Protein kinases (PKs) are important enzymes that play critical cellular roles by controlling cell signalling and a myriad of associated functions. They act by catalysing the phosphorylation of specific target proteins, thereby modulating their activity, subcellular localisation, and function. Through protein phosphorylation, kinases orchestrate essential cellular processes such as cell growth, differentiation, metabolism, and intricate signal transduction. Their mode of action relies on the catalytic transfer of a phosphate group from adenosine triphosphate (ATP) to their selective target proteins (Ubersax and Ferrell, 2007). This phosphorylation acts as a switch in their function by initiating a cascade of molecular events controlling selective cellular behaviours (Taylor and Kornev, 2011). Following binding to the catalytic domain of the kinase, ATP is converted to adenosine diphosphate (ADP) and an inorganic phosphate (Pi) which is covalently transferred to a protein substrate, leading to the modification of its activity (Cohen, 2001). PKs exhibit a high degree of diversity, with various ways to classify them (Modi and Dunbrack, 2019) based on their specific structural motifs (eukaryotic/atypical) (Kanev et al., 2019) and target amino acid residues. The three main classes of PKs are serine/threonine protein kinases (STPKs), tyrosine kinases (TKs), and dual specificity protein kinases (DSPKs) (Fabbro et al., 2015).

STPKs represent most of the eukaryotic PKs. As indicated by their name, they act by phosphorylating serine and threonine residues in their substrate/target protein. These kinases are involved in regulating various cellular processes, including cell growth, proliferation, differentiation, and apoptosis. STPKs play critical roles in signalling pathways, such as the transforming growth factor-beta pathway and the mitogen-activated protein kinase (MAPK) pathway. They can be further subdivided into ‘classical’ STPKs and atypical STPKs that are only found in certain cell types.

TKs constitute a distinct class of PKs that selectively phosphorylate tyrosine residues. TKs play crucial roles in cellular communication, proliferation, differentiation, and survival. They can be further categorised into receptor-associated TKs, which participate in receptor signalling pathways, and non-receptor-associated TKs, which interact with DNA in the nucleus. Prominent examples of TKs include the epidermal growth factor receptor (EGFR), the insulin receptor, and the JAK, and Src family kinases (SFKs).

DSPKs possess the unique ability to phosphorylate both serine/threonine and tyrosine residues. These kinases exhibit versatility in their substrate specificity and often participate in complex cellular processes. DSPKs are involved in regulating cell cycle progression, DNA damage response, and cellular stress signalling.

PKs can also be classified based on their specific functions into signalling PKs, metabolic PKs, and housekeeping PKs. Signalling PKs are involved in signal transduction, metabolic PKs regulate cellular metabolism, and housekeeping PKs perform essential functions within the cell.

PK nanoclustering: implications for signalling

The subcellular organisation of PKs is of great importance for their signalling function. Our understanding of signalling mainly revolves around a vertical integration of the signal, with ligand binding to receptor, receptor activation and PK initiation of the signalling cascade (Figure 1). With the super-resolution microscopy revolution in cell biology, came the realisation that PKs are organised in clusters with sizes below the diffraction limit of light (Martínez-Mármol et al., 2023a; Owen et al., 2010; Padmanabhan et al., 2019). The mechanism(s) underpinning such clustering and its functional outcome for PK activity is/are still open questions, but the emerging concept points to a horizontal integration of the signal revolving around the lateral trapping of receptors and associated PKs in nanoclusters (Figure 1). Upon binding of the ligand, these nanoscale hubs can, in turn, generate hubs of PKs and of other downstream effectors raising the possibility that nanoclusters could serve as quantal units of signalling function. Such horizontal organisation of receptors and their effectors creates dynamic discrete signalling hubs, likely relying on structural pro-clustering sequences and interfaces leading to dimerisation and oligomerisation. This may involve the non-catalytic PK (or association of pseudokinases), as well as post-translational modifications (PTMs; e.g. phosphorylation) controlling these interactions. Understanding the modality of PK clustering will require multidisciplinary approaches to grasp how the output signal is integrated both vertically and horizontally in space and time to generate a downstream functions.

