Lecture 4. Biological Insights From Single-Cell and Spatial Transcriptomics Data: Unraveling The Heterogeneous Make-Up Of The Human Brain (ENG)

Julio LEON, PhD (UCSF, USA)

Lecture Abstract

Single-cell and spatial multi-omics technologies have revolutionized molecular and cell biology research, allowing us to study better the complexity and heterogeneity of human tissues. In this lecture, I will show how we use integrative single-cell and spatial transcriptomics to understand the complexity of the human entorhinal (EC) cortex and its enhanced vulnerability to Alzheimer’s disease (AD). Neurons from layer 2/3 of the EC exhibit early tau neurofibrillary inclusions and degenerate early during AD. However, its microenvironment and spatially restricted drivers of the selective vulnerability of L2/3 neurons remain elusive. We used an integrative analysis of spatial and snRNAseq to profile cell heterogeneity and neuronal-glial interactions within the spatial niches of the laminar organization of the EC. We identified spatially restricted coding and non- coding genes, receptor-ligand interactions, and dysregulated inflammatory pathways in AD. Our analysis revealed that L1 astrocytes, an important cell type for brain metabolism, positioned in proximity to L2/3 neurons, exhibited a distinct pro-inflammatory transcriptomic signature. This enhanced inflammatory profile indicates the potential for L1 astrocytes to impact neuronal populations in the 2/3 cortical layers. Development of a spatial multimodal method to capture metabolomics and transcriptomics from the same slide

To provide a comprehensive understanding of the intricate dynamics of the astrocyte-neuron in AD, taking into account the heterogeneous nature of the astrocyte-neuronal interaction in distinct regions of the brain, we developed a multimodal-omics technology to extract metabolome and transcriptome data in relation to plaque locations in an AD mouse model. The data obtained allowed me to identify main brain regions unbiasedly using transcriptomic and metabolomic data separately, indicating region-specific signatures. After performing the integration of these multimodal data, I was able to identify brain micro-niches associated and non-associated with amyloid beta deposition. We are using this technology to explore further brain micro-niches related to AD pathology in the human brain to identify regions of heightened vulnerability and resilience, enabling targeted interventions.