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Hawkinson, T.

Publications and source records attributed to Hawkinson, T..

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Cortical inhibitory parvalbumin interneurons exhibit metabolic specializations coordinated by PGC-1α that are lost in rodents and humans after traumatic brain injury

Parvalbumin-positive interneurons (PV-INs) regulate neuronal and circuit activity, and their dysfunction is observed across neurological conditions, including traumatic brain injury (TBI), epilepsy, Alzheimers disease, and schizophrenia. PV-INs are particularly vulnerable to cell loss, potentially due to their increased metabolic demands arising from their uniquely high level of electrical activity, which render them susceptible to metabolic pressure. Here, we use single-nucleus RNA-sequencing (snRNAseq) data from a rodent model of TBI, as well as human TBI data, and demonstrate PV-INs have unique metabolic specializations that are lost after injury and can be rescued by in vivo treatment with the glycolytic inhibitor, 2-deoxyglucose. We generated a novel PV-IN transcriptional identity module comprised primarily of genes encoding specialized ion channels, metabolic enzymes, and synaptic machinery, that identifies heterogenous subsets of injury-associated PV-INs with loss of PV-IN transcriptional identity. We show that changes in metabolic specialization are coupled to changes in transcriptional identity in PV-INs and implicate the PV-IN-enriched transcriptional co-activator, Ppargc1a, as a key driver of PV-IN transcriptional metabolic dysfunction. We also identify a family of long non-coding RNAs enriched in this subset of transcriptionally dysfunctional PV-INs that negatively correlates with PV-IN metabolic specialization. Lastly, we utilize these tools to interrogate a published human TBI snRNAseq data set and find nearly identical changes, underscoring the importance of PV-IN metabolic dysfunction in the pathology of TBI.

neuroscience↗

MetaVision3D: Automated framework for the Generation of Spatial Metabolome Atlas in 3D

High-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the dissection of the complex metabolic landscape and heterogeneity from a thin tissue section. Currently, spatial metabolism highlights the remarkable complexity in two-dimensional space and is poised to be extended into the three-dimensional world of biology. Here, we introduce MetaVision3D, a novel pipeline driven by computer vision techniques for the transformation of serial 2D MALDI mass spectrometry imaging sections into a high-resolution 3D spatial metabolome. Our framework employs advanced algorithms for image registration, normalization, and interpolation to enable the integration of serial 2D tissue sections, thereby generating a comprehensive 3D model of unique diverse metabolites across host tissues at mesoscale. As a proof of principle, MetaVision3D was utilized to generate the mouse brain 3D metabolome atlas (available at https://metavision3d.rc.ufl.edu/) as an interactive online database and web server to further advance brain metabolism and related research.

neuroscience↗