Figure 1

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Due to the large variety of PKs, this review will focus primarily on a limited number of PKs that have been studied with super-resolution microscopy. We will also attempt to draw a roadmap detailing the use of super-resolution microscopy to assess the nanoscale organisation of PKs. We will first discuss the main advantages of using sub-diffraction microscopy to assess the dynamics of PK nanoscale organisation, and how this can alter protein signalling cascades in health and disease. We will also briefly cover the main technologies used in these studies. We will then discuss PK membrane clustering, cytoplasmic re-organisation of clustering mediated by lipidation and biomolecular condensates. The review will finally explore the ramifications of alterations in PK clustering in neurodegeneration and cancer.

Advancing PK research with super-resolution microscopy

Fluorescence microscopy has been instrumental in revealing the subcellular localisation of most PKs, providing additional information on their signalling functions. However, the relatively low resolution of fluorescence microscopy limits our ability to address critical questions inherent to the nanoscale environment in which they operate. The resolution of light microscopy is constrained by the diffraction limit of light, which depends on the wavelength of the illumination light used (typically ~250 nm). Furthermore, many hundreds, if not thousands, of molecules are needed to achieve a signalling output in discrete subcellular locations. In such inherently crowded nano-environments, the fluorescence of so many emitters largely overlaps, leading to the detection of large blobby structures lacking spatial resolution. To some extent, super-resolution microscopy can overcome the diffraction limit of light. There are three commonly used types of super-resolution microscopy techniques (Figure 2) including stimulated emission depletion (STED) (Heine et al., 2018), structured illumination microscopy (SIM) (Gustafsson, 2008), and single-molecule localisation microscopy (SMLM) (Betzig et al., 2006; Rust et al., 2006; Heilemann et al., 2008).

Figure 2

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Common SMLM approaches include, stochastic optical reconstruction microscopy (STORM), photo-activated localization microscopy (PALM) as well as point accumulation for imaging in nanoscale topography (PAINT). Regardless of the methods used, the output of SMLM is a list of co-ordinates of each single-molecule’s detection. Spatially separated localisations are achieved for each approach by either imaging organic dyes in a reducing buffer to induce blinking (STORM), by visualising photoconvertable/switchable proteins applying targeted lasers (PALM), or by incubating with fluorescent binding probes that can bind reversibly or at sparse concentrations (PAINT) (Godin et al., 2014). SMLM uses the point spread function (PSF) of single fluorescence emitters to fit a Gaussian function and establish the Cartesian coordinates of each emitter with sub-pixel precision, depending on the method used. The PSF is usually circular in the two dimensions (x, y) and elliptical in depth (z), and its size depends on the wavelength of the light and the numerical aperture of the objective lens. By fitting a Gaussian function to the PSF, the localisation of the fluorophore can be estimated with relatively high accuracy. Many excellent reviews describe the pros and cons of each super-resolution technique (Valli et al., 2021; Mahecic et al., 2019; Cox, 2015). One of the key advantages of SMLM is that it can be used both in fixed and live cells (Manley et al., 2008) and is amenable to tracking endogenous proteins via a range of methods including intrabody expression of selective single-chain antibodies (e.g. camelid nanobodies) (Gormal et al., 2020) or CRISPR/Cas9-based endogenous tagging (Willems et al., 2020). They require specialised tags and rely on the activation of a photoswitchable fluorophore that emits sufficient photons for reliable localisation before becoming bleached or going into a dark state (Lelek et al., 2021). When this is used to track molecules over several consecutive images, it is called single-particle tracking (SPT) and can be used to derive mobility measurements such as the diffusion coefficient (Gormal et al., 2020). Since SPT computes the mobility of molecules as they perform their actions, some important temporal metrics can be obtained. Applying specialised analysis methods to SPT data can capture single-molecule dynamics, including diffusion, active transport, and spatiotemporal clustering (Cho et al., 2016; Wallis et al., 2023). One of the drawbacks of photoactivatable, photoswitchable, and photoconvertible tags is that the trajectories generated are relatively short. Depending on the requirement of the experiments, other techniques based on self-labelling enzymatic tags can be used, such as SNAP tags and Halo tags that are also genetically fused to the target protein. They covalently bind their bright fluorescent cell-permeant ligands (Lelek et al., 2021). The sparse labelling obtained with these techniques can be used in fixed and live cells. In the latter case, long trajectories can be generated with high localisation precision. Classically, SMLM techniques have relatively good localisation precision in the range of 10–40 nm (Figure 2), but new technologies can achieve higher spatiotemporal resolutions (Fujiwara et al., 2023; Deguchi et al., 2023). STED microscopy uses a different approach to achieve improved localisation precision by reducing the size of the PSF. STED employs two super-imposed laser beams with different excitation patterns, that raster-scan the entire sample, pixel by pixel. Since the second laser beam can de-excite the periphery of the excited fluorophore, the resulting emission is much smaller (Hell and Wichmann, 1994; Vicidomini et al., 2018). For live imaging experiments, SIM provides a direct approach that can achieve super-resolved images with conventional fluorescent microscopy sample preparation. By applying the light to the sample through known patterns at different lateral positions (rotating), emissions from out of focus light can be removed during post-processing, achieving super-resolved images with an improved lateral resolution of ~150 nm (Gustafsson, 2000). Since both SIM and STED both remove photons from the imaging sample, it is critical that the samples are sufficiently bright to achieve the best results. Overall, a range of super-resolution techniques are well suited to the study of PKs, providing a better understanding of the relationship between their spatiotemporal localisation and the signalling generated. These sub-diffractional methods provide an opportunity to characterise the organisation of PKs into discrete clusters, to varying degrees, by applying tailored analytical approaches (Chenouard et al., 2014; Khater et al., 2020). For instance, fixed STED and SMLM approaches can identify metrics such as cluster size and density, whereas SPT approaches can provide information on other temporal aspects such as mobility and time spent immobilised within hotspots.

Dynamics of RTK clustering at the plasma membrane

Unique to PK-linked receptors is that their intracellular catalytic activity can be triggered in response to an external ligand (Figure 3A). Receptor dimerisation or clustering was proposed to be sufficient to initiate the catalytic activity of many RTKs. This is supported by the finding that antibody-mediated clustering (two epitope IgG binding to two receptors) of some RTKs was sufficient to promote their activation (Davis et al., 1994). However, it is becoming increasingly evident that the precise control of signalling cascades is likely to be multifaceted. For instance, there are a large number of RTKs containing pseudokinase domains that lack conventional catalytic activity. The organisation of pseudokinases into homo-dimers or heterodimerisation with other PKs can regulate their active counterparts (Mace and Murphy, 2021). In addition, there are ‘bona-fide’ catalytically active kinases that have regulatory functions independent of their kinase activity (Shaw et al., 2014).

Figure 3

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With the exception of kinase-associated receptors, the other four members (outlined above) are able to sense external stimuli through intrinsic catalytic activity/transactivation. Alternatively, kinase-associated receptors act through ligand/effector downstream pathways, including activation by non-receptor tyrosine kinases (NRTKs). The RTK family of ‘receptor’ kinases is the largest superfamily (20 classes) and contains a range of receptors that regulate cell differentiation, proliferation, survival, metabolism, and migration. RTKs utilise several signalling pathways, including MAPK/ERK, PI3K/Akt/mTOR, PLCG1/PKC, JAK/STAT, Ras/Raf, Rac/MEK, and NF-κB. Interestingly, RTK signalling occurs in endosomes and at the cell surface, with different signalling pathways recruited depending on the location of the receptor. For kinase-associated receptors to propagate a signalling cascade, it is thought that several distinct processes must occur: (1) ligand binding, (2) activation of the intrinsic kinase domains, and/or (3) binding and activated NRTK to cell signalling (potentially through effectors). Despite the structural differences between kinase-associated receptors (e.g. cytokine receptors) and other intrinsically activated RTKs, the mechanism of ligand binding and re-organisation appears to be somewhat conserved. There are two major, distinct concepts to explain the activation of transmembrane and cell-surface receptors. Ligand binding induces either (1) dimerisation of receptors or (2) rearrangement of constitutively preformed dimeric receptors. A recent study seeking to investigate how two pseudokinases in the Eph family (EphB6 and EphA10) regulate signalling despite lacking kinase activity, found key intracellular residues that mediated the interaction of the kinases with SH2 domains of effectors for downstream signalling (Liang et al., 2021). The role of kinases extends far beyond phosphorylation, and non-catalytic activity, such as allosteric regulation, effector binding and scaffolding roles, are fundamental to biological mechanisms and signal transduction (Kung and Jura, 2016). The catalytic activity of EGFR receptors (human protein often referred to as ‘HER1/2’) is regulated by their interaction with the pseudokinase family member HER3. In a study combining the SMLM method dSTORM with protein cluster analysis, lapatinib-bound HER2 was found to form a complex with HER3 via a non-canonical kinase dimer structure that induced the formation of higher-order oligomers (Roberts et al., 2018).

PK clustering via association with plasma membrane receptors

