Spinal cord perfusion impairments in the M83 mouse model of Parkinson’s disease

Spinal cord perfusion impairments in the M83 mouse model of Parkinson’s disease | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Spinal cord perfusion impairments in the M83 mouse model of Parkinson’s disease Benjamin F. Combes , Sandeep Kumar Kalva , Pierre-Louis Benveniste , Agathe Tournant , Man Hoi Law , Joshua Newton , Maik Krüger , Rebecca Z. Weber , View ORCID Profile Inês Dias , Daniela Noain , Xose Luis Dean-Ben , Uwe Konietzko , Christian R. Baumann , Per-Göran Gillberg , Christoph Hock , Roger M. Nitsch , Julien Cohen-Adad , Daniel Razansky , View ORCID Profile Ruiqing Ni doi: https://doi.org/10.1101/2024.04.27.591432 Benjamin F. Combes 1 Institute for Regenerative Medicine, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sandeep Kumar Kalva 2 Institute for Biomedical Engineering, University of Zurich & ETH Zurich , Zurich, Switzerland 3 Institute of Pharmacology and Toxicology, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pierre-Louis Benveniste 4 NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal , Montreal, QC, Canada 5 Mila - Quebec AI Institute , Montreal, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Agathe Tournant 2 Institute for Biomedical Engineering, University of Zurich & ETH Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Man Hoi Law 1 Institute for Regenerative Medicine, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joshua Newton 4 NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal , Montreal, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maik Krüger 1 Institute for Regenerative Medicine, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rebecca Z. Weber 1 Institute for Regenerative Medicine, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Inês Dias 6 Department of Neurology, University Hospital Zurich, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Inês Dias Daniela Noain 6 Department of Neurology, University Hospital Zurich, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xose Luis Dean-Ben 2 Institute for Biomedical Engineering, University of Zurich & ETH Zurich , Zurich, Switzerland 3 Institute of Pharmacology and Toxicology, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Uwe Konietzko 1 Institute for Regenerative Medicine, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christian R. Baumann 6 Department of Neurology, University Hospital Zurich, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Per-Göran Gillberg 7 Department of Neurobiology, Care Sciences and Society, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christoph Hock 1 Institute for Regenerative Medicine, University of Zurich , Zurich, Switzerland 8 Neurimmune , Schlieren, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Roger M. Nitsch 1 Institute for Regenerative Medicine, University of Zurich , Zurich, Switzerland 8 Neurimmune , Schlieren, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julien Cohen-Adad 4 NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal , Montreal, QC, Canada 5 Mila - Quebec AI Institute , Montreal, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Razansky 2 Institute for Biomedical Engineering, University of Zurich & ETH Zurich , Zurich, Switzerland 3 Institute of Pharmacology and Toxicology, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: Razansky{at}biomed.ee.ethz.ch ruiqing.ni{at}uzh.ch Ruiqing Ni 1 Institute for Regenerative Medicine, University of Zurich , Zurich, Switzerland 2 Institute for Biomedical Engineering, University of Zurich & ETH Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ruiqing Ni For correspondence: Razansky{at}biomed.ee.ethz.ch ruiqing.ni{at}uzh.ch Abstract Full Text Info/History Metrics Preview PDF Abstract Metabolism and bioenergetics in the central nervous system play important roles in the pathophysiology of Parkinson’s disease (PD). Here, we employed a multimodal imaging approach to assess oxygenation changes in the spinal cord of a transgenic M83 murine model of PD in comparison to non-transgenic littermates at 9-12 months-of-age. A lower oxygen saturation (SO 2 ) SVOT was detected in vivo with spiral volumetric optoacoustic tomography (SVOT) in the spinal cord of M83 mice compared to non-transgenic littermate mice. Ex-vivo high-field T1-weighted magnetic resonance imaging (MRI) and immunostaining for alpha-synuclein (phospho-S129) and vascular organisation (CD31 and GLUT1) were used to investigate the nature of the abnormalities detected via in vivo imaging. Ex-vivo analysis showed that the vascular network in the spinal cord was not impaired in the spinal cord of M83 mice. Ex-vivo MRI assisted with deep learning-based automatic segmentation showed no volumetric atrophy in the spinal cord of M83 mice compared to non-transgenic littermates, whereas nuclear alpha-synuclein phosphorylated at Ser129 site could be linked to early pathology and metabolic dysfunction. The proposed and validated non-invasive high-resolution imaging tool to study oxygen saturation in the spinal cord of PD mice holds promise for assessing early changes preceding motor deficits in PD mice. 1. Introduction Parkinson’s disease (PD) is the second most common neurodegenerative disease affecting 1 to 3% of the elderly population (≥60 years old), with an increased prevalence over the past generation(2018). PD is defined by the appearance of clinical features, including bradykinesia, rigidity, and resting tremor ( Poewe et al. 2017 ). The proposed new biological definition ( Simuni et al. 2024 ) and classification of PD ( Höglinger et al. 2024 ) suggest disease staging based on the presence of abundant intracellular aggregates termed Lewy bodies, which are mainly composed of insoluble alpha-synuclein (α-syn) fibrils that hypothesized to trigger selective and progressive neuronal death, regardless of any clinical manifestations. α-Syn undergoes posttranslational modifications, particularly phosphorylation at the Ser129 site( Anderson et al. 2006 ; Fujiwara et al. 2002 ), inducing endogenous α-syn to form these amyloid fibrils and spread within the central nervous system in a prion-like manner ( Goedert et al. 2010 ; Hijaz and Volpicelli-Daley 2020 ). According to the Braak staging of the spreading of α-syn pathology, the spinal cord is affected early in the disease process, at stage 2, even prior to the nigrostriatal pathway (stage 3) and limbic system (stage 4). This is also associated with functional connectivity changes ( Braak and Del Tredici 2017 ; Braak et al. 2003 ; Landelle et al. 2023 ), which may contribute to clinical non-motor and motor symptoms commonly developing in PD patients, including pain, constipation, and poor balance, etc ( Del Tredici and Braak 2012 ). Recent study in animal models of PD showed that spinal cord stimulation could restores locomotion ( Fuentes et al. 2009 ), indicating the its importance in disease progression and