Advancing applications of super-resolution imaging
Protein clustering and spatial organization in T-cells Michael J. Shannon*, Garth Burn*, Andrew Cope†, Georgina Cornish†1 and Dylan M. Owen*1 *Department of Physics and Randall Division of Cell and Molecular Biophysics, King’s College London, London SE1 1UL, U.K. †Academic Department of Rheumatology, Division of Immunology, Infection and Inflammatory Disease, Faculty of Life Sciences and Medicine, King’s College London, London SE1 1UL, U.K.
Abstract T-cell protein microclusters have until recently been investigable only as microscale entities with their composition and structure being discerned by biochemistry or diffraction-limited light microscopy. With the advent of super resolution microscopy comes the ability to interrogate the structure and function of these clusters at the single molecule level by producing highly accurate pointillist maps of single molecule locations at ∼ 20 nm resolution. Analysis tools have also been developed to provide rich descriptors of the pointillist data, allowing us to pose questions about the nanoscale organization which governs the local and cell wide responses required of a migratory T-cell.
Introduction T-cells travel huge distances in the blood and the lymphatics, undergoing thousands of transient interactions with antigen presenting cells (APCs) each day. To do this, T-cells migrate, relying on coordination of actin retrograde flow in the leading edge and integrin engagement with ligands on the surface of endothelial cells [1]. Transmigration from the blood to the extracellular environment/through tissues uses the same systems, but relies on a different schema of actin flow and clutch engagement [2,3]. When a T-cell finds an APC in the lymph nodes or tissues, numerous ligands and receptors communicate with one another to form a specialized junction. T-cell receptor (TCR) complexes recognize unique peptide–major histocompatibility complex (p-MHC) receptors expressed on the surface of the APC. If there is antigen recognition, the cell switches from treadmilling to centripetal actin flow [4] which aids the cellwide reorganization of signalling molecules to form the ‘immune synapse’ (IS). The supramolecular organization of this synapse [5] and the ability of cells to migrate and respond to a changeable extracellular environment [6,7] relies on the nanoscale spatio-temporal organization of proteins in and near the cell membrane, itself containing transient lipid nanodomains [8,9]. This precise and tunable organization relies on the dynamic clustering of proteins. The clustering of molecules in cells works spatiotemporally to gather together the signalling components Key words: clusters, immune synapse (IS), migration, super resolution localization microscopy. Abbreviations: APC, antigen presenting cell; CD, cluster of differentiation; cSMAC, central region of SMAC; dSMAC, distal region of SMAC; FA, focal adhesion; FP, fluorescent protein; ICAM-1, intercellular adhesion molecule 1; IS, immune synapse; LAT, linker of activated T-cells; LFA1, lymphocyte function-associated antigen 1; PALM, photo activation localization microscopy; pSMAC, peripheral region of SMAC; SMLM, single molecule localization microscopy; STORM, stochastic optical reconstruction microscopy; TCR, T-cell receptor. 1 Correspondence may be addressed to either author (email [email protected] or [email protected]).
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required for a particular response [10]. Protein clusters occur in the cell membrane and in vesicles represent dynamic control centres for molecules which must be made rapidly available to regulate T-cell migration [LFA-1 (lymphocyte function-associated antigen)] and signalling {LAT (linker of activated T-cells) [11], Lck (lymphocyte-specific protein tyrosine kinase) [12]}. Downstream signalling events are also regulated at the level of clustering and participate both in T-cell activation and in integrin inside-out signalling pathways (VAV, SH2 domain containing leukocyte protein of 76kDa (SLP-76)) [13]. Cluster formation requires scaffold proteins, direct protein–protein interactions, trafficking in membranous vesicles or partitioning into lipid ordered membrane microdomains. These lipid ordered domains support the formation of signalling complexes such as the TCR and its subunits [14] as well as integrin LFA1 heterodimers [8], which are both required for successful engagement with APCs [9]. Information about the clustering of molecules in T-cells has been gleaned mainly from biochemistry to identify multimeric complexes in whole cell populations. The use of fluorescence microscopy has since provided 200– 300 nm resolution images and the identification of protein microclusters at the synapse, but richer information lies beyond the diffraction limit of conventional fluorescent/light microscopy. Super resolution fluorescence microscopy, especially single molecule localization microscopy (SMLM) techniques [15– 17], have allowed us to probe beyond the diffraction limit and achieve nanometre scale resolution images of protein clusters in cells. Although electron microscopy (EM) has achieved this type of resolution previously, it is limited to fixed samples, heavy metal staining artefacts often arise and very specialized set-ups are required for vitrification in cryo-EM correlated to fluorescent microscopy. SMLM combines the high specificity C The
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of antibodies or fluorescent constructs for proteins of interest with the resolution required to access rich information about the spatial localization of molecules. SMLM data are represented by a pointillist map of fluorophore localizations and moving from digital pixelated information to a detailed pointillist molecular coordinate list requires different tools for the analysis of the data. For this, specialist cluster analysis tools have been developed [11,18,19].
T-cell protein clustering T-cells move rapidly through different environments in the body and form ISs with APCs. In highly motile cells, integrins, focal complex linkers and actin coordinate their action based on the extracellular nano-environment. In 2D, actin flow is achieved by actin related protein (Arp 2/3)dependent polymerization of actin filaments in the leading edge at the front of the cell and force generation by myosin II. This flow is coupled to the formation of integrin-based adhesions which act as a ‘molecular clutch’ [20] resulting in forward movement and simultaneous actomyosin-based retraction of the tail. The clutch mechanism is exemplified by the interaction between clusters of LFA-1 and its cognate ligand intercellular adhesion molecule 1 (ICAM-1). Precise and dynamic reorganization of LFA-1 in micro- and nanoclusters works in concert with affinity states to regulate adhesion of cells in different environments, for example in interstitial movement compared with migration along blood vessels [2]. Clusters can be pre-formed within the membrane or newly recruited and subtle control of integrin clustering dynamics seems to have an important role in persistent cell migration in T-cells. Super resolution microscopy has allowed us to probe the spatial characteristics of integrin LFA-1 nanoclusters, for example showing that LFA-1 preorganizes in quiescent T-cells and localizes to areas of ordered membrane aided by glycosylphosphatidylinositol anchored proteins [21]. LFA-1 localization to lipid microdomains in steady state and during activation is further evidenced by imaging of ganglioside GM1/LFA-1 which laterally aggregates when induced by the GM1 ligand, cholera toxin [22]. Clustering is also important in the force transduction link between integrin and the actin cytoskeleton, commonly known as focal adhesions (FAs). Controversy has surrounded the nature of this link, as although FA components are expressed, large FAs in T-cells are undetectable by conventional microscopy, with the molecules instead forming highly dynamic nanoclusters [23]. Interplay between clusters of integrin and FAs may be crucial for cell movement. In fibroblasts, the model for study of FAs, arginylglycylaspartic acid (RGD) ligands must be less than 58 nm apart to induce the formation of stable clusters, defined as such by the inclusion of zyxin [6]. In fast moving cancer cells, integrin α5β3 clusters can be induced by addition of Mn2 + , which then cause the formation of early FAs composed of talin and phosphatidylinositol 4,5-bisphosphate (PIP2 ) [24]. In Tcells, integrin LFA-1 spacing has been shown to be important C The
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for successful cell migration using micropatterned ICAM1 and single particle tracking [7,25]. It is possible therefore that small, transient integrin regulated adhesions predominate in T-cells, which must move quickly and be adaptable to different environments. T-cells form an IS on contact with APCs. This involves the spatio-temporal arrangement of signalling molecules into micro- and nanoclusters [26,27], ordering of the plasma membrane [28] and reorganization of the actin cytoskeleton [29,30]. IS formation has been shown to be a highly coordinated ‘stop signal’ for T-cells which involves LFA1 engagement and a switch from retrograde to centripetal flow as compared with a migrating cell. Centripetal flow of actin in IS formation has been imaged by super resolution microscopy and quantified by correlative image analysis and is likely to be important for the reorganization of signalling complexes [29]. In the formation of the IS, supramolecular clusters form in a bull’s-eye fashion which are important for signalling. A ring of integrins forms in the peripheral region (peripheral supramolecular activation cluster (SMAC)), which is surrounded by a distal region (dSMAC) consisting of protein tyrosine phosphatase CD45 and TCR undergoing active signalling and a central region (cSMAC) consists of TCR and CD3 which have already undergone signalling [31]. It has therefore been hypothesized that clustering in the cSMAC may be important for controlling the cellular response (Figure 1).
Super resolution SMLM imaging to investigate clusters in T-cells Typical T-cell protein microclusters exist on a size-scale smaller than the diffraction limit of conventional microscopy. Super resolution localization microscopy techniques like stochastic optical reconstruction microscopy (STORM) [15] and photo activation localization microscopy (PALM) [32] combine the biochemical specificity of fluorescence microscopy with imaging resolution of ∼ 20 nm. They work by temporally separating the fluorescence emission from each emitter, by exciting only a subset of fluorophores to the singlet state (S1) and recording them in a single frame using a high-sensitivity CCD (charge-coupled device) camera (Figure 2). Individual point spread functions can be imaged and their centroids localized assuming a Gaussian distribution of intensity. Reconstruction of many frames and subsets of localized fluorophores produces a pointillist map of all of the localized molecules in a given field of view. Although SMLM is generally not yet suitable for use in live tissue due to constraints on the thickness of the sample, there are many in vitro models which mimic physiological systems and are conducive to the study of migration and synapse formation in T-cells by SMLM. Whereas the simplest system uses a coverslip coated with a ligand (e.g. ICAM-1 or antiCD3/anti-CD28), surface ligand concentration and spacing can be altered to known values [6] or patterns, as can its stiffness and even its nanotopography [33]. Planar supported
Figure 1 The formation of the IS: clustering patterns of TCR, CD3, LAT and LCK dictate spatiotemporal signalling events Event (a) shows LAT (red) clusters in vesicles being recruited to the membrane to join pre-existing LAT nanoclusters upon activation of the TCR (green). (b) TCR signalling occurs mainly in the dSMAC of mature synapses. (c) Micro- and nanoclusters of high, medium and low affinity LFA-1 occur in the pSMAC, binding to cognate ligand ICAM-1 present on the APC surface. (d) clusters of TCR, CD3 and LAT which have already undergone signalling occur in the cSMAC.
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lipid bilayers allow lateral freedom of ligands and have been used to study T-cells. Two-colour STORM set-ups can provide detailed data for analysis of co-localization [34] and multi-colour STORM is possible by using relocation grids, fiducials and antibody quenching/re-labelling [35]. Twocolour 3D STORM has been used in the past to generate 20–30 nm spatial resolution images of microtubules and mitochondria, and can be adapted for use in T-cells [36]. Two examples of T-cell cluster-based signalling events which occur at the IS involve LCK and LAT (Figure 1). LCK is a kinase which signals downstream of TCR engagement. Around 40 % of LCK is constitutively active in resting Tcells as well as in activated T-cells [37]. This points towards a spatial/temporal clustering mechanism of regulation/activity of both LCK itself and its targets and interaction partners in the TCR complex. Pre-existing LCK nanoclusters become denser, larger and contain more molecules after activation of the TCR pathway by glass-bound anti-CD3/CD28. LCK has different conformations and these help the protein to self-associate in clusters and regulate its signalling activity. Clustering dynamics were regulated by a combination of lipid association and protein–protein interactions from electrostatic interactions and availability of Src homology 2 and 3 (SH2-SH3) domains present in the open formation of the protein. Conformational changes in LCK thus govern clustering dynamics [12]. LAT is an adaptor protein that links antigen recognition with downstream signalling and amplification and illustrates dynamic clustering in T-cells. Super resolution imaging has shown that pre-existing LAT forms nanoclusters at the plasma membrane but that early initiation of the T-cell response appears to be related to the en masse recruitment of LAT containing vesicles to the plasma membrane [11].
