Completed Cells, Biochemistry & Physiology Cancer

Understanding Chemotaxis Towards Self-Generated Gradients Using Computational Models, Model Organisms and T cells

In plain English

AI plain-English summary

Cells carve their own steering signals by breaking down widely scattered attractants, creating local gradients that guide their own movement. This matters because cell migration is fundamental to embryonic development and immune function, yet scientists understand little about how steering cues actually form. The standard view assumes cells follow pre-existing chemical trails, but this research proposes that cells often generate those trails themselves through a process called chemotaxis up self-generated gradients (SGGs). The mechanism is hard to study because attractant breakdown and migration happen simultaneously, creating complex feedback loops. The project uses a four-pronged approach: computational models to explore possible mechanisms, microfluidic experiments with *Dictyostelium* cells, cultured T cells migrating toward the immune signal CCL19, and finally a 3D computer model of a lymph node. If successful, it will reveal how immune cells navigate lymph nodes to find pathogens and coordinate responses. This is fundamental science with no immediate practical application. However, understanding how cells steer themselves could eventually inform therapies for immune disorders, cancer metastasis, or developmental abnormalities—conditions where cell migration goes wrong. Past fundamental work on chemotaxis, for instance, led directly to drugs that modulate immune cell trafficking.

View original technical description
Cell migration is essential to processes throughout biology, especially embryonic development and immune function. Migration must be steered to be physiologically effective. Steering cues are not well understood; we know a lot about how cells interpret them, but relatively little about how they are generated. The basic premise of this work is that the cells often generate their own cues, by breaking down attractants that are widely present (and thus initially give no steering information) into local gradients. Attractant breakdown and migration happen simultaneously. This mechanism - chemotaxis up self-generated gradients (SGGs) - is hard to dissect because it is complex, and based on positive feedback loops. We therefore propose a four-pronged, iterative approach, in which we combine less challenging components to create an understanding of the underlying biology. Key goals are: (1) explore possible mechanisms and new extensions using computational models; (2) test outcomes using chemotactic Dictyostelium in custom microfluidic devices; (3) verify these data by establishing cultured T cells chemotaxing to CCL19 as a model SGG; (4) combine findings from parts 1-3 to make a 3D, SGG-based model of a lymph node. Together they will illuminate chemotactic steering in general, by focussing on one physiologically important system.

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Researchers

Robert Insall (EPMC Awardee)

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Original classification

Investigator Award in Science

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