intervention. Although the disease mechanisms underlying PD are still not fully understood and disease-modifying therapies still represent an unmet clinical need, growing evidence shows a tight relation between reductions in oxygen supply and α-syn pathology, PD development, and neurodegeneration ( Guo et al. 2022 ; Lestón Pinilla et al. 2021 ). Indeed, the high energetic demand of neurons renders them particularly vulnerable to hypoxia. Oxygen intake and utilization disorders, such as cerebral hypoperfusion ( Melzer et al. 2011 ) and hypoxia in the brain, have been implicated in the pathogenesis of PD ( Guo et al. 2022 ; Pang et al. 2019 ). Risk genes for PD, such as leucine-rich-repeat kinase 2, ATP13A2, PTEN-induced kinase 1 and Parkin ( Blauwendraat et al. 2020 ), are often associated with mitochondrial dysfunction and cellular respiration. In addition, environmental risk factors associated with PD, including air pollution, pesticide and heavy metal exposure, are directly linked to oxygen uptake and utilization disorders by modulating ventilation, competing for haemoglobin binding, or affecting the mitochondrial electron transport chain ( Murata et al. 2022 ) ( Hatcher et al. 2008 ) ( Burtscher et al. 2024 ). Phospho-α-syn (p-α-syn) at Ser129 site (pS129-α-syn) exists at a low level in the healthy brain and plays a physiological role at synapses ( Parra-Rivas et al. 2023 ; Ramalingam et al. 2023 ). Nevertheless, Ser129 site is implicated in all pathologic α-syn aggregates, making this post-translational modification an established pathological marker of α-synucleinopathy ( Anderson et al. 2006 ; Fujiwara et al. 2002 ). Recent in vitro studies have shown that hypoxic stress induced abnormal accumulation of pS129-α-syn, α-syn oligomer and degeneration of dopaminergic neurons ( Guo et al. 2023 ; Li et al. 2022 ). Transient focal ischemia has also been shown to upregulate pS129-α-syn in a stroke mouse model ( Kim et al. 2016 ). In humans, studies have shown increased plasma levels of total α-syn and pS129-α-syn in patients suffering from chronic intermittent hypoxia or obstructive sleep apnea ( Sun et al. 2019 ), which are predisposing factors for PD development. Further exploration of hypoxia signalling mechanisms in α-syn pathology and neurodegeneration will facilitate a better understanding of PD pathogenesis and allow for the exploration of new therapeutic strategies. The current study aims to develop a tool to measure in vivo changes in oxygenation in the spinal cord of a mouse model of α-synucleinopathy using spiral volumetric optoacoustic tomography (SVOT) ( Kalva et al. 2023a , b ; Ron et al. 2019b ) and to assess potential structural volumetric reduction by using ex vivo high-field magnetic resonance imaging (MRI). A number of imaging modalities, such as perfusion and functional MRI ( Laakso et al. 2021 ; Matsubayashi et al. 2018 ; Meyer et al. 2021 ), two-photon phosphorescence lifetime microscopy ( Esipova et al. 2019 ; Wu et al. 2022 ), positron emission tomography ( Mondal et al. 2021 ), contrast enhanced ( Harmon et al. 2024 ) and ultrasound localization microscopy ( Claron et al. 2021 ), have previously been employed for imaging the functional alterations in the spinal cord of rodent models. However, SVOT provides the unique combination of rapid whole-body imaging capacity ( Kalva et al. 2022 ), high spatio-temporal resolution, and rich spectroscopic optical contrast, enabling the direct oxygenation measurements that are unachievable with other modalities ( Ron et al. 2019a ). We used the M83 PD model overexpressing the mutated A53T α-syn form at the early stage of pathology ( Giasson et al. 2002 ) and assessed spinal oxygenation by mapping oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and oxygen saturation (sO 2 ) in vivo with SVOT. This was followed by ex vivo MRI and deep learning-based grey and white matter quantification, as well as immunofluorescence staining for validation ex vivo ( Fig. 1 ). Download figure Open in new tab Figure 1. Workflow and experimental setup of the study. A) Schematic of the SVOT system for head-to-tail volumetric imaging of mice. FB: fiber bundle, DAQ: data acquisition system, OA: optoacoustic. B) Ex vivo MRI of the mouse spinal cord. C) Schematic of spinal cord extraction, sectioning, embedding in SpineRacks ( Fiederling et al. 2021 ) and immunostaining. Scale bar = 50 µm. (Illustration created on Biorender.com ). 2. Results 2.1 Reduction in oxygenation saturation in the spinal cord of M83 mice We first assessed in vivo whether there was alternation in the oxygenation saturation levels in the spinal cord of asymptomatic M83 mice and NTL mice at 9-12 months of age ( Fig. 1A ). SVOT imaging was performed using the spherical array transducer together with the fiber bundle on the back of the mice from neck-to-tail in a step-and-go scanning manner. At each position of the spherical array, multispectral optoacoustic data was spectrally unmixed to differentiate HbO ( Fig. 2A ) and HbR ( Fig. 2B ) content along the spinal cord ( Kalva et al. 2023b ), followed by calculation of the SVOT-derived oxygen saturation (sO SVOT ) ( Fig. 2C ). We observed that sO SVOT levels were lower in M83 mice than in NTL mice. To visualize the differences in the sO 2 SVOT across the spinal cord segments, ex vivo structural T1-weighted (T1w) scans were acquired to use landmarks for segmentation ( Fig. 1B and Fig. 3A ). sO SVOT sagittal maximum intensity projections were segmented into thoracic, lumbar, and sacral regions of interest ( Fig. 3B and 3C ), and the mean sO 2 SVOT was computed ( Fig. 3D ). The cervical part of the spinal cord could not be assessed as too little optoacoustic signal was measured. This may be due to the brown adipose tissue covering this region that is highly absorbing, preventing the light from reaching the spinal cord underneath. Compared to age-matched NTL mice, M83 mice exhibited significant reductions in the sO 2 SVOT in the thoracic (by 15%, p=0.0013), lumbar (by 13%, p=0.0015), sacral regions (by 16%, p=0.0005) as well as total spinal cord (by 14%, p=0.0014) ( Fig. 3D ). This suggested a reduction in sO 2 level in the spinal cord of M83 mice preceding motor symptoms. Download figure Open in new tab Figure 2. In vivo SVOT images of the spinal cords of M83 and NTL (control) mice. A-C) Representative maximum intensity projection images of HbO (A), HbR (B), and sO 2 SVOT (C) in the spinal cords of NTL and M83 mice. Scale bar = 1 cm. BAT, brown adipose tissue. Download figure Open in new tab Figure 3. Reduced sO 2 SVOT in the spinal cord of M83 mice compared to NTL mice. A) Ex vivo MRI showing sections of the thoracic (T, left), lumbar (L, right) and sacral (S, right) vertebral segments from a M83 mouse. First row: sagittal view, second row: transverse view, grid = 2.5 mm. Transverse views display six landmarks of the spinal cord (T1, T6, T11, L1, S1 and S4) used for segmentation. B) Representative