Quantifying molecular clustering Moving from digital pixelated information to a molecular coordinate list requires different tools for the analysis of the data. Current tools include a Getis’ variation on Ripley’s Kfunction [11,18] and pair-correlation [19] to discern between random and non-random molecular distributions. These methods provide information on the level and size scale of clustering. Richer descriptors based on the generation of heat and binary maps for visualization and quantification can also be generated. The size, shape and density of clusters can be extracted from these maps and such information has been used to investigate changes in clustering behaviour in T-cell signalling during the formation of the IS [11,12]. The methods are also generally applicable to two-colour colocalization analysis in such data sets [34] and to the analysis of 3D SMLM data [38]. Co-localization of molecules at the nanoscale, especially those within clusters will reveal new information previously hidden behind the diffraction barrier [34,39]. In T-cells, fluorescence resonance energy transfer (FRET) experiments have previously shown that TCR, Zeta-chain-associated protein kinase 70 (ZAP-70), LAT, Phospholipase Cγ 1 (PLCγ 1), casitas B-lineage lymphoma C The
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protein (CBL) and VAV1/SLP76 all interact within TCR microclusters (reviewed in [40]). SMLM data have since been gathered and analysed for levels of co-localization at the nanoscale and co-localization analysis tools have replicated the results/interactions seen in FRET [41].
The future of SMLM for application in T-cells Live SMLM is certainly possible both in vitro and in vivo, but two broad problems remain for its implementation. The first is a reliance on fluorescent proteins (FPs) encoded in genetic constructs with the protein of interest. These FPs suffer from low quantum yield and poor photostability, which limits their usefulness for super resolution. They also require overexpression, which can result in artefacts such as inappropriate localization and aggregation. Finally, FPs can dramatically increase the size of the protein under investigation, which can interfere with its normal function and accurate localization. To solve these problems, many new FPs and fluorophores are being developed which are brighter, more photostable and smaller. One example is live-cell compatible anti-GFP camelid nanobodies with high photon yields, their tiny size resulting in low ‘linkage error’ as compared with antibodies [42]. New methods of delivery such as the use of cell permeable peptides with highly customizable fluorophores and recognition regions to tag proteins could be used for live SMLM in cells [43]. The second problem with live SMLM is imaging fast enough to view dynamic clustering events at the nanometre scale. SMLM represents a trade-off between spatial and temporal resolution. The temporal resolution (how fast you can image), is limited by the time required to obtain sufficient numbers of molecules for clusters to become apparent and be analysable, which relies on a certain number of molecules per field of view for accurate readouts. New methods of Bayesian localization (such as 3B [44]) may address this trade-off, allowing for higher temporal resolution with comparable spatial resolution and good information output when implemented on biological samples/data. Whereas 3D STORM/PALM achieved with an astigmatic lens provides information about protein activity at the membrane and to a small depth (∼ 700 nm) above it, accurate localizations in larger volumes would be preferable. Single plane illumination microscopy (SPIM) is a technique compatible with SMLM which achieves fast optical sectioning by selectively illuminating a single plane at any height in z in a 3D volume [45]. This could be used for looking at clustering of molecules in the IS in a 3D matrix.
Conclusion At the moment, advances in in vivo imaging and in vitro super resolution serve to complement each other and provide answers through correlation. Commercially produced ‘outof-the-box’ SMLM set-ups are now available with high
Figure 2 The principle of SMLM is to temporally separate point spread functions and localize their centres to produce a pointillist map Subsets of fluorescent molecules are turned on either by exploiting blinking properties and high laser power (STORM) or photoactivatable molecules, which are imaged and then photobleached (PALM). Each frame is recorded (top left panel) and each spatially separated point spread function (PSF) centroided (bottom left panel). Thousands of frames consisting of localized pointillist centroids are then summed to give a reconstructed coordinate map which is no longer diffraction limited. The right hand panes show (a) diffraction limited and (b) reconstructed pointillist dSTORM map of LAT molecules close to the cell membrane.
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quality fluorophores and reliable data analysis tools for use with in vitro systems. Improvements in fluorophores, techniques and analysis in this young area of experimentation will allow us to further elucidate the 3D dynamic structure and function of clusters in live T-cells and subsequently investigate these phenomena in whole organisms. Super resolution maps of cluster dynamics in T-cells will also provide insight into disease, where clustering of signalling components may be modulated to affect T-cell function.
Funding This work was supported by the Marie Curie Career Integration Grants [grant number 334303]; the European Research Council Starter Grant [grant number 337187]; and Arthritis Research UK Grants [grant number 19652], [grant number 20525].
