Genome ResearchNeurosciencePeer-reviewed journal article
LCM-seq reveals unique transcriptional adaption mechanisms of resistant neurons in spinal muscular atrophy
Susanne NichterwitziD, Jik NijsseniD, Helena StorvalliD, Christoph SchweingruberiD, Laura Helen ComleyiD, Ilary AllodiiD, Mirjam van der Lee, Qiaolin DengiD, Rickard SandbergiD, Eva HedlundiD
Abstract
Somatic motor neurons are selectively vulnerable in spinal muscular atrophy (SMA), a lethal disease caused by a deficiency of the ubiquitously expressed survival of motor neuron (SMN) protein. However, some brainstem motor neuron groups, including oculomotor and trochlear (ocular), which innervate the muscles around the eyes, are for unknown reasons spared. Here, using laser capture microdissection coupled with RNA sequencing (LCM-seq), we investigate the transcriptional dynamics in discrete neuronal populations in health and SMA to reveal mechanisms of vulnerability and resistance. Using gene correlation network analysis, we reveal a p53-mediated stress response that is intrinsic to all somatic motor neurons independent of their vulnerability, but absent in resistant red nucleus and visceral motor neurons. However, our temporal and spatial differential expression analysis across neuron types clearly demonstrates that the majority of SMA-induced modulations are cell-type specific. Notably, using gene ontology and protein-network analyses we show that ocular motor neurons present unique disease-adaptation mechanisms that could explain their resilience. In particular, ocular motor neurons up-regulate; i) Syt1, Syt5 and Cplx2, which modulate neurotransmitter release; ii) the motor neuron survival factors Chl1 and Lif, iii) Aldh4, that can protect cells from oxidative stress and iv) the caspase inhibitor Pak4. In conclusion, our in-depth longitudinal analysis of gene expression changes in SMA reveal novel cell-type specific changes that present compelling targets for future gene therapy studies aimed towards preserving vulnerable motor neurons.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 48
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