Comparative Study of Molecular Taxonomy in Cortical Regions of Primates and Rodents

The brain structure is complex, showing high complexity in both molecular and cellular composition. Current research on brain molecular taxonomy is mainly based on rodents. However, although primates and rodents have common ancestors, they diverged through different evolutionary paths 75 million years ago. Therefore, studying other species alone cannot fully explain the unique cognitive abilities of primates. Previously, cross-species analysis of gene expression profiles mainly focused on the hippocampus and prefrontal cortex.

Authors and Publication Information

This article was co-authored by Lei Zhang, Yanyong Cheng, Zhenyu Xue, Shihao Wu, Zilong Qiu, et al., and published in the 2024 issue of Neuroscience Bulletin. The main research units include the Department of Anesthesiology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Institute of Neuroscience, Chinese Academy of Sciences, State Key Laboratory, and others.

Research Background and Objectives

This study focuses on the molecular taxonomy of the anterior cingulate cortex (ACC) and retrosplenial cortex (RSC) in the cingulate cortex. ACC and RSC show homology in cortical structure across species and have synaptic connections between them, participating in the regulation of cognitive and social behaviors. However, existing research on the molecular composition of these brain regions is mostly based on rodents and not fully applicable to primates. Therefore, studying the molecular taxonomy of these brain regions in primates and rodents helps to understand their differences and commonalities in cognitive functions and pathological processes.

Research Methods and Procedures

The study used single-nucleus RNA sequencing (snRNA-seq) technology, with ACC and RSC tissues from two rhesus monkeys and six mice as research subjects. The specific process is as follows:

1. Sample Collection and Processing: Tissues were collected from the ACC and RSC regions of rhesus monkeys and mice for snRNA-seq analysis.
2. Cell Type Identification and Clustering: Six main cell types were identified using known cell type marker genes: inhibitory neurons, excitatory neurons, astrocytes, oligodendrocytes, progenitor cells, and microglia. t-SNE algorithm was used for cell clustering analysis.
3. Cell Subgroup and Gene Function Analysis: Excitatory neurons were further divided into L2/3, L2/3/4, L4, and L5/6 subtypes based on cortical layer-specific markers; inhibitory neurons were divided into SST, PV, VIP, and SV2C subtypes, followed by Gene Ontology (GO) analysis.
4. Differential Gene Expression Analysis: Analyzed differentially expressed genes (DEGs) between ACC and RSC, and verified expression marker genes across different species through RNA fluorescence in situ hybridization (FISH).

Research Results

1. Cell Types and Clustering: 80,484 and 16,164 transcriptomes were obtained from rhesus monkey and mouse ACC and RSC, respectively. t-SNE analysis showed cell clustering status in different species and brain regions.

2. Molecular Characteristics of Excitatory and Inhibitory Neurons:

   • Excitatory Neurons: Divided into L2/3, L2/3/4, L4, and L5/6 subtypes based on cortical layer-specific markers. Results showed significantly different distribution patterns of excitatory neurons in rhesus monkey ACC and RSC.
   • Inhibitory Neurons: Divided into SST, PV, VIP, and SV2C subtypes. Transcriptional characteristics of inhibitory neurons were generally similar in ACC and RSC.

3. Differential Gene Expression Analysis: In excitatory neurons, significantly more differentially expressed genes (such as PCDH17, PHGFC, CDH4) were found between ACC and RSC; in inhibitory neurons, this difference was less pronounced.

4. Cross-species Analysis: Unbiased t-SNE analysis revealed significant differences in the distribution of excitatory neuron subtypes and marker gene expression in ACC and RSC between rhesus monkeys and mice. In rhesus monkeys, the transcriptional characteristics of excitatory neurons in these regions differed significantly, while this difference was not obvious in mice.

Research Conclusions

This study used snRNA-seq technology to map the molecular profiles of ACC and RSC regions in primates and rodents. Significant differences were found in the transcriptional characteristics of excitatory and inhibitory neurons in these brain regions between primates and rodents. These results provide an important molecular basis for understanding primate-specific functions and cross-species integration of brain region functions.

Research Significance

This study revealed molecular characteristic differences in ACC and RSC brain regions between primates and rodents through single-nucleus RNA sequencing, enriching our understanding of brain region-specific functions. The identification of these molecular markers provides a foundation for developing new genetic tools, such as targets for transcriptional and epigenetic regulation. Additionally, the study of cellular composition and molecular characteristics can provide important insights into understanding the biological functions of these brain regions.

Research Highlights and Limitations

1. Highlights: For the first time, systematically compared the molecular characteristics of ACC and RSC regions in primates and rodents, particularly discovering significant differences in primate excitatory neurons across different brain regions, which is important for further studying unique cognitive functions in primates.
2. Limitations: Due to sample size and tissue acquisition limitations, this study only included two rhesus monkeys and six mice. Future studies need to increase sample size and include samples from multiple age groups to improve the generalizability and accuracy of research results.

Other Important Content

While providing a molecular framework for transcriptomics research in primates and rodents, this study also lays the foundation for further functional studies, such as transcriptional and epigenetic regulation. Additionally, the gene expression data and code reported in this paper are publicly available on the Gene Expression Omnibus and GitHub platforms, facilitating subsequent research.