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Protein clustering and spatial organization in T-cells Michael J. Shannon*, Garth Burn*, Andrew Cope†, Georgina Cornish†1 and Dylan M. Owen*1 *Department of Physics and Randall Division of Cell and Molecular Biophysics, King’s College London, London SE1 1UL, U.K. †Academic Department of Rheumatology, Division of Immunology, Infection and Inflammatory Disease, Faculty of Life Sciences and Medicine, King’s College London, London SE1 1UL, U.K.
Abstract T-cell protein microclusters have until recently been investigable only as microscale entities with their composition and structure being discerned by biochemistry or diffraction-limited light microscopy. With the advent of super resolution microscopy comes the ability to interrogate the structure and function of these clusters at the single molecule level by producing highly accurate pointillist maps of single molecule locations at ∼ 20 nm resolution. Analysis tools have also been developed to provide rich descriptors of the pointillist data, allowing us to pose questions about the nanoscale organization which governs the local and cell wide responses required of a migratory T-cell.
Introduction T-cells travel huge distances in the blood and the lymphatics, undergoing thousands of transient interactions with antigen presenting cells (APCs) each day. To do this, T-cells migrate, relying on coordination of actin retrograde flow in the leading edge and integrin engagement with ligands on the surface of endothelial cells [1]. Transmigration from the blood to the extracellular environment/through tissues uses the same systems, but relies on a different schema of actin flow and clutch engagement [2,3]. When a T-cell finds an APC in the lymph nodes or tissues, numerous ligands and receptors communicate with one another to form a specialized junction. T-cell receptor (TCR) complexes recognize unique peptide–major histocompatibility complex (p-MHC) receptors expressed on the surface of the APC. If there is antigen recognition, the cell switches from treadmilling to centripetal actin flow [4] which aids the cellwide reorganization of signalling molecules to form the ‘immune synapse’ (IS). The supramolecular organization of this synapse [5] and the ability of cells to migrate and respond to a changeable extracellular environment [6,7] relies on the nanoscale spatio-temporal organization of proteins in and near the cell membrane, itself containing transient lipid nanodomains [8,9]. This precise and tunable organization relies on the dynamic clustering of proteins. The clustering of molecules in cells works spatiotemporally to gather together the signalling components Key words: clusters, immune synapse (IS), migration, super resolution localization microscopy. Abbreviations: APC, antigen presenting cell; CD, cluster of differentiation; cSMAC, central region of SMAC; dSMAC, distal region of SMAC; FA, focal adhesion; FP, fluorescent protein; ICAM-1, intercellular adhesion molecule 1; IS, immune synapse; LAT, linker of activated T-cells; LFA1, lymphocyte function-associated antigen 1; PALM, photo activation localization microscopy; pSMAC, peripheral region of SMAC; SMLM, single molecule localization microscopy; STORM, stochastic optical reconstruction microscopy; TCR, T-cell receptor. 1 Correspondence may be addressed to either author (email [email protected] or [email protected]).
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required for a particular response [10]. Protein clusters occur in the cell membrane and in vesicles represent dynamic control centres for molecules which must be made rapidly available to regulate T-cell migration [LFA-1 (lymphocyte function-associated antigen)] and signalling {LAT (linker of activated T-cells) [11], Lck (lymphocyte-specific protein tyrosine kinase) [12]}. Downstream signalling events are also regulated at the level of clustering and participate both in T-cell activation and in integrin inside-out signalling pathways (VAV, SH2 domain containing leukocyte protein of 76kDa (SLP-76)) [13]. Cluster formation requires scaffold proteins, direct protein–protein interactions, trafficking in membranous vesicles or partitioning into lipid ordered membrane microdomains. These lipid ordered domains support the formation of signalling complexes such as the TCR and its subunits [14] as well as integrin LFA1 heterodimers [8], which are both required for successful engagement with APCs [9]. Information about the clustering of molecules in T-cells has been gleaned mainly from biochemistry to identify multimeric complexes in whole cell populations. The use of fluorescence microscopy has since provided 200– 300 nm resolution images and the identification of protein microclusters at the synapse, but richer information lies beyond the diffraction limit of conventional fluorescent/light microscopy. Super resolution fluorescence microscopy, especially single molecule localization microscopy (SMLM) techniques [15– 17], have allowed us to probe beyond the diffraction limit and achieve nanometre scale resolution images of protein clusters in cells. Although electron microscopy (EM) has achieved this type of resolution previously, it is limited to fixed samples, heavy metal staining artefacts often arise and very specialized set-ups are required for vitrification in cryo-EM correlated to fluorescent microscopy. SMLM combines the high specificity C The
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of antibodies or fluorescent constructs for proteins of interest with the resolution required to access rich information about the spatial localization of molecules. SMLM data are represented by a pointillist map of fluorophore localizations and moving from digital pixelated information to a detailed pointillist molecular coordinate list requires different tools for the analysis of the data. For this, specialist cluster analysis tools have been developed [11,18,19].
