Immunoreceptor MerTK: A journey from the membrane into the nucleus of human dendritic cells

[eng] Discrimination between foreign and potentially harmful antigens, and the body’s own tissue is one of the most crucial first steps that lays at the basis of a proper immune response. Immunoreceptors are cell membrane embedded molecules that aid immune cells in identifying and interacting with its environment. Because of their key importance, they are therefore a frequent subject of research in immunology. It is becoming increasingly clear that the organization of immunoreceptors in space and time on the plasma membrane directly impacts on the way they function. Over the past two decades, novel microscopy techniques and biophysical tools have been developed and exploited to directly visualize molecular events in immune cells with unprecedented spatial and temporal resolution. These technical advances have led to the emergence of a new, active field of research: Nano-immunology. Biophysical tools and super-resolution imaging have been exploited in this thesis to unravel the spatiotemporal behaviour of different immunoreceptors, with a particular emphasis on the tyrosine kinase immunoreceptor MerTK. These studies have contributed to further our understanding of immune cell biology at the molecular level. In Part I of this thesis, I will discuss several advanced imaging techniques and microfabrication approaches that I used throughout my doctoral studies. For each technique, the fundamental principles as well as the quantitative analysis associated to them will be explained. Their specific advantages in the field of nano-immunology will be highlighted. In addition, an example of how each technique has been exploited to answer a specific biological question in the field will be given. All of the examples presented in part I correspond to publications that I co-authored during my PhD research. In Part II, I will address the subcellular organization of the immunoreceptor MerTK in human dendritic cells (DCs). By exploiting super-resolution STED nanoscopy, we discovered that MerTK organizes in small nanoclusters on the plasma membrane of tolerogenic DCs, where MerTK is highly expressed. Moreover, we will show that even though MerTK is a membrane receptor, it is also found at very high levels in the nucleus of DCs. To place this finding in the context of immunity, we established a direct correlation between DC differentiation and the amount of MerTK found in the nucleus. We enquired the route by which MerTK translocate to the nucleus, and dissected some of the main molecular factors involved in promoting this translocation. In a first attempt to identify its nuclear function, we additionally mapped the spatial relationship between MerTK and chromatin with nanometre accuracy using super-resolution STORM nanoscopy, in single intact DCs nuclei at different stages of their differentiation. We will finally place our findings in a broader perspective and suggest future lines of investigation that may further unravel the molecular mechanism of action of MerTK in particular, and the functional role of membrane receptors in the nucleus in general.[spa] Discriminar entre los antígenos ajenos y potencialmente peligrosos y los propios es uno de los pasos más cruciales en el inicio de una respuesta inmune. Los inmuno-receptores son moléculas inmersas en la membrana celular que ayudan a las células del sistema inmune a identificar y relacionarse con su entorno. Debido a su importancia, son sujeto intenso de estudio en el campo de la inmunología. Cada vez está más claro que la organización espaciotemporal de los inmuno-receptores en la membrana celular tiene un impacto directo en su función. En las dos últimas décadas, se han desarrollado un gran número de técnicas de microscopía y de biofísica innovadoras que están siendo utilizadas para visualizar de manera directa procesos a nivel molecular en células del sistema inmune con una resolución espacial y temporal sin precedentes. Estos avances técnicos han dado lugar a la aparición de un nuevo y activo campo de investigación: la nano-inmunología. En esta tesis se han utilizado técnicas de biofísica y microscopía de super-resolución para descifrar el comportamiento espaciotemporal de varios inmuno- receptores, con especial énfasis en el inmuno-receptor tirosina quinasa MerTK. Estos estudios han contribuido al mejor entendimiento de la biología celular del sistema inmune a nivel molecular. En la Parte I de esta tesis, analizaré varias técnicas de imagen y estrategias de micro-fabricación avanzadas utilizadas a lo largo de mi doctorado. En cada una de las técnicas se explicarán tanto los principios fundamentales como el análisis cuantitativo asociado. Se destacarán también sus ventajas específicas en el campo de la nano-inmunología. Además, se dará un ejemplo de cómo cada una de estas técnicas se ha aprovechado para dar respuesta a preguntas biológicas concretas. Todos los ejemplos expuestos en la Parte I de esta tesis se corresponden con publicaciones que he co-escrito durante mi doctorado. En la Parte II, abordaré el estudio de la organización sub-celular del inmuno-receptor MerTK expresado en células dendríticas humanas (DCs). Haciendo uso de la técnica de nanoscopía de superresolución STED, hemos descubierto que MerTK está organizado en pequeños nano-agregados en la membrana de células dendríticas tolerogénicas, donde MerTK está altamente expresado. Además, hemos descubierto que, aunque MerTK es un receptor de membrana, también se encuentra expresado a niveles muy altos en el núcleo de células dendríticas. Para posicionar este hallazgo en el contexto de la inmunología, hemos establecido una correlación directa entre la diferenciación de células dendríticas y la cantidad de MerTK en el núcleo. Adicionalmente, hemos investigado la ruta a través de la cual MerTK es translocado al núcleo y hemos analizado algunos de los principales factores moleculares involucrados en promover esta translocación. En un primer intento de identificar su función en el núcleo, hemos cartografiado además la relación espacial entre MerTK y cromatina con precisión nanométrica, utilizando la técnica de nanoscopía de superresolución STORM en células dendríticas intactas en varios estados de diferenciación. Finalmente, pondremos todos nuestros hallazgos en una perspectiva más amplia con la finalidad de sugerir líneas de investigación futuras que puedan descifrar con mas detalle el mecanismo molecular de acción de MerTK en particular, y la función de receptores de membrana en el núcleo en general