The cytokine receptor, growth hormone receptor (GHR) has recently been shown to form nanoclusters at the plasma membrane (Chhabra et al., 2023; Figure 4A). This clustering has been shown to be highly dependent on the competitive binding of two PKs with distinct downstream signalling (Chhabra et al., 2023). It is unclear whether these clusters are fostering the formation of dimers (Brooks et al., 2014), which is needed for effective signalling (Figure 1B). In the absence of ligand, GHR-ICD is in an inactive conformation. Structural changes in the GHR extracellular domain (ECD) induced by the ligand results in altered ratio of JAK2/STAT5 to ERK1/2 signalling, which has also been shown for other receptors, including the prolactin receptor (PRLR) and erythropoietin receptors (EpoR) (Rowlinson et al., 2008; Liu et al., 2009; Moraga et al., 2015; Zhang et al., 2015). The impact of the competitive binding of PKs for the GHR receptor nanoscale organisation and two alternative signalling pathways have recently been demonstrated (Chhabra et al., 2023). Upon growth hormone (GH) addition and Lyn binding/activation (ERK1/2 pathway), the GHR redistributes into larger clusters on the plasma membrane. Conversely, the activation of the JAK2/STAT pathway did not alter the distribution of the GHR within clusters. A recent study using single-molecule imaging to co-track the movement of monomeric GHR and its effectors showed that JAK2 was important in contributing to GHR dimerisation at the membrane (Wilmes et al., 2020). GHR is able to signal through at least two divergent NTRK pathways, (1) the JAK2/STAT and (2) the LYN/ERK1/2 pathway (Chhabra et al., 2021). Since the activation of GHR through the LYN/ERK pathway correlates with increased GHR endocytosis and degradation, its overall membrane organisation may be a key determinant by which a signalling cascade is initiated. In a similar line of research, the cytokine receptor interleukin-2 receptor gamma (IL-2Rγ) rarely exist as monomers, and cluster membership increased in response to IL-2 ligand stimulation. In addition, three distinct cluster sub-populations have been described: small ‘active’ clusters, medium clusters associated with endocytosis, and large ‘static’ clusters (Salavessa et al., 2021). Perturbation of either lipid rafts or F-actin increased the size of clusters to a similar level, but differentially affected signalling. Cholesterol depletion promoted assembly and sustained STAT5 and ERK signalling, whereas F-actin disruption blocked signalling all together. However, a live cell single-molecule imaging study on the cytokine receptor for thrombopoietin (TpoR) identified that in the absence of Tpo the ratio of monomer–dimer receptors was in equilibrium, and the presence of Tpo did not significantly perturb this ratio, but prolonged the stability of dimers. This study did not extend to investigate the clustering of TpoR (Sakamoto et al., 2016). Another single-molecule tracking study indicated that EGFRs reside outside lipid rafts in the absence of ligand but move into raft microdomains upon EGF binding (Lin et al., 2016). The pseudokinases PEAK1/3 are important scaffolding regulators for the EGFR signalling (Paul et al., 2022). A recent study investigating the PEAK3 interactome revealed that it acts as a dynamic scaffold together with essential adaptor proteins to regulate signal transduction (Roy et al., 2023). Lipid raft environment has been shown to enhance LYN kinase activity (Young et al., 2003). It is, therefore, likely that the presence of clusters, their size and sub-membrane distribution in regions with different lipidic composition, such as cholesterol-enriched microdomains, jointly act to mediate a tight control over the initiation and duration of signalling events. Pseudokinases likely play an integral role in the organisation and propagation of signalling hubs, yet it is a field that has not been fully explored using super-resolution approaches.

