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A Semi-Automated Software for Myelin G-Ratio Quantification Research Article: Open Source Tools and Methods | Novel Tools and Methods MyelTracer: A semi-automated software for myelin g-ratio quantification https://doi.org/10.1523/ENEURO.0558-20.2021 Cite as: eNeuro 2021; 10.1523/ENEURO.0558-20.2021 Received: 23 December 2020 Revised: 14 June 2021 Accepted: 17 June 2021 This Early Release article has been peer-reviewed and accepted, but has not been through the composition and copyediting processes. The final version may differ slightly in style or formatting and will contain links to any extended data. Alerts: Sign up at www.eneuro.org/alerts to receive customized email alerts when the fully formatted version of this article is published. Copyright © 2021 Kaiser et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. 1 Manuscript Title Page Instructions 2 Please list the following information on your separate title page in Word .DOC format in the order 3 listed below and upload as a Title Page file at submission 4 1. Manuscript Title (50 word maximum) 5 MyelTracer: A semi-automated software for myelin g-ratio quantification 6 2. Abbreviated Title (50 character maximum) 7 Myelin g-ratio software 8 3. List all Author Names and Affiliations in order as they would appear in the published article 9 Tobias Kaiser1,2,*, Harrison Allen1,3,*, Ohyoon Kwon1,2,*, Boaz Barak4, Jing Wang5, Zhigang He5, Minqing 10 Jiang1,2,#, Guoping Feng1,2,6,# 11 1McGovern Institute for Brain Research 12 2Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 13 02139, USA 14 3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 15 Cambridge, MA 02139, USA 16 Massachusetts Institute of Technology, Cambridge, MA 02139, USA 17 4The Sagol School of Neuroscience and The School of Psychological Sciences, Tel Aviv University, 18 Israel 19 5F.M. Kirby Neurobiology Center, Boston Children’s Hospital, and Department of Neurology and 20 Ophthalmology, Harvard Medical School, Boston, MA, USA 21 6Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, 22 USA 23 24 *these authors contributed equally 25 #corresponding author 26 27 4. Author Contributions: 28 TK, HA, MJ, and GF designed the research. HA wrote the software. TK and MJ generated the electron 29 microscopy data. TK, MJ, and OK analyzed the data and benchmarked the software. BB, JW, ZH 30 contributed electron micrographs for additional analysis. TK, MJ, and GF wrote the paper with inputs 31 from all authors. 32 5. Correspondence should be addressed to (include email address) 33 Minqing Jiang; [email protected] 34 Guoping Feng; [email protected] 35 1 36 6. Number of Figures: 3 37 7. Number of Tables: 1 38 8. Number of Multimedia: 0 39 9. Number of words for Abstract: 159 40 10. Number of words for Significance Statement: 46 41 11. Number of words for Introduction: 562 42 12. Number of words for Discussion: 817 43 13. Acknowledgements 44 We thank all members of the Feng lab for helpful discussions. We would also like to acknowledge 45 Nicholas Sanders and Dongqing Wang for excellent technical support and Boaz Barak for sharing data 46 from Williams Syndrome mice. We further thank Maria Ericsson and Louise Trakimas at the Harvard 47 Electron Microscopy Core facility. TK is supported by the Tan-Yang center for Autism Research at MIT. 48 GF is supported by the National Institute of Mental Health (5R01MH097104), the Poitras Center for 49 Affective Disorders Research at MIT, Tan-Yang center for Autism Research at MIT, Stanley Center for 50 Psychiatric Research at Broad Institute of MIT and Harvard, Nancy Lurie Marks Family Foundation, 51 Simons Foundation Autism Research Initiative (SFARI) and Simons Center for the Social Brain at MIT. 52 14. Conflict of Interest 53 A. No (State ‘Authors report no conflict of interest’) 54 The authors report no conflict of interest. 55 B. Yes (Please explain) 56 15. Funding sources 57 Tan-Yang Center for Autism Research at MIT 58 Simons Foundation Autism Research Initiative 59 Simons Center for the Social Brain at MIT 60 NIMH (R01MH097104) 61 Poitras Center for Affective Disorders Research at MIT 62 Stanley Center for Psychiatric Research at Broad Institute of MIT and Harvard 63 Nancy Lurie Marks Family Foundation 64 2 65 MyelTracer: A semi-automated software for myelin g-ratio quantification 66 67 Abstract 68 69 In the central and peripheral nervous systems, the myelin sheath promotes neuronal 70 signal transduction. The thickness of the myelin sheath changes during development and in 71 disease conditions like multiple sclerosis. Such changes are routinely detected using electron 72 microscopy through g-ratio quantification. While g-ratio