Biology subjects

Aksoy, P.

Publications and source records attributed to Aksoy, P..

4 recordsLinked to original sources

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↗

bioRxiv2019

Towards a scaled-up T cell-mediated cytotoxicity assay in 3D cell culture using microscopy

Three-dimensional (3D) cell culture systems with tumor spheroids are being adopted for research on the antitumor activity of drug treatments and cytotoxic T cells. Analysis of the cytotoxic effect on 3D tumor cultures within a 3D scaffold, such as collagen, is challenging. Image-based approaches often use confocal microscopy, which greatly limits the sample size of tumor spheroids that can be assayed. We explored a system where tumor spheroids growing in a collagen gel within a microfluidics chip can be treated with drugs or co-cultured with T cells. We attempted to adapt the system to measure the death of cells in the tumor spheroids directly in the microfluidics chip via automated widefield fluorescence microscopy. We were able to successfully measure drug-induced cytotoxicity in tumor spheroids, but had difficulties extending the system to measure T cell-mediated tumor killing. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/842039v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@c95725org.highwire.dtl.DTLVardef@785728org.highwire.dtl.DTLVardef@a23dceorg.highwire.dtl.DTLVardef@187bd74_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2018

Electroporation characteristics of human primary T cells

Electroporation is the most feasible non-viral material delivery system for manipulating human T cells given its time- and cost-effectiveness. However, efficient delivery requires electroporation settings to be optimized for different devices, cellular states, and materials to be delivered. Here, we used electroporation to either induce exogenous gene expression in human primary T cells by plasmids or in vitro transcribed (IVT) mRNA and also target endogenous genes by Cas9 ribonucleoproteins (RNPs). We characterized the electroporation conditions both for activated and unstimulated human T cells. Although naive cells are non-dividing and therefore their genetic manipulation is harder compared to activated T cells, we developed the technical ability to manipulate both naive and memory cells within the unstimulated T cell population by IVT mRNA and Cas9 RNP electroporation. Here, we outline the best practices for achieving highly-efficient genetic manipulation in primary T cells without causing significant cytotoxicity to the cells. Because there is increasing evidence for \"less-differentiated\" T cells to have better anti-tumor activity for immunotherapy, manipulating naive T cells with high efficiency is also of high importance to clinical applications and to study the biology of these cells.

immunology↗

bioRxiv2018

Cytokit: A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging

BackgroundMultiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind.\n\nResultsCytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset.\n\nConclusionCytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit.

bioinformatics↗