McIntosh, B. J. | bioRxiv Science

bioRxiv2023

Engineered CD47 protects T cells for enhanced antitumor immunity

Adoptively transferred T cells and agents designed to block the CD47/SIRP axis are promising antitumor therapeutics, which activate distinct arms of the immune system. We administered anti-CD47 (CD47) with adoptively transferred T cells with the goal of enhancing antitumor efficacy but observed rapid macrophage-mediated clearance of T cells expressing chimeric antigen receptors (CARs) or engineered T cell receptors, which blunted therapeutic benefit. CD47 mediated CAR T clearance was potent and rapid enough to serve as an effective safety switch. To overcome this challenge, we engineered a CD47 variant (47E) that engaged SIRP and provided a "dont-eat-me" signal that was not blocked by CD47 antibodies. TCR or CAR T cells expressing 47E were resistant to clearance by macrophages following CD47, and mediated significant, sustained macrophage recruitment into the TME. Although many of the recruited macrophages manifested an M2-like profile, the combined therapy resulted in synergistic enhancement in antitumor efficacy. This work identifies macrophages as major regulators of T cell persistence and illustrates the fundamental challenge of combining T cell directed therapeutics with those designed to activate macrophages. It further delivers a therapeutic approach capable of simultaneously harnessing the antitumor effects of T cells and macrophages that manifests markedly enhanced potency against solid tumors.

bioengineering↗

bioRxiv2021

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource.

bioinformatics↗