Figure 4

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PK nanoclustering for optimal output signal

The binding affinity of ligands to their targets, such as a ligand to a cell-surface receptor, or a PK to its substrate is an important metric that ultimately controls signalling strength and duration. However, there are other factors that can make a substantial contribution to the signal output, such as local concentrations, protein conformational states, and molecular crowding. The terms affinity and avidity are often used in the context of antibody binding, but the principles of avidity/multivalency have also been applied to characterise several other types of interactions (Erlendsson and Teilum, 2020), such as inter-molecular interactions (allosteric proteins and IDPs) (Tompa, 2014), and interactions associated with short linear motifs (Bugge et al., 2020). Since receptors and associated PKs and pseudokinases can potentially participate in more than one signalling cascade, the mechanism by which they can favour one over the other is still mostly elusive. Other factors in determining the nature and strength of the output signal are (1) the probability of ligand/binding, activation and dissociation (Vauquelin and Charlton, 2010), (2) their multimeric state (Chhabra et al., 2023; Brooks et al., 2014), and (3) their diffusive and clustering properties (Small et al., 2020), and the local concentration (Caré and Soula, 2011; Figure 5). It has therefore been proposed that the residence of proteins within subcellular compartments, such as lipid rafts or BMCs, likely serves to modulate their activity through dynamic clustering mechanisms (Erlendsson and Teilum, 2020). More experimental and in silico work are needed to elucidate the precise cellular organisation of PKs to optimally amplify and locally control signalling. Since the precise organisation of PKs can now be explored using various super-resolution microscopy methods, much can be learnt about the mechanistic details of PK signalling. For instance, how the discrete signalling hubs at the membrane are formed and maintained, and how they are affected by various ligands and effectors. Furthermore, by applying SPT, some key questions in PK biology will begin to be answered, such as (1) is PK organisation affected during cancer progression and metastasis; (2) how do protein effectors modulate signalling strength; and (3) how do PKs differentiate alternative down-stream signalling pathways. Furthermore, the nanoscale distribution of pseudokinases (or ‘kinase-dead’ variants), and their effect on binding partners and effectors (including active PK binders) will require further investigation. The ability of a single type of receptor to activate numerous signalling responses likely relies upon these biophysical properties to modulate RTKs, kinase-associated receptors, non-receptor PKs, and pseudokinases.

Figure 5

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Author details

1. Rachel S Gormal

   Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, Australia

   Contribution

   Visualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1604-0253

2. Ramon Martinez-Marmol

   Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, Australia

   Contribution

   Funding acquisition, Visualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0929-8627

3. Andrew J Brooks

   Frazer Institute, The University of Queensland, Woolloongabba, Australia

   Contribution

   Funding acquisition, Writing – original draft, Writing – review and editing

   For correspondence

   a.brooks@uq.edu.au

   Competing interests

   No competing interests declared

4. Frédéric A Meunier

   1. Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
   2. School of Biomedical Sciences, The University of Queensland, St Lucia, Australia

   Contribution

   Funding acquisition, Writing – original draft, Writing – review and editing

   For correspondence

   f.meunier@uq.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6400-1107

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