sO 2 SVOT distribution (sagittal projection) from a M83 mouse; grid = 3 mm. C) Representation of the same image transformed into grey scale with regions-of-interest marker for further quantification. Cyan: thoracic, orange: lumbar and red: sacral segments. D) Comparison of mean sO 2 SVOT between M83 and NTL mice in the thoracic, lumbar, and sacral segments and in three combined segments (total). sO 2 SVOT scale: 0-1. N = 4 M83 and n = 3 NTLs. 2.2 Low oxygenation in the spinal cord is not associated with spinal volumetric reduction Spinal cord structural organisation was analysed by applying automatic segmentation generated by a deep learning-based model to study potential volumetric changes associated with low spinal oxygenation. To validate the automatic segmentation generated by the deep learning model, we first compared cross-sectional area (CSA) values of grey matter (GM) and white matter (WM) from the model ( Fig. 4B ) with manually segmented CSAs ( Fig. 4A ) on the same sections. Spearman’s correlation analysis shown a very strong positive linear relationship between the deep learning and manual segmentation (n = 26, r = 0.9596, p < 0.0001) ( Fig. 4C ). This indicates that the deep learning-generated GM and WM CSAs model is an efficient and reliable tool to quantify potential structural spinal changes in mice. Download figure Open in new tab Figure 4. Absence of spinal atrophy in the ex vivo T1w MRI images of M83 mice compared to their NTLs (controls). A) Representative transverse section of the thoracic spinal segment. Upper and lower panels show the same image where manual segmentation is depicted in the lower panel. Yellow: grey matter, red: white matter, R: right, L: left, A: anterior, P: posterior; scale bar = 1 cm. B) Representation of deep learning computed segmentation on the same section shown in A. C) Spearman correlation between deep learning-generated and manually segmented CSAs. Yellow and red dots represent values for grey and white matter respectively. D) Comparison of mean grey matter cross-sectional areas (upper panel), white matter cross-sectional areas (middle panel) and grey matter / white matter cross-sectional areas (lower panel) between M83 and NTL mice in the thoracic, lumbar, and sacral segments. N=15 M83 and n=7 NTLs. Quantification using CSAs indicated that there was no volumetric alteration in the spinal cord GM or WM between M83 mice and their NTLs in any of the thoracic, lumbar, or sacral segments of the spinal cord, as well as in the ratio of GM/WM ( Fig. 4D ). NeuN staining showed intact Clark’s column in the thoracic and lumbar spinal segments of M83 mice, although the number of motor neurons appear to be relatively low ( Supplemental Fig. 1A and B ). We further examined whether there was atrophy in the brain of the M83 mice using 9.4T MRI ex vivo. No structural abnormality or volumetric alterations was observed in the M83 mice compared to NTL mice by using MRI ( Supplemental Fig. 2A ). Download figure Open in new tab Suppl Fig 1. Neuronal and astroglial staining in the spinal cord of the M83 mouse model. A) Representative immunofluorescence images of NeuN (yellow) and GFAP (green) in the thoracic spinal region of M83 mice showing intact Clarke’s column (white arrows) and GFAP-positive astrocytes. B) Representative immunofluorescence images of NeuN and GFAP in the lumbar spinal region of M83 mice showing intact motor neurons (white arrows) and GFAP-positive astrocytes. Nuclei were counterstained using DAPI (blue). Scale bars = 200 µm. Download figure Open in new tab Suppl Fig 2. No regional atrophy in the brains of M83 mice compared to NTL mice, despite presence of cerebral pS129-positive (clone EP1536Y) α-Synuclein accumulation. A) High-resolution ex vivo T1w MR image acquired at 9.4 T. No apparent atrophy was observed in the brains of M83 mice compared to NTL mice. B) Representative immunofluorescence images of colocalised pS129 (green) and DAPI (blue) showing intranuclear pS129-positive signals in the cortex of the M83 mouse brain, which is absent in the NTL mouse brain. Scale bar = 100 µm. Earlier studies have shown a vicious circle between neuroinflammation and hypoxia such as in multiple sclerosis ( Yang and Dunn 2019 ) as well as in neurodegenerative diseases of disrupted brain energy metabolism ( Butovsky and Weiner 2018 ) ( van Horssen et al. 2019 ). Next, we performed staining for astrocytes (GFAP) and microglia (Iba1) in the different spinal segments of M83 mice. No apparent astrocytosis and microgliosis was observed throughout the spinal cord of M83 mice. Both astrocytes and microglia displayed homeostatic non-reactive morphology in the sections examined ( Supplemental Fig. 1A and B and Supplemental Fig. 3A and B ). Download figure Open in new tab Suppl Fig 3. No apparent microglia or astrocyte activation in the spinal cord of M83 mice. A) Representative immunohistochemistry images of Iba1-positive microglia in the thoracic spinal segment of the M83 mouse model. B) Representative immunohistochemistry images of GFAP-positive astrocytes in the thoracic spinal segment of the M83 mouse model. Scale bar = 200 µm and 50 µm. 2.3 Low oxygenation in the spinal cord is not due to vascular organization impairment To explore the cause of the reduction in the oxygenation in the spinal cord of M83 mice, we analysed the spinal vascular organization after in vivo imaging ( Fig. 1C ). For this, we first performed immunostaining of the thoracic, lumbar, and sacral regions using the platelet endothelial cell adhesion molecule (CD31), expressed by differentiated endothelial cells ( Fig. 5A ) ( Kecheliev et al. 2023 ). To assess the vasculature network, we used an ImageJ (Fiji) script to automatically calculate (1) the area fraction of blood vessels, (2) the length of blood vessels and (3) the number of branches and junctions ( Rust et al. 2019 ) based on the CD31 staining. No significant differences were observed in the vasculature area (p=0.1586, p=0.1827 and p=0.1314), in the number of branches (p=0.3300, p=0.3300 and p=0.3127), or in the vascular length (p=0.2262, p=0.1335 and p=0.2001) in the thoracic, lumbar, and sacral segments between M83 and NTL control mice ( Fig. 5B, 5C and 5D ). Download figure Open in new tab Figure 5. Vascular density, organization and function are not impaired in the spinal cord of M83 mice. A) Representative immunofluorescence images of CD31 (red), GLUT1 (green) and DAPI (blue) at the level of the thoracic, lumbar and sacral vertebras in NTL and M83 mice with a zoom-in showing close colocalization of CD31 and GLUT1; scale bar = 1 cm. B) Representation of the automated vascular organization analysis. C, D and E) Quantitative evaluation of the vascular area fraction, number of branches and blood vessel length in the different spinal segments of both groups. F) Quantitative evaluation of GLUT1-positive area fraction. N=9 M83 and n=6 NTLs. After analyzing the blood endothelial cell structure, we performed glucose transporter 1 (GLUT1) immunostaining to study the functionality of the vessel in the spinal cord. GLUT1 protein is critical for transporting glucose across the blood-brain barrier to the central nervous system and is highly expressed by endothelial cells ( Kong et al. 2024 ). The GLUT1-positive area fraction was not significantly different between M83 and NTL mice in any of the three spinal segments (p=0.2559, p=0.2559 and p=0.3998, respectively, in the thoracic, lumbar and sacral segments) ( Fig. 5E ). These results suggest that M83 mice do not exhibit vascular network disruption. 