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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1. 1. Araya-Secchi R
   2. Bugge K
   3. Seiffert P
   4. Petry A
   5. Haxholm GW
   6. Lindorff-Larsen K
   7. Pedersen SF
   8. Arleth L
   9. Kragelund BB

   (2023) The prolactin receptor scaffolds Janus kinase 2 via co-structure formation with phosphoinositide-4,5-bisphosphate

   eLife 12:e84645.

   https://doi.org/10.7554/eLife.84645

   • PubMed
   • Google Scholar
2. 1. Ayala A
   2. Muñoz MF
   3. Argüelles S

   (2014) Lipid peroxidation: production, metabolism, and signaling mechanisms of malondialdehyde and 4-hydroxy-2-nonenal

   Oxidative Medicine and Cellular Longevity 2014:360438.

   https://doi.org/10.1155/2014/360438

   • PubMed
   • Google Scholar
3. 1. Banani SF
   2. Lee HO
   3. Hyman AA
   4. Rosen MK

   (2017) Biomolecular condensates: organizers of cellular biochemistry

   Nature Reviews. Molecular Cell Biology 18:285–298.

   https://doi.org/10.1038/nrm.2017.7

   • PubMed
   • Google Scholar
4. 1. Betzig E
   2. Patterson GH
   3. Sougrat R
   4. Lindwasser OW
   5. Olenych S
   6. Bonifacino JS
   7. Davidson MW
   8. Lippincott-Schwartz J
   9. Hess HF

   (2006) Imaging intracellular fluorescent proteins at nanometer resolution

   Science 313:1642–1645.

   https://doi.org/10.1126/science.1127344

   • PubMed
   • Google Scholar
5. 1. Brede G
   2. Solheim J
   3. Stang E
   4. Prydz H

   (2003) Mutants of the protein serine kinase PSKH1 disassemble the Golgi apparatus

   Experimental Cell Research 291:299–312.

   https://doi.org/10.1016/j.yexcr.2003.07.009

   • PubMed
   • Google Scholar
6. 1. Brooks AJ
   2. Dai W
   3. O’Mara ML
   4. Abankwa D
   5. Chhabra Y
   6. Pelekanos RA
   7. Gardon O
   8. Tunny KA
   9. Blucher KM
   10. Morton CJ
   11. Parker MW
   12. Sierecki E
   13. Gambin Y
   14. Gomez GA
   15. Alexandrov K
   16. Wilson IA
   17. Doxastakis M
   18. Mark AE
   19. Waters MJ

   (2014) Mechanism of activation of protein kinase JAK2 by the growth hormone receptor

   Science 344:1249783.

   https://doi.org/10.1126/science.1249783

   • PubMed
   • Google Scholar
7. 1. Brooks AJ
   2. Putoczki T

   (2020) JAK-STAT signalling pathway in cancer

   Cancers 12:1971.

   https://doi.org/10.3390/cancers12071971

   • PubMed
   • Google Scholar
8. 1. Bugge K
   2. Brakti I
   3. Fernandes CB
   4. Dreier JE
   5. Lundsgaard JE
   6. Olsen JG
   7. Skriver K
   8. Kragelund BB

   (2020) Interactions by disorder - a matter of context

   Frontiers in Molecular Biosciences 7:110.

   https://doi.org/10.3389/fmolb.2020.00110

   • PubMed
   • Google Scholar
9. 1. Caré BR
   2. Soula HA

   (2011) Impact of receptor clustering on ligand binding

   BMC Systems Biology 5:48.

   https://doi.org/10.1186/1752-0509-5-48

   • Google Scholar
10. 1. Carr SA
    2. Biemann K
    3. Shoji S
    4. Parmelee DC
    5. Titani K

    (1982) n-Tetradecanoyl is the NH2-terminal blocking group of the catalytic subunit of cyclic AMP-dependent protein kinase from bovine cardiac muscle

