Biology subjects

Textor, J.

Publications and source records attributed to Textor, J..

17 recordsLinked to original sources

bioRxiv2026

A human-in-the-loop approach for faster cell tracking

Traditional cell tracking methods focus on fully automated tracking. The quality of the generated tracks, however, is heavily influenced by factors such as cell speed and density. In complex scenarios where track correctness is essential, an efficient and reliable tool for manual track correction remains a key component of the cell tracking process. Most tracking tools that allow for such manual correction do so only as a post-processing step, using human feedback to correct one track error at a time -- without improving the quality of other tracks in the dataset. This highlights the need for a more efficient track correction method that iteratively integrates human feedback into the automated tracking process. In this paper we propose a human-in-the-loop approach to cell tracking and investigate the extent to which a tracking algorithm can learn from human input during the tracking process. We combine a graph representation of cell detections, Dijkstra's shortest path algorithm, and a neural network that predicts cell displacement to generate initial track suggestions. We introduce a tool that enables human feedback on individual tracks suggested by the algorithm; this feedback is then propagated to improve subsequent track generation. Compared to the traditional approach of fully automated tracking with correction as a post-processing step, our human-in-the-loop tracking framework reduces the number of corrections required. Finally, we show how this approach can be integrated with existing state-of-the-art tracking algorithms by using pre-generated tracks as a prior, improving the quality of tracks suggested to the human annotator.

bioinformatics↗

bioRxiv2025

Hiltonol and Protamine-RNA stimulation induce an immune-activating transcriptome profile in cDC1s

Ex-vivo stimulation of dendritic cells (DCs) is a critical step in DC-based cancer immunotherapies. In humans, conventional type 1 dendritic cells (cDC1s) are a rare myeloid dendritic cell (mDC) subset that express BDCA-3 (CD141). cDC1s promote CD8+ T cell cross-priming against tumor antigens and are therefore being explored for use in immunotherapy. We evaluated the impact of ex-vivo stimulation on human peripheral blood cDC1s. In contrast to routine evaluation, which focuses on pre-defined surface maturation markers or soluble factors released from the activated cells, we investigated the impact of stimulation on the transcriptome using both RNA-sequencing (RNA-seq) and microarrays. Specifically, we analyzed the mRNA of cDC1s upon activation with two clinical-grade adjuvants, Hiltonol (poly IC, a TLR3 ligand) and protamine-stabilized RNA (pRNA, a TLR7/8 ligand) compared to unstimulated controls. Both RNA-seq and microarray analysis showed profound and similar effects of both Hiltonol and pRNA on the transcriptome of cDC1s. A gene ontology (GO) analysis suggested that these changes were mainly related to activation and maturation pathways, including induction of type-I interferon (IFN) and interleukin (IL)-12 transcription, while pathways related to adverse effects or cell damage did not appear to be affected. Combination of both reagents did not appear to have a synergistic effect, as the transcriptome changes were similar to those induced by each stimulus alone. Together, our results indicate that both adjuvants have comparable effects on cDC1 maturation within an immunogenic short-term culture as performed in immunotherapy.

immunology↗

bioRxiv2025

Differential T cell receptor gene expression analysis using the Wilcoxon test with similarity-based weighting

To better understand immune responses, comparing the abundance of T cell receptors (TCRs) between conditions can provide insights into which T cells have proliferated or were involved in immune activation. This requires methods that can accurately identify significant differences in TCR-seq data. For conventional RNA-seq data, well-established differential gene expression (DGE) analysis tools such as DESeq2 and edgeR have been developed. However, applying these methods to TCR sequencing (TCR-seq) data presents additional challenges. TCR-seq data is highly sparse, overdispersed, and contains many artificial zeros, which can lead to inflated false-positive rates when using traditional approaches. While non-parametric methods like the Wilcoxon test better control false positives, they may suffer from lower statistical power. To address these issues, we propose a novel pre-processing step for TCR-seq data using network-based local weighting based on TCR sequence similarity. This pre-processing step improves the sensitivity and reduces the false-positive rates of methods like DESeq2 and edgeR while enhancing the power of the Wilcoxon test. Through empirical analysis of both simulated and real datasets, we show that combining our pre-processing step with the Wilcoxon test achieves robust performance, outperforming traditional RNA-seq methods. This simple but powerful approach to TCR-seq analysis could help advance our understanding of adaptive immune responses.