2.4 Αlpha-Synucleinopathy throughout the spinal cord in M83 mice Next, we assessed the distribution of α-syn pathology in the spinal cord sections of M83 mice using anti-pS129 α-syn antibodies. Given the complex finding reported in α-syn staining using different antibodies, we used three widely used antibodies against pS129 α-syn, i.e. EP1536Y clone, 81A clone and MJR-R13 clone. The pS129 α-syn signal (EP1536Y clone) was more abundant at the level of the thoracic, lumbar and sacral spinal segments of the spinal cord in M83 mice compared to NTLs, and mainly in the GM ( Fig. 6A ). The % pS129-positive area (EP1536Y clone) was significantly higher in M83 mice than NTLs in the thoracic (1.227% vs 0.006%, p=0.001198), lumbar (1.100% vs 0.010%, p=0.001198) and sacral spinal segments (1.192% vs 0.008%, p=0.002525) ( Fig. 6B ). Next, we calculated the nuclear and the cytoplasmic pS129-positive signals (EP1536Y clone, background subtracted from control immunostaining without primary antibody). The % pS129-positive area (EP1536Y clone) was significantly higher in the nucleus than in the cytoplasmic area in the thoracic (4.08% vs 1.02%, p=0.011673), lumbar (4.21% vs 0.83%, p=0.011673) and sacral spinal segments (3.17% vs 0.96%, p=0.015625) ( Fig. 6C ). Notably, intranuclear pS129 α-syn staining (EP1536Y clone) was also found in the brain, mainly in the cortex of the M83 mice ( Supplemental Fig. 3B ). In contrast, using the other two anti-pS129 α-syn antibodies, 81A clone or MJR-R13 clones, no apparent difference in signal intensity and localization of pS129 α-syn was found throughout the spinal cord of M83 mice and NTLs ( Supplemental Fig. 4 ). Download figure Open in new tab Suppl Fig 4. No apparent difference in pS129 expression in the spinal cord of M83 mice compared to NTL mice revealed by the antibodies 81A and MJR-R13 clones. Representative immunofluorescence images of pS129 targeted with 81A (yellow) and MJR-R13 (green) clones showing similar signal in the thoracic spinal cord segment of the M83 mouse model and NTLs. Nuclei were counterstained using DAPI (blue). Scale bar = 50 µm. Download figure Open in new tab Figure 6. pS129 immunostaining in the spinal cord of M83 and NTL (control) mice using EP1536Y clone. A) Representative immunofluorescence images of colocalised pS129 (green) and DAPI (blue) showing intranuclear pS129-positive signals at the lumbar level of the spinal cord of M83 mice compared to NTL mice. Scale bars = 500 µm and 50 µm. B) Quantitative evaluation of the pS129-positive areas in M83 and NTL control mice, at the level of the thoracic, lumbar, and sacral spinal cord segments. C) Higer levels of relative nuclear pS129-positive areas (grey) than cytoplasmic pS129-positive areas (red dots) in thoracic, lumbar and sacral spinal cord segments. N=9 M83 and n=6 NTLs. 3. Discussion In this work, we demonstrated the preclinical potential of SVOT in measuring the reduced oxygen perfusion in the spinal cord of the M83 mouse model of α-synucleinopathy, with unimpaired GM/WM structure (by MRI) and vascular network in the spinal cord. In addition, we found higher pS129 α-syn accumulation in the spinal cord of M83 mice compared to NTL mice, Higher nuclear than cytoplasmic location of pS129 α-syn was observed in the spinal cord of M83 mice, which might be linked to early pathology and metabolic dysfunction. Optoacoustic imaging has previously been employed to study brain dynamics ( Gottschalk et al. 2019 ), hemodynamic alterations in neurodegenerative disease ( Ni et al. 2018 ) and stroke ( Deán-Ben et al. 2023 ), as well as other proteinopathies using extrinsic contract agents ( Ni et al. 2022 ; Straumann et al. 2023 ; Vagenknecht et al. 2022 ). This method has also been used to detect changes in the spinal cord of an ischemic stroke mouse model ( Ni et al. 2023 ), as well as in an experimental autoimmune encephalomyelitis (EAE) model of MS ( Ramos-Vega et al. 2022 ). To our knowledge, the current study is the first optoacoustic imaging study in PD mouse models. We demonstrated mapping of oxygenation abnormalities in the spinal cord of PD mice with sub 100-micron spatial resolution. SVOT is a state-of-the-art optoacoustic tomography imaging system for preclinical volumetric 3D imaging of the whole-body of small animals with scalable spatio-temporal resolution ( Kalva et al. 2023b ). While other cross-sectional optoacoustic imaging systems can potentially be utilized for whole-body imaging via scanning of a circular ring-shaped array transducer ( Kalva et al. 2023a , b ), the proposed SVOT configuration attains true volumetric (3D) imaging capacity on a whole-body scale with nearly isotropic resolution, making it highly suitable for accurate signal quantification in the spinal cord. By using in vivo SVOT, we showed a 13-16% reduction in the oxygenation level in the thoracic, lumbar, and sacral spinal segments of asymptomatic M83 mice compared to NTLs. Lower spinal cord [ 18 F]fluorodeoxyglucose hypometabolism has been observed in M83 mice at 9 months of age ( Mondal et al. 2021 ). The low oxygen saturation found in the spinal cord of M83 mice is in line with previous studies on cerebral α-syn-associated mitochondrial degeneration in this model when treated with the pesticide paraquat (which is absent in the wild-type human α-syn M7 line) ( Norris et al. 2007 ). In the M83 mouse model, the administration of the iron chelator clioquinol compound that stabilized functional hypoxia-inducible factor-1α, led to improvement in motor function ( Finkelstein et al. 2016 ). This indicates an important role of hypoxia in the motor function of PD rodent model. Hypoperfusion is hypothesized to contribute to neurodegeneration by lowering the neuronal energetic supply in human ( Hernandez-Gerez et al. 2019 ) ( Wolters et al. 2017 ). In mouse models, chronic cerebral hypoperfusion led to WM lesions in the brain ( Shibata et al. 2004 ). In aditional, neuronal functional deficits are associated with spinal cord hypoxia in an EAE model. To study potential structural changes and volumetric atrophy associated with oxygenation impairments, we developed a deep learning model that able to use 3D MRI images as input and automatically segment the GM and the WM. Upon validation of the model, no atrophy was found in the thoracic, lumbar or sacral spinal segments of