Tiger Cross, Rasika Navarange, Joon-ho Son, William Burr, Arjun Singh, Kelvin Zhang, M. Rusu, Konstantinos Gkoutzis, A. Osborne, Bart Nieuwenhuis Department of Computing, I. -. London, John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, U. Cambridge, L. Systems, Netherlands Institute for Neuroscience, R. Arts, Sciences
{"title":"简单的RGC: ImageJ插件用于计数视网膜神经节细胞和确定病毒载体在视网膜整体的转导效率","authors":"Tiger Cross, Rasika Navarange, Joon-ho Son, William Burr, Arjun Singh, Kelvin Zhang, M. Rusu, Konstantinos Gkoutzis, A. Osborne, Bart Nieuwenhuis Department of Computing, I. -. London, John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, U. Cambridge, L. Systems, Netherlands Institute for Neuroscience, R. Arts, Sciences","doi":"10.5334/jors.342","DOIUrl":null,"url":null,"abstract":"Simple RGC consists of a collection of ImageJ plugins to assist researchers investigating retinal ganglion cell (RGC) injury models in addition to helping assess the effectiveness of treatments. The first plugin named RGC Counter accurately calculates the total number of RGCs from retinal wholemount images. The second plugin named RGC Transduction measures the co-localisation between two channels making it possible to determine the transduction efficiencies of viral vectors and transgene expression levels. The third plugin named RGC Batch is a batch image processor to deliver fast analysis of large groups of microscope images. These ImageJ plugins make analysis of RGCs in retinal wholemounts easy, quick, consistent, and less prone to unconscious bias by the investigator. The plugins are freely available from the ImageJ update site this https URL.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Simple RGC: ImageJ Plugins for Counting Retinal Ganglion Cells and Determining the Transduction Efficiency of Viral Vectors in Retinal Wholemounts\",\"authors\":\"Tiger Cross, Rasika Navarange, Joon-ho Son, William Burr, Arjun Singh, Kelvin Zhang, M. Rusu, Konstantinos Gkoutzis, A. Osborne, Bart Nieuwenhuis Department of Computing, I. -. London, John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, U. Cambridge, L. Systems, Netherlands Institute for Neuroscience, R. Arts, Sciences\",\"doi\":\"10.5334/jors.342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Simple RGC consists of a collection of ImageJ plugins to assist researchers investigating retinal ganglion cell (RGC) injury models in addition to helping assess the effectiveness of treatments. The first plugin named RGC Counter accurately calculates the total number of RGCs from retinal wholemount images. The second plugin named RGC Transduction measures the co-localisation between two channels making it possible to determine the transduction efficiencies of viral vectors and transgene expression levels. The third plugin named RGC Batch is a batch image processor to deliver fast analysis of large groups of microscope images. These ImageJ plugins make analysis of RGCs in retinal wholemounts easy, quick, consistent, and less prone to unconscious bias by the investigator. The plugins are freely available from the ImageJ update site this https URL.\",\"PeriodicalId\":298664,\"journal\":{\"name\":\"arXiv: Neurons and Cognition\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv: Neurons and Cognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5334/jors.342\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv: Neurons and Cognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5334/jors.342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Simple RGC: ImageJ Plugins for Counting Retinal Ganglion Cells and Determining the Transduction Efficiency of Viral Vectors in Retinal Wholemounts
Simple RGC consists of a collection of ImageJ plugins to assist researchers investigating retinal ganglion cell (RGC) injury models in addition to helping assess the effectiveness of treatments. The first plugin named RGC Counter accurately calculates the total number of RGCs from retinal wholemount images. The second plugin named RGC Transduction measures the co-localisation between two channels making it possible to determine the transduction efficiencies of viral vectors and transgene expression levels. The third plugin named RGC Batch is a batch image processor to deliver fast analysis of large groups of microscope images. These ImageJ plugins make analysis of RGCs in retinal wholemounts easy, quick, consistent, and less prone to unconscious bias by the investigator. The plugins are freely available from the ImageJ update site this https URL.
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