TY - JOUR
T1 - Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA
AU - Hu, Jian
AU - Coleman, Kyle
AU - Zhang, Daiwei
AU - Lee, Edward B.
AU - Kadara, Humam
AU - Wang, Linghua
AU - Li, Mingyao
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/5/17
Y1 - 2023/5/17
N2 - Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases.
AB - Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases.
KW - spatial transcriptomics
KW - super-resolution
KW - tertiary lymphoid structures
KW - tumor core
KW - tumor edge
KW - tumor microenvironment
KW - tumor-infiltrating lymphocytes
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U2 - 10.1016/j.cels.2023.03.008
DO - 10.1016/j.cels.2023.03.008
M3 - Article
C2 - 37164011
AN - SCOPUS:85158846310
SN - 2405-4712
VL - 14
SP - 404-417.e4
JO - Cell Systems
JF - Cell Systems
IS - 5
ER -