Planar Optical Nanoantennas Resolve Cholesterol-Dependent Nanoscale Heterogeneities in the Plasma Membrane of Living Cells

Optical nanoantennas can efficiently confine light into nanoscopic hotspots, enabling single-molecule detection sensitivity at biological relevant conditions. This innovative approach to breach the diffraction limit offers a versatile platform to investigate the dynamics of individual biomolecules in living cell membranes and their partitioning into cholesterol-dependent lipid nanodomains. Here, we present optical nanoantenna arrays with accessible surface hotspots to study the characteristic diffusion dynamics of phosphoethanolamine (PE) and sphingomyelin (SM) in the plasma membrane of living cells at the nanoscale. Fluorescence burst analysis and fluorescence correlation spectroscopy performed on nanoantennas of different gap sizes show that, unlike PE, SM is transiently trapped in cholesterol-enriched nanodomains of 10 nm diameter with short characteristic times around 100 μs. The removal of cholesterol led to the free diffusion of SM, consistent with the dispersion of nanodomains. Our results are consistent with the existence of highly transient and fluctuating nanoscale assemblies enriched by cholesterol and sphingolipids in living cell membranes, also known as lipid rafts. Quantitative data on sphingolipids partitioning into lipid rafts is crucial to understand the spatiotemporal heterogeneous organization of transient molecular complexes on the membrane of living cells at the nanoscale. The proposed technique is fully biocompatible and thus provides various opportunities for biophysics and live cell research to reveal details that remain hidden in confocal diffraction-limited measurements.Peer ReviewedPostprint (author's final draft

Priming by Chemokines Restricts Lateral Mobility of the Adhesion Receptor LFA-1 and Restores Adhesion to ICAM-1 Nano-Aggregates on Human Mature Dendritic Cells

