A Spatial Dispersion Approach Qualifies to Quantify Vascular Alterations in a Swine Amyotrophic Lateral Sclerosis Model

Main Article Content

Righi Marco Morara Stefano Petrillo Giulia Perota Andrea Cagnotti Giulia Corona Cristiano


Amyotrophic Lateral Sclerosis is still a poorly understood neurological syndrome showing muscle impairment and leading to death because of respiratory failure. Recently, a new, transgenic swine model overexpressing the human superoxide-dismutase 1 gene, promised the chance to investigate animals before disease onset, and we planned to investigate vascular alterations that we recently learned to quantify. In order to address for feasibility, we checked angioarchitectures in spinal cord samples of at least one animal for each of three health conditions: healthy, asymptomatic, clear motor symptoms. Furthermore, analyses were carried out in three different regions: cervical, thoracic and lumbar districts.

            In our approach, we relied on described ImageJ automatic routines, measuring amounts and dispersion of microvascular structures, classified according to their calibers and in spite of the low height of the sample slice. As in previous papers, we investigated amount and volume dispersion of 7 progressively reconstructed angioarchitectures, built from larger calibers through addition of vessels or voxels of smaller and smaller caliber. Results were processed by linear regression to depict a 2D summary pattern, specific for that micro-angioarchitecture.

            Healthy samples presented well dispersed vascular layouts, depicted by near-flat linear regressions. However, they were characterized by large dispersion variances, apparently due to district of origin of the sample itself. On the contrary, results from pathological samples presented lines with increased slopes while retaining the observed inter-sample dispersion variances. Absence of samples from different animals in the same health status prevented us to observe inter-animal variances. Therefore, we could not derive significant biological conclusions on reduced vascularization. Nevertheless, results demonstrated the success of our image analysis approach and provided a “tantalizing” observation of vascular alterations in a swine model for Amyotrophic Lateral Sclerosis.

Article Details

How to Cite
MARCO, Righi et al. A Spatial Dispersion Approach Qualifies to Quantify Vascular Alterations in a Swine Amyotrophic Lateral Sclerosis Model. Medical Research Archives, [S.l.], v. 10, n. 9, sep. 2022. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/3152>. Date accessed: 29 may 2023. doi: https://doi.org/10.18103/mra.v10i9.3152.
Research Articles


