Mapping the brain: How cell types and location influence Parkinson’s disease

NewsGuard 100/100 Score

In a recent study published in Cell Reports, researchers conducted a single-cell spatial transcriptome analysis on murine brain expression in age and disease using a Parkinson's disease (PD) transgenic model, focusing on dopaminergic neurons (DA) spanning 29 cell types.

Study: Single-cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging, and disease. Image Credit: solarseven/Shutterstock.comStudy: Single-cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging, and disease. Image Credit: solarseven/Shutterstock.com

Background

The spatially structured brain contains various cells, each with a distinct purpose. PD is a neurodegenerative condition characterized by DA loss and alpha-synuclein buildup due to overexpression via locus multiplication.

Single-cell ribonucleic acid sequencing (RNS-seq) has improved the knowledge of cell-based expression in organs such as the brain, but current methods fail to attain high-throughput resolution. Fluorescent in situ hybridization (FISH) technologies provide higher sensitivity at an individual level.

About the study

In the present study, researchers performed single-cell spatial transcriptomic and translatomic profiling of DA to find markers for healthy and aged cells.

The researchers crossed Rosa26fsTRAP::DATIREScre (DAT-TRAP) mice with SNCA-OVX mice to enable DA messenger RNA (mRNA) capture in a PD model.

The mice were aged to 18 months to assess the influence of human ⍺-synuclein overexpression and aging on dopaminergic neuron genetic expression and study the effect of healthy and Parkinsonian aging.

The researchers created a single-cell-level spatial transcriptomic map of gene expression in the adult mouse brain and a high-fidelity translatome-level profile of DA neuron expression.

They analyzed stereo-seq arrays with special transcript maps. They converted the transcript expression maps to segmented individual cells, identifying 29 types of cells, including astrocytes and inhibitory cortical neurons, in 18 brain slices.

The team filtered the segmented cells by transcriptome size and complexity and analyzed spatially distinct cortex, hippocampus, and thalamus populations. They also examined the ventral midbrain and striatum to enrich transcripts confined to the cell body and putative axon.

They used data from gene enrichment and genome-wide association studies (GWAS) to identify potential disease-causing genes.

They evaluated each cell's location and compared DA neuronal gene expression in various cells.

The researchers examined enhanced green fluorescent protein (eGFP)-tagged ribosomes in DAT-expressing cells and confirmed eGFP colocalization with tyrosine hydroxylase (TH), a DA neuronal marker.

They split cells from stereo-seq brain slices, filtering them based on detected genes, and performed Uniform Manifold Approximation and Projection (UMAP) analysis.

They also performed short- and long-read RNA sequencing of the translating mRNA collected by TRAP and used stereo-sequencing and TRAP data to rank prospective genes for inquiry into sporadic PD.

The researchers demonstrated specific enrichment of DA marker genes and depletion of marker genes of other neighboring cell types in DAT-TRAP mRNA.

They confirmed specific calcium-sensing receptor (CASR) protein expression in mouse ventral midbrain neurons and investigated CASR expression in human-induced pluripotent stem cell-derived DA neurons. They also examined age-related gene variations in cells revealed by stereo-seq.

Results

The team studied PD in young and aged brains to identify genes having spatially varying expression in dopaminergic neurons of the ventral tegmental area (VTA) and substantia nigra (SN) and particular markers such as copine-7 (Cpne7) and Solute carrier family 10 member 4 (Slc10a4) genes.

They also detected splice variants unique to DA. They demonstrated ways of using TRAP and stereo-sequencing expression specificity measurements to identify potentially relevant genes from GWAS areas, indicating that CASR regulates intracellular DA neuronal calcium.

The findings demonstrated substantia nigra-specific DA neuronal loss and increased microglial activation with age. They highlighted aging- and disease-associated genetic alterations in various cells, including dopaminergic neurons, across many PD-related pathways.

Stereo-seq detected expression alterations caused by aging and illness across different cell types, loss of nigral DA neurons, and the neuroinflammatory expansion of microglia.

Pathway enrichment research revealed that various biological processes were altered, including axon ensheathment, synaptic transmission modulation, intracellular calcium ion homeostasis, and catecholamine secretion control.

The team extracted 355,307 transcriptomes of high quality with spatial coordinate details from 18 murine brains, identifying a total of 14,494 genes.

They observed synaptosomal-associated protein, 25kDa (Snap25) expression, and structured localization in places like the thalamus or hippocampus differentiated neurons from glia.

They also examined neuronal cells in the CA1 region, CA3 region, subiculum, and dentate gyrus in the hippocampus region or gamma-aminobutyric acid (GABA)-releasing nuclei in the midbrain.  

Marker expression enabled oligodendrocyte, astrocyte, microglia, and erythrocyte identification. In DAT-TRAP samples, classical markers of dopaminergic neurons showed significant enrichment, whereas those of other cell types decreased in the ventral midbrain.

Conclusion

Overall, the study identified 29 unique brain cell types by examining variations in spatial gene expression linked to aging and illness. The stereo-seq data indicated differences in transcript use across more than a thousand genes.

There were 817 occurrences of alternative splicing, suggesting that more genes were being translated than gene-level count data showed. The study also discovered an age-dependent drop in SN DA neuron cell number, which supports earlier findings.

Journal reference:
Pooja Toshniwal Paharia

Written by

Pooja Toshniwal Paharia

Dr. based clinical-radiological diagnosis and management of oral lesions and conditions and associated maxillofacial disorders.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Toshniwal Paharia, Pooja Toshniwal Paharia. (2024, February 27). Mapping the brain: How cell types and location influence Parkinson’s disease. News-Medical. Retrieved on April 14, 2024 from https://www.news-medical.net/news/20240227/Mapping-the-brain-How-cell-types-and-location-influence-Parkinsone28099s-disease.aspx.

  • MLA

    Toshniwal Paharia, Pooja Toshniwal Paharia. "Mapping the brain: How cell types and location influence Parkinson’s disease". News-Medical. 14 April 2024. <https://www.news-medical.net/news/20240227/Mapping-the-brain-How-cell-types-and-location-influence-Parkinsone28099s-disease.aspx>.

  • Chicago

    Toshniwal Paharia, Pooja Toshniwal Paharia. "Mapping the brain: How cell types and location influence Parkinson’s disease". News-Medical. https://www.news-medical.net/news/20240227/Mapping-the-brain-How-cell-types-and-location-influence-Parkinsone28099s-disease.aspx. (accessed April 14, 2024).

  • Harvard

    Toshniwal Paharia, Pooja Toshniwal Paharia. 2024. Mapping the brain: How cell types and location influence Parkinson’s disease. News-Medical, viewed 14 April 2024, https://www.news-medical.net/news/20240227/Mapping-the-brain-How-cell-types-and-location-influence-Parkinsone28099s-disease.aspx.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
Study reveals skin bacteria removal boosts brain attention signals