LFA-1 is a leukocyte specific β2 integrin that plays a major role in regulating adhesion and migration of different immune cells. Recent data suggest that LFA-1 on mature dendritic cells (mDCs) may function as a chemokine-inducible anchor during homing of DCs through the afferent lymphatics into the lymph nodes, by transiently switching its molecular conformational state. However, the role of LFA-1 mobility in this process is not yet known, despite that the importance of lateral organization and dynamics for LFA-1-mediated adhesion regulation is broadly recognized. Using single particle tracking approaches we here show that LFA-1 exhibits higher mobility on resting mDCs compared to monocytes. Lymphoid chemokine CCL21 stimulation of the LFA-1 high affinity state on mDCs, led to a significant reduction of mobility and an increase on the fraction of stationary receptors, consistent with re-activation of the receptor. Addition of soluble monomeric ICAM-1 in the presence of CCL21 did not alter the diffusion profile of LFA-1 while soluble ICAM-1 nano-aggregates in the presence of CCL21 further reduced LFA-1 mobility and readily bound to the receptor. Overall, our results emphasize the importance of LFA-1 lateral mobility across the membrane on the regulation of integrin activation and its function as adhesion receptor. Importantly, our data show that chemokines alone are not sufficient to trigger the high affinity state of the integrin based on the strict definition that affinity refers to the adhesion capacity of a single receptor to its ligand in solution. Instead our data indicate that nanoclustering of the receptor, induced by multi-ligand binding, is required to maintain stable cell adhesion once LFA-1 high affinity state is transiently triggered by inside-out signals.Peer ReviewedPostprint (published version

Lateral mobility and nanoscale spatial arrangement of chemokine-activated α4β1 integrins on T cells

11p.-8 fig.Chemokine stimulation of integrin α4β1-dependent T lymphocyte adhesion is a key step during lymphocyte trafficking. A central question regarding α4β1 function is how its lateral mobility and organization influence its affinity and avidity following cell stimulation with chemokines and/or ligands. Using single particle tracking and super-resolution imaging approaches, we explored the lateral mobility and spatial arrangement of individual α4β1 integrins on T cells exposed to different activating stimuli. We show that CXCL12 stimulation leads to rapid and transient α4β1 activation, measured by induction of the activation epitope recognized by the HUTS-21 anti-β1 antibody and by increased talin-β1 association. CXCL12-dependent α4β1 activation directly correlated with restricted lateral diffusion and integrin immobilization. Moreover, co-stimulation by CXCL12 together with soluble VCAM-1 potentiated integrin immobilization with a five-fold increase in immobile integrins as compared to unstimulated conditions. Our data indicate that docking by talin of the chemokine-activated α4β1 to the actin cytoskeleton favors integrin immobilization, which likely facilitates ligand interaction and increased adhesiveness. Super-resolution imaging showed that the nanoscale organization of high-affinity α4β1 remains unaffected following chemokine and/or ligand addition. Instead, newly activated α4β1 integrins organize on the cell membrane as independent units without joining pre-established integrin sites to contribute to cluster formation. Altogether, our results provide a rationale to understand how the spatiotemporal organization of activated α4β1 integrins regulates T lymphocyte adhesion.This work was supported by Erasmus Mundus Doctorate Program Europhotonics Grant 159224-1-2009-1-FR-ERA MUNDUS-EMJD), Spanish Ministry of Economy and Competitiveness “Severo Ochoa” Programme for Centres of Excellence in R&D Grants SEV-2015–0522, FIS2014 –5617-R, SAF2014–53059-R, and RD12/0036/0061; Fundacio Privada CELLEX (Barcelona);HFSP Grant GA RGP0027/2012; and LaserLab Europe 4 Grant GA 654148.Peer reviewe

Mobility of LFA-1 on monocytes and mDCs.

