Biology subjects

Theorell, J.

Publications and source records attributed to Theorell, J..

3 recordsLinked to original sources

bioRxiv2024

Natural Killer Cell Dysfunction In Human Bladder Cancer Is Caused By Tissue-Specific Suppression of SLAMF6 Signaling

NK cells are innate lymphocytes critical for surveillance of viruses and tumors, however the mechanisms underlying NK cell dysfunction in cancer are incompletely understood. We assessed the effector function of NK cells from bladder cancer patients and found severe dysfunction in NK cells derived from tumors versus peripheral blood. While both peripheral and tumor-infiltrating NK cells exhibited conserved patterns of inhibitory receptor over-expression, this did not explain the observed defects in NK surveillance in bladder tumors. Rather, TME-specific TGF-{beta} and metabolic perturbations such as hypoxia directly suppressed NK cell function. Specifically, an oxygen-dependent reduction in signaling through SLAMF6 was mechanistically responsible for poor NK cell function, as tumor-infiltrating NK cells cultured ex vivo under normoxic conditions exhibited complete restoration of function, while deletion of SLAMF6 abrogated NK cell cytolytic function even under normoxic conditions. Collectively, this work highlights the role of tissue-specific factors in dictating NK cell function, and implicates SLAMF6 signaling as a rational target for immuno-modulation to improve NK cell function in bladder cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/591366v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@18ff2faorg.highwire.dtl.DTLVardef@1991cb6org.highwire.dtl.DTLVardef@12c1a94org.highwire.dtl.DTLVardef@84f87b_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2019

A topological data analysis based classification method for multiple measurement

BackgroundMachine learning models for repeated measurements are limited. Using topological data analysis (TDA), we present a classifier for repeated measurements which samples from the data space and builds a network graph based on the data topology. When applying this to two case studies, accuracy exceeds alternative models with additional benefits such as reporting data subsets with high purity along with feature values.\n\nResultsFor 300 examples of 3 tree species, the accuracy reached 80% after 30 datapoints, which was improved to 90% after increased sampling to 400 datapoints. Using data from 100 examples of each of 6 point processes, the classifier achieved 96.8% accuracy. In both datasets, the TDA classifier outperformed an alternative model.\n\nConclusionsThis algorithm and software can be beneficial for repeated measurement data common in biological sciences, as both an accurate classifier and a feature selection tool.

bioinformatics↗

bioRxiv2018

Determination of essential phenotypic elements of clusters in high-dimensional entities - DEPECHE

Technological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make this highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi-and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better as the best performing state-of-the-art clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. An open source R implementation of DEPECHE is available at https://github.com/theorell/DepecheR.\n\nAuthor summaryDEPECHE-a data-mining algorithm for mega-variate data\n\nModern experimental technologies facilitate an array of single cells measurements, e.g. at the RNA-level, generating enormous datasets with thousands of annotated biological markers for each of thousands of cells. To analyze such datasets, researchers routinely apply automated or semi-automated techniques to order the cells into biologically relevant groups. However, even after such groups have been generated, it is often difficult to interpret the biological meaning of these groups since the definition of each group often dependends on thousands of biological markers. Therefore, in this article, we introduce DEPECHE, an algorithm designed to simultaneously group cells and enhance interpretability of the formed groups. DEPECHE defines groups only with respect to biological markers that contribute significantly to differentiate the cells in the group from the rest of the cells, yielding more succinct group definitions. Using the open source R software DepecheR on RNA sequencing data and mass cytometry data, the number of defining markers were reduced up to 1000-fold, thereby increasing interpretability vastly, while maintaining or improving the biological relevance of the groups formed compared to state-of-the-art algorithms.

bioinformatics↗