CellNeighborEX:从空间转录组学数据中破译邻居依赖性基因表达。

IF 8.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular Systems Biology Pub Date : 2023-11-09 Epub Date: 2023-10-10 DOI:10.15252/msb.202311670
Hyobin Kim, Amit Kumar, Cecilia Lövkvist, António M Palma, Patrick Martin, Junil Kim, Praveen Bhoopathi, Jose Trevino, Paul Fisher, Esha Madan, Rajan Gogna, Kyoung Jae Won
{"title":"CellNeighborEX:从空间转录组学数据中破译邻居依赖性基因表达。","authors":"Hyobin Kim, Amit Kumar, Cecilia Lövkvist, António M Palma, Patrick Martin, Junil Kim, Praveen Bhoopathi, Jose Trevino, Paul Fisher, Esha Madan, Rajan Gogna, Kyoung Jae Won","doi":"10.15252/msb.202311670","DOIUrl":null,"url":null,"abstract":"<p><p>Cells have evolved their communication methods to sense their microenvironments and send biological signals. In addition to communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach to investigate cell neighbor-dependent gene expression (CellNeighborEX) in spatial transcriptomics (ST) data. To categorize cells based on their microenvironment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on ST technologies. For each cell type, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. We found that cells express different genes depending on their neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. Those genes are associated with critical biological processes such as development or metastases. We further validated that gene expression is induced by neighboring partners via spatial visualization. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"e11670"},"PeriodicalIF":8.5000,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632736/pdf/","citationCount":"0","resultStr":"{\"title\":\"CellNeighborEX: deciphering neighbor-dependent gene expression from spatial transcriptomics data.\",\"authors\":\"Hyobin Kim, Amit Kumar, Cecilia Lövkvist, António M Palma, Patrick Martin, Junil Kim, Praveen Bhoopathi, Jose Trevino, Paul Fisher, Esha Madan, Rajan Gogna, Kyoung Jae Won\",\"doi\":\"10.15252/msb.202311670\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Cells have evolved their communication methods to sense their microenvironments and send biological signals. In addition to communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach to investigate cell neighbor-dependent gene expression (CellNeighborEX) in spatial transcriptomics (ST) data. To categorize cells based on their microenvironment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on ST technologies. For each cell type, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. We found that cells express different genes depending on their neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. Those genes are associated with critical biological processes such as development or metastases. We further validated that gene expression is induced by neighboring partners via spatial visualization. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.</p>\",\"PeriodicalId\":18906,\"journal\":{\"name\":\"Molecular Systems Biology\",\"volume\":\" \",\"pages\":\"e11670\"},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2023-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632736/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Systems Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.15252/msb.202311670\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/10/10 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Systems Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.15252/msb.202311670","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/10/10 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

细胞已经进化出了感知微环境和发送生物信号的通信方法。除了使用配体和受体进行交流外,细胞还使用包括间隙连接在内的多种通道与近邻进行交流。然而,目前的方法无法有效地捕捉各种微环境的影响。在这里,我们提出了一种新的方法来研究空间转录组学(ST)数据中的细胞邻居依赖性基因表达(CellNeighborEX)。为了根据细胞的微环境对其进行分类,CellNeighborEX根据ST技术使用直接的细胞定位或来自多个细胞的转录组的混合。对于每种细胞类型,CellNeighborEX都能识别出与伴侣细胞类型相关的不同基因集,从而提供进一步的见解。我们发现,细胞在各种组织中表达不同的基因,这取决于其相邻的细胞类型,包括小鼠胚胎、脑和肝癌。这些基因与关键的生物学过程有关,如发育或转移。我们通过空间可视化进一步验证了基因表达是由相邻伴侣诱导的。邻居依赖性基因表达表明,参与细胞-细胞相互作用的新的潜在基因超出了配体-受体共表达所能发现的范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
CellNeighborEX: deciphering neighbor-dependent gene expression from spatial transcriptomics data.

Cells have evolved their communication methods to sense their microenvironments and send biological signals. In addition to communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach to investigate cell neighbor-dependent gene expression (CellNeighborEX) in spatial transcriptomics (ST) data. To categorize cells based on their microenvironment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on ST technologies. For each cell type, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. We found that cells express different genes depending on their neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. Those genes are associated with critical biological processes such as development or metastases. We further validated that gene expression is induced by neighboring partners via spatial visualization. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Molecular Systems Biology
Molecular Systems Biology 生物-生化与分子生物学
CiteScore
18.50
自引率
1.00%
发文量
62
审稿时长
6-12 weeks
期刊介绍: Systems biology is a field that aims to understand complex biological systems by studying their components and how they interact. It is an integrative discipline that seeks to explain the properties and behavior of these systems. Molecular Systems Biology is a scholarly journal that publishes top-notch research in the areas of systems biology, synthetic biology, and systems medicine. It is an open access journal, meaning that its content is freely available to readers, and it is peer-reviewed to ensure the quality of the published work.
期刊最新文献
Understanding the biological processes of kidney carcinogenesis: an integrative multi-omics approach. Enhancers and genome conformation provide complex transcriptional control of a herpesviral gene. Global atlas of predicted functional domains in Legionella pneumophila Dot/Icm translocated effectors. Subcellular mRNA kinetic modeling reveals nuclear retention as rate-limiting. Identifying T-cell clubs by embracing the local harmony between TCR and gene expressions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1