bioinformatics↗

bioRxiv2025

Compartment size and proliferation shape the murine T cell receptor repertoire in splenic T- and B cell zones during an immune response

The adaptive immune system recognizes billions of unique antigens using highly variable T cell receptors (TCRs). To what extent TCR features shape the spatial distribution of the TCR repertoire (TCR-R) within lymphoid organs remains unclear. Here, we compared the TCR-R composition in T cell zones (TCZ), B cell zones (BCZ), and germinal centers (GC) of the spleen by analyzing the complementarity-determining region 3 of the beta chain (CDR3{beta}) in microdissected splenic compartments from naive and immunized mice. While we found substantial differences in TCR-R composition between these compartments, these differences largely disappeared once the effects of compartment size and proliferation were accounted for: During steady state, the few T cells found in BCZs strongly resemble random subsets of those found in TCZs. After immunization, expanded clones started appearing in the TCZ at day 3 and later shifted to the BCZ. GCs harbored even further expanded clones that were grouped into clusters with high sequence similarity. TCR-R differences during the immune response were well explained by a Galton-Watson model of T cell proliferation, suggesting that this could be the central factor shaping the observed TCR-R differences. Similarly, while TCR length and closeness to germline appeared to differ between the compartments, our models of random distribution and proliferation explained these differences almost entirely. Hence, our findings suggest that splenic TCR-Rs are shaped predominantly by compartment size and proliferation rates rather than TCR sequence features. This would imply that quantitative assessment of TCR-R dynamics across heterogeneous spatial compartments during an immune response is only possible when these effects are explicitly accounted for.

immunology↗

bioRxiv2024

Systematic analysis of immune cell motility leveraging Immunemap, an open intravital microscopy atlas

Studying the spatiotemporal dynamics of cells in living organisms is a current frontier in bioimaging. Intravital Microscopy (IVM) provides direct, long-term observation of cell behavior in living animals, from tissue to sub-cellular resolution. Hence, IVM has become crucial for studying complex biological processes in motion and across scales, such as the immune response to pathogens and cancer. However, IVM data are typically kept in private repositories inaccessible to the scientific community, hampering large-scale analysis that aggregates data from multiple laboratories. To solve this issue, we introduce Immunemap, an atlas of immune cell motility based on an Open Data platform that provides access to over 58000 single-cell tracks and 1049000 cell-centroid annotations from 360 videos in murine models. Leveraging Immunemap and unsupervised learning, we systematically analyzed cell trajectories, identifying four main patterns of cell migration in immune cells. Two patterns correspond to behaviors previously characterized: directed movement and arresting. However, we identified two other patterns, characterized by low directionality and twisted paths, often considered random migration. We show that the newly defined patterns can be subdivided into two distinct types: within small areas, suggesting a focused patrolling around one or a few cells, and over larger areas, indicative of a more extended tissue patrolling. Furthermore, we show that the percentage of cells displaying these motility patterns changes in response to immune stimuli. Altogether, Immunemap embraces the FAIR principles, promoting data reuse to extract novel insights from immune cell dynamics through an image-based systems biology approach.

systems biology↗

bioRxiv2024

Cooperative motility emerges in crowds of T cells but not neutrophils

Interacting, self-propelled particles are prone to jamming when crowded. This well-described phenomenon is shared by diverse systems including cars, animal colonies, and pedestrians. T cells, essential effectors of adaptive immunity, seemingly defy this principle: the rapid migration enabling their protective function persists even in tightly packed tissue environments - from the thymus where T cells develop, to lymphoid organs they survey for antigen, to tissues they clear from infection. Here we studied T cell crowds by combining experiments of T cells migrating in microfluidic devices with in silico models. We observed that while single T cells are highly heterogeneous in their motility, in crowds they synchronized their speeds and formed stable, motile trains. Our models showed that the emergence of this flocking-like behavior can be explained by a combination of two interaction mechanisms at the cell-cell interface: adhesion maintains cohesive T cell groups, and force transmission accelerates slower cells. Not all immune cells flock when they are crowded: neutrophils in the same settings slowed down with increasing cell density. Thus, cooperative motion may enable T cells to remain motile in densely packed tissue environments, preventing jams that curtail the motion of other crowded systems.

