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Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks.
Sui, Xin; Lo, Jennifer A; Luo, Shuchen; He, Yichun; Tang, Zefang; Lin, Zuwan; Zhou, Yiming; Wang, Wendy Xueyi; Liu, Jia; Wang, Xiao.
Afiliación
  • Sui X; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Lo JA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Luo S; These authors contributed equally.
  • He Y; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Tang Z; Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA USA.
  • Lin Z; These authors contributed equally.
  • Zhou Y; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Wang WX; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Liu J; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Wang X; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
bioRxiv ; 2024 Aug 08.
Article en En | MEDLINE | ID: mdl-39149316
ABSTRACT
Characterizing the transcriptional and translational gene expression patterns at the single-cell level within their three-dimensional (3D) tissue context is essential for revealing how genes shape tissue structure and function in health and disease. However, most existing spatial profiling techniques are limited to 5-20 µm thin tissue sections. Here, we developed Deep-STARmap and Deep-RIBOmap, which enable 3D in situ quantification of thousands of gene transcripts and their corresponding translation activities, respectively, within 200-µm thick tissue blocks. This is achieved through scalable probe synthesis, hydrogel embedding with efficient probe anchoring, and robust cDNA crosslinking. We first utilized Deep-STARmap in combination with multicolor fluorescent protein imaging for simultaneous molecular cell typing and 3D neuron morphology tracing in the mouse brain. We also demonstrate that 3D spatial profiling facilitates comprehensive and quantitative analysis of tumor-immune interactions in human skin cancer.

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos