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Cordano C, Werneburg S, Abdelhak A, Bennett DJ, Beaudry-Richard A, Duncan GJ, Oertel FC, Boscardin WJ, Yiu HH, Jabassini N, Merritt L, Nocera S, Sin JH, Samana IP, Condor Montes SY, Ananth K, Bischof A, Nourbakhsh B, Hauser SL, Cree BAC, Emery B, Schafer DP, Chan JR, Green AJ. Synaptic injury in the inner plexiform layer of the retina is associated with progression in multiple sclerosis. Cell Rep Med 2024; 5:101490. [PMID: 38574736 PMCID: PMC11031420 DOI: 10.1016/j.xcrm.2024.101490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 02/01/2024] [Accepted: 03/12/2024] [Indexed: 04/06/2024]
Abstract
While neurodegeneration underlies the pathological basis for permanent disability in multiple sclerosis (MS), predictive biomarkers for progression are lacking. Using an animal model of chronic MS, we find that synaptic injury precedes neuronal loss and identify thinning of the inner plexiform layer (IPL) as an early feature of inflammatory demyelination-prior to symptom onset. As neuronal domains are anatomically segregated in the retina and can be monitored longitudinally, we hypothesize that thinning of the IPL could represent a biomarker for progression in MS. Leveraging our dataset with over 800 participants enrolled for more than 12 years, we find that IPL atrophy directly precedes progression and propose that synaptic loss is predictive of functional decline. Using a blood proteome-wide analysis, we demonstrate a strong correlation between demyelination, glial activation, and synapse loss independent of neuroaxonal injury. In summary, monitoring synaptic injury is a biologically relevant approach that reflects a potential driver of progression.
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Affiliation(s)
- Christian Cordano
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Sebastian Werneburg
- Department of Neurobiology, Brudnik Neuropsychiatric Research Institute, University of Massachusetts Chan Medical School, Worcester, MA, USA; Department of Ophthalmology & Visual Sciences, Michigan Neuroscience Institute, University of Michigan - Michigan Medicine, Ann Arbor, MI, USA
| | - Ahmed Abdelhak
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel J Bennett
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Alexandra Beaudry-Richard
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Greg J Duncan
- Jungers Center for Neurosciences Research, Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Frederike C Oertel
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - W John Boscardin
- Department of Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Hao H Yiu
- Department of Biology, University of Maryland, College Park, MD, USA
| | - Nora Jabassini
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Lauren Merritt
- Department of Neurobiology, Brudnik Neuropsychiatric Research Institute, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Sonia Nocera
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Jung H Sin
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Isaac P Samana
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Shivany Y Condor Montes
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Kirtana Ananth
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Antje Bischof
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Bardia Nourbakhsh
- Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Stephen L Hauser
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Bruce A C Cree
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Ben Emery
- Jungers Center for Neurosciences Research, Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Dorothy P Schafer
- Department of Neurobiology, Brudnik Neuropsychiatric Research Institute, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Jonah R Chan
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Ari J Green
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
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Condor Montes SY, Bennett D, Bensinger E, Rani L, Sherkat Y, Zhao C, Helft Z, Roorda A, Green AJ, Sheehy CK. Characterizing Fixational Eye Motion Variance Over Time as Recorded by the Tracking Scanning Laser Ophthalmoscope. Transl Vis Sci Technol 2022; 11:35. [PMID: 35201339 PMCID: PMC8883154 DOI: 10.1167/tvst.11.2.35] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose The purpose of this study was to characterize the benign biological variance of fixational microsaccades in a control population using a tracking scanning laser ophthalmoscope (TSLO), accounting for machine accuracy and precision, to determine ideal testing conditions to detect pathologic change in fixational eye motion (FEM). Methods We quantified the accuracy and precision of the TSLO, analyzing measurements made by three operators on a model eye. Repeated, 10-second retinal motion traces were then recorded in 17 controls, 3 times a day (morning, afternoon, and evening), on 3 separate days. Microsaccade metrics (MMs) of frequency, average amplitude, peak velocity, and peak acceleration were extracted. Trace to trace, interday, and intraday variability were calculated across all subjects. Results Intra-operator and machine variation contributed minimally to total variation, with only 0.007% and 0.14% contribution for frequency and amplitude respectively. Bias was detected, with lower accuracy for higher amplitudes. Participants had an average (SD) microsaccade frequency of 0.84 Hz (0.52 Hz), amplitude of 0.32 degrees (0.11 degrees), peak velocity of 43.68 degrees/s (14.02 degrees/s), and peak acceleration of 13,920.04 degrees/s2 (4,186.84 degrees/s2). The first trace recorded within a session significantly differed from the second two in both microsaccade acceleration and velocity (P < 0.05), and frequency was 0.098 Hz higher in the evenings (P < 0.05). There was no MM difference between days and no evidence of a session-level learning effect (P > 0.05). Conclusions The TSLO is both accurate and precise. However, biological inter- and intra-individual variance is present. Trace to trace variability and time of day should be accounted for to optimize detection of pathologic change.
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Affiliation(s)
| | - Daniel Bennett
- University of California - San Francisco, Department of Neurology, San Francisco, CA, USA
| | - Ethan Bensinger
- University of California Berkeley, Vision Science Graduate Group, Berkeley, CA, USA.,University of California Berkeley, School of Optometry, Berkeley, CA, USA
| | - Lakshmisahithi Rani
- University of California - San Francisco, Department of Neurology, San Francisco, CA, USA
| | - Younes Sherkat
- University of California Berkeley, College of Engineering, Berkeley, CA, USA
| | - Chao Zhao
- University of California - San Francisco, Department of Neurology, San Francisco, CA, USA
| | | | - Austin Roorda
- University of California Berkeley, Vision Science Graduate Group, Berkeley, CA, USA.,University of California Berkeley, School of Optometry, Berkeley, CA, USA
| | - Ari J Green
- University of California - San Francisco, Department of Neurology, San Francisco, CA, USA.,University of California - San Francisco, Department of Ophthalmology, San Francisco, CA, USA
| | - Christy K Sheehy
- University of California - San Francisco, Department of Neurology, San Francisco, CA, USA.,C. Light Technologies, Inc. Berkeley, CA, USA
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