HippoGenes Toolbox: Transcriptomic and Cellular Mapping of the Human Hippocampus
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M4 (Mezzanine Level)
Poster No:
701
Submission Type:
Abstract Submission
Authors:
Alexander Ngo1, Sara Larivière2, Jessica Royer1, Raúl Rodríguez-Cruces1, Alan Evans1, Andrea Bernasconi3, Neda Bernasconi1, Alexander Barnett3, Jacob Vogel4, Jordan DeKraker1, Boris Bernhardt1
Institutions:
1McGill University, Montreal, Quebec, 2Université de Sherbrooke, Sherbrooke, Quebec, 3Montreal Neurological Institute, Montreal, Quebec, 4Lund University, Lund, Sweden
First Author:
Co-Author(s):
Introduction:
The hippocampus is a unique cortical structure, key to understanding brain function and plasticity. Unravelling its complex organization requires the integration of multiscale data, linking molecular features to macroscale hierarchies. Gene and cell type expression are fundamental microscale phenotypes, and their profiling can provide a reference description of how microstructural features are distributed across the brain. Post-mortem samples, however, are often discontinuous and have limited spatial coverage, thus potentially overlooking fine-grained information. Here, we charted gene and cell type expression patterns within the hippocampus with unprecedented resolution.
Methods:
Allen Human Brain Atlas. We used the structural T1w magnetic resonance imaging (MRI) and microarray expression data of six deceased human donors (five males, mean ± SD age = 42.5 ± 13.4 years) from the Allen Human Brain Atlas-a brain-wide atlas comprised of bulk transcriptomic measures from over 20,000 genes sampled across 3,702 spatially distinct tissue samples (Hawrylycz, 2012).
Vertex-wise mapping of hippocampal gene expression and cell type prominence. Donor-specific hippocampal surfaces were generated from individual structural scans using HippUnfold-an automated pipeline for hippocampal unfolding, subfield segmentation, and surface-based hippocampal registration (DeKraker, 2022; DeKraker, 2023). In parallel, we preprocessed the microarray expression data through intensity-based filtering of microarray probes, selection of a representative probe for each gene across both hemispheres, normalization, and aggregation across donors (Arnatkeviciute, 2023). Tissues sampled within the hippocampus (n = 107) were mapped to hippocampal surfaces. We interpolated expression values across the hippocampus, weighted by the geodesic distance of a given vertex to its nearest sampled neighbour. Continuous donor-specific maps were averaged to generate a single expression map for each gene (Fig 1A). Cell type prominence was also derived from the principal transcriptomic component of a priori, cell-specific gene lists (Fig 2A; Ayhan, 2021).
Vertex-wise mapping of hippocampal gene expression and cell type prominence. Donor-specific hippocampal surfaces were generated from individual structural scans using HippUnfold-an automated pipeline for hippocampal unfolding, subfield segmentation, and surface-based hippocampal registration (DeKraker, 2022; DeKraker, 2023). In parallel, we preprocessed the microarray expression data through intensity-based filtering of microarray probes, selection of a representative probe for each gene across both hemispheres, normalization, and aggregation across donors (Arnatkeviciute, 2023). Tissues sampled within the hippocampus (n = 107) were mapped to hippocampal surfaces. We interpolated expression values across the hippocampus, weighted by the geodesic distance of a given vertex to its nearest sampled neighbour. Continuous donor-specific maps were averaged to generate a single expression map for each gene (Fig 1A). Cell type prominence was also derived from the principal transcriptomic component of a priori, cell-specific gene lists (Fig 2A; Ayhan, 2021).
·Figure 1 | Transcriptomic mapping of the hippocampus
·Figure 2 | Cell type characterization from gene expression and their spatial variation
Results:
We generated a vertex-wise atlas of hippocampal expression for 13,561 genes. Non-linear dimensionality reduction identified two main axes of transcriptome-wide variation, namely anterior-posterior (57% of variance explained) and medial-lateral (28%) gradients (Fig 1B). Translating gene expression to 21 cell type prominences, we found differential expression across all cells, with a majority of them following subfield divisions of the hippocampus (Fig 2B).
Conclusions:
Capitalizing on recent imaging-transcriptomic initiatives, we provide normative references for gene and cell expression in the human hippocampus. Gene expression patterns mainly recapitulate anterior-posterior distinctions, previously identified as main functional axes (Vogel, 2020), and subfield-related cell and microcircuit variation (Paquola, 2020). These atlases provide a bridge across different neural scales in the hippocampus, and thereby advance fundamental, cognitive, and clinical neurosciences.
Genetics:
Genetic Modeling and Analysis Methods
Transcriptomics 1
Neuroinformatics and Data Sharing:
Brain Atlases 2
Informatics Other
Keywords:
Cellular
Data analysis
Multivariate
Open Data
Open-Source Software
Other - Transcriptomics; Hippocampus
1|2Indicates the priority used for review
Abstract Information
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Ayhan, F. (2021). Resolving cellular and molecular diversity along the hippocampal anterior-to-posterior axis in humans. Neuron, 109(13), 2091–2105.e6.
DeKraker, J. (2022). Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold. eLife, 11, e77945.
DeKraker, J. (2023). Evaluation of surface-based hippocampal registration using ground-truth subfield definitions. eLife, 12, RP88404.
Hawrylycz, M. J. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416), 391–399.
Paquola, C. (2020). Convergence of cortical types and functional motifs in the human mesiotemporal lobe. eLife, 9, e60673.
Vogel, J. W. (2020). A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems. Nature communications. 11(1), 960