M83 mice compared to their NTLs. No difference was observed tin the GM, WM areas, or WM/GM ratio (computed with CSAs) for each segment bewteen M83 and NTL mice. An earlier study has shown spinal axonal degeneration with deteriorating myelin in 12 month-old M83 mice ( Giasson et al. 2002 ). Previous studies have shown impaired neurons in the spinal cord of rodent models of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine or rotenone-induced experimental parkinsonism by immunostaining ( Chera et al. 2002 ; Chera et al. 2004 ; Samantaray et al. 2007 ). Here we did not observe apparent alterations in the NeuN staining of the spinal cord slices of M83 mice. One possibility is that asymptomatic M83 mice in this study had oxygen saturation impairments before presenting strong markers of neurodegeneration. Ex vivo MRI might not be sensitive enough to detect axonal degeneration at this stage. The histopathology in the brain, and behavior abnormality development of the transgenic M83 PD mouse model have been well characterized ( Giasson et al. 2002 ). Although the developed motor symptoms are thought to be an outcome of pyramidal or motor neurodegeneration rather than the result of nigrostriatal degeneration in this model ( Benskey et al. 2016 ), little is known about the related pathological processes in the spinal cord. Very few in vivo or ex vivo MRI or optical studies have been performed in the spinal cord of PD animal models ( Chu et al. 2020 ; Ni 2022 ). Functional and structural MRI has been performed on the spinal cord of wild-type ( Bilgen et al. 2005 ; Saito et al. 2012 ), tau mice ( Sartoretti et al. 2022 ), amyotrophic lateral sclerosis mice ( Gao et al. 2020 ; Gatto et al. 2018 ) and experimental autoimmune encephalomyelitis model. To our knowledge, our study is the first MRI study investigating the alterations in spinal cord of PD animal model. To understand the possible reasons for the observed reduction in sO SVOT in M83 compared to NTLs, we investigated possible causes, including alterations in the vasculature, α-syn level as well as glial activation. Our ex vivo analysis of spinal vascular organization and functionality suggested that the vascular network was not impaired. Quantification of vascular density and organization with CD31 expression did not show any significant differences in the vascular area fraction, number of branches or blood vessel length between M83 and NTL mice. GLUT1 expression did not display significant changes between the two strains, suggesting that vessel functionality was also not impaired and was not the cause of the reduction in oxygen saturation. It is also important to note that GLUT1 expression can be enhanced under hypoxic conditions, mainly in tumor environment, which was not the case in our study, in which the impairment in oxygenation is probably only mild ( Bukkuri et al. 2022 ). Neuroinflammation is closely linked to cerebral energy metabolism impariments in neurodegenerative diseases. Recently, induction of neuroinflammation in a mouse model of Alzheimer’s disease has been shown to elicit reductions in cerebral intravascular oxygen and increases in oxygen extraction in the brain ( Davies et al. 2013 ; Liu et al. 2024 ). The neuroinflammatory pathology in the EAE mouse model leads to hypoxia accompanied by reductions in spinal vascular perfusion ( Ramos-Vega et al. 2022 ). In this study, we did not observe any sign of inflammation in the spinal cord of M83 mice by using immunohistochemistry, as astrocytes and microglia do not display reactive phenotypes. This is in line with earlier study showing gliosis only in the symptomatic M83 and M83 mice injected with α-syn preformed fibrils ( Sorrentino et al. 2018 ). Therefore, further study is needed to elucidate the cause of low oxygen saturation in the spinal cord of M83 mice. pS129 α-syn has been the focus in understanding α-synucleinopathies because it is one of the most robust pathological markers of early α-syn aggregation, with almost all the aggregates containing this posttranslational modification ( Anderson et al. 2006 ; Fujiwara et al. 2002 ). On the other hand, pS129 α-syn is known to play a physiological role at the synaptic level in healthy brains and it triggers protein-protein interactions( Parra-Rivas et al. 2023 ; Ramalingam et al. 2023 ). Accumulation of pS129 α-syn has also been observed in the context of oxygen intake and utilization disorders in cells, as well as in middle cerebral artery occlusion rodent models,( Kim et al. 2016 ; Unal-Cevik et al. 2011 ) and in patients with obstructive sleep apnea syndrome ( Chen et al. 2014 ). Nuclear translocation of pS129 α-syn has also been shown following transient focal ischemia in a mouse model of stroke ( Kim et al. 2016 ). Our study revealed an increase in the pS129 α-syn-positive signal within the thoracic, lumbar, and sacral spinal segments in M83 mice compared to their NTLs. Immunofluorescence staining revealed that the main location of pS129 α-syn was in the vicinity of the nucleus. Several studies have suggested a possible nuclear role of α-syn in modulating gene expression, possibly by binding to DNA ( Miller et al. 2007 ), in mouse and cellular models ( Kontopoulos et al. 2006 ; Outeiro et al. 2008 ; Siddiqui et al. 2012 ) as well as in human PD brains ( Garcia-Esparcia et al. 2015 ; Siddiqui et al. 2012 ). Studies have shown the presence of nuclear and perinuclear pS129-α-syn-positive signals on brain slices from transgenic ((Thy1)-[A30P]αSYN) or α-syn fibril-injected rodent models using high-affinity antibodies against the specific α-syn phosphorylated at the Ser129 site (EP1536Y or D1R1R)( Schell et al. 2009 ) ( Delic et al. 2018 ; Elfarrash et al. 2021 ). pS129 α-syn has been shown to modulate the nuclear localization of the wild-type form of α-syn, affecting gene expression, suggesting an important role of the phosphorylation status as well as the different α-syn species within the nucleus (Pinho et al. 2019). The presence of pS129 α-syn in the nucleus remains controversial as numerous antibodies targeting this posttranslational modification of α-syn have been developed with off-target, as well as non-specific binding and disparity in staining signal using these antibodies as reported earlier ( Delic et al. 2018 ; Huang et al. 2011 ; Kumar et al. 2020 ; Lashuel et al. 2022 ). It is noted that α-syn staining appeared to be antibody (clone-dependent) in the spinal cord of M83 mice, with negative staining observed using two other clones targeting pS129 (81A and MJR-R13). Nevertheless, the clone EP1536Y is one of the most robust and widely used antibodies to study alpha-synuclein pathology ( Delic et al. 2018 ). The difference in the pS129-positive signal observed and quantified between M83 mice and NTLs was genotype dependent in our study. These observed alterations can be very useful for studying early pathological changes in this model of PD. Further mechanistic studies on