cell biology↗

bioRxiv2022

Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity

The T cell receptor (TCR) determines the specificity and affinity for both foreign and self-peptides presented by MHC. It is established that self-pMHC reactivity impacts T cell function, but it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naive CD4+ T cells with low versus high self-pMHC reactivity, we used data from 42 mice to train a machine learning (ML) algorithm that predicts self-reactivity directly from TCR{beta} sequences. This approach revealed that n-nucleotide additions and acidic amino acids weaken self-reactivity. We tested our ML predictions of TCR{beta} sequence self-reactivity using retrogenic mice. Extrapolating our analyses to independent datasets, we found high predicted self-reactivity for regulatory CD4+ T cells and low predicted self-reactivity for T cells responding to chronic infection. Our analyses suggest a potential trade-off between repertoire diversity and self-reactivity intrinsic to the architecture of a TCR repertoire.

immunology↗

bioRxiv2022

Constraints and trade-offs shape the evolution of T cell search strategies

Two decades of in vivo imaging have revealed how diverse the shapes and motion patterns of migrating T cells can be. This finding has sparked the notion of "search strategies": T cells may have evolved ways to search for antigen efficiently and might even adapt their motion to the task at hand. Mathematical models have indeed confirmed that observed T-cell migration patterns resemble a theoretical optimum in several contexts; for example, frequent turning, stop-and-go motion, or alternating short and long motile runs have all been interpreted as deliberately tuned behaviours, optimising the cells chance of finding antigen. But the same behaviours could also arise simply because T cells cant follow a straight, regular path through the tight spaces they navigate. Even if T cells can be shown to follow a theoretically optimal pattern, the question remains: has that pattern truly been evolved for this particular searching task, or does it merely reflect how the cells migration machinery and surroundings constrain motion paths? We here examine to what extent cells can evolve search strategies when faced with realistic constraints. Using a cellular Potts model (CPM), where motion arises from interactions between intracellular dynamics, cell shape, and a constraining environment, we simulate an evolutionary process in which cells "optimise" a simple task: explore as much area as possible. We find that cells evolve several motility characteristics previously attributed to search optimisation, even though these features were not beneficial for the task given here. Our results stress that "optimal" search strategies do not always stem from evolutionary adaptation: instead, they may be the inevitable side effects of interactions between cell shape, intracellular actin dynamics, and the diverse environments T cells face in vivo.

immunology↗

bioRxiv2021

A Segmentation-Free Machine Learning Architecture for Immune Landscape Phenotyping in Solid Tumors by Multichannel Imaging

Tissue specimens taken from primary tumors or metastases contain important information for diagnosis and treat-ment of cancer patients. Multiplex imaging allows in situ visualization of heterogeneous cell populations, such as immune cells, in tissue samples. Most image processing pipelines first segment cell boundaries and then measure marker expression to assign cell phenotypes. In dense tissue environments, this segmentation-first approach can be inaccurate due to segmentation errors or overlapping cells. Here we introduce the machine learning pipeline "ImmuNet" that identifies positions and phenotypes of cells without segmenting them. ImmuNet is easy to train: human annotators only need to click on an immune cell and score its expression of each marker. This approach al-lowed us to annotate 34,458 cells. We show that ImmuNet consistently outperforms a state-of-the-art segmentation-based pipeline for multiplex immunohistochemistry analysis across tissue types, cell types and tissue densities, achieving error rates below 5-10% on challenging detection and phenotyping tasks. We externally validate Im-muNet results by comparing them to flow cytometric measurements from the same tissue. In summary, ImmuNet is an effective, simpler alternative to segmentation-based approaches when only cell positions and phenotypes, but not their shapes, are required for downstream analyses. Thus, ImmuNet helps researchers to analyze multiplex tissue images more easily and accurately.

immunology↗

bioRxiv2021

Pre-existing chromatin accessibility and gene expression differences among naïve CD4+ T cells influence effector potential

CD4+ T cells have a remarkable potential to differentiate into diverse effector lineages following activation. Here, we probed the heterogeneity present among naive CD4+ T cells before encountering their cognate antigen to ask whether their effector potential is modulated by pre-existing transcriptional and epigenetic differences. Using single-cell RNA sequencing, we showed that key drivers of variability are genes involved in T cell receptor (TCR) signaling. Using CD5 expression as a read-out of the strength of tonic TCR interactions with self-peptide MHC, and sorting on the ends of this self-reactivity spectrum, we find that pre-existing transcriptional differences among naive CD4+ T cells impact follicular helper cell (TFH) versus non-TFH effector lineage choice. Moreover, our data implicate TCR signal strength during thymic development in establishing differences in naive CD4 T cell chromatin landscapes that ultimately shape their effector potential.

