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TARGET-seq: Linking single-cell transcriptomics of human dopaminergic neurons with their target specificity

Fiorenzano, Alessandro LU ; Storm, Petter LU orcid ; Sozzi, Edoardo LU orcid ; Bruzelius, Andreas LU ; Corsi, Sara LU ; Kajtez, Janko LU orcid ; Mudannayake, Janitha LU orcid ; Nelander, Jenny LU orcid ; Mattsson, Bengt LU and Åkerblom, Malin LU , et al. (2024) In Proceedings of the National Academy of Sciences of the United States of America 121(47). p.1-12
Abstract

Dopaminergic (DA) neurons exhibit significant diversity characterized by differences in morphology, anatomical location, axonal projection pattern, and selective vulnerability to disease. More recently, scRNAseq has been used to map DA neuron diversity at the level of gene expression. These studies have revealed a higher than expected molecular diversity in both mouse and human DA neurons. However, whether different molecular expression profiles correlate with specific functions of different DA neurons or with their classical division into mesolimbic (A10) and nigrostriatal (A9) neurons, remains to be determined. To address this, we have developed an approach termed TARGET-seq (Tagging projections by AAV-mediated RetroGrade Enrichment... (More)

Dopaminergic (DA) neurons exhibit significant diversity characterized by differences in morphology, anatomical location, axonal projection pattern, and selective vulnerability to disease. More recently, scRNAseq has been used to map DA neuron diversity at the level of gene expression. These studies have revealed a higher than expected molecular diversity in both mouse and human DA neurons. However, whether different molecular expression profiles correlate with specific functions of different DA neurons or with their classical division into mesolimbic (A10) and nigrostriatal (A9) neurons, remains to be determined. To address this, we have developed an approach termed TARGET-seq (Tagging projections by AAV-mediated RetroGrade Enrichment of Transcriptomes) that links the transcriptional profile of the DA neurons with their innervation of specific target structures in the forebrain. Leveraging this technology, we identify molecularly distinct subclusters of human DA neurons with a clear link between transcriptome and axonal target-specificity, offering the possibility to infer neuroanatomical-based classification to molecular identity and target-specific connectivity. We subsequently used this dataset to identify candidate transcription factors along DA developmental trajectories that may control subtype identity, thus providing broad avenues that can be further explored in the design of next-generation A9 and A10 enriched DA-neurons for drug screening or A9 enriched DA cells for clinical stem cell-based therapies.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Humans, Dopaminergic Neurons/metabolism, Transcriptome, Single-Cell Analysis/methods, Animals, Mice, Gene Expression Profiling/methods, Prosencephalon/metabolism, Axons/metabolism
in
Proceedings of the National Academy of Sciences of the United States of America
volume
121
issue
47
article number
e2410331121
pages
1 - 12
publisher
National Academy of Sciences
external identifiers
  • pmid:39541349
  • scopus:85209368357
ISSN
1091-6490
DOI
10.1073/pnas.2410331121
language
English
LU publication?
yes
id
ed605dc7-6a04-4424-8050-2fa45e61a313
date added to LUP
2024-12-09 10:57:43
date last changed
2025-06-10 17:55:03
@article{ed605dc7-6a04-4424-8050-2fa45e61a313,
  abstract     = {{<p>Dopaminergic (DA) neurons exhibit significant diversity characterized by differences in morphology, anatomical location, axonal projection pattern, and selective vulnerability to disease. More recently, scRNAseq has been used to map DA neuron diversity at the level of gene expression. These studies have revealed a higher than expected molecular diversity in both mouse and human DA neurons. However, whether different molecular expression profiles correlate with specific functions of different DA neurons or with their classical division into mesolimbic (A10) and nigrostriatal (A9) neurons, remains to be determined. To address this, we have developed an approach termed TARGET-seq (Tagging projections by AAV-mediated RetroGrade Enrichment of Transcriptomes) that links the transcriptional profile of the DA neurons with their innervation of specific target structures in the forebrain. Leveraging this technology, we identify molecularly distinct subclusters of human DA neurons with a clear link between transcriptome and axonal target-specificity, offering the possibility to infer neuroanatomical-based classification to molecular identity and target-specific connectivity. We subsequently used this dataset to identify candidate transcription factors along DA developmental trajectories that may control subtype identity, thus providing broad avenues that can be further explored in the design of next-generation A9 and A10 enriched DA-neurons for drug screening or A9 enriched DA cells for clinical stem cell-based therapies.</p>}},
  author       = {{Fiorenzano, Alessandro and Storm, Petter and Sozzi, Edoardo and Bruzelius, Andreas and Corsi, Sara and Kajtez, Janko and Mudannayake, Janitha and Nelander, Jenny and Mattsson, Bengt and Åkerblom, Malin and Björklund, Tomas and Björklund, Anders and Parmar, Malin}},
  issn         = {{1091-6490}},
  keywords     = {{Humans; Dopaminergic Neurons/metabolism; Transcriptome; Single-Cell Analysis/methods; Animals; Mice; Gene Expression Profiling/methods; Prosencephalon/metabolism; Axons/metabolism}},
  language     = {{eng}},
  month        = {{11}},
  number       = {{47}},
  pages        = {{1--12}},
  publisher    = {{National Academy of Sciences}},
  series       = {{Proceedings of the National Academy of Sciences of the United States of America}},
  title        = {{TARGET-seq: Linking single-cell transcriptomics of human dopaminergic neurons with their target specificity}},
  url          = {{http://dx.doi.org/10.1073/pnas.2410331121}},
  doi          = {{10.1073/pnas.2410331121}},
  volume       = {{121}},
  year         = {{2024}},
}