    PNAS 79:6128–6131.

    https://doi.org/10.1073/pnas.79.20.6128

    • PubMed
    • Google Scholar
11. 1. Casaletto JB
    2. McClatchey AI

    (2012) Spatial regulation of receptor tyrosine kinases in development and cancer

    Nature Reviews. Cancer 12:387–400.

    https://doi.org/10.1038/nrc3277

    • PubMed
    • Google Scholar
12. 1. Chenouard N
    2. Smal I
    3. de Chaumont F
    4. Maška M
    5. Sbalzarini IF
    6. Gong Y
    7. Cardinale J
    8. Carthel C
    9. Coraluppi S
    10. Winter M
    11. Cohen AR
    12. Godinez WJ
    13. Rohr K
    14. Kalaidzidis Y
    15. Liang L
    16. Duncan J
    17. Shen H
    18. Xu Y
    19. Magnusson KEG
    20. Jaldén J
    21. Blau HM
    22. Paul-Gilloteaux P
    23. Roudot P
    24. Kervrann C
    25. Waharte F
    26. Tinevez JY
    27. Shorte SL
    28. Willemse J
    29. Celler K
    30. van Wezel GP
    31. Dan HW
    32. Tsai YS
    33. Ortiz de Solórzano C
    34. Olivo-Marin JC
    35. Meijering E

    (2014) Objective comparison of particle tracking methods

    Nature Methods 11:281–289.

    https://doi.org/10.1038/nmeth.2808

    • PubMed
    • Google Scholar
13. 1. Chhabra Y
    2. Lee CMM
    3. Müller AF
    4. Brooks AJ

    (2021) GHR signalling: receptor activation and degradation mechanisms

    Molecular and Cellular Endocrinology 520:111075.

    https://doi.org/10.1016/j.mce.2020.111075

    • PubMed
    • Google Scholar
14. 1. Chhabra Y
    2. Seiffert P
    3. Gormal RS
    4. Vullings M
    5. Lee CMM
    6. Wallis TP
    7. Dehkhoda F
    8. Indrakumar S
    9. Jacobsen NL
    10. Lindorff-Larsen K
    11. Durisic N
    12. Waters MJ
    13. Meunier FA
    14. Kragelund BB
    15. Brooks AJ

    (2023) Tyrosine kinases compete for growth hormone receptor binding and regulate receptor mobility and degradation

    Cell Reports 42:112490.

    https://doi.org/10.1016/j.celrep.2023.112490

    • PubMed
    • Google Scholar
15. 1. Cho W-K
    2. Jayanth N
    3. English BP
    4. Inoue T
    5. Andrews JO
    6. Conway W
    7. Grimm JB
    8. Spille J-H
    9. Lavis LD
    10. Lionnet T
    11. Cisse II

    (2016) RNA Polymerase II cluster dynamics predict mRNA output in living cells

    eLife 5:e13617.

    https://doi.org/10.7554/eLife.13617

    • PubMed
    • Google Scholar
16. 1. Cohen P

    (2001) The role of protein phosphorylation in human health and disease: the Sir Hans Krebs Medal Lecture

    European Journal of Biochemistry 268:5001–5010.

    https://doi.org/10.1046/j.0014-2956.2001.02473.x

    • PubMed
    • Google Scholar
17. 1. Cordeddu V
    2. Di Schiavi E
    3. Pennacchio LA
    4. Ma’ayan A
    5. Sarkozy A
    6. Fodale V
    7. Cecchetti S
    8. Cardinale A
    9. Martin J
    10. Schackwitz W
    11. Lipzen A
    12. Zampino G
    13. Mazzanti L
    14. Digilio MC
    15. Martinelli S
    16. Flex E
    17. Lepri F
    18. Bartholdi D
    19. Kutsche K
    20. Ferrero GB
    21. Anichini C
    22. Selicorni A
    23. Rossi C
    24. Tenconi R
    25. Zenker M
    26. Merlo D
    27. Dallapiccola B
    28. Iyengar R
    29. Bazzicalupo P
    30. Gelb BD
    31. Tartaglia M