1. Brown RH, Al-Chalabi A. Amyotrophic lateral sclerosis. NEJM. 2017; 377:162-172. doi: 10.1056/NEJMra1603471.
2. Andersen PM. Amyotrophic lateral sclerosis associated with mutations in the CuZn superoxide dismutase gene. Curr Neurol Neurosci Rep. 2006; 6:37–46. doi: 10.1007/s11910-996-0008-9.
3. Rosen DR, Siddique T, Patterson D, et al. Mutations in Cu/Zn superoxide dismutase gene are associated with familial amyotrophic lateral sclerosis. Nature 1993; 362:59-62. doi: 10.1038/362059a0.
4. Turner BJ, Talbot K. Transgenics, toxicity and therapeutics in rodent models of mutant SOD1-mediated familial ALS. Prog Neurobiol. 2008; 85:94–134. doi: 10.1016/j.pneurobio.2008.01.001.
5. Wu CH, Fallini C, Ticozzi N, et al. Mutations in the profilin 1 gene cause familial amyotrophic lateral sclerosis. Nature. 2012; 488:499-503. doi: 10.1038/nature11280.
6. Zuo X, Zhou J, Li Y, et al. TDP-43 aggregation induced by oxidative stress causes global mitochondrial imbalance in ALS. Nat Struct Mol Biol. 2021; 28(2):132-142. doi: 10.1038/s41594-020-00537-7.
7. Smith EF, Shaw PJ, De Vos KJ. The role of mitochondria in amyotrophic lateral sclerosis. Neurosci Lett. 2019; 710:132933 doi: 10.1016/j.neulet.2017.06.052.
8. Leigh PN, Meldrum BS. Excitotoxicity in ALS. Neurology. 1996; 47(6 Suppl 4):S221-7. doi: 10.1212/wnl.47.6_suppl_4.221s.
9. King AE, Woodhouse A, Kirkcaldie MT, Vickers JC. Excitotoxicity in ALS: Overstimulation, or overreaction? Exp Neurol. 2016; 275:162-71. doi: 10.1016/j.expneurol.2015.09.019.
10. Peviani M, I Caron, C Pizzasegola, F Gensano, M Tortarolo, C Bendotti. Unraveling the complexity of amyotrophic lateral sclerosis: recent advances from the transgenic mutant SOD1 mice. CNS Neurol Disord Drug Targets 2010; 9:491–503. doi: 10.2174/187152710791556096.
11. Sreedharan J, Blair IP, Tripathi VB, et al. TDP-43 mutations in familial and sporadic amyothropic lateral sclerosis. Science 2008; 319:1668-1672. doi: 10.1126/science.1154584.
12. Perrin S. Preclinical research: make mouse studies work. Nature 2014; 507:423–425. doi: 10.1038/507423a.
13. Oskarsson B, Gendron TF, Staff NP. Amyotrophic Lateral Sclerosis: An Update for 2018. Mayo Clin Proc. 2018; 93:1617-1628. doi: 10.1016/j.mayocp.2018.04.007.
14. Crociara P, Chieppa MN, Costassa EV, et al. Motor neuron degeneration, severe myopathy and TDP-43 increase in a transgenic pig model of SOD1-linked familiar ALS Neurobiol Dis. 2019; 124:263-275. doi: 10.1016/j.nbd.2018.11.021.
15. Eisen A. Response to a letter by Dr T Ramesh. J Neurol. Neurosurg Psychiatry. 2014; 85:1289. doi: 10.1136/jnnp-2014-308588.
16. Talbot K. Amyotrophic lateral sclerosis: cell vulnerability or system vulnerability? J Anat. 2014; 224:45–51. doi: 10.1111/joa.12107.
17. Månberg A, Skene N, Sanders F, et al. Altered perivascular fibroblast activity precedes ALS disease onset. Nature Med. 2021; 27:640-646. doi: 10.1038/s41591-021-01295-9.
18. Guo M, Hao Y, Feng Y, et al. Microglial Exosomes in Neurodegenerative Disease. Front Mol Neurosci. 2021; 14:630808. doi: 10.3389/fnmol.2021.630808.
19. Lewandowski SA, Nilsson I, Fredriksson L, et al. Presymptomatic activation of the PDGF-CC pathway accelerates onset of ALS neurodegeneration. Acta Neuropathol. 2016; 131:453-464. doi: 10.1007/s00401-015-1520-2.
20. Zhong Z, Deane R, Ali Z, et al. ALS-causing SOD1 mutants generate vascular changes prior to motor neuron degeneration. Nat. Neurosci. 2008; 11:420-422. doi: 10.1038/nn2073.
21. Ehling J, Theek B, Gremse F, et al. Micro-CT Imaging of Tumor angiogenesis quantitative measures describing micromorphology and vascularization. Am J Pathol. 2014; 184:431-441. doi: 10.1016/j.ajpath.2013.10.014.
22. Helmberger M, Pienn M, Urschler M, et al. Quantification of tortuosity and fractal dimension of the lung vessels in pulmonary hypertension patients. PLoS ONE 2014; 9:e87515 doi: 10.1371/journal.pone.0087515.
23. Scott A Powner MB, Fruttiger M. Quantification of vascular tortuosity as an early outcome measure in oxigen induced retinopathy (OIR). Exp Eye Res. 2014; 120:55-60 doi: 10.1016/j.exer.2013.12.020.
24. Di Ieva A, Grizzi F, Ceva-Grimaldi G, et al. Fractal dimension as a quantitator of the microvasculature of normal and adenomatous pituitary tissue. J Anat. 2007; 211:673-680. doi: 10.1111/j.1469-7580.2007.00804.x.
