Cells On Patrol Understanding, quantifying, and Lymphocyte - - PowerPoint PPT Presentation

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Cells On Patrol

Understanding, quantifying, and modeling lymphocyte migration

Course Immunobiology, May 8th, 2015

Johannes Textor Theoretical Biology & Bioinformatics Universiteit Utrecht

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Learning Objectives

1 Understand why and how lymphocytes migrate 2 Learn about modern intravital imaging 3 Be able to quantify cell migration 4 Appreciate how modeling can contribute to understanding

lymphocyte migration

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Lecture Outline

1 Lymphocyte migration 2 Intravital imaging 3 Motion in numbers 4 Modeling migration

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Why cell migration?

Example: T cells in mice vs a previously unseen antigen

cells 30,000,000,000 20g 100% lymphocytes 300,000,000 0.2g 1% T cells 150,000,000 0.1g 0.5% ag-specific T cells 20-200 10−7g 0.0000005% lymph nodes 35 ∼2-3 ag-specific naive T cells per lymph node! T cells migrate because the immune system does not (cannot!) provide complete protection everywhere in the

• body. Instead, T cells patrol the

body by migrating constantly.

Il était une fois – la vie

Migration is necessary to provide protection everywhere.

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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T cell migration takes place on two scales

migration between organs

• rhythm: hours
• studied in the 1960s-70

migration within tissue

• rhythm: minutes
• studied since 2002

B cells migrate similarly, but more slowly (2-4fold). In this lecture, we’ll focus on T cells to illustrate the basic principle.

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Circulation routes of Naive T Cells To find their antigen, naive T Cells circulate between blood, secondary lymhpoid organs (SLOs), and lymph.

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Circulation routes of Naive T Cells To find their antigen, naive T Cells circulate between blood, secondary lymhpoid organs (SLOs), and lymph.

1 Circulation in the blood

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Circulation routes of Naive T Cells To find their antigen, naive T Cells circulate between blood, secondary lymhpoid organs (SLOs), and lymph.

1 Circulation in the blood 2 Recruitment into SLO

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Circulation routes of Naive T Cells To find their antigen, naive T Cells circulate between blood, secondary lymhpoid organs (SLOs), and lymph.

1 Circulation in the blood 2 Recruitment into SLO 3 Search for antigen in SLO

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Circulation routes of Naive T Cells To find their antigen, naive T Cells circulate between blood, secondary lymhpoid organs (SLOs), and lymph.

1 Circulation in the blood 2 Recruitment into SLO 3 Search for antigen in SLO 4 Exit to lymphatic system

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Circulation routes of Naive T Cells To find their antigen, naive T Cells circulate between blood, secondary lymhpoid organs (SLOs), and lymph.

1 Circulation in the blood 2 Recruitment into SLO 3 Search for antigen in SLO 4 Exit to lymphatic system 5 Re-entry into blood

Thoracic duct drains into left subclavian vein

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Circulation routes of Naive T Cells To find their antigen, naive T Cells circulate between blood, secondary lymhpoid organs (SLOs), and lymph. One round takes ≈6-30h, depending on the SLO and the cell type (CD4/CD8, Naive/Memory)

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Classic measurements of migration dynamics Experimental protocol:

• Inject labeled naive T cells

into subclavian vein

• Measure reappearance in

thoracic duct

Westermann, 1994

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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How fast do T cells arrive at an infection site? We can use a simulation to investigate arrival speed at infection sites.

Influenza infection 20 40 60 80 1 2 3 4 5 6 7 cells arriving at dLNs in silico (%) time p.i. (days) priming in vivo 1 dLN 2 d L N s 6 dLNs 9 dLNs cells recruited into response in vivo (%)

• Depends on number
• f draining lymph

nodes (dLNs)

• With few dLNs,

these get more blood and swell

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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In lymph nodes, T cells meet dendritic cells that present antigen

Antigen Normal tissue HEV Postcapillary venule Inflamed venule Inflamed tissue Dendritic cell Afferent lymph vessel Tissue- specific homing Naive T cell Central memory T cell Central memory T cell (long-lived) Lymphoid

• rgan

Effector memory T cell (long-lived) Effector T cell (short-lived) Inflammation- induced recruitment Differentiation Constitutive homing Activated T cells

• Clonal expansion

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Basic structure of lymph nodes Cortex > 80% B cells

germinal centers

Paracortex > 80% T cells

T/DC interactions

Medulla Macrophages

efferent lymph

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Migration of naive T cells through lymph nodes (1) entry T cells are recruited by surface molecules in high endothelial venules (HEV) in the paracortex.

2 Medulla Paracortex Cortex Vascular System Efferent Lymph 3 Lymphatic System

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HEV

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Migration of naive T cells through lymph nodes (2) search T cells search for antigen in the paracortex. Most of them don’t enter the cortex (B zone) unless activated.