T-cell protein clustering T-cells move rapidly through different environments in the body and form ISs with APCs. In highly motile cells, integrins, focal complex linkers and actin coordinate their action based on the extracellular nano-environment. In 2D, actin flow is achieved by actin related protein (Arp 2/3)dependent polymerization of actin filaments in the leading edge at the front of the cell and force generation by myosin II. This flow is coupled to the formation of integrin-based adhesions which act as a ‘molecular clutch’ [20] resulting in forward movement and simultaneous actomyosin-based retraction of the tail. The clutch mechanism is exemplified by the interaction between clusters of LFA-1 and its cognate ligand intercellular adhesion molecule 1 (ICAM-1). Precise and dynamic reorganization of LFA-1 in micro- and nanoclusters works in concert with affinity states to regulate adhesion of cells in different environments, for example in interstitial movement compared with migration along blood vessels [2]. Clusters can be pre-formed within the membrane or newly recruited and subtle control of integrin clustering dynamics seems to have an important role in persistent cell migration in T-cells. Super resolution microscopy has allowed us to probe the spatial characteristics of integrin LFA-1 nanoclusters, for example showing that LFA-1 preorganizes in quiescent T-cells and localizes to areas of ordered membrane aided by glycosylphosphatidylinositol anchored proteins [21]. LFA-1 localization to lipid microdomains in steady state and during activation is further evidenced by imaging of ganglioside GM1/LFA-1 which laterally aggregates when induced by the GM1 ligand, cholera toxin [22]. Clustering is also important in the force transduction link between integrin and the actin cytoskeleton, commonly known as focal adhesions (FAs). Controversy has surrounded the nature of this link, as although FA components are expressed, large FAs in T-cells are undetectable by conventional microscopy, with the molecules instead forming highly dynamic nanoclusters [23]. Interplay between clusters of integrin and FAs may be crucial for cell movement. In fibroblasts, the model for study of FAs, arginylglycylaspartic acid (RGD) ligands must be less than 58 nm apart to induce the formation of stable clusters, defined as such by the inclusion of zyxin [6]. In fast moving cancer cells, integrin α5β3 clusters can be induced by addition of Mn2 + , which then cause the formation of early FAs composed of talin and phosphatidylinositol 4,5-bisphosphate (PIP2 ) [24]. In Tcells, integrin LFA-1 spacing has been shown to be important C The
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for successful cell migration using micropatterned ICAM1 and single particle tracking [7,25]. It is possible therefore that small, transient integrin regulated adhesions predominate in T-cells, which must move quickly and be adaptable to different environments. T-cells form an IS on contact with APCs. This involves the spatio-temporal arrangement of signalling molecules into micro- and nanoclusters [26,27], ordering of the plasma membrane [28] and reorganization of the actin cytoskeleton [29,30]. IS formation has been shown to be a highly coordinated ‘stop signal’ for T-cells which involves LFA1 engagement and a switch from retrograde to centripetal flow as compared with a migrating cell. Centripetal flow of actin in IS formation has been imaged by super resolution microscopy and quantified by correlative image analysis and is likely to be important for the reorganization of signalling complexes [29]. In the formation of the IS, supramolecular clusters form in a bull’s-eye fashion which are important for signalling. A ring of integrins forms in the peripheral region (peripheral supramolecular activation cluster (SMAC)), which is surrounded by a distal region (dSMAC) consisting of protein tyrosine phosphatase CD45 and TCR undergoing active signalling and a central region (cSMAC) consists of TCR and CD3 which have already undergone signalling [31]. It has therefore been hypothesized that clustering in the cSMAC may be important for controlling the cellular response (Figure 1).
