Jaewoo Mo, Junseong Bae, Jahanzeb Saqib, Dohyun Hwang, Yunjung Jin, Beomsu Park, Jeongbin Park, Junil Kim
{"title":"癌症生物学中空间转录组学的当前计算方法。","authors":"Jaewoo Mo, Junseong Bae, Jahanzeb Saqib, Dohyun Hwang, Yunjung Jin, Beomsu Park, Jeongbin Park, Junil Kim","doi":"10.1016/bs.acr.2024.06.006","DOIUrl":null,"url":null,"abstract":"<p><p>Cells in multicellular organisms constitute a self-organizing society by interacting with their neighbors. Cancer originates from malfunction of cellular behavior in the context of such a self-organizing system. The identities or characteristics of individual tumor cells can be represented by the hallmark of gene expression or transcriptome, which can be addressed using single-cell dissociation followed by RNA sequencing. However, the dissociation process of single cells results in losing the cellular address in tissue or neighbor information of each tumor cell, which is critical to understanding the malfunctioning cellular behavior in the microenvironment. Spatial transcriptomics technology enables measuring the transcriptome which is tagged by the address within a tissue. However, to understand cellular behavior in a self-organizing society, we need to apply mathematical or statistical methods. Here, we provide a review on current computational methods for spatial transcriptomics in cancer biology.</p>","PeriodicalId":94294,"journal":{"name":"Advances in cancer research","volume":"163 ","pages":"71-106"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Current computational methods for spatial transcriptomics in cancer biology.\",\"authors\":\"Jaewoo Mo, Junseong Bae, Jahanzeb Saqib, Dohyun Hwang, Yunjung Jin, Beomsu Park, Jeongbin Park, Junil Kim\",\"doi\":\"10.1016/bs.acr.2024.06.006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Cells in multicellular organisms constitute a self-organizing society by interacting with their neighbors. Cancer originates from malfunction of cellular behavior in the context of such a self-organizing system. The identities or characteristics of individual tumor cells can be represented by the hallmark of gene expression or transcriptome, which can be addressed using single-cell dissociation followed by RNA sequencing. However, the dissociation process of single cells results in losing the cellular address in tissue or neighbor information of each tumor cell, which is critical to understanding the malfunctioning cellular behavior in the microenvironment. Spatial transcriptomics technology enables measuring the transcriptome which is tagged by the address within a tissue. However, to understand cellular behavior in a self-organizing society, we need to apply mathematical or statistical methods. Here, we provide a review on current computational methods for spatial transcriptomics in cancer biology.</p>\",\"PeriodicalId\":94294,\"journal\":{\"name\":\"Advances in cancer research\",\"volume\":\"163 \",\"pages\":\"71-106\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in cancer research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/bs.acr.2024.06.006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in cancer research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/bs.acr.2024.06.006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/25 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Current computational methods for spatial transcriptomics in cancer biology.
Cells in multicellular organisms constitute a self-organizing society by interacting with their neighbors. Cancer originates from malfunction of cellular behavior in the context of such a self-organizing system. The identities or characteristics of individual tumor cells can be represented by the hallmark of gene expression or transcriptome, which can be addressed using single-cell dissociation followed by RNA sequencing. However, the dissociation process of single cells results in losing the cellular address in tissue or neighbor information of each tumor cell, which is critical to understanding the malfunctioning cellular behavior in the microenvironment. Spatial transcriptomics technology enables measuring the transcriptome which is tagged by the address within a tissue. However, to understand cellular behavior in a self-organizing society, we need to apply mathematical or statistical methods. Here, we provide a review on current computational methods for spatial transcriptomics in cancer biology.