Xiaoyong Chen, Shichao Lin, Honghai You, Jinyuan Chen, Qiaoyi Wu, Kun Yin, Fanghe Lin, Yingkun Zhang, Jia Song, Chenyu Ding, Dezhi Kang, Chaoyong Yang
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Integrating Metabolic RNA Labeling-Based Time-Resolved Single-Cell RNA Sequencing with Spatial Transcriptomics for Spatiotemporal Transcriptomic Analysis.
Metabolic RNA labeling-based time-resolved single-cell RNA sequencing (scRNA-seq) has provided unprecedented tools to dissect the temporal dynamics and the complex gene regulatory networks of gene expression. However, this technology fails to reveal the spatial organization of cells in tissues, which also regulates the gene expression by intercellular communication. Herein, it is demonstrated that integrating time-resolved scRNA-seq with spatial transcriptomics is a new paradigm for spatiotemporal analysis. Metabolic RNA labeling-based time-resolved Well-TEMP-seq is first applied to profile the transcriptional dynamics of glioblastoma (GBM) cells and discover two potential pathways of EZH2-mediated mesenchymal transition in GBM. With spatial transcriptomics, it is further revealed that the crosstalk between CCL2+ malignant cells and IL10+ tumor-associated macrophages in the tumor microenvironment through an EZH2-FOSL2-CCL2 axis contributes to the mesenchymal transition in GBM. These discoveries show the power of integrative spatiotemporal scRNA-seq to elucidate the complex gene regulatory mechanism and advance the understanding of cellular processes in disease.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.