immunology↗

bioRxiv2020

Artistoo: build, share, and explore simulations of cells and tissues in the web browser

SummaryThe Cellular Potts Model (CPM) is a powerful in silico method for simulating diverse biological processes at tissue scale. Because of its inherently graphical nature, this model should in theory be accessible to a large audience of biologists - without requiring extensive mathematical expertise. But in practice, CPMs are mostly implemented in specialized frameworks that users need to master before they can run and modify the simulation. We here present Artistoo (Artificial Tissue Toolbox), a JavaScript library for building "explorable" CPM simulations where users can change model parameters and see their effects in real time. Artistoo simulations run directly in the web browser and do not require any third-party software, plugins, or back-end servers. Although implemented in JavaScript, Artistoo does not suffer from a major performance loss compared to frameworks written in C++; it remains sufficiently fast to let users interact with simulations in real time. Artistoo provides an opportunity to unlock CPM models for a broader audience: interactive simulations can be shared through a simple URL in a zero-install setting. We discuss how such model sharing may benefit modelling research, science dissemination, open science, and education. Availability and ImplementationArtistoo is an open-source library released under the MIT license, and is freely available on GitHub at https://github.com/ingewortel/artistoo. ContactJohannes.Textor@radboudumc.nl

bioinformatics↗

bioRxiv2020

Cancer cell elimination by cytotoxic T cell cooperation and additive damage

Cytotoxic T lymphocytes (CTL) eliminate tumor target cells in an antigen and cell-contact dependent manner. Lethal hit delivery occurs as a rapid and binary, "yes/no" process when immunogenicity is very high1-3, however in vivo CTL often fail to kill solid tumor cells during 1:1 conjugations4-6. Using long-term time-lapse microscopy in three distinct tumor cytotoxicity models and statistical modeling, we here show that migrating CTL transit between target cells and initiate apoptosis by a series of sublethal interactions ( additive cytotoxicity), while individual conjugations rarely induced apoptosis. Sublethal damage included perforin-dependent membrane pore formation, nuclear lamina rupture and DNA damage, and these events resolved within minutes to hours. In immunogenic B16F10 melanoma tumors in vivo, frequent serial engagements and sublethal hit delivery of CTL was largely confined to interstitial niches in the invasion front, resulting in eradication of invading tumor cells. Thus, additive cytotoxicity is a probabilistic process achieved by a series of CTL-target cell engagements and sublethal events. The need for additive "hits" has implications for the topographic mechanisms of elimination or immune evasion of tumor cells and microenvironmental regulation of CTL accumulation and cooperation by targeted therapy.

immunology↗

bioRxiv2019

CelltrackR: an R package for fast and flexible analysis of immune cell migration data

SummaryVisualization of cell migration via time-lapse microscopy has greatly advanced our understanding of the immune system. However, subtle differences in migration dynamics are easily obscured by biases and imaging artifacts. While several analysis methods have been suggested to address these issues, an integrated tool implementing them is currently lacking. Here, we present CelltrackR, an R package containing a diverse set of state-of-the-art analysis methods for (immune) cell tracks. CelltrackR supports the complete pipeline for track analysis by providing methods for data management, quality control, extracting and visualizing migration statistics, clustering tracks, and simulating cell migration.\n\nAvailability and ImplementationCelltrackR is an open-source package released under the GPL-2 license, and is freely available on GitHub at https://github.com/ingewortel/celltrackR.\n\nContactmmiller23@wustl.edu, Johannes.Textor@radboudumc.nl

bioinformatics↗

bioRxiv2018

Robust self-nonself discrimination requires negative T cell selection on non-random peptides

Self-foreign discrimination by the immune system was long thought to arise because negative selection in the thymus silences self-reactive T cells. Yet recent data show that this silencing is remarkably incomplete. Here we ask how a repertoire containing many self-reactive cells can nevertheless discriminate self from foreign. We address this question using realistic-scale computational models of the T cell repertoire. Our models show that when foreign peptides dif-fer systematically from self, moderate T cell cross-reactivity skews the post-selection repertoire towards foreign recognition. When no such systematic differences exist, self-foreign discrim-ination is only possible if peptide presentation in the thymus minimizes the co-occurrence of similar, redundant self peptides. These results imply that negative selection needs to be based on non-random self peptides to allow robust self-foreign discrimination for both self-similar and -dissimilar pathogens.

