Visual genetic typing of glioma using proximity-anchored in situ spectral coding amplification

This research developed a novel multiplexed imaging method of RNA mutations, named proximity-anchored in situ spectral coding amplification (ProxISCA), enabling visual typing of brain gliomas with different pathological grades at the single-cell and tissue levels. ProxISCA provides a tool for glioma research and precise diagnosis, which can reveal the relationship between cellular heterogeneity and glioma occurrence or development, and assist in pathological prognosis.

Abstract:

Gliomas are histologically and genetically heterogeneous tumors. However, classical histopathological typing often ignores the high heterogeneity of tumors and thus cannot meet the requirements of precise pathological diagnosis. Here, proximity-anchored in situ spectral coding amplification (ProxISCA) is proposed for multiplexed imaging of RNA mutations, enabling visual typing of brain gliomas with different pathological grades at the single-cell and tissue levels. The ligation-based padlock probe can discriminate one-nucleotide variations, and the design of proximity primers enables the anchoring of amplicons on target RNA, thus improving localization accuracy. The DNA module-based spectral coding strategy can dramatically improve the multiplexing capacity for imaging RNA mutations through one-time labelling, with low cost and simple operation. One-target-one-amplicon amplification confers ProxISCA the ability to quantify RNA mutation copy number with single-molecule resolution. Based on this approach, it is found that gliomas with higher malignant grades express more genes with high correlation at the cellular and tissue levels and show greater cellular heterogeneity. ProxISCA provides a tool for glioma research and precise diagnosis, which can reveal the relationship between cellular heterogeneity and glioma occurrence or development and assist in pathological prognosis.

Author list:

Xiaolei Chen, Ruijie Deng, Dongdong Su*, Xiaochen Ma, Xu Han, Shizheng Wang, Yuqing Xia, Zifu Yang, Ningqiang Gong, Yanwei Jia, Xueyun Gao*, Xiaojun Ren*

How to cite:

X. Chen, R. Deng, D. Su, X. Ma, X. Han, S. Wang, Y. Xia, Z. Yang, N. Gong, Y. Jia, X. Gao, X. Ren, Exploration 2023, 20220175.
https://doi.org/10.1002/EXP.20220175