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Mapping Cell-Type-Specific Transcriptomic Entropy Across the Cortex

Poster No:

702

Submission Type:

Abstract Submission

Authors:

Wenkun Lei¹^,2, Lin Du¹^,2, Xiaohan Tian¹^,2, Jing Lou¹^,2, Xinyi Dong¹^,2, Yuqing Sun¹^,2, Ruoxin Yang¹^,2, Xinghui Zhao¹^,2, Meng Wang¹^,2, Bing Liu¹^,2

Institutions:

¹State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China, ²Beijing normal university, Beijing, China

First Author:

Wenkun Lei
State Key Laboratory of Cognitive Neuroscience and Learning|Beijing normal university
Beijing, China|Beijing, China

Co-Author(s):

Lin Du
State Key Laboratory of Cognitive Neuroscience and Learning|Beijing normal university
Beijing, China|Beijing, China

Xiaohan Tian
State Key Laboratory of Cognitive Neuroscience and Learning|Beijing normal university
Beijing, China|Beijing, China

Jing Lou
State Key Laboratory of Cognitive Neuroscience and Learning|Beijing normal university
Beijing, China|Beijing, China

Xinyi Dong
State Key Laboratory of Cognitive Neuroscience and Learning|Beijing normal university
Beijing, China|Beijing, China

Yuqing Sun
State Key Laboratory of Cognitive Neuroscience and Learning|Beijing normal university
Beijing, China|Beijing, China

Ruoxin Yang
State Key Laboratory of Cognitive Neuroscience and Learning|Beijing normal university
Beijing, China|Beijing, China

Xinghui Zhao
State Key Laboratory of Cognitive Neuroscience and Learning|Beijing normal university
Beijing, China|Beijing, China

Meng Wang
State Key Laboratory of Cognitive Neuroscience and Learning|Beijing normal university
Beijing, China|Beijing, China

Bing Liu
State Key Laboratory of Cognitive Neuroscience and Learning|Beijing normal university
Beijing, China|Beijing, China

Introduction:

Cellular transcriptomic entropy quantifies the variability or complexity in gene expression within a specific cell type (Teschendorff & Enver, 2017), High entropy values indicate greater diversity and unpredictability in gene expression, while low values reflect more consistent expression patterns (Jorstad et al., 2023). In this study, we use data from the Allen Human Brain Atlas (AHBA) and transcriptomic profiles of various cell types to create a whole-cortex map of cell-type-specific transcriptomic entropy.

Methods:

We used the Abagen (Markello et al., 2021) tool to extract transcriptomic data from the AHBA for cortical regions of the brain. Probes that exceeded background noise in at least 30% of tissue samples were included, and for each gene, the probe with the highest differential stability was selected. This process retained 16,383 genes for analysis. Expression values for each donor were normalized across genes, and the resulting values were further normalized across samples. The data were then mapped onto the Schaefer 400 ROI atlas, focusing on the left hemisphere due to the characteristics of the AHBA dataset. We employed a set of conserved differentially expressed genes (DEGs) found to be reliable across all eight cortical areas from the single-cell dataset (Jorstad et al., 2023). Subsequently, transcriptomic entropy was calculated for each cell type in each ROI, followed by a whole-cortex mapping of these entropy values. We also examined the spatial correlation between the entropy distribution and the deconvoluted distribution for each cell type (Zhang et al., 2024).

Results:

We found that primary brain regions have relatively low entropy values, while higher-order brain regions tend to exhibit higher entropy values. Additionally, brain regions closer to the subcortex also display lower entropy values. We also observed a significant spatial correlation between entropy values and the distribution of cell types such as L2/3 IT, L4 IT, L5/6 NP, L6b, Sncg, and Astro.

Conclusions:

Our findings suggest that the distribution of entropy values is consistent with the level of brain differentiation. Furthermore, entropy distributions exhibit specificity across different cell types. A strong correlation between the distribution of excitatory neurons and their entropy values likely reflects the functional roles of these neurons across cortical regions.

Genetics:

Transcriptomics ¹

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Cortical Anatomy and Brain Mapping ²

Keywords:

Cellular

Neuron

Other - Transcriptomics

^1|2Indicates the priority used for review

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Provide references using APA citation style.

Jorstad, N. L., Close, J., Johansen, N., Yanny, A. M., Barkan, E. R., Travaglini, K. J., Bertagnolli, D., Campos, J., Casper, T., Crichton, K., Dee, N., Ding, S.-L., Gelfand, E., Goldy, J., Hirschstein, D., Kiick, K., Kroll, M., Kunst, M., Lathia, K., … Lein, E. S. (2023). Transcriptomic cytoarchitecture reveals principles of human neocortex organization. Science, 382(6667), eadf6812. https://doi.org/10.1126/science.adf6812
Markello, R. D., Arnatkeviciute, A., Poline, J.-B., Fulcher, B. D., Fornito, A., & Misic, B. (2021). Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife, 10, e72129. https://doi.org/10.7554/eLife.72129
Teschendorff, A. E., & Enver, T. (2017). Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nature Communications, 8(1), 15599. https://doi.org/10.1038/ncomms15599
Zhang, X.-H., Anderson, K. M., Dong, H.-M., Chopra, S., Dhamala, E., Emani, P. S., Gerstein, M. B., Margulies, D. S., & Holmes, A. J. (2024). The cell-type underpinnings of the human functional cortical connectome. Nature Neuroscience, 1–11. https://doi.org/10.1038/s41593-024-01812-2

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