Ulrike Haessler
Dendritic cells (DCs) are crucial for the onset of adaptive immunity. Their outstanding migratory capacities in combination with their antigen presenting properties make them perfectly suited to pick up antigens in the periphery and transport them to the lymph nodes, where antigen presentation to T-cells is most efficient [1]. Their migration from the periphery to the lymph nodes, called DCs cell homing, is involved in both mounting effective adaptive immune responses as well as maintaining tolerance to self-antigens [2]. Both of them need to be balanced to maintain a healthy organism. So far two alternative mechanisms have been proposed to guide DCs from the interstitium through the extracellular matrix to the proximal lymphatic vessel [3]. One of these mechanisms is based on paracrine chemotaxis towards a CCL21 gradient created by the proximal lymphatic vessel. The second mechanism, called autologous chemotaxis, describes how cells can follow an autocrine gradient, created by the biasing of self-secreted chemokine by interstitial flow [4, 5]. To answer the question of how these two mechanisms contribute to DC homing, a quantitative understanding of how cells respond to gradients and interstitial flow present in their physiological microenvironment is needed [6]. To date, appropriate tools to study DC migration under defined gradients and in the presence of tunable interstitial flow rates in a 3D environment have been lacking. Within this thesis, we present two micro-scale devices that allow live cell observation on an inverted microscope. The first was optimized to expose cells to a steady-state chemokine gradient within a 3D microenvironment and is based on a microfluidic agarose scaffold which functions as a gradient buffer (Chapter 3). The novelty of a gradient buffer allows us to quickly establish stable gradients, which is an important feature in order to create stable and precisely defined gradients during tracking of fast-moving cells like DCs. We then performed an in-depth study of DC cell chemotactic responses towards the CCR7 ligands (CCL19 and CCL21) in 3D using this new agarose chamber (Chapter 4). With the help of an integrated computational model, we could precisely predict the gradient formation of matrix-bound CCL21 versus soluble CCL19 in the microcellular environment and found that DC answer differently to the two chemokines in response to gradient steepness. Furthermore, CCL21 was more chemoattractive than CCL19 when cells were exposed to competing gradients of both chemokines, and this was independent of the CCL21-binding properties. This represents the first quantitative study of CCR7-mediated DC chemotaxis in 3D in response to well-defined ligand gradients. The second device we developed for studying the effects of the physiological flow environment, which cells experience in capillary-fed tissues, on cell migration [6]. A 2-gel strategy allowed us to flexibly fine tune interstitial flow velocities and to precisely determine the actual flow rate for each position imaged on a microscope. We exposed DCs to a wide range of flow rates and observed an increase in persistence under flow compared to static (Chapter 6). In conclusion, this work presents quantitative insights into DC migration within precisely controlled 3D-microenvironments using new devices. It also highlights the role of a quantitative understanding of biological processes to distinguish between chemotactic mechanisms of DC cells.
EPFL2010
