.png){kind=icon}
返回
Mapping cellular interactions from spatially resolved transcriptomics data
.png){kind=icon}
PRE
AI
DOI：10.1038/s41592-024-02408-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications. Spacia is a multiple-instance learning model for cell-cell communication (CCC) interference in single-cell resolution spatially resolved transcriptomics data. Spacia can map complex CCCs by modeling cell proximity and CCC-driven gene perturbation.
Keyword:
EPITHELIAL-MESENCHYMAL TRANSITION
GENOME-WIDE EXPRESSION
GASTRIC-CANCER
CELLS
METASTASIS
MIGRATION
SEQ
期刊
IF:
32.1
论文数:
7.2K
被引数:
12.7W
机构
引用论文
Large scale poly(vinyl alcohol-co-ethylene)/TiO2hybrid nanofibrous filters with efficient fine particle filtration and repetitive-use performance.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Comparative analysis of dimension reduction methods for cytometry by time-of-flight dataNATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer乳腺癌患者anti-PD1治疗期间肿瘤内变化的单细胞图NATURE MEDICINEOAAI
NATURE MEDICINEIF50
OAAI分享收藏分享收藏CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexesCellPhoneDB: 从多亚基配体-受体复合物的联合表达推断细胞-细胞通讯NATURE PROTOCOLSOAAI
NATURE PROTOCOLSIF16
OAAI分享收藏TGF-β1 exposure induces epithelial to mesenchymal transition both in CSCs and non-CSCs of the A549 cell line, leading to an increase of migration ability in the CD133+ A549 cell fractionTGF-β1暴露诱导A549细胞系的CSCs和非CSCs中的上皮间质转化，导致CD133 A549细胞级分的迁移能力增加CELL DEATH & DISEASEOAAI
CELL DEATH & DISEASEIF9.6
OAAI分享收藏A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization基于组织学的细胞核分割和肿瘤微环境表征的深度学习方法MODERN PATHOLOGYOAAI
MODERN PATHOLOGYIF5.5
OAAI分享收藏
查看更多内容