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Spatial epitranscriptomics reveals A-to-I editome specific to cancer stem cell microniches.

Amos Chungwon LeeYongju LeeAhyoun ChoiHan-Byoel LeeKyoungseob ShinHyunho LeeJi Young KimKyung-Min LeeHoe Suk KimSeung Yeon RyuSangeun LeeJong-Ho CheunDuck Kyun YooSumin LeeHansol ChoiTaehoon RyuHuiran YeomNamphil KimJinsung NohYonghee LeeInyoung KimSangwook BaeJinhyun KimWooseok LeeOkju KimYushin JungChanghoe KimSeo Woo SongYeongjae ChoiJunho ChungByung Gee KimWonshik HanSunghoon Kwon
Published in: Nature communications (2022)
Epitranscriptomic features, such as single-base RNA editing, are sources of transcript diversity in cancer, but little is understood in terms of their spatial context in the tumour microenvironment. Here, we introduce spatial-histopathological examination-linked epitranscriptomics converged to transcriptomics with sequencing (Select-seq), which isolates regions of interest from immunofluorescence-stained tissue and obtains transcriptomic and epitranscriptomic data. With Select-seq, we analyse the cancer stem cell-like microniches in relation to the tumour microenvironment of triple-negative breast cancer patients. We identify alternative splice variants, perform complementarity-determining region analysis of infiltrating T cells and B cells, and assess adenosine-to-inosine base editing in tumour tissue sections. Especially, in triple-negative breast cancer microniches, adenosine-to-inosine editome specific to different microniche groups is identified.
Keyphrases
  • cancer stem cells
  • single cell
  • rna seq
  • crispr cas
  • stem cells
  • genome wide
  • papillary thyroid
  • drinking water
  • big data
  • gene expression
  • artificial intelligence
  • childhood cancer
  • machine learning
  • young adults
  • nucleic acid