    (2009) Mutation of SHOC2 promotes aberrant protein N-myristoylation and causes Noonan-like syndrome with loose anagen hair

    Nature Genetics 41:1022–1026.

    https://doi.org/10.1038/ng.425

    • PubMed
    • Google Scholar
18. 1. Cox S

    (2015) Super-resolution imaging in live cells

    Developmental Biology 401:175–181.

    https://doi.org/10.1016/j.ydbio.2014.11.025

    • PubMed
    • Google Scholar
19. 1. Davis S
    2. Gale NW
    3. Aldrich TH
    4. Maisonpierre PC
    5. Lhotak V
    6. Pawson T
    7. Goldfarb M
    8. Yancopoulos GD

    (1994) Ligands for EPH-related receptor tyrosine kinases that require membrane attachment or clustering for activity

    Science 266:816–819.

    https://doi.org/10.1126/science.7973638

    • PubMed
    • Google Scholar
20. 1. Deguchi T
    2. Iwanski MK
    3. Schentarra E-M
    4. Heidebrecht C
    5. Schmidt L
    6. Heck J
    7. Weihs T
    8. Schnorrenberg S
    9. Hoess P
    10. Liu S
    11. Chevyreva V
    12. Noh K-M
    13. Kapitein LC
    14. Ries J

    (2023) Direct observation of motor protein stepping in living cells using MINFLUX

    Science 379:1010–1015.

    https://doi.org/10.1126/science.ade2676

    • PubMed
    • Google Scholar
21. 1. Du Z
    2. Lovly CM

    (2018) Mechanisms of receptor tyrosine kinase activation in cancer

    Molecular Cancer 17:58.

    https://doi.org/10.1186/s12943-018-0782-4

    • PubMed
    • Google Scholar
22. 1. Elowitz MB
    2. Surette MG
    3. Wolf PE
    4. Stock JB
    5. Leibler S

    (1999) Protein mobility in the cytoplasm of Escherichia coli

    Journal of Bacteriology 181:197–203.

    https://doi.org/10.1128/JB.181.1.197-203.1999

    • PubMed
    • Google Scholar
23. 1. Erlendsson S
    2. Teilum K

    (2020) Binding revisited-avidity in cellular function and signaling

    Frontiers in Molecular Biosciences 7:615565.

    https://doi.org/10.3389/fmolb.2020.615565

    • PubMed
    • Google Scholar
24. 1. Fabbro D
    2. Cowan-Jacob SW
    3. Moebitz H

    (2015) Ten things you should know about protein kinases: IUPHAR Review 14

    British Journal of Pharmacology 172:2675–2700.

    https://doi.org/10.1111/bph.13096

    • PubMed
    • Google Scholar
25. 1. Flynn N
    2. Syed NI

    (2020) Src family kinases play a role in the functional clustering of central postsynaptic nicotinic acetylcholine receptors

    Frontiers in Marine Science 7:00008.

    https://doi.org/10.3389/fmars.2020.00008

    • Google Scholar
26. 1. Fujiwara TK
    2. Takeuchi S
    3. Kalay Z
    4. Nagai Y
    5. Tsunoyama TA
    6. Kalkbrenner T
    7. Iwasawa K
    8. Ritchie KP
    9. Suzuki KGN
    10. Kusumi A

    (2023) Development of ultrafast camera-based single fluorescent-molecule imaging for cell biology

    The Journal of Cell Biology 222:e202110160.

    https://doi.org/10.1083/jcb.202110160

    • PubMed
    • Google Scholar
27. 1. Godin AG
    2. Lounis B
    3. Cognet L

    (2014) Super-resolution microscopy approaches for live cell imaging

    Biophysical Journal 107:1777–1784.