Super resolution SMLM imaging to investigate clusters in T-cells Typical T-cell protein microclusters exist on a size-scale smaller than the diffraction limit of conventional microscopy. Super resolution localization microscopy techniques like stochastic optical reconstruction microscopy (STORM) [15] and photo activation localization microscopy (PALM) [32] combine the biochemical specificity of fluorescence microscopy with imaging resolution of ∼ 20 nm. They work by temporally separating the fluorescence emission from each emitter, by exciting only a subset of fluorophores to the singlet state (S1) and recording them in a single frame using a high-sensitivity CCD (charge-coupled device) camera (Figure 2). Individual point spread functions can be imaged and their centroids localized assuming a Gaussian distribution of intensity. Reconstruction of many frames and subsets of localized fluorophores produces a pointillist map of all of the localized molecules in a given field of view. Although SMLM is generally not yet suitable for use in live tissue due to constraints on the thickness of the sample, there are many in vitro models which mimic physiological systems and are conducive to the study of migration and synapse formation in T-cells by SMLM. Whereas the simplest system uses a coverslip coated with a ligand (e.g. ICAM-1 or antiCD3/anti-CD28), surface ligand concentration and spacing can be altered to known values [6] or patterns, as can its stiffness and even its nanotopography [33]. Planar supported
Figure 1 The formation of the IS: clustering patterns of TCR, CD3, LAT and LCK dictate spatiotemporal signalling events Event (a) shows LAT (red) clusters in vesicles being recruited to the membrane to join pre-existing LAT nanoclusters upon activation of the TCR (green). (b) TCR signalling occurs mainly in the dSMAC of mature synapses. (c) Micro- and nanoclusters of high, medium and low affinity LFA-1 occur in the pSMAC, binding to cognate ligand ICAM-1 present on the APC surface. (d) clusters of TCR, CD3 and LAT which have already undergone signalling occur in the cSMAC.
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lipid bilayers allow lateral freedom of ligands and have been used to study T-cells. Two-colour STORM set-ups can provide detailed data for analysis of co-localization [34] and multi-colour STORM is possible by using relocation grids, fiducials and antibody quenching/re-labelling [35]. Twocolour 3D STORM has been used in the past to generate 20–30 nm spatial resolution images of microtubules and mitochondria, and can be adapted for use in T-cells [36]. Two examples of T-cell cluster-based signalling events which occur at the IS involve LCK and LAT (Figure 1). LCK is a kinase which signals downstream of TCR engagement. Around 40 % of LCK is constitutively active in resting Tcells as well as in activated T-cells [37]. This points towards a spatial/temporal clustering mechanism of regulation/activity of both LCK itself and its targets and interaction partners in the TCR complex. Pre-existing LCK nanoclusters become denser, larger and contain more molecules after activation of the TCR pathway by glass-bound anti-CD3/CD28. LCK has different conformations and these help the protein to self-associate in clusters and regulate its signalling activity. Clustering dynamics were regulated by a combination of lipid association and protein–protein interactions from electrostatic interactions and availability of Src homology 2 and 3 (SH2-SH3) domains present in the open formation of the protein. Conformational changes in LCK thus govern clustering dynamics [12]. LAT is an adaptor protein that links antigen recognition with downstream signalling and amplification and illustrates dynamic clustering in T-cells. Super resolution imaging has shown that pre-existing LAT forms nanoclusters at the plasma membrane but that early initiation of the T-cell response appears to be related to the en masse recruitment of LAT containing vesicles to the plasma membrane [11].
