Biology subjects

Wortel, I. M. N.

Publications and source records attributed to Wortel, I. M. N..

8 recordsLinked to original sources

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↗

bioRxiv2022

Listeria motility increases the efficiency of goblet cell invasion during intestinal infection

Listeria monocytogenes (Lm) is a food-borne pathogen that causes severe bacterial gastroenteritis, with high rates of hospitalization and mortality. Lm is ubiquitous in soil, water and livestock, and can survive and proliferate at low temperatures. Following oral ingestion of contaminated food, Lm crosses the epithelial through intestinal goblet cells in a mechanism depending on Lm InlA and host E-cadherin. Importantly, human infections typically occur with Lm growing at or below room temperature, which are flagellated and motile. Even though many important human bacterial pathogens are flagellated, little is known regarding the effect of bacterial motility on invasion and immune evasion. Here, we used complementary imaging and computer modeling approaches to test the hypothesis that bacterial motility helps Lm locate and engage target cells permissive for invasion. Imaging explanted mouse and human intestine, we confirmed that Lm grown at room temperature uses motility to scan the epithelial surface and preferentially attach to target cells. Furthermore, we integrated quantitative parameters from our imaging experiments to construct a versatile "layered" cellular Potts model (L-CPM) that simulates host-pathogen dynamics. Simulated data are consistent with the hypothesis that bacterial motility enhances invasion by allowing bacteria to search the epithelial surface for their preferred invasion targets. Indeed, our model consistently predicts that motile bacteria have invaded [~]2-fold more at the 1-hour mark. This invasion advantage persists even in the presence of host phagocytes, with the balance between invasion and phagocytosis governed almost entirely by bacterial motility. In conclusion, our simulations provide insight into host pathogen interactions and challenge fundamental assumptions regarding how phagocytes might limit bacterial invasion early during infection.

microbiology↗

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↗

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

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↗