Timmers, C. | bioRxiv Science

bioRxiv2021

Discovering tumor-reactive T-cell receptors through single-cell sequencing of tumor-infiltrating lymphocytes

We evaluated the utility of single-cell sequencing of tumor-infiltrating lymphocytes (TIL) for tumor-reactive T-cell receptor (TCR) discovery. Using the MC38 cell line as our tumor model in mice, we show that expression of exogenous TCRs via mRNA electroporation in human T cells provides an easy and quick path to validating tumor-specific candidate TCRs. We detail the identification and validation of four novel MC38-reactive mouse TCRs with varying levels of reactivity to the target cells. Validating our process, one of the MC38 TCRs is specific against a previously reported neoantigen (ASMTNMELM in the Adpgk gene). Consideration of these methodologies may aid in the development of rapid TCR-based therapies for the treatment of cancer and human disease. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/470597v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@792d44org.highwire.dtl.DTLVardef@18af6aforg.highwire.dtl.DTLVardef@498c2aorg.highwire.dtl.DTLVardef@9127e8_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Deep learning tools and modeling to estimate the temporal expression of E2Fs over the cell cycle from 2D still images

Automatic characterization of fluorescent labeling in intact mammalian tissues remains a challenge due to the lack of quantifying techniques capable of segregating densely packed nuclei and intricate tissue patterns. Here, we describe a powerful deep learning-based approach that couples remarkably precise nuclear segmentation with quantitation of fluorescent labeling intensity within segmented nuclei, and then apply it to the analysis of cell cycle dependent protein concentration in mouse tissues using 2D fluorescent still images. First, several existing deep learning-based methods were evaluated to accurately segment nuclei using different imaging modalities with a small training dataset. Next, we developed a deep learning-based approach to identify and measure fluorescent labels within segmented nuclei, and created an ImageJ plugin to allow for efficient manual correction of nuclear segmentation and label identification. Lastly, using fluorescence intensity as a readout for protein concentration, a three-step global estimation method was applied to the characterization of the cell cycle dependent expression of E2F proteins in the developing mouse intestine.

bioinformatics↗