Quantifying molecular clustering Moving from digital pixelated information to a molecular coordinate list requires different tools for the analysis of the data. Current tools include a Getis’ variation on Ripley’s Kfunction [11,18] and pair-correlation [19] to discern between random and non-random molecular distributions. These methods provide information on the level and size scale of clustering. Richer descriptors based on the generation of heat and binary maps for visualization and quantification can also be generated. The size, shape and density of clusters can be extracted from these maps and such information has been used to investigate changes in clustering behaviour in T-cell signalling during the formation of the IS [11,12]. The methods are also generally applicable to two-colour colocalization analysis in such data sets [34] and to the analysis of 3D SMLM data [38]. Co-localization of molecules at the nanoscale, especially those within clusters will reveal new information previously hidden behind the diffraction barrier [34,39]. In T-cells, fluorescence resonance energy transfer (FRET) experiments have previously shown that TCR, Zeta-chain-associated protein kinase 70 (ZAP-70), LAT, Phospholipase Cγ 1 (PLCγ 1), casitas B-lineage lymphoma C The
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protein (CBL) and VAV1/SLP76 all interact within TCR microclusters (reviewed in [40]). SMLM data have since been gathered and analysed for levels of co-localization at the nanoscale and co-localization analysis tools have replicated the results/interactions seen in FRET [41].
The future of SMLM for application in T-cells Live SMLM is certainly possible both in vitro and in vivo, but two broad problems remain for its implementation. The first is a reliance on fluorescent proteins (FPs) encoded in genetic constructs with the protein of interest. These FPs suffer from low quantum yield and poor photostability, which limits their usefulness for super resolution. They also require overexpression, which can result in artefacts such as inappropriate localization and aggregation. Finally, FPs can dramatically increase the size of the protein under investigation, which can interfere with its normal function and accurate localization. To solve these problems, many new FPs and fluorophores are being developed which are brighter, more photostable and smaller. One example is live-cell compatible anti-GFP camelid nanobodies with high photon yields, their tiny size resulting in low ‘linkage error’ as compared with antibodies [42]. New methods of delivery such as the use of cell permeable peptides with highly customizable fluorophores and recognition regions to tag proteins could be used for live SMLM in cells [43]. The second problem with live SMLM is imaging fast enough to view dynamic clustering events at the nanometre scale. SMLM represents a trade-off between spatial and temporal resolution. The temporal resolution (how fast you can image), is limited by the time required to obtain sufficient numbers of molecules for clusters to become apparent and be analysable, which relies on a certain number of molecules per field of view for accurate readouts. New methods of Bayesian localization (such as 3B [44]) may address this trade-off, allowing for higher temporal resolution with comparable spatial resolution and good information output when implemented on biological samples/data. Whereas 3D STORM/PALM achieved with an astigmatic lens provides information about protein activity at the membrane and to a small depth (∼ 700 nm) above it, accurate localizations in larger volumes would be preferable. Single plane illumination microscopy (SPIM) is a technique compatible with SMLM which achieves fast optical sectioning by selectively illuminating a single plane at any height in z in a 3D volume [45]. This could be used for looking at clustering of molecules in the IS in a 3D matrix.
Conclusion At the moment, advances in in vivo imaging and in vitro super resolution serve to complement each other and provide answers through correlation. Commercially produced ‘outof-the-box’ SMLM set-ups are now available with high
Figure 2 The principle of SMLM is to temporally separate point spread functions and localize their centres to produce a pointillist map Subsets of fluorescent molecules are turned on either by exploiting blinking properties and high laser power (STORM) or photoactivatable molecules, which are imaged and then photobleached (PALM). Each frame is recorded (top left panel) and each spatially separated point spread function (PSF) centroided (bottom left panel). Thousands of frames consisting of localized pointillist centroids are then summed to give a reconstructed coordinate map which is no longer diffraction limited. The right hand panes show (a) diffraction limited and (b) reconstructed pointillist dSTORM map of LAT molecules close to the cell membrane.
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quality fluorophores and reliable data analysis tools for use with in vitro systems. Improvements in fluorophores, techniques and analysis in this young area of experimentation will allow us to further elucidate the 3D dynamic structure and function of clusters in live T-cells and subsequently investigate these phenomena in whole organisms. Super resolution maps of cluster dynamics in T-cells will also provide insight into disease, where clustering of signalling components may be modulated to affect T-cell function.
Funding This work was supported by the Marie Curie Career Integration Grants [grant number 334303]; the European Research Council Starter Grant [grant number 337187]; and Arthritis Research UK Grants [grant number 19652], [grant number 20525].
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