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Grant-Peters, M.

Publications and source records attributed to Grant-Peters, M..

2 recordsLinked to original sources

Biochemical and metabolic maladaption defines pathological niches in progressive multiple sclerosis

Progressive multiple sclerosis (MS) is driven by demyelination, neuroaxonal loss, and mitochondrial damage occurring behind a closed blood-brain barrier (BBB).1,2 Patients with progressive MS typically fail to respond to available immunomodulatory drugs that reduce relapses in early disease.2 This indicates a dire need to identify non-canonical therapeutic avenues to limit neurodegeneration and promote protection and repair.3 Here, we have employed high-resolution multiomic profiling to characterise the biochemical and metabolic adaptations underpinning MS pathology, as these have been incompletely described but critically, may be amenable to BBB-permeable drug targeting. Using synchrotron radiation (SR)- and focal plane array (FPA)-based Fourier transform infrared microspectroscopy (FTIR), we spatially mapped the biochemical features present in human progressive MS and control post-mortem brain and rare spinal cord tissue. By employing single-nuclear RNA sequencing (snRNA-seq), 10x Genomics Visium spatial transcriptomics and spatial proteomics to resolve their cellular context, we found that these biochemical features provide a uniquely and highly disease-specific barcode for distinct pathological niches within the tissue. Characterisation of the metabolic processes underpinning these niches revealed an associated re-organisation of the astrocytic landscape in the grey and white matter, with implications for the treatment of progressive MS.

neuroscience↗

Photizo: an open-source library for cross-sample analysis of FTIR spectroscopy data

With continually improved instrumentation, Fourier transform infrared (FTIR) microspectroscopy can now be used to capture thousands of high-resolution spectra for chemical characterisation of a sample. The spatially resolved nature of this method lends itself well to histological characterisation of complex biological specimens. However, commercial software currently available can make joint analysis of multiple samples challenging and, for large datasets, computationally infeasible. In order to overcome these limitations, we have developed Photizo - an open-source Python library for spectral analysis which includes functions for pre-processing, visualisation and downstream analysis, including principal component analysis, clustering, macromolecular quantification and biochemical mapping. This library can be used for analysis of spectroscopy data without a spatial component, as well as spatially-resolved data, such as data obtained via infrared (IR) microspectroscopy in scanning mode and IR imaging by focal plane array (FPA) detector. Contactcdendrou@well.ox.ac.uk

bioinformatics↗