A Temporal Cortex Cell Atlas Highlights Gene Expression Dynamics During Human Brain Maturation

2025-01-27 Mon
temporal cortex single-nucleus RNA sequencing brain maturation gene expression cell subtypes cognition synaptic plasticity
Gene Expression Dynamics During Human Brain Maturation: A Novel Temporal Cortex Cell AtlasAcademic BackgroundThe development and maturation of the human brain are critical areas in neuroscience, though many mysteries remain unresolved. Guided by changes in gene expression, the human brain undergoes a prolonged and complex postnatal maturation process. While prior bulk tissue-based transcriptomics studies have revealed significant changes in gene expression—particularly during the transition from late fetal to early infancy stages, as well as the dramatic structural and functional changes of the brain in childhood and adolescence—these studies have been limited in their ability to identify cell-type-specific gene expression dynamics. A critical unanswered question is how different cell types undergo gene expression changes during brain maturation from childhood to adulthood.
Moreover, current global human brain cell atlases primarily focus on adults and lack coverage of childhood stages. As Africa represents the region with the highest global genetic diversity and a rapidly growing child population, establishing a reference brain cell atlas incorporating samples from African pediatric populations is particularly significant. Such an effort not only aids the study of human brain development mechanisms in the global population but also provides a framework for understanding the effects of local prevalent conditions such as tuberculous meningitis (TBM) and HIV on brain development.
Research SourceThis study, titled “A temporal cortex cell atlas highlights gene expression dynamics during human brain maturation,” was conducted by Christina Steyn and a collaborative team from multiple institutions, including the University of Cape Town, Oxford University’s MRC Weatherall Institute of Molecular Medicine, and the Stowers Institute for Medical Research. It was published in the December 2024 issue of Nature Genetics (Volume 56, DOI: 10.1038/s41588-024-01990-6).
Research Design and MethodsThe research team employed single-nucleus RNA sequencing (snRNA-seq) to analyze high-resolution transcriptomes of temporal lobe tissue samples from African pediatric and adult populations. They constructed a brain cell atlas combining both pediatric and adult samples to reveal the gene expression differences underlying brain maturation processes.
a) Experimental Design and WorkflowThe study’s first step involved sequencing temporal lobe tissue samples from five pediatric and three adult donors using snRNA-seq. These included 23 datasets from 12 donors, yielding 144,438 high-quality nuclei. By integrating 17 previously published datasets, the researchers employed UMAP (Uniform Manifold Approximation and Projection) to identify and annotate 75 cell subtypes. Annotations were based on the Allen Brain Map’s middle temporal gyrus (MTG) cell taxonomy.
To precisely localize these cell subtypes, the researchers incorporated spatial transcriptomics techniques. Using the Visium platform, they sequenced tissue sections from two donors (aged 15 and 31 years) and estimated cell-type abundance. By applying nonnegative matrix factorization (NMF), the study identified 15 cell compartments and analyzed their cortical layer distributions.
The team also utilized the machine learning algorithm NS-Forest v.2.0 to define minimal marker genes for each cell subtype, exploring whether marker genes could generalize across age-diverse datasets.
Finally, through differential gene expression analysis (DGE) and Gene Set Enrichment Analysis (GSEA), the research highlighted functional differences and pathway dynamics in various cell types between pediatric and adult samples.
b) Experimental Results and Analysis1. Sample Organization and Cell Type Distribution

The data revealed that 75 cell subtypes encompassed major neuronal and non-neuronal brain cell types and their respective subtypes. Neurons significantly differed from non-neuronal cells by displaying a higher number of genes and unique molecular identifiers (UMIs). Both pediatric and adult samples exhibited similar overall cell-type distributions, indicating minimal changes in cell composition during brain maturation. Among non-neuronal cells, oligodendrocytes were the most prevalent, while exc_l2-3_linc00507_frem3 was the most common neuronal subtype.
2. Cellular Spatial Localization

Spatial transcriptomics data showed that both adult and pediatric samples displayed highly similar tissue architectures. The distribution of neuronal subtypes correlated closely with cortical layer characteristics—for example, the exc_l2_lamp5_ltk subtype was primarily located in cortical layer 2, while exc_l6_fezf2_scube1 extended into the white matter. Non-neuronal types, such as the two astrocyte subtypes, revealed distinct distribution patterns consistent with their functional properties.
3. Differential Gene Expression

Across 21 cell subtypes, a total of 165 significantly differentially expressed genes (DEGs) were identified—123 were upregulated and 42 downregulated in pediatric samples. Genes such as lamc3 and sox11, which are known to play key roles in cortical lamination and neurogenesis, were upregulated in multiple excitatory neuronal subtypes. Additionally, genes like fnbp1l were specifically expressed in certain neuronal subtypes (e.g., exc_l2-3_linc00507_frem3), potentially driving the unique processes of brain maturation during childhood.
4. Functional Pathway Analysis

GSEA revealed that pathways related to cellular respiration, synaptic plasticity, and protein translation regulation were significantly enriched in pediatric samples, likely reflecting the higher metabolic demands and neural circuit formation during childhood. Conversely, pathways promoting synaptic growth and axon ensheathment were significantly depleted, potentially linked to synaptic pruning processes.
5. Pediatric Stage Biomarker Analysis

Using the atlas, the team analyzed the cell-type-specific expression of potential biomarkers for pediatric tuberculous meningitis (TBM). Many biomarker genes were significantly elevated in non-neuronal cell types, such as astrocytes, though certain neuronal subtypes may also contribute to TBM-associated neurotoxicity.
c) Research ConclusionThis study constructed a comprehensive temporal lobe cell atlas combining pediatric and adult samples, revealing cell-type-specific gene expression dynamics during human brain maturation. The resource contributes significantly to understanding human brain development mechanisms and provides valuable references for studying pathological states of gene expression. Notably, by incorporating African pediatric samples, the research broadens the racial and age coverage of the global Human Cell Atlas (HCA).
d) Research HighlightsInnovative Data: Inclusion of African pediatric samples for the first time.
Advanced Technology: Integration of single-nucleus sequencing and spatial transcriptomics allows highly detailed cell-type dynamic analysis.
Differential Gene Expression: Detailed elucidation of subtle gene expression changes during the transition from childhood to adulthood.
Applications: Expands the data’s utility for investigating the mechanisms of neurodevelopmental disorders.
e) Additional InsightsThe study enriches the global single-cell data repertoire and provides a practical pathway for creating more diverse human reference atlases. By exploring pathological traits at the cellular gene expression level, it paves the way for the development of precision medicine tailored to individual patients.