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Comparative study of cross-modal cortical transcriptomics

Presented During:

Genetics: Transcriptomics, Phenotypes and Disorders

Wednesday, June 25, 2025: 5:45 PM - 7:00 PM

Brisbane Convention & Exhibition Centre

Room: M2 (Mezzanine Level)

Poster No:

703

Submission Type:

Abstract Submission

Authors:

Shangzheng Huang¹, Ang Li¹, Tongyu Zhang¹, Yingjie Peng¹, Changsheng Dong¹

Institutions:

¹State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

First Author:

Shangzheng Huang
State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences
Beijing, China

Co-Author(s):

Ang Li
State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences
Beijing, China

Tongyu Zhang
State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences
Beijing, China

Yingjie Peng
State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences
Beijing, China

Changsheng Dong
State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences
Beijing, China

Introduction:

The human cortical architecture exhibits complex functional divisions and organizational patterns, which are often altered in various psychiatric disorders, with these changes being closely regulated at the molecular level. Understanding the molecular mechanisms underlying cortical organizational patterns relies on transcriptomic data at the whole-cortex scale. Historically, the Allen Human Brain Atlas (AHBA) has been used to correlate different macroscopic networks, deviant patterns, and gene expression, providing insights into the molecular mechanisms. However, due to the limitations of microarray sequencing, while the AHBA data provides transcriptomic information at whole-cortex spatial resolution, it is constrained by sample size and lacks information on specific cell types. Recently, the release of human single-nucleus datasets has partly addressed these shortcomings, though due to sampling constraints, these datasets do not provide full cortical coverage across brain regions. Therefore, combining the strengths of both datasets can deepen our understanding of the complex molecular mechanisms underlying the brain cortex. A key question is whether data from different modalities can represent similar cortical transcriptional patterns. In this study, we analyzed the consistencies and differences between two modalities of data in representing cortical transcriptional patterns.

Methods:

For the single-nucleus data, we selected two subjects, H19.30.001 and H19.30.002, with minimal batch effects and a high overlap in the sampled brain regions. Preprocessing was performed separately for each subject. After preprocessing, the single-nucleus data were aggregated at the brain region level to obtain the average gene expression matrix for each subject. Next, using a digitized atlas that overlaps with the single-nucleus sampled brain regions, we extracted the regional average expression matrix from AHBA using the abagen toolkit. Since our focus was on the reproducibility of cortical expression patterns, subcortical regions were excluded. The similarity between the regional average expressions of the two modalities and and the cross-modal spatial expression consistency for each gene were evaluated using Pearson's correlation coefficient (r). We then selected the intersection of the top 20% cross-modal genes from both H19.30.001 and H19.30.002, totaling 2,172 genes, for Gene Ontology (GO) enrichment analysis.

Results:

We found significant consistency between the two datasets at the regional transcriptional level (Fig.1). Analysis of the spatial expression patterns of individual gene revealed that genes with consistent cross-modal spatial patterns exhibited stable expression across individuals (Fig.2 top). These genes were enriched in modules related to cellular connectivity, ion transmembrane transport, neuronal projection, and neuron communication, and were associated with various behavioral abnormalities and diseases (Fig.2 bottom).

Supporting Image: crossmodal-gene-FIG11.png

·Figure 1

Supporting Image: crossmodal-gene-FIG21.png

·Figure 2

Conclusions:

Our study reveals both the consistency and divergence of different modalities of data in cortical representation. At the brain region scale, the AHBA and single-nucleus datasets show overall consistent expression patterns. At the gene level, genes related to core brain functions are better able to resist the differential effects introduced by sequencing methods. Our findings suggest that when integrating transcriptomic data from different modalities to elucidate brain mechanisms, it is crucial to consider the consistency of gene expression across modalities to avoid discrepancies in interpretation.

Genetics:

Genetic Modeling and Analysis Methods ²

Transcriptomics ¹

Keywords:

Other - Transcriptomics

^1|2Indicates the priority used for review

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