the potential association between low oxygen saturation and intranuclear pS129 α-syn, and the consequences of this nuclear location of pS129 α-syn will be informative. Heterozygous and homozygous M83 mice develop motor impairment at 22-28 and 8-16 months of age respectively. We focused on studying early changes in asymptomatic mice of both hetero- and homozygous M83 mice for the expression of the human mutated A53T α-syn. Similar results in the spinal perfusion, spinal volume and pS129 load was observed in M83 hetero- and homozygous mice. There are several limitations in the present study: 1) unmatched in vivo and ex vivo sample sizes and, sex imbalance in the samples (mice with pigments on the skin inside the region of interest after shaving were excluded from the in vivo study and were only used for ex vivo analysis); 2) spectral colouring effect due to wavelength dependent light attenuation ( Cox et al. 2012 ; Hochuli et al. 2019 ), which can potentially be reduced by correcting for light attenuation with depth ( Chen et al. 2022 ); 3) the cervical part of the spine region displayed very little or no signal and thus could not be measured due to the high absorption of the BAT. Further studies are needed to elucidate the cause underlying the reduction in sO 2 in the spinal cord of M83 mice. 4) We did not further segment GM and WM in the analysis of immunostaining results. 4. Conclusion In conclusion, we demonstrated the use of a non-invasive high-resolution imaging tool to study oxygenation in vivo in the spinal cord of the PD M83 mouse model. SVOT has the potential to measure in vivo spinal cord sO 2 , which is an early pathological marker preceding the motor deficits in M83 mice. Ex vivo high-resolution MRI combined with deep learning automated quantification has further shown that low spinal cord oxygen saturation was not associated with volumetric atrophy in this model of α-synucleinopathy. This new approach thus might facilitate our understanding of metabolic and potential structural changes in the spinal cord of rodent models of PD and the monitoring of treatment effects. 5. Methods Animal model In total, 22 M83 mice (14 homozygous and 8 heterozygous) between 9 and 12 months of age overexpressing human mutated A53T α-syn under the mouse prion promoter (C57Bl/C3H background) ( Giasson et al. 2002 ; Straumann et al. 2023 ) and 13 age-matched non-transgenic littermates (NTLs) of both sexes (M83 10/12 males/females and NTLs 6/7 males/females) were used for different purposes throughout the study. Four M83 (2/2 males/females) and three NTLs (0/3 males/females) were scanned using the in vivo SVOT system, 15 M83 (9/6) and 7 NTLs (2/5) were imaged ex vivo with MRI, and 9 M83 (3/6 males/females) and 6 NTLs (4/2 males/females) were included in the histological tissue analysis. None of the animals displayed abnormal posture, seizures, or paralysis, as can be observed in end-stage aged M83 mice. All the M83 mice were asymptomatic. Animals were housed in individually ventilated cages inside a temperature-controlled room under a 12-h dark/light cycle. Pelleted food (3437PXL15, Cargill) and water were provided ad libitum. All the experiments were performed in accordance with the Swiss Federal Act on Animal Protection and were approved by the Cantonal Veterinary Office Zurich (ZH024/2021). Spiral volumetric optoacoustic tomography (SVOT) The SVOT system employs a Nd:YAG-pumped optical parametric oscillator laser (SpitLight, Innolas Laser GmbH, Krailling, Germany) as an excitation light source. It delivers < 10 ns duration pulses at a repetition rate of 10 Hz over a broad tunable wavelength range (680–1250 nm) with per-pulse energies up to ∼180 mJ. Five wavelengths (700, 730, 760, 800 and 850 nm) were employed repeatedly in succession (20 averages per wavelength) to efficiently unmix the HbO, HbR and total hemoglobin components using the spectral absorption profiles of hemoglobin in oxygenated and deoxygenated forms ( Jacques 2013 ). The light beam was guided through a custom-made fiber bundle (CeramOptec GmBH, Bonn, Germany) placed at a radial distance of 40 mm from the center of a custom-made spherical array. The output bundle created a Gaussian illumination profile with a size of 10 mm at full width at half maximum on the mouse skin surface. The optical fluence on the skin surface was maintained within safe limits according to the American National Standards Institute regulations throughout all experiments (American National Standards and Laser Institute of 2022). A custom-made spherical array, comprising 512 distinct piezoelectric sensor elements, each with a surface area of ∼7 mm², a central detection frequency of 7 MHz, and a detection bandwidth of ∼85% (spanning from 2.6 to 8.6 MHz at the full width at half maximum), was employed to collect the optoacoustic responses ( Kalva et al. 2023a ). These elements were organized on a hemispherical surface with a radius of 40 mm and an angular coverage of 110 degrees (0.85π solid angle). Simultaneously, the optoacoustic signals were digitized at a rate of 40 Mega samples per second using a tailored parallel data acquisition unit, Falkenstein Mikrosysteme GmBH, Taufkirchen, Germany). This data acquisition unit was synchronized with the laser’s Q-switch output and connected to a PC via a 1 Gb/s Ethernet link for data storage and subsequent analysis. The data acquisition process was performed under a custom MATLAB interface (Version R2020b, MathWorks Inc., Natick, MA, USA) running on a PC. Mice (n=4 M83 and n=3 NTLs) were anesthetized with isoflurane (induction: 4% and maintenance: 1.5-2%) and imaged following protocol previously described ( Kalva et al. 2023b ) and maintained under constant anaesthesia and at 36 ± 0.5□°C throughout the experiments. SVOT image reconstruction and spectral unmixing At each scanning position of the spherical array on the back of the mouse, signals were initially averaged 20 times for each wavelength (700, 730, 760, 800 and 850 nm), band-pass filtered between 0.1-12 MHz, and eventually deconvolved with the impulse response of the spherical array sensing elements. Next, a GPU-implemented back-projection (BP) reconstruction technique was employed for the reconstruction of individual volumetric frames, with each US sensing element split into 16 sub-elements ( Kalva et al. 2021 ). Whole-spinal cord images were obtained by stitching individual reconstructed volumes at each corresponding position of the spherical array. To quantify the HbO- and HbR levels in the spinal cord, a linear spectral unmixing algorithm was employed ( Deán-Ben et al. 2023 ; Gottschalk et al. 2019 ). Before unmixing, the reconstructed whole-spinal cord volumes for each wavelength were normalized to the respective optical fluence values. The oxygenation values rendered with SVOT on a voxel-by-voxel basis (sO 2 SVOT ) were then calculated as (HbO/(HbO + HbR))*100. All the image reconstruction and processing steps were