Investigating post-traumatic syringomyelia and local fluid osmoregulation via a rat model.

BACKGROUND: Syringomyelia (SM) is characterized by the development of fluid-filled cavities, referred to as syrinxes, within the spinal cord tissue. The molecular etiology of SM post-spinal cord injury (SCI) is not well understood and only invasive surgical based treatments are available to treat SM clinically. This study builds upon our previous omics studies and in vitro cellular investigations to further understand local fluid osmoregulation in post-traumatic SM (PTSM) to highlight important pathways for future molecular interventions. METHODS: A rat PTSM model consisting of a laminectomy at the C7 to T1 level followed by a parenchymal injection of 2 µL quisqualic acid (QA) and an injection of 5 µL kaolin in the subarachnoid space was utilized 6 weeks after initial surgery, parenchymal fluid and cerebrospinal fluid (CSF) were collected, and the osmolality of fluids were analyzed. Immunohistochemistry (IHC), metabolomics analysis using LC-MS, and mass spectrometry-based imaging (MSI) were performed on injured and laminectomy-only control spinal cords. RESULTS: We demonstrated that the osmolality of the local parenchymal fluid encompassing syrinxes was higher compared to control spinal cords after laminectomy, indicating a local osmotic imbalance due to SM injury. Moreover, we also found that parenchymal fluid is more hypertonic than CSF, indicating establishment of a local osmotic gradient in the PTSM injured spinal cord (syrinx site) forcing fluid into the spinal cord parenchyma to form and/or expand syrinxes. IHC results demonstrated upregulation of betaine, ions, water channels/transporters, and enzymes (BGT1, AQP1, AQP4, CHDH) at the syrinx site as compared to caudal and rostral sites to the injury, implying extensive local osmoregulation activities at the syrinx site. Further, metabolomics analysis corroborated alterations in osmolality at the syrinx site by upregulation of small molecule osmolytes including betaine, carnitine, glycerophosphocholine, arginine, creatine, guanidinoacetate, and spermidine. CONCLUSIONS: In summary, PTSM results in local osmotic disturbance that propagates at 6 weeks following initial injury. This coincides with and may contribute to syrinx formation/expansion.

Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem.

Tumors are comprised of a multitude of cell types spanning different microenvironments. Mass spectrometry imaging (MSI) has the potential to identify metabolic patterns within the tumor ecosystem and surrounding tissues, but conventional workflows have not yet fully integrated the breadth of experimental techniques in metabolomics. Here, we combine MSI, stable isotope labeling, and a spatial variant of Isotopologue Spectral Analysis to map distributions of metabolite abundances, nutrient contributions, and metabolic turnover fluxes across the brains of mice harboring GL261 glioma, a widely used model for glioblastoma. When integrated with MSI, the combination of ion mobility, desorption electrospray ionization, and matrix assisted laser desorption ionization reveals alterations in multiple anabolic pathways. De novo fatty acid synthesis flux is increased by approximately 3-fold in glioma relative to surrounding healthy tissue. Fatty acid elongation flux is elevated even higher at 8-fold relative to surrounding healthy tissue and highlights the importance of elongase activity in glioma.

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity.

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19.