is one of the most critical measurements 73 in myelin studies, a major drawback is that g-ratio quantification is extremely laborious and 74 time-consuming. Here, we report the development and validation of MyelTracer, an installable, 75 stand-alone software for semi-automated g-ratio quantification based on the Open Computer 76 Vision Library (OpenCV). Compared to manual g-ratio quantification, using MyelTracer 77 produces consistent results across multiple tissues and animal ages, as well as in remyelination 78 after optic nerve crush, and reduces total quantification time by 40-60%. With g-ratio 79 measurements via MyelTracer, a known hypomyelination phenotype can be detected in a 80 Williams Syndrome mouse model. MyelTracer is easy to use and freely available for Windows 81 and Mac OS X [GitHub link, blinded for review]. 82 83 1 84 85 Significance statement 86 An easy-to-use software suite for g-ratio quantification in myelin ultrastructure 87 micrographs is currently unavailable, but it is much needed to streamline the study of myelin in 88 homeostasis and disease-related conditions. We used computer vision libraries to develop a 89 freely available software to facilitate studies of myelination. 90 91 Introduction 92 93 The myelin sheath is essential for proper neuronal functions in central and peripheral 94 nervous systems. Oligodendrocytes and Schwann cells produce the myelin sheath through 95 lamellar enwrapping of axons (Simons and Trotter, 2007; Salzer, 2015; Hughes and Appel, 2016; 96 Stadelmann et al., 2019). The myelin sheath insulates axons, thereby preserving axonal integrity 97 and promoting neuronal signal transduction (Sherman and Brophy, 2005; Nave, 2010). Under 98 physiological conditions, changes in myelin contribute to behaviorally relevant neural plasticity 99 mechanisms and experience-dependent sensory adaptations (Gibson et al., 2014; McKenzie et 100 al., 2014; Hughes et al., 2018). 101 Abnormal changes in myelin are prominent features of many clinical pathologies. 102 Ultrastructural myelin abnormalities, such as hypomyelination, myelin degeneration, and 103 tomaculi, are cardinal features of prototypic white-matter diseases like multiple sclerosis, 104 Pelizaeus Merzbacher disease, and Charcot-Marie-Tooth disease (Krajewski et al., 2000; Sander 105 et al., 2000; Franklin and ffrench-Constant, 2008; Lin and Popko, 2009; Duncan and Radcliff, 106 2016). In addition, myelin abnormalities have recently been discovered as common features of 2 107 neurodevelopmental disorders, including Pitt Hopkins Syndrome, Rett Syndrome, autism 108 spectrum disorders (ASDs), and William’s Syndrome (Zhao et al., 2018; Barak et al., 2019; Phan 109 et al., 2020). 110 In both preclinical and clinical settings, pathological myelin abnormalities are often 111 identified by studying myelin ultrastructure. Specifically, researchers use g-ratio as a metric for 112 the relative thickness of the myelin sheath in cross-sectional micrographs (Rushton, 1951). Since 113 manual myelin tracing is a major bottleneck in g-ratio quantification, tools that streamline this 114 process will tremendously facilitate future studies investigating activity-dependent homeostatic 115 and pathological changes to the myelin sheath. 116 Several non-automated, semi-automated, and fully automated toolboxes are currently 117 available. They include G-ratio for ImageJ, AxonSeg, and AxonDeepSeg (Goebbels et al., 2010; 118 More et al., 2011; Bégin et al., 2014; Zaimi et al., 2016, 2018; Janjic et al., 2019). Collectively, 119 these tools have facilitated different quantitative measurements of myelin ultrastructure, such as 120 the number of axons and g-ratio. However, the current tools with more automation impose 121 difficult requirements on their users, such as pre-processing data or modifying parameters by 122 writing code. These drawbacks have impeded the tools’ widespread adoption by the scientific 123 community. We identified four major requirements of an open access toolbox that would 124 significantly facilitate myelin analyses: (1) an intuitive graphical user interface that can be 125 installed and used without running code, (2) consistent quantification results compared to manual 126 analysis, (3) significantly less time-consuming, and (4) well-organized output data files and 127 overlay images for publication and post-hoc quality control. 128 Here we report the development and validation of MyelTracer, an easy-to-use software 129 that fulfills these critical requirements. Built using OpenCV and PyQt5's GUI toolkit, 3 130 MyelTracer replaces the manual tracing of axons and myelin with semi-automated,
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