Dataset Information

Robust Identification of Suitable T-Cell Subsets for Personalized CMV-Specific T-Cell Immunotherapy Using CD45RA and CD62L Microbeads.

ABSTRACT: Viral infections and reactivations remain a serious obstacle to successful hematopoietic stem cell transplantation (HSCT). When antiviral drug treatment fails, adoptive virus-specific T-cell transfer provides an effective alternative. Assuming that naive T cells (TN) are mainly responsible for GvHD, methods were developed to generate naive T-cell-depleted products while preserving immune memory against viral infections. We compared two major strategies to deplete potentially alloreactive T cells: CD45RA and CD62L depletion and analyzed phenotype and functionality of the resulting CD45RA-/CD62L- naive T-cell-depleted as well as CD45RA⁺/CD62L⁺ naive T-cell-enriched fractions in the CMV pp65 and IE1 antigen model. CD45RA depletion resulted in loss of terminally differentiated effector memory T cells re-expressing CD45RA (TEMRA), and CD62L depletion in loss of central memory T cells (TCM). Based on these differences in target cell-dependent and target cell-independent assays, antigen-specific T-cell responses in CD62L-depleted fraction were consistently 3⁻5 fold higher than those in CD45RA-depleted fraction. Interestingly, we also observed high donor variability in the CD45RA-depleted fraction, resulting in a substantial loss of immune memory. Accordingly, we identified donors with expected response (DER) and unexpected response (DUR). Taken together, our results showed that a naive T-cell depletion method should be chosen individually, based on the immunophenotypic composition of the T-cell populations present.

SUBMITTER: Mangare C

PROVIDER: S-EPMC6471767 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Robust Identification of Suitable T-Cell Subsets for Personalized CMV-Specific T-Cell Immunotherapy Using CD45RA and CD62L Microbeads.

Mangare Caroline C Tischer-Zimmermann Sabine S Riese Sebastian B SB Dragon Anna C AC Prinz Immo I Blasczyk Rainer R Maecker-Kolhoff Britta B Eiz-Vesper Britta B

International journal of molecular sciences 20190320 6

Viral infections and reactivations remain a serious obstacle to successful hematopoietic stem cell transplantation (HSCT). When antiviral drug treatment fails, adoptive virus-specific T-cell transfer provides an effective alternative. Assuming that naive T cells (T<sub>N</sub>) are mainly responsible for GvHD, methods were developed to generate naive T-cell-depleted products while preserving immune memory against viral infections. We compared two major strategies to deplete potentially alloreact ...[more]

PMID: 30897843

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Project description:MotivationT cell heterogeneity presents a challenge for accurate cell identification, understanding their inherent plasticity, and characterizing their critical role in adaptive immunity. Immunologists have traditionally employed techniques such as flow cytometry to identify T cell subtypes based on a well-established set of surface protein markers. With the advent of single-cell RNA sequencing (scRNA-seq), researchers can now investigate the gene expression profiles of these surface proteins at the single-cell level. The insights gleaned from these profiles offer valuable clues and a deeper understanding of cell identity. However, CD45RA, the isoform of CD45 which distinguishes between naive/central memory T cells and effector memory/effector memory cells re-expressing CD45RA T cells, cannot be well profiled by scRNA-seq due to the difficulty in mapping short reads to genes.ResultsIn order to facilitate cell-type annotation in T cell scRNA-seq analysis, we employed machine learning and trained a CD45RA+/- classifier on single-cell mRNA count data annotated with known CD45RA antibody levels provided by cellular indexing of transcriptomes and epitopes sequencing data. Among all the algorithms we tested, the trained support vector machine with a radial basis function kernel with optimized hyperparameters achieved a 99.96% accuracy on an unseen dataset. The multilayer perceptron classifier, the second most predictive method overall, also achieved a decent accuracy of 99.74%. Our simple yet robust machine learning approach provides a valid inference on the CD45RA level, assisting the cell identity annotation and further exploring the heterogeneity within human T cells. Based on the overall performance, we chose the support vector machine with a radial basis function kernel as the model implemented in our Python package scCD45RA.Availability and implementationThe resultant package scCD45RA can be found at https://github.com/BrubakerLab/ScCD45RA and can be installed from the Python Package Index (PyPI) using the command "pip install sccd45ra."

Dataset Information

Robust Identification of Suitable T-Cell Subsets for Personalized CMV-Specific T-Cell Immunotherapy Using CD45RA and CD62L Microbeads.

Publications

Robust Identification of Suitable T-Cell Subsets for Personalized CMV-Specific T-Cell Immunotherapy Using CD45RA and CD62L Microbeads.

Similar Datasets

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