<p>(<b>A</b>) Representative frame from a recorded movie on a mDC to which SDT was applied (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099589#pone.0099589.s004" target="_blank">Movie S1</a>). Representative examples of stationary, slow and fast trajectories are displayed. (<b>B</b>) Overlay semi-log histogram of LFA-1 diffusion on both monocytes (dashed black lines) and mDCs (grey bars). (<b>C</b>) Percentage of stationary, slow and fast diffusing LFA-1 molecules on monocytes and mDCs, as extracted from the cumulative probability distribution analysis (see Methods). (<b>D</b>) Diffusion coefficient of the total mobile population, and slow and fast diffusing fractions of LFA-1 on monocytes and mDCs. 25 monocytes divided over 6 independent samples (4587 trajectories) and 117 mDCs from 5 different donors, divided over 117 independent samples (26756 trajectories) were imaged. Means ± SEM are depicted. The Student T-test was used to determine significant differences between means. The resulting P values are indicated as follows: *P<0.05; ***P<0.0001.</p

Localization of Talin1 to LFA-1 on monocytes and mDCs.

<p>(<b>A, B</b>) Representative images of (<b>A</b>) a monocyte seeded on a TS2/4 pattern, (<b>B</b>) a resting mDC on irrelevant IgG1 pattern, (<b>C</b>) a resting mDC seeded on a TS2/4 pattern and (<b>D</b>) a CCL21 activated mDC seeded on a ICAM-1 pattern. Green corresponds to Talin1 and red to the location of IgG1, TS2/4 or ICAM-1 positive squares. Cells are delineated by white lines. (<b>E</b>) Quantification of the degree of Talin1 enhancement to the positive areas in monocytes in different conditions (see Methods). Mean enhancement factor is displayed in red per condition. (<b>F</b>) Percentage of positive squares per experiment (n = 3, each a different donor) that showed significantly enhanced Talin1 signal per condition in monocytes. An enhancement factor of ≥1.5 was considered significantly enhanced, since 95% of the control sample (monocytes on IgG1) showed an enhancement factor below this value. (<b>G</b>) Quantification of the degree of Talin1 enhancement to the positive areas in mDCs in different conditions (see Methods). Mean enhancement factor is displayed in red per condition. (<b>H</b>) Percentage of positive squares per experiment (n = 3, each a different donor) that showed significantly enhanced Talin1 signal per condition in mDCs. Around 60 cells of 3 different donors were analyzed per condition. Monocytes contained 10 positive areas on average per cell, while mDCs contained around 50 positive areas. Means ± SEM are depicted. The Kruskal-Wallis test, followed by Dunn’s multiple comparison test was used to determine significant differences between means in E and G. The One-way ANOVA followed by the Tukey’s multiple comparison test were used to determine significant differences between means in F and H. The resulting P values are indicated as follows: <i>ns (</i>P>0.05); * (P<0.05) and *** (P<0.0001).</p

Mobility of LFA-1 on resting and CCL21-activated mDCs after soluble monomeric and nano-clustered ICAM-1.