immunology↗

bioRxiv2018

Local Attachment Explains Small-World-Like Properties of Fibroblastic Reticular Cell Networks in Lymph Nodes

Fibroblastic reticular cells (FRCs) form a cellular network that serves as the structural backbone of lymph nodes and facilitates lymphocyte migration. This FRC network has been found to have small-world properties. Using a model based on geographical preferential attachment, we simulated the formation of a variety of cellular networks and show that similar small-world properties robustly emerge under such natural conditions. By estimating the parameters of this model, we generated FRC network representations with realistic topological properties. We found that these properties change markedly when the network is expanded from a thin slice to a 3D cube. Typical small-world properties were found to persist as network size was increased. The simulated networks were very similar to 2D and 3D lattice networks. According to the used metrics, these lattice networks also have small-world properties, indicating that lattice-likeness is sufficient to become classified as a small-world network. Our results explain why FRC networks have small-world properties and provide a framework for simulating realistic FRC networks.

immunology↗

bioRxiv2018

Both cell-intrinsic and environmental factors constrain speed and persistence in T cell migration

Cell migration is astoundingly diverse. Molecular signatures, cell-cell and cell-matrix interactions, and environmental structures each play their part in shaping cell motion, yielding numerous different cell morphologies and migration modes. Nevertheless, in recent years, a simple unifying law was found to describe cell migration across many different cell types and contexts: faster cells turn less frequently. Given this universal coupling between speed and persistence (UCSP), from a modelling perspective it is important to know whether computational models of cell migration capture this speed-persistence link. Here, we present an in-depth characterisation of an existing Cellular Potts Model (CPM). We first show that this model robustly reproduces the UCSP without having been designed for this task. Instead, we show that this fundamental law of migration emerges spontaneously through a crosstalk of intracellular mechanisms, cell shape, and environmental constraints, resembling the dynamic nature of cell migration in vivo. Our model also reveals how cell shape dynamics can further constrain cell motility by limiting both the speed and persistence a cell can reach, and how a rigid environment such as the skin can restrict cell motility even further. Our results further validate the CPM as a model of cell migration, and shed new light on the speed-persistence coupling that has emerged as a fundamental property of migrating cells. SIGNIFICANCEThe universal coupling between speed and persistence (UCSP) is the first general quantitative law describing motility patterns across the versatile spectrum of migrating cells. Here, we show - for the first time - that this migration law emerges spontaneously in an existing, highly popular computational model of cell migration. Studying the UCSP in entirely different model frameworks, not explicitly built with this law in mind, can help uncover how intracellular dynamics, cell shape, and environment interact to produce the diverse motility patterns observed in migrating cells.

immunology↗

bioRxiv2017

Deep Sequencing Reveals Transient Segregation of T Cell Repertoires in Splenic T Cell Zones During an Immune Response

Immunological differences between hosts, such as diverse T-cell receptor (TCR) repertoires, are widely credited for reducing the risk of pathogen spread and adaptation in a population. Within-host immunological diversity might likewise be important for robust pathogen control, but to what extent naive TCR repertoires differ across different locations in the same host is unclear. T-cell zones (TCZs) in secondary lymphoid organs provide secluded micro-environmental niches. By harboring distinct TCRs, such niches could enhance within-host immunological diversity. On the other hand, rapid T cell migration is expected to dilute such diversity. Here, we combined tissue micro-dissection and deep sequencing of the TCR {beta} chain to examine the extent to which TCR repertoires differ between TCZs in murine spleens. In the absence of antigen, we found little evidence for differences between different TCZs of the same spleen. Yet, three days after immunization with sheep red blood cells, we observed a >10-fold rise in the number of clones that appeared to localize to individual zones. Remarkably, these differences largely disappeared at 4 days after immunization, when hallmarks of an ongoing immune response were still observed. These data suggest that in the absence of antigen, any repertoire differences observed between TCZs of the same host can largely be attributed to random clone distribution. Upon antigen challenge, segregated TCR compartments appear and disappear within days. Such \"transient mosaic\" dynamics could be an important barrier for pathogen adaptation and spread during an immune response.

immunology↗