Abstract

Striatal projection neurons (SPNs), which progressively degenerate in human patients with Huntington’s disease (HD), are classified along two axes: the canonical direct-indirect pathway division and the striosome-matrix compartmentation. It is well established that the indirect-pathway SPNs are susceptible to neurodegeneration and transcriptomic disturbances, but less is known about how the striosome-matrix axis is compromised in HD in relation to the canonical axis. Here we show, using single-nucleus RNA-sequencing data from male Grade 1 HD patient post-mortem brain samples and male zQ175 and R6/2 mouse models, that the two axes are multiplexed and differentially compromised in HD. In human HD, striosomal indirect-pathway SPNs are the most depleted SPN population. In mouse HD models, the transcriptomic distinctiveness of striosome-matrix SPNs is diminished more than that of direct-indirect pathway SPNs. Furthermore, the loss of striosome-matrix distinction is more prominent within indirect-pathway SPNs. These results open the possibility that the canonical direct-indirect pathway and striosome-matrix compartments are differentially compromised in late and early stages of disease progression, respectively, differentially contributing to the symptoms, thus calling for distinct therapeutic strategies.

In human and mouse models of Huntington’s disease, Matsushima, Pineda et al. show, using snRNAsequencing, the two axes defining identities of striatal projection neurons are multiplexed and differentially compromised, calling for distinct therapies.

Details

Title
Transcriptional vulnerabilities of striatal neurons in human and rodent models of Huntington’s disease
Author
Matsushima, Ayano 1 ; Pineda, Sergio Sebastian 2   VIAFID ORCID Logo  ; Crittenden, Jill R. 1   VIAFID ORCID Logo  ; Lee, Hyeseung 3 ; Galani, Kyriakitsa 4 ; Mantero, Julio 4 ; Tombaugh, Geoffrey 5 ; Kellis, Manolis 6   VIAFID ORCID Logo  ; Heiman, Myriam 7   VIAFID ORCID Logo  ; Graybiel, Ann M. 1   VIAFID ORCID Logo 

 Massachusetts Institute of Technology, McGovern Institute for Brain Research, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
 Massachusetts Institute of Technology, Picower Institute for Learning and Memory, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623); MIT, Department of Electrical Engineering and Computer Science, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
 Massachusetts Institute of Technology, Picower Institute for Learning and Memory, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623) 
 Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623); MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
 PyschoGenics Inc., Paramus, USA (GRID:grid.116068.8) 
 Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623); MIT, Department of Electrical Engineering and Computer Science, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
 Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); Massachusetts Institute of Technology, Picower Institute for Learning and Memory, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
Pages
282
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2766279878
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.