performed in MATLAB (MathWorks, Natick, MA, USA). maximum intensity projection images (sagittal view) of the grey scale sO 2 SVOT from the MATLAB files were captured and used for analysis. Regions-of-interest (ROIs) were drawn on these images using ImageJ (NIH) to measure the mean sO SVOT signal intensities in the spinal cord segments. using the atlas of the mouse spinal cord ( Watson et al. 2009 ) as reference. Sample Preparation Immediately following SVOT imaging, M83 and NTLs were intracardially perfused under deep anaesthesia with 0.1 M phosphate buffered saline (PBS, pH 7.4) followed by 4% paraformaldehyde in 0.1 M PBS (pH 7.4). Mouse head and vertebral column samples were postfixed in 4% paraformaldehyde in 0.1 M PBS for 6 days and stored in 0.1 M PBS (pH 7.4) at 4 °C as described earlier ( Sartoretti et al. 2022 ). Ex vivo MRI of the M83 mouse head and spinal cord Mouse head and vertebral column samples were placed in a 15 ml centrifuge tube filled with perfluoropolyether (Fomblin Y, LVAC 16/6, average molecular weight 2,700; Sigma□Aldrich, USA) ( Massalimova et al. 2021 ). Data were acquired on a BioSpec 94/30 preclinical MRI scanner (Bruker BioSpin AG, Fällanden, Switzerland) with a cryogenic 2×2 radio frequency phased-array surface coil (overall coil size of 20×27 mm 2 ). The coil system operated at 30□K for reception in combination with a circularly polarized 86□mm volume resonator for transmission ( Ni et al. 2020 ). For the spinal cord, a structural T1w scan was acquired with a 3D multishot echo planar imaging sequence (4 shots) with a field of view =□25 mm × 10 mm × 10 mm and matrix dimension =□500 × 200 × 200, resulting in a nominal voxel resolution of□50 × 50 × 50 μm. The following imaging parameters were chosen: echo time = 8 ms, repetition time =□50 ms, number of averages□=□4 The total acquisition time was 2 h 28 min for each segment ( Sartoretti et al. 2022 ). For the brain, a structural T1 scan was acquired with a 3D multishot echo planar imaging sequence (4 shots) with a FOV of 18 × 12 × 9 mm and matrix dimension of 180 × 120 × 90, resulting in a nominal voxel resolution of 100 × 100 × 100 μm. Structural T2 scans were acquired using a T2-Turbo rapid acquisition with relaxation-3D sequence with a field of view of 15 × 15 × 8 mm and matrix dimension of 150 ×150×80, resulting in a nominal voxel resolution of 100 × 100 × 100 μm, echo time □=□ 49 ms, repetition time =□ 2000 ms, number of averages□=□1, acquisition scan time □= 52 min 48 sec ( Kecheliev et al. 2023 ). MRI data postprocessing and analysis ITK-SNAP software (v4.0.1, Penn Image Computing and Science Laboratory - PICSL, University of Pennsylvania, Philadelphia, PA) was used to inspect and manually reorient the MR images of the spinal cord. By using an anatomical atlas ( Watson et al. 2009 ) for guidance, representative axial T1w image sections matching the 23 spinal segments from the thoracic, lumbar and sacral parts (T1-T13, L1-L6 and S1-S4) were identified. GM and WM in the spinal cords were automatically segmented using a deep learning-based model trained on manually annotated slices and improved by active learning ( https://github.com/ivadomed/model_seg_mouse-sc_wm-gm_t1 ). The model is based on a 3D nnUNet architecture ( Isensee et al. 2021 ), which takes as input a 3D MRI image and outputs a 3D segmentation file with values 1 for GM, 2 for WM and 0 for background. The GM and WM CSAs were then extracted using the sct_process_segmentation command from the Spinal Cord Toolbox ( De Leener et al. 2017 ) (v6.1) for all 23 identified segments. The average of five CSAs from 5 consecutive sections was calculated per segment and per mouse. GM and WM CSAs as well as WM/GM ratios were further analysed to study potential spinal volumetric atrophy. Validation of the deep learning method was performed by comparing the automatically computed CSAs (without angle correction) with the manually segmented CSAs from the same 13 thoracic sections (T1-T13) for one mouse. GM and WM CSAs were analysed to study potential volumetric alterations. Immunofluorescence and immunohistochemical staining Mouse brain and spinal cords were extracted and placed in 0.1 M PBS for 24 h in 15% sucrose and in 30% sucrose in 0.1 M PBS until the tissue sank or for a maximum of 5 days. Coronal brain sections (30 µm) were obtained using a HM 450 Epredia sliding microtome (Thermo Scientific, UK). Spinal cords were embedded in OCT in SpineRacks ( Fiederling et al. 2021 ) made of natural PVA and printed with the Ultimaker S5 3D printer (Ultimaker B.V.; Geldermalsen, Netherlands). Transverse sectioning (30 µm) was performed using a Leica CM1900 cryostat (Leica Biosystems, Germany). After cutting, free floating sections were stored in 0.1 M PBS + 0.1% sodium azide at 4 °C. For immunofluorescence staining, sections were rinsed three times in 0.1 M PBS for 10 min, followed by a 1 h incubation in 0.1 M PBS, 5% v/v normal donkey serum (NDS), 5% v/v normal goat serum (NGS), and 0.5% v/v Triton-X for blocking and permeabilization. Primary antibodies against NeuN, GFAP, CD31, GLUT-1 and pS129 (EP1536Y, 81A and MJR-R13 clones), were incubated overnight at 4°C (in 0.1 M PBS, 3% v/v NDS, 3% v/v NGS and 0.3% v/v Triton-X) ( Kecheliev et al. 2023 ; Sobek et al. 2023 ). The sections were then rinsed three times for 10 min each in 0.1 M PBS before being incubated for 2 h in species specific secondary antibodies (suspended in 0.1 M PBS with 3% v/v NDS, % v/v NGS and 0.3% v/v Triton-X). The tissues were then counterstained with 4’,6-diamidino-2-phenylindole (DAPI) and mounted on microscope slides in Prolong Diamond antifade mounting medium. For immunochemistry staining, sections were rinsed three times in 0.1 M PBS for 10 min, followed by a 30 min incubation in 0.1 M PBS, 1% H 2 O 2 , before another three times washing and 1 h incubation in 5% v/v NGS, 0.5% v/v Triton-X for blocking and permeabilization. Primary antibodies against Iba1 suspended in 0.1 M PBS, 3% v/v NGS, 0.3% v/v Triton-X, was incubated over night at 4°C. The sections were then rinsed three times for 10 min each in 0.1 M PBS before being incubated in biotinylated anti-mouse secondary antibody (suspended in 0.1 M PBS with 0.5% v/v Triton-X) for 2 h. The sections were then incubated in avidin-biotin complex– horseradish peroxidase for 1 h at room temperature. The sections were developed with 0.025 % 3,3′-diaminobenzidine and 0.05 % H 2 O 2 in triphosphate buffered saline (TBS, pH 7.4) for 3 min. After being mounted on slides, the sections were dehydrated by ascending alcohol series 70%, 90%, and 100% (twice each) and Roticlear for 2 min. Coverslip was finally mounted with roticlear/rotimount mounting medium. Image acquisition and microscopic analysis For vascular organization analysis, platelet endothelial cell adhesion molecule, also known as CD31 staining and GLUT1 staining of two or three sections per segment (thoracic, lumbar and sacral) were imaged at 