TIM June 4th, 2018
The fast and the curious:
Can T cells tune speed and turning behavior
to optimize their search for antigen?
Inge Wortel
inge.wortel@radboudumc.nl
Department of Tumor Immunology, Radboudumc
Search strategies: the exploration-exploitation dilemma
1. How fast do I move? (speed)
| Faster : | cover more area, |
| but less time to look |
2. How long until I turn? (persistence)
| Short time : | more thorough |
| but get less far |
Images adapted from: Benichou et al. Review of Modern Physics, 2011.
Searching in immunology
|
T cells in LN (no infection) A. Peixoto, Harvard Medical School |
Neutrophils in LN (with infection)
Chtanova et al, Immunity 2008. |
How T cells search
T cells show highly different migratory behavior depending on context:
$\rightarrow$ Does this reflect different "optimal" search strategies?
Image adapted from: Krummel et al. Nature Reviews Immunology, 2016.
How T cells search
Random walk models:
Vary parameters $\rightarrow$ how long does it take to find target?
Problems:
- Can cells tune speed and persistence independently?
Can cells tune speed and persistence independently?
Migrating cells: universal coupling between speed and persistence (UCSP)
Maiuri et al, Cell 2015.
Can cells tune speed and persistence independently?
Migrating cells: universal coupling between speed and persistence (UCSP)
Maiuri et al, Cell 2015.
Maiuri et al, Cell 2015.
How T cells search
Random walk models:
Vary parameters $\rightarrow$ how long does it take to find target?
Problems:
- Can cells tune speed and persistence independently?
- Is behavior cell-intrinsic, or shaped by environment?
How do intrinsic (UCSP) and extrinsic (tissue) factors
constrain T cell migration patterns?
Approach: Modeling T cell migration in silico
This model must:
| 1 | describe migration | $\rightarrow$ T cells should actively move |
| 2 | reproduce UCSP | $\rightarrow$ speed/persistence as output, not input |
| 3 | be spatial | $\rightarrow$ T cells should have a shape |
| 4 | be multicellular | $\rightarrow$ allow cells to interact with surrounding tissue |
The Cellular Potts Model (CPM)
Pixels belong to cells, which
move by copying pixels:
Copy success chance (Pcopy) is higher when it helps the cell:
| Stay together: | Maintain its size: | Maintain its membrane: |
| $\searrow$ | $\downarrow$ | $\swarrow$ |
Approach: Modeling T cell migration in silico
This model must:
| 1 | describe migration | $\rightarrow$ T cells should actively move |
| 2 | reproduce UCSP | $\rightarrow$ speed/persistence as output, not input |
| 3 | be spatial | $\rightarrow$ T cells should have a shape |
| 4 | be multicellular | $\rightarrow$ allow cells to interact with surrounding tissue |
Migration: Act model
Cells move if we add positive feedback on protrusive activity ($\approx$ actin polymerization)1:
| Parameters: | ||
| λact | $\approx$ | protrusive force |
| maxact | $\approx$ | polymerized actin lifetime |
1Niculescu et al. PLoS Computational Biology, 2015.
Approach: Modeling T cell migration in silico
This model must:
| 1 | describe migration | $\rightarrow$ T cells should actively move |
| 2 | reproduce UCSP | $\rightarrow$ speed/persistence as output, not input |
| 3 | be spatial | $\rightarrow$ T cells should have a shape |
| 4 | be multicellular | $\rightarrow$ allow cells to interact with surrounding tissue |
Set-up: microchannels ("1D")
In silico microchannel:
Maiuri et al, Cell 2015.
Set-up: 2D and 3D
Speed-persistence coupling emerges in the Act model
All tested λact / maxact combinations:
$\rightarrow$ Speed-persistence coupling exists in all 3 settings
Speed-persistence coupling emerges in the Act model
Similar cells grouped (microchannels):
$\rightarrow$ Exponential coupling is strong in cells with same maxact
Speed-persistence coupling emerges in the Act model
Similar cells grouped (2D and 3D):
 |
$\rightarrow$ Exponential coupling is strong in cells with same maxact
Speed-persistence coupling spans different migration modes
Similar cells grouped (2D and 3D):
 |
 |
$\rightarrow$ Coupling holds throughout different shapes & behaviors
Persistence saturates at high λact
Model:
Experimental data:
$\rightarrow$ Model reproduces saturation observed in vitro
Not only persistence, but also speed saturates
Speed and persistence versus λact:
Why do speed and persistence saturate?
Speed and persistence versus λact:
Low maxact: protrusion splitting
High maxact: angular diffusion
Back to T cells: the epidermis
The epidermis is packed tightly with keratinocytes
...Yet T cells remaining after an infection are highly motile:
 |
|
| Ariotti et al, PNAS 2012. |
$\rightarrow$ Can we still see the UCSP for T cells in such a dense environment?
Act T cells migrate between keratinocytes in the epidermis
Low λact:
High λact:
Environmental constraints obscure UCSP in the epidermis
For different tissue rigidity (λP):
$\rightarrow$ Stringent environmental constraints can overrule the UCSP
Summary
Cells cannot freely "choose" their speed and persistence:
| - | Both have a natural upper bound (cell-intrinsic) |
| - | Speed and persistence are coupled (cell-intrinsic) |
| - | Environmental constraints restrict options even further, and can overrule the UCSP (cell-extrinsic) |
$\rightarrow$ "Optimizing" speed and persistence is not so straightforward!
Relevance: Lévy walks?
Back to T cells in the brain:
Cells alternate between high & low persistence, while keeping the
same speed.
$\rightarrow$ But how could they do that when there is a UCSP?
Fitting these models on limited data is hard...
Acknowledgements
Tumor Immunology, Nijmegen
- Johannes Textor
- Jolanda de Vries
- Carl Figdor
- Human DLM
Funding
- Radboudumc PhD grant
Theoretical Biology, Utrecht
- Rob de Boer
- Martijn Kolijn
- Ioana Niculescu
Chemical & Biological Physics, Weizmann Institute, Israel
- Nir Gov