2 Medulla Paracortex Cortex Vascular System Efferent Lymph 3 Lymphatic System

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HEV

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Migration of naive T cells through lymph nodes (3) exit T cells reach the medulla and exit via efferent lymphatic vessels to the lymphatic system.

2 Medulla Paracortex Cortex Vascular System Efferent Lymph 3 Lymphatic System

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HEV

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Summary on migration

• T and B cells migrate because it would be too expensive to

maintain a sizable number of cells of every specificity in every location.

• T and B cells recirculate between blood, secondary

lymphatic organs (SLOs) and lymph ≈ once per day.

• Lymph nodes and other SLOs provide hubs for migrating

cells to come together and exchange information.

Fun fact to remember

A lymph node consists > 90% of constantly moving cells!

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Intravital imaging – a breakthrough for cell migration research

• Traditional approaches like thoracic duct draining only

allowed to indirectly observe cell motion in vivo.

• This changed with the advent of two-photon microscopy.
• First immunological applications of two-photon imaging in

2002 – three back-to-back papers in Science.

Terminology

intravital “into the living” – any experiment where we

• bserve something within a living being

two-photon microscopy – one technique that allows penetrating the skin for intravital imaging

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Fluorescence microscopy How do we make cells visible in dense tissues?

ground state excited state

Emit low energy photon Excitation of fluorophores Green fluorescent protein

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Two-photon vs confocal microscopy

Basic idea

Instead of a single, strong beam shooting through the sample, we have two, weak beams that meet in a single point.

Dichroic Beam Splitter Objective Scanning mirror Flurophores Focal Plane Red Filter Green Filter Red PMT Green PMT PMT=photomultiplier tube Infrared laser (Mode-locked)

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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From stacks to movies One stack of z-planes is acquired every ∼15-30 sec. Due to this process, depth resolution (z) is much poorer than planar resolution (x, y).

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Intravital lymph node imaging

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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T and B cells migrating in a lymph node

Miller et al, Science 2002

• No apparent synchronization or directionality
• This was a huge surprise to the field

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Summary on intravital imaging

• In two-photon imaging, two weak laser beams of low

frequency (red) are used instead of one strong laser beam

• f high frequency.
• Fluorescent proteins like GFP and “tomato” are used to

visualize injected cells within tissue.

• T and B cells search for antigen in lymph nodes by

swarming randomly.

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Motion in numbers

Things we want to describe

• How do the cells move
• Where do they go

This lecture and the practicals will focus mainly on the how. We will learn a little bit on the where in the last part.

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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5-minute-exercise

Exercise

Describe the migration of the green cells in these videos. Which differences do you observe?

T cells in a lymph node in the absence of infection

• A. Peixoto, Harvard Medical School

Neutrophils in lymph nodes during infection with Toxoplasma gondii Chtanova et al, Immunity 2008

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Cell tracking The basis for most quantitative analyses are cell tracks. Making tracks requires following each cell through the movie. This can be done by hand or automatically (often poor quality).

Terminology

step two consecutive locations of the same cell track a sequence of steps

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Two extreme forms of motion Linear motion – move continuously along a straight line, never change direction. t=0 t=1 t=2 t=3 t=4 t=5 Brownian motion – move discontinuously, change direction randomly at every step.

t=0 t=7 t=3 t=5 t=4 t=1 t=2 t=6 t=8 Typical question

“How linear” or “how Brownian” are cells in a given movie?

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Displacement plots Displacement plots were the first attempt to quantify how lymphocytes move. Though not optimal, they have become a tradition that we still see in many papers.

Mean square displacement function d2(∆t)

• Measure distance

between track positions time ∆t apart

• Average the square

distance for each ∆t

∆t=4 : 176µm ∆t=4 : 129µm

d2(4) = 1762 + 1292 2 = 47617µm2

• Upward: linear motion
• Straight: Brownian motion
• Hits ceiling: confined motion

l i n e a r Brownian confined

∆t α(∆t)

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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What’s wrong with displacement plots? Displacement plots were designed by physicists to study long-term observations of molecules that move extremely fast. Issues with applying this to cell migration:

• We can often not track cells long enough to start seeing

the difference between migration modes.

• The imaging region itself apparently “confines" cell

migration → artifact. f u l l v

• l

u m e i m a g i n g r e g i

• n

cells in full volume cells in imaging region

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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A better option: autocorrelation plots

Autocorrelation function α(∆t)

• Average correlation between

cell directions time ∆t apart

• Correlation of two directions

= cosine of their angle:

• cor(→, →) = 1
• cor(→, ↑) = 0
• cor(→, ←) = −1

∆t = 4

cor( , ) = 0.89

∆t = 4

cor( , ) = −0.82 α(4) = 0.89−0.82

2

= 0.035

• Linear motion: α(∆t) = 1

(maximum possible)

• Brownian motion: α(∆t) = 0

(minimum possible)

• Realistic motion:

somewhere in between Difference visible from the start! 1 linear Brownian cell ∆t α(∆t)

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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5-minute-exercise

Exercise

Compute the value α(4) for the following track: t=1 t=2 t=3 t=4 t=5 t=6 t=7 What about α(0)?