10× magnification using a TCS SP8 confocal laser scanning microscope (Leica, Germany). For CD31 staining, the vascular area fraction, length, and number of branches of the spinal cord section were assessed by using automated analysis as previously described ( Rust et al. 2019 ). For GLUT1 staining, the area fraction of the spinal cord section was analysed. Three or four pictures per segment (thoracic, lumbar and sacral) were taken at 63× magnification using a confocal laser scanning microscope to study the pS129-positive signal in the of the spinal cord section. Automatic quantification of the colocalization of pS129-positive pixel with DAPI-positive pixel was performed with ImageJ (Fiji, NIH) to quantify its presence in the vicinity of the nucleus. To study the relative nuclear fraction of pS129 α-syn, we divided the pS129-positive signal colocalized with DAPI by the DAPI-positive area. This fraction was named “relative nuclear pS129-positive signal area”. On the other hand, the remaining fraction of the pS129-positive signal was divided by the total area of the image subtracted by the DAPI area to analyse the fraction occupied by this signal in the cytoplasmic compartment (“relative cytoplasmic pS129-positive signal area”). Representative images of CD31, GLUT1 and pS129 staining of the spinal cord section were taken using Zeiss Axio Scan Z1 slidescanner (Carl Zeiss Meditec AG, Germany) at 20× magnification and TCS SP8 confocal laser scanning microscope at 63× magnification. Representative images of GFAP and NeuN staining were taken using TCS SP8 confocal laser scanning microscope at 10× and 20× magnification. Representative images of Iba1 staining were taken using Zeiss Axio Scan Z1 slidescanner at 20× magnification. Statistical analysis All statistical analyses were performed using GraphPad Prism 9.5.0 (GraphPad Software, Inc., USA). Data distributions were first tested for normality by visually assessing the histograms and Shapiro-Wilk test. For distributed data ( Fig. 3 and 5 ), groups were compared using two-way analysis of variance (ANOVA) followed by Holm-Sidak’s multiple comparison post hoc analysis. The data are presented as violin plots with all the points, quartiles and median. data were not normally distributed. Unpaired Mann-Whitney test and Wilcoxon matched-pairs signed rank test, followed by Holm-Sidak’s multiple comparison post hoc analyses have been performed for Fig. 6B and 6C as data was not normally distributed. Violin plots display the mean and bar plots present the mean ± standard deviation. Significance was set at ∗p < 0.05. Declaration Data and code availability All raw data are available upon request to corresponding authors. Code for deep learning-based MRI analysis is available at https://github.com/ivadomed/model_seg_mouse-sc_wm-gm_t1 . Author contributions BC, SK, DR and RN conceived and designed the study. BC, SK and DR developed the SVOT system. SK and XLDB performed the SVOT experiments. AT and LMH performed the MRI experiments. PLB, JN and JCA developed the deep learning-based model for spinal cord segmentation. BC, PLB, JN and JCA contributed to the extraction of relevant data from automatic segmentation. ID performed genotyping. BC performed histopathological immunostaining. BC, SK, MK, RW, PG and R.N. analysed the data. BC wrote the first draft. All authors contributed to the revision of the manuscript. All authors read and approved the final manuscript. Funding RN acknowledges funding from Olga Mayenfisch Stiftung, Swiss Center for Applied Human Toxicology (AP22-1), and Fondation Gustave et Simone Prévot. The authors acknowledge support from the EU Joint Programme – Neurodegenerative Disease Research grant JPND2022-083 (to DR and RN), the Innosuisse – Swiss Innovation Agency grant 51767.1 IP-LS (to XLDB, DR, and RN), and the Swiss National Science Foundation grant 310030_192757 (to DR). ID and DN acknowledge funding from Parkinson Schweiz. JCA acknowledges the Canada Research Chair in Quantitative Magnetic Resonance Imaging [CRC-2020-00179]. Conflict of interest CH and RMN are employees and share-holders of Neurimmune AG, Schlieren, Switzerland. View this table: View inline View popup Download powerpoint Supplementary Table 1. Antibodies, chemicals, and materials used. Acknowledgements The authors would like to thank Center for Microscopy and Image Analysis (ZMB), University of Zurich; Michael Reiss, Mark-Aurel Augath and Diana Kindler at Institute for Biomedical Engineering, ETH Zurich and Daniel Schuppli, and Uwe Konietzko at Institute for Regenerative Medicine, University of Zurich. for technical assistance. List of abbreviations CD31 Cluster of differentiation 31 CSA Cross-sectional areas DAPI 4’,6-diamidino-2-phenylindole GLUT1 Glucose transporter 1 GM Grey matter HbO Oxygenated hemoglobin HbR deoxygenated hemoglobin MRI Magnetic resonance imaging NTL Non-transgenic littermate PD Parkinson’s disease pS129-α-syn Phospho-α-syn (p-α-syn) at Ser129 site sO 2 Oxygen saturation SVOT Spiral volumetric optoacoustic tomography T1w T1-weighted WM White matter α-syn Alpha-synuclein References 2018 . Global, regional, and national burden of Parkinson’s disease, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016 . 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Share Spinal cord perfusion impairments in the M83 mouse model of Parkinson’s disease Benjamin F. Combes , Sandeep Kumar Kalva , Pierre-Louis Benveniste , Agathe Tournant , Man Hoi Law , Joshua Newton , Maik Krüger , Rebecca Z. Weber , Inês Dias , Daniela Noain , Xose Luis Dean-Ben , Uwe Konietzko , Christian R. Baumann , Per-Göran Gillberg , Christoph Hock , Roger M. Nitsch , Julien Cohen-Adad , Daniel Razansky , Ruiqing Ni bioRxiv 2024.04.27.591432; doi: https://doi.org/10.1101/2024.04.27.591432 Share This Article: Copy Citation Tools Spinal cord perfusion impairments in the M83 mouse model of Parkinson’s disease Benjamin F. Combes , Sandeep Kumar Kalva , Pierre-Louis Benveniste , Agathe Tournant , Man Hoi Law , Joshua Newton , Maik Krüger , Rebecca Z. Weber , Inês Dias , Daniela Noain , Xose Luis Dean-Ben , Uwe Konietzko , Christian R. Baumann , Per-Göran Gillberg , Christoph Hock , Roger M. Nitsch , Julien Cohen-Adad , Daniel Razansky , Ruiqing Ni bioRxiv 2024.04.27.591432; doi: https://doi.org/10.1101/2024.04.27.591432 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7644) Biochemistry (17726) Bioengineering (13916) Bioinformatics (42033) Biophysics (21486) Cancer Biology (18635) Cell Biology (25549) Clinical Trials (138) Developmental Biology (13397) Ecology (19940) Epidemiology (2067) Evolutionary Biology (24361) Genetics (15620) Genomics (22541) Immunology (17763) Microbiology (40468) Molecular Biology (17207) Neuroscience (88739) Paleontology (667) Pathology (2842) Pharmacology and Toxicology (4834) Physiology (7659) Plant Biology (15175) Scientific Communication and Education (2047) Synthetic Biology (4304) Systems Biology (9834) Zoology (2272)