<p>(<b>A,B</b>) ICAM-1 (either monomeric: ICAMm, or as nano-aggregates: ICAMagg) was added to resting mDCs and mobility was measured before, and between 1 and 5 min after addition. (<b>A</b>) Stationary fraction of LFA-1 molecules on resting mDCs and mDCs + either monomeric ICAM-1 or ICAM-1 nano-aggregates, displayed as the difference with respect to the total stationary fraction on resting mDCs, which serve here as the default. (<b>B</b>) Diffusion coefficient of the total mobile population, and slow and fast diffusing fractions of LFA-1 on resting mDCs and after addition of either monomeric ICAM-1 or ICAM-1 nano-aggregates. 30 cells divided over 2 independent experiments (3393 trajectories) were imaged for the ICAMm condition and 10 cells (684 trajectories) for ICAMagg. (<b>C, D</b>) ICAM-1 (either monomeric or nano-aggregates) was added together with CCL21 to mDCs and mobility was measured before, and 2 minutes after addition. (<b>C</b>) Stationary fraction of LFA-1 molecules on resting mDCs (serving as reference control), CCL21 activated mDCs and CCL21 activated mDCs + either monomeric ICAM-1 or ICAM-1 nano-aggregates, displayed as the difference with respect to the total stationary fraction on resting mDCs. (<b>D</b>) Diffusion coefficient of the total mobile population, and slow and fast diffusing fractions of LFA-1 on resting mDCs, CCL21 activated mDCs and after simultaneous addition of CCL21 and either monomeric ICAM-1 or ICAM-1 nano-aggregates. 16 cells (8423 trajectories) from 2 different donors divided over 5 independent samples (ICAMm) and 7 cells (314 trajectories) from 2 different donors divided over 7 independent samples (ICAMagg) were imaged. (<b>E</b>) Quantification of fluorescent ICAM-1 dimers (monomers bound together due to antibody labelling) and nano-aggregates binding in resting and CCL21 activated mDCs, normalized to the area quantified and to the background signal outside of the cell. For this, regions of the cell in between the obvious fluorescent ICAM-1 aggregates were selected, the fluorescent intensity was measured using ImageJ, and used to compare the baseline fluorescent signal across all 4 conditions. 20 cells per condition were imaged. (<b>A–E</b>) Means ± SEM are depicted. The One-way ANOVA followed by the Tukey multiple comparison test were used to determine significant differences between means. The resulting P values are indicated as follows: <i>ns (</i>P>0.05); * (P<0.05), ** (P<0.001) and *** (P<0.0001). (<b>F</b>) Quantification of bound ICAM-1 nano-aggregates to resting and CCL21 activated mDCs. After applying a threshold of 25% of the fluorescent signal, all visible fluorescent spots per cell were counted. 20 cells per donor and 3 different donors were imaged. Each data point represents the mean value for 1 donor. Means ± SEM and individual data points are depicted, and dotted lines connecting datapoint of the same experiment indicate that not just in average, but in each individual experiment using a different donor, an increase of ICAM-1 nano-aggregate binding is observed after CCL21 activation. The paired two-tailed Student T-test was used to determine significant differences between means. (<b>G–I</b>) Representative examples of confocal images of ICAM-1 binding to mDCs: (<b>G</b>) dimeric ICAM-1 to resting cells, (<b>H</b>) nano-aggregates of ICAM-1 to resting cells and (<b>I</b>) nano-aggregates to CCL21 activated cells. Arrows in <b>H</b> and <b>I</b> point to the binding of individual ICAM-1 nano-aggregates to LFA-1.</p

Mobility of LFA-1 on mDCs after activation with CCL21.

<p>(<b>A</b>) Overlay histograms of LFA-1 diffusion on monocytes (4578 trajectories), resting mDCs (26756 trajectories) and CCL21 activated mDCs (4213 trajectories). (<b>B</b>) Percentage of total mobile LFA-1 population (normalized to 100%) displaying slow and fast diffusion on monocytes, mDCs and 2 min CCL21 activated mDCs. (<b>C</b>) Diffusion coefficient of the total mobile population, and slow and fast diffusing fractions of LFA-1 on monocytes, mDCs and 2 min CCL21 activated mDCs. (<b>D</b>) Stationary fraction of LFA-1 on monocytes, mDCs and CCL21 activated mDCs, displayed as the difference from the total stationary fraction on mDCs, which serve here as the default. Data from <b>A–D</b> on CCL21 activated mDCs is based on 22 cells in independent experiments from 3 different donors. (<b>E</b>) Percentage of the stationary, slow and fast diffusing LFA-1 molecules at different time points after CCL21 activation. (<b>F</b>) D values for the total mobile, and slow and fast fractions of LFA-1 at different time points after CCL21 activation. Data from <b>E</b> and <b>F</b> is based on 11 cells, 11 independent samples and around 2000 trajectories per time point. <b>A</b>–<b>F</b> Means ± SEM are depicted. The One-way ANOVA followed by the Tukey multiple comparison test were used to determine significant differences between means. The resulting P values are indicated as follows: <i>ns (P</i>>0.05); * (P<0.05) and *** (P<0.0001).</p