Jessica Maxey, Marshall A. Thompson, Katie M. Campbell, B. Kamphaus, Zaid Bustami, Sandra Santulli-Marotto, Daniel K. Wells, S. Boffo, Lisa Salvador, P. Scumpia, Christine N. Spencer, Adam Schoenfeld, Antoni Ribas, L. Kitch
{"title":"1292用于多重成像(MIBI)数据分析的可重复管道,用于揭示肿瘤免疫微环境的新特征","authors":"Jessica Maxey, Marshall A. Thompson, Katie M. Campbell, B. Kamphaus, Zaid Bustami, Sandra Santulli-Marotto, Daniel K. Wells, S. Boffo, Lisa Salvador, P. Scumpia, Christine N. Spencer, Adam Schoenfeld, Antoni Ribas, L. Kitch","doi":"10.1136/jitc-2022-sitc2022.1292","DOIUrl":null,"url":null,"abstract":"Background Although immune checkpoint inhibition (ICI) has been transformational, tumor-associated factors regulating response have not been elucidated. High-dimensional spatial profiling technologies have enabled simultaneous investigation of many protein targets on individual cells within the spatial context of the tumor microenvironment (TME). Analysis of these data to uncover immune and tumor profiles relies on identification of individual cells and characterization of their specific marker expression to classify lineage and functional state. However, robust automated cell type assignment remains a key challenge in multiplex image data analysis. Here, we describe a reproducible pipeline for single cell identification and typing from multiplex ion beam imaging (MIBI) data uti-lizing lineage protein expression, which has applications in the context of precision immunotherapy and beyond.","PeriodicalId":398566,"journal":{"name":"Regular and Young Investigator Award Abstracts","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"1292 A reproducible pipeline for analysis of multiplex imaging (MIBI) data with application to uncovering novel features of the tumor-immune microenvironment\",\"authors\":\"Jessica Maxey, Marshall A. Thompson, Katie M. Campbell, B. Kamphaus, Zaid Bustami, Sandra Santulli-Marotto, Daniel K. Wells, S. Boffo, Lisa Salvador, P. Scumpia, Christine N. Spencer, Adam Schoenfeld, Antoni Ribas, L. Kitch\",\"doi\":\"10.1136/jitc-2022-sitc2022.1292\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background Although immune checkpoint inhibition (ICI) has been transformational, tumor-associated factors regulating response have not been elucidated. High-dimensional spatial profiling technologies have enabled simultaneous investigation of many protein targets on individual cells within the spatial context of the tumor microenvironment (TME). Analysis of these data to uncover immune and tumor profiles relies on identification of individual cells and characterization of their specific marker expression to classify lineage and functional state. However, robust automated cell type assignment remains a key challenge in multiplex image data analysis. Here, we describe a reproducible pipeline for single cell identification and typing from multiplex ion beam imaging (MIBI) data uti-lizing lineage protein expression, which has applications in the context of precision immunotherapy and beyond.\",\"PeriodicalId\":398566,\"journal\":{\"name\":\"Regular and Young Investigator Award Abstracts\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Regular and Young Investigator Award Abstracts\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1136/jitc-2022-sitc2022.1292\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Regular and Young Investigator Award Abstracts","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/jitc-2022-sitc2022.1292","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
1292 A reproducible pipeline for analysis of multiplex imaging (MIBI) data with application to uncovering novel features of the tumor-immune microenvironment
Background Although immune checkpoint inhibition (ICI) has been transformational, tumor-associated factors regulating response have not been elucidated. High-dimensional spatial profiling technologies have enabled simultaneous investigation of many protein targets on individual cells within the spatial context of the tumor microenvironment (TME). Analysis of these data to uncover immune and tumor profiles relies on identification of individual cells and characterization of their specific marker expression to classify lineage and functional state. However, robust automated cell type assignment remains a key challenge in multiplex image data analysis. Here, we describe a reproducible pipeline for single cell identification and typing from multiplex ion beam imaging (MIBI) data uti-lizing lineage protein expression, which has applications in the context of precision immunotherapy and beyond.