25. Van Craenendonck T, Gerrits N, Buelens B, et al. Retinal microvascular complexity comparing mono- and multifractal dimensions in relation to cardiometabolic risk factors in a Middle Eastern population. Acta Ophthalmol. 2021; 99:e368-e377. doi: 10.1111/aos.14598.
26. Wang L, Murphy O, Caldito NG, Calabresi PA, Saidha S. Emerging applications of Optical Coherence Tomography Angiography (OCTA) in neurological research. Eye Vis. (Lond) 2018; 5:11-22 doi: 10.1186/s40662-018-0104-3.
27. O’Bryhim BE, Apte RS, Kung N, Coble D, Van Stevern GP. Association of Preclinical Alzheimer Disease with Optical Coherence Tomography Angiography Findings. JAMA Ophthalmology 2018; 136:1242-1248. doi: 10.1001/jamaophthalmol.2018.3556.
28. Sorrentino FS, Matteini S, Bonifazzi C, Sebastiani A, Parmeggiani F. Diabetic retinopathy and endothelin system: microangiopathy versus endothelial dysfunction. Eye (Lond) 2018; 32:1157-1163. doi: 10.1038/s41433-018-0032-4.
29. Kwapong WR, Ye H, Peng C, et al. Retinal Microvascular Impairment in the Early Stages of Parkinson’s Disease. Invest Ophthalmol Vis. Sci. 2018; 59:4115-4122. doi: 10.1167/iovs.17-23230.
30. Britze J, Frederiksen JL. Optical coherence tomography in multiple sclerosis Eye (Lond) 2018; 32:884-888 doi: 10.1038/s41433-017-0010-2.
31. Righi M, Locatelli SL, Carlo-Stella C, Presta M, Giacomini A. Sci. Rep. 2018; 8:7520-17531. doi: 10.1038/s41598-018-35788-4.
32. Namba M, Oktsuki T, Mori M, et al. Establishment of five human myeloma cell lines. In Vitro Cell Dev Biol. 1989; 25:723-729. doi: 10.1007/BF02623725.
33. Righi M, Belleri M, Presta M, Giacomini A. Quantification of 3D brain micro-angioarchitectures in an animal model of Krabbe Disease. IJMS 2019; 20:2384-2398 doi: 10.3390/ijms20102384.
34. Giacomini A, Ackermann M, Belleri M, et al. Brain angioarchitecture and intussusceptive microvascular growth in a murine model of Krabbe Disease. Angiogenesis 2015; 18:499-510. doi: 10.1007/s10456-015-9481-6.
35. Carol A, Graziano E, Cardile V. History, genetic, and recent advances on Krabbe Disease. Gene 2015; 555: 2-13. doi: 10.1016/j.gene.2014.09.046.
36. Chieppa MN, Perota A, Corona C, et al. Modeling amyotrophic lateral sclerosis in hSOD1 transgenic swine. Neurodegener Dis. 2014; 13:246–254. doi: 10.1159/000353472.
37. Robertson RT, Levine ST, Haynes SM, et al. Use of labeled tomato lectin for imaging vasculature structures. Histochem Cell Biol. 2015; 143:225-34. doi: 10.1007/s00418-014-1301-3.
38. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nature Methods 2012; 9:671-675. doi: 10.1038/nmeth.2089.
39. Righi M, Presta M, Giacomini A. Quantification of Tumor Vasculature by Analysis of Amount and Spatial Dispersion of Caliber-Classified Vessels. Methods Mol Biol. 2021; 2206:151-178. doi: 10.1007/978-1-0716-0916-3_12.
40. Carlo-Stella C, Locatelli SL, Giacomini A, et al. Sorafenib inhibits lymphoma xenografts by targeting MAPK/ERK and AKT pathways in tumor and vascular cells. PLoS ONE. 2013; 8(4):e61603. doi: 10.1371/journal.pone.0061603.
41. Korbecki J, Simińska D, Gąssowska-Dobrowolska M, et al. Chronic and Cycling Hypoxia: Drivers of Cancer Chronic Inflammation through HIF-1 and NF-κB Activation: A Review of the Molecular Mechanisms. Int J Mol Sci. 2021; 22:10701. doi: 10.3390/ijms221910701.
42. LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature 2015; 521:436-444. doi: 10.1038/nature14539.
43. Kermany DS, Goldbaum M, Cai W, et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell 2018; 172:1122-1131. doi: 10.1016/j.cell.2018.02.010.
44. Bogduck Nikolai. Functional anatomy of the spine. Handb Clin Neurol. 2016; 136:675-88. doi: 10.1016/B978-0-444-53486-6.00032-6.
45. Kashima TG, Dongre A, Athanasou NA. Lymphatic involvement in vertebral and disc pathology. Spine (Phila Pa 1976). 2011; 36:899-904. doi: 10.1097/BRS.0b013e3182050284.
46. Arkesteijn ITM, Smolders LA, Spillekom S, et al. Effect of coculturing canine notochordal, nucleus pulposus and mesenchymal stromal cells for intervertebral disc regeneration. Arthritis Res Ther. 2015; 17:60-72. doi: 10.1186/s13075-015-0569-6.