pTau act in concert to orchestrate complex pathobiological events underlying AD, but their impact on the local microenvironment remains poorly understood. Here, we profiled spatial gene expression patterns surrounding local amyloid and pTau elements within the human inferior temporal cortex (ITC) of individuals with AD. We employed 10x Genomics Visium spatial transcriptomics coupled with immunofluorescence (Visium Spatial Proteogenomics; Visium-SPG) to generate an integrated proteomic and transcriptomic spatial map of the human ITC in AD. Visium-SPG pairs IF staining with on-slide cDNA synthesis across 5000 oligonucleotide-barcoded spots to recreate the spatial landscape of gene and protein expression in the same tissue section. For this study, the human ITC blocks were dissected from 3 brain donors diagnosed with late-onset AD (Amyloid C/Braak III) and 1 age-matched neurotypical adult donor. Cryosections from each block were collected in duplicate on Visium arrays and immuno-stained for Aβ and pTau Ser202/Thr205 to visualize amyloid plaques and neurofibrillary structures. Multispectral images were acquired, and libraries were prepared for whole transcriptome sequencing. Following segmentation of Aβ and pTau fluorescent signals, gene expression spots were annotated by pathological burden in the following categories: no pathology (none), Aβ+, Aβ-adjacent (next_Aβ), pTau+, pTau-adjacent (next_pTau), both Aβ & pTau, and both-adjacent (next_both). Within the gray matter region, we pseudo-bulked these spots by category for AD donors and performed differential gene expression analysis to identify transcriptional signatures associated with each pathological combination. Candidate differentially expressed genes were validated by IF-combined RNAscope in situ hybridization technology and characterized by gene ontology analysis to implicate their molecular functions in putative disease mechanisms and pathways. Finally, we are performing multi-marker analysis of genomic annotation (MAGMA) to identify which pathological domains harbored aggregated genome-wide association study (GWAS) risk for neurodegenerative diseases and dementia and further investigated spatial enrichment of genes associated with AD using publicly available bulk and single-nucleus RNA-seq datasets. Our novel approach can disentangle molecular dynamics and regional heterogeneity in gene expression while refining annotation of genes that are functionally linked to pathology-afflicted local microenvironments in the human brain. Astrogliosis Influence of Amyloid and pTau Pathology on Local Microenvironment Gene Expression in the Human Inferior Temporal Cortex in Alzheimer’s Disease ABSTRACT REFERENCES ACKNOWLEDGEMENTS GENERATING SPATIAL PROTEOGENOMIC DATA Funding for these studies was provided by the Lieber Institute for Brain Development. Visium-SPG data was acquired in collaboration with 10x Genomics. All the schematics were drawn with BioRender. We would like to gratefully acknowledge the families of the subjects whose donations made this research possible. The authors declare that they have no conflicts of interest. CONCLUSIONS B • We established Visium Spatial Proteogenomics (Visium-SPG) for integration of proteomic and transcriptomic data to study local gene-expression patterns with respect to pathohistological changes in Alzheimer’s Disease. • We identified significant differentially expressed genes in the vicinity of Aβ plaques and their surrounding areas with Visium-SPG, which were successfully cross-validated with RNAscope-immunofluorescence. • Gene set enrichment analysis between our Visium dataset and other publicly available scRNA-seq datasets may help better understand cellular phase of the local niches around Aβ plaques. • Unsupervised approach may better predict broad regional differences (e.g. cortical layers) than subtle changes in the local niches (e.g. dispersed Aβ plaques) LIEBER INSTITUTE for BRAIN DEVELOPMENT MALTZ RESEARCH LABORATORIES Sang Ho Kwon1,2, Madhavi Tippani1, Sowmya Parthiban3,4, Jashandeep S. Lobana1,5, Charles Bruce6, Stephen Williams6, Michelle Mak6, Guixia Yu6, Julianna Avalos-Gracia6, Thomas M. Hyde1,7,8, Stephanie C. Page1, Stephanie C. Hicks3, Keri Martinowich*1,2,7, Kristen R. Maynard*1,7, Leonardo Collado-Torres*1 PATHOLOGY CLASSIFICATION IN GRAY MATTER EXEMPLAR DIFFERENTIALLY EXPRESSED GENES SINGLE-CELL AD GENE SET ENRICHMENT RNASCOPE-BASED VALIDATION UNSUPERVISED IDENTIFICATION OF AD NICHES 1.Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA 2.Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA 3.Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA 7.Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA 8.Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA *:Equal contributors 4.Department of Biomedical engineering, Johns Hopkins Whiting School of Engineering, Baltimore, MD, 21202, USA 5.Department of Neuroscience, Johns Hopkins Krieger School of Arts and Sciences, Baltimore, MD, 21202, USA 6.10x Genomics, Pleasanton, CA, 94588, USA. METHODS FOR VISIUM-SPG PRINCIPAL COMPONENT ANALYSIS PSEUDO-BULKING STRATEGY BayesSpace k=28 1 mm 20 um 1 mm GFAP PLEKHA5 AHNAK DIO2 CNTNAP2 ITGB4 PREX2 FRMD4A LRRC7 PTPRG NR4A2 SST SYN1 TSPAN7 CHD5 RAB3A ST6GALNAC6 PER1 RTF1 STMN2 SLC38A2 MID1IP1 FKBP4 LDLRAD4 ATF4 RPS27 PPA1 Associated DEGs (with Grubman’s AD vs Control dataset) snRNA-seq analysis of Grubman et al 2019 reports differentially expressed genes (DEGs) between AD and control across cell types in the human entorhinal cortex. Grubman et al compared their dataset with that of Mathys et al 2019 and reported overlapping DEGs co-expressed in the human entorhinal cortex and dorsolateral prefrontal cortex (DLPFC). The list of the overlapping DEGs was used as our reference dataset for this gene set enrichment analysis and provided above to show concordant genes between our dataset and Grubman/Mathys’s overlapping datasets for each cell type. Up Down Aβ PPP3CA DAPI Inflammation Synaptic vesicles Neuronal growth Stress responses 50 um By sample • Maynard and Collado-Torres et al., “Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex,” Nat Neurosci 24, 425- 436, (2021). • Grubman et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation. Nat Neurosci 22, 2087–2097 (2019). • Mathys et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019). N/A N/A IF-based ground-truth AD pathology QUESTIONS? skwon17@jh.edu @sanghokwon17 Download this poster: (k=2) By pathology