Biology subjects

Driessen, A.

Publications and source records attributed to Driessen, A..

4 recordsLinked to original sources

bioRxiv2024

Modeling CAR Response at the Single-Cell Level Using Conditional Optimal Transport

Chimeric Antigen Receptor (CAR) T cell therapy is a promising area of cancer immunotherapy. However, many challenges such as loss of persistence, T cell exhaustion, and therapy associated toxicities hamper further advancement of CAR T cell therapy. Therefore, recent efforts have focused on designing improved CARs that show better therapeutic characteristics. However, it is unfeasible to test all CAR variants in lab-based assays as CARs consist of multiple intracellular signalling domains. This results in over 100000 possible variants. We leverage computational modeling to navigate this vast combinatorial space by learning the relationship between CAR design and T cell functionality, thereby proposing promising CAR T cell designs. CAR T cells expressing different variants can be viewed as cells that underwent different perturbations. Neural Optimal Transport is an upcoming field that can model single cell perturbations and predict unseen cells and conditions. In this work we leverage the conditional Monge Gap to model the response to CAR expression at a single-cell level and generate gene expression of cells that express an unseen CAR design. We show that CAR OT (CAROT) significantly outperforms the baseline for gene expression prediction for in-distribution CAR variants, with distinct gene expression patterns per CAR that capture biological characteristics. When predicting unseen CAR variants, we demonstrate promising results in terms of gene expression prediction and show the model learns gene expression patterns linked to domains in the training set. This work demonstrates that optimal transport may support discovery and development of new CAR T cell designs.

synthetic biology↗

bioRxiv2024

Leukocytes use endothelial membrane tunnels to extravasate the vasculature

Upon inflammation, leukocytes extravasate through endothelial cells. When they extravasate in a paracellular manner, it is generally accepted that neighbouring endothelial cells physically disconnect to open cell-cell junctions, allowing leukocytes to cross. When carefully examining endothelial junctions, we found a partial membrane overlap of endothelial cells beyond VE-cadherin distribution. These overlaps are regulated by actin polymerization and, although marked by, do not require PECAM-1, nor VE-cadherin. Neutrophils prefer wider membrane overlaps as exit sites. Detailed 3D analysis of endothelial membrane dynamics during paracellular neutrophil transmigration in real-time, at high spatiotemporal resolution using resonant confocal and lattice light-sheet imaging, revealed that overlapping endothelial membranes form a tunnel during neutrophil transmigration. These tunnels are formed by the neutrophil lifting the membrane of the upper endothelial cell while indenting and crawling over the membrane of the underlying endothelial cell. Our work shows that endothelial cells do not simply retract upon passage of neutrophils but provide membrane tunnels, allowing neutrophils to extravasate. This discovery defines the 3D multicellular architecture in which the paracellular transmigration of neutrophils occurs.

cell biology↗

bioRxiv2024

Dissecting the role of CAR signaling architectures on T cell activation and persistence using pooled screening and single-cell sequencing

Chimeric antigen receptor (CAR) T cells represent a promising approach for cancer treatment, yet challenges remain such as limited efficacy due to a lack of T cell persistence. Given its critical role in promoting and modulating T cell responses, it is crucial to understand how alterations in the CAR signaling architecture influence T cell function. Here, we designed a combinatorial CAR signaling domain library and performed repeated antigen stimulation assays, pooled screening and single-cell sequencing to investigate T-cell responses triggered by different CAR architectures. Parallel comparisons of CAR variants, at early, middle and late timepoints during chronic antigen stimulation systematically assessed the impact of modifying signaling domains on T cell activation and persistence. Our data reveal the predominant influence of membrane-proximal domains in driving T cell phenotype. Additionally, we highlight the critical role of CD40 costimulation in promoting potent and persistent T cell responses, followed by CTLA4, which induces a long-term cytotoxic phenotype. This work deepens the understanding of CAR T cell biology and may be used to guide the future engineering of CAR T cell therapies.

synthetic biology↗

bioRxiv2021

Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell-types, with important implications to understand and treat diseases such as cancer. These technologies are however limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organised the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry data set, covering 36 markers in over 4,000 conditions totalling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=58 SRC="FIGDIR/small/436603v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@a95337org.highwire.dtl.DTLVardef@966d5aorg.highwire.dtl.DTLVardef@1e55b18org.highwire.dtl.DTLVardef@bfb5d7_HPS_FORMAT_FIGEXP M_FIG C_FIG Key pointsO_LIOver 80 million single-cell multiplexed measurements across 67 cell lines, 54 conditions and 10 time points to benchmark predictive models of single cell signaling C_LIO_LI73 approaches from 27 teams for predicting response to kinase inhibitors on single cell level, and dynamic response from unperturbed basal omics data C_LIO_LIPredictions of single marker models correlate with measurements with a correlation coefficient of 0.76 C_LIO_LITop models of whole signaling response models perform almost as well as a biological replicate C_LIO_LICell-line specific variation in dynamics can be predicted from basal omics C_LI

bioinformatics↗