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Summary on migration quantification

• The basis for quantifying migration are steps and tracks.
• Care must be taken because of the finite imaging region.
• One way to quantify cell migration is to contrast it with

Brownian and linear motion. In the practical, we will quantify cell tracks ourselves.

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Modeling migration

• How can we model of lymphocyte migration in tissue?
• What can we do with such a model?

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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The Beauchemin model – a simple model

Migration program

• Wait for time tpause
• Turn in a random direction
• Move for time tfree at speed vfree
• Repeat as long as desired

Beauchemin, Dixit, Perelson, Journal of Immunology, 2007

Only 3 parameters – cells have no mass and no shape. Example: tpause = 1min, tfree = 2min, vfree = 10µm/min 20µm 20µm 2 µ m . . . t=0-1min t=3-4min t=6-7min

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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A small example application for the Beauchemin model

Naive T cells in uninfected lymph node (A. Peixoto)

Are these cells really just searching randomly?

“There has been no systematic study demonstrating how many lymphocytes would need to be moving in a nonrandom fashion to be detected [...]. For instance, would current analysis techniques detect 10% of T cells moving in a non-random, directed fashion, or would as many as 30% or 50% of the cells [be needed] for it to be recognized?” – Bajénoff et al, Trends Immunol, 2007

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Simulating random migration with a small bias

• We modify the Beauchemin model to give it a small bias
• We simulate migration in a realistic-sized imaging region
• How strong does the bias need to be to see it by eye?

simulation parameters vfree=19.1µm/min, tfree=2min, tpause=0.5min imaging volume 225 × 175 × 52µm like Miller et al., Science 02

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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HSV infection in skin epidermis How do T cells fight a viral infection in the skin? Research by Joost Beltman, Silvia Ariotti, Ton Schumacher, and Rob de Boer

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Experimental setup to study HSV infection in skin Silvia Ariotti & Ton Schumacher, NKI Amsterdam day -1 3 6 10 11 tattoo vaccination HSV infection intravital imaging naive T cell transfer (CD8+) Naive T cells (GFP+) are gB or OTI transgenic HSV (tomato+) expresses OTI peptide (SIINFEKL) or not

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Patches of HSV infection Immunohistochemistry staining with anti-HSV antibody Black line: basal membrane Confocal microscopy

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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CTLs limit HSV disease progression Appearance of skin 7 days post infection with gBT cells (nonspecific) with OTI cells (specific)

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Migrating effector CD8 T cells in epidermis specific T cell virus ≈100 min 440x440x30µm Confocal 3-D tracks of individual cells

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Migrating effector CD8 T cells in epidermis

• ther T cell

virus ≈100 min 440x440x30µm Confocal 3-D tracks of individual cells

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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How do T cells reach microlesions?

• By random or directed migration?
• Differences close to/far away from infection?
• Differences by presence of matching antigen?
• Not apparent from visual inspection.

Quantitative analysis of cell tracks is required.

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Step-based analysis of “approach angles” Analyze the angle formed by each cell step and the shortest line to the infection. no effect mean angle = 90◦ attraction mean angle < 90◦ repulsion mean angle > 90◦

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Antigen-specific arrest Only the antigen specific cells stop at the infection, the non-specific ones migrate away

100 200 100 200

distance from infection (µm)

specific cell non specific cell (OTI) specific cell (OTI)

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Small bias for all cells to migrate towards the microlesions

specific cell non specific cell (OTI) specific cell (OTI)

Both antigen-specific and antigen-nonspecific cells are attracted by the infection

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Research question for modeling There is a small preference to travel towards the microlesions. It is not antigen specific, and difficult to appreciate in videos.

Is this small preference biologically relevant?

• Use Beauchemin-like model of cell migration
• Run long-term simulations both with and without the

preference built in

• Preference is simulated by tweaking the cell turning angle

(build in small bias)

• Compare simulations to estimate impact on arrival

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Directionality strongly contributes to arriving at microlesions

Cells On Patrol Johannes Textor Lymphocyte migration Intravital imaging Motion in numbers Modeling migration

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Summary

1 Most lymphocytes spend most of their time migrating. This

way, immune surveillance requires fewer cells.

2 Two-photon microscopy allows (short-term) tracking of

lymphocyte migration in the living, intact animal.

3 Combining data analysis with modeling allows for

quantitative interpretation.

4 Small biases in cell migration can be very important. 5 It’s not easy to quantify cell migration – but it’s important to

get it right. Come to the practical!

• J. B. Beltman, A. F

. M. Marée, and R. J. de Boer. Analysing immune cell migration. Nature Reviews Immunology, 9:789–798, 2009.