Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA

• Authors: Jian Hu, Emory University; Kyle Coleman, University of Pennsylvania; Daiwei Zhang, University of Pennsylvania; Edward B Lee, University of Pennsylvania; Human Kadara, The University of Texas MD Anderson Cancer Center; Linghua Wang, The University of Texas MD Anderson Cancer Center; Mingyao Li, University of Pennsylvania
• Journal Title: CELL SYSTEMS
• Volume: Volume 14, Number 5
• Publisher: CELL PRESS | 2023-05-17, Pages 404-+
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.cels.2023.03.008
• Subjects: Health Sciences, Medicine and Surgery
• Keywords: Science & Technology; Life Sciences & Biomedicine; Biochemistry & Molecular Biology; Cell Biology; INFILTRATING LYMPHOCYTES; METASTATIC MELANOMA; B-CELLS; IMMUNOTHERAPY; SURVIVAL; ARCHITECTURE; PROGRESSION; EXPRESSION

Abstract

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.