    https://doi.org/10.1016/j.bpj.2014.08.028

    • PubMed
    • Google Scholar
28. 1. Gormal RS
    2. Padmanabhan P
    3. Kasula R
    4. Bademosi AT
    5. Coakley S
    6. Giacomotto J
    7. Blum A
    8. Joensuu M
    9. Wallis TP
    10. Lo HP
    11. Budnar S
    12. Rae J
    13. Ferguson C
    14. Bastiani M
    15. Thomas WG
    16. Pardon E
    17. Steyaert J
    18. Yap AS
    19. Goodhill GJ
    20. Hilliard MA
    21. Parton RG
    22. Meunier FA

    (2020) Modular transient nanoclustering of activated β2-adrenergic receptors revealed by single-molecule tracking of conformation-specific nanobodies

    PNAS 117:30476–30487.

    https://doi.org/10.1073/pnas.2007443117

    • PubMed
    • Google Scholar
29. 1. Gustafsson MG

    (2000) Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy

    Journal of Microscopy 198:82–87.

    https://doi.org/10.1046/j.1365-2818.2000.00710.x

    • PubMed
    • Google Scholar
30. 1. Gustafsson MGL

    (2008) Super-resolution light microscopy goes live

    Nature Methods 5:385–387.

    https://doi.org/10.1038/nmeth0508-385

    • PubMed
    • Google Scholar
31. 1. Hantschel O
    2. Nagar B
    3. Guettler S
    4. Kretzschmar J
    5. Dorey K
    6. Kuriyan J
    7. Superti-Furga G

    (2003) A myristoyl/phosphotyrosine switch regulates c-Abl

    Cell 112:845–857.

    https://doi.org/10.1016/s0092-8674(03)00191-0

    • PubMed
    • Google Scholar
32. 1. Harding AS
    2. Hancock JF

    (2008) Using plasma membrane nanoclusters to build better signaling circuits

    Trends in Cell Biology 18:364–371.

    https://doi.org/10.1016/j.tcb.2008.05.006

    • PubMed
    • Google Scholar
33. 1. Haxholm GW
    2. Nikolajsen LF
    3. Olsen JG
    4. Fredsted J
    5. Larsen FH
    6. Goffin V
    7. Pedersen SF
    8. Brooks AJ
    9. Waters MJ
    10. Kragelund BB

    (2015) Intrinsically disordered cytoplasmic domains of two cytokine receptors mediate conserved interactions with membranes

    The Biochemical Journal 468:495–506.

    https://doi.org/10.1042/BJ20141243

    • PubMed
    • Google Scholar
34. 1. Heidbreder M
    2. Zander C
    3. Malkusch S
    4. Widera D
    5. Kaltschmidt B
    6. Kaltschmidt C
    7. Nair D
    8. Choquet D
    9. Sibarita JB
    10. Heilemann M

    (2012) TNF-α influences the lateral dynamics of TNF receptor I in living cells

    Biochimica et Biophysica Acta 1823:1984–1989.

    https://doi.org/10.1016/j.bbamcr.2012.06.026

    • PubMed
    • Google Scholar
35. 1. Heilemann M
    2. van de Linde S
    3. Schüttpelz M
    4. Kasper R
    5. Seefeldt B
    6. Mukherjee A
    7. Tinnefeld P
    8. Sauer M

    (2008) Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes

    Angewandte Chemie 47:6172–6176.

    https://doi.org/10.1002/anie.200802376

    • PubMed
    • Google Scholar
36. 1. Heine J
    2. Wurm CA
    3. Keller-Findeisen J
    4. Schönle A
    5. Harke B
    6. Reuss M
    7. Winter FR
    8. Donnert G

    (2018) Three dimensional live-cell STED microscopy at increased depth using a water immersion objective

    The Review of Scientific Instruments 89:053701.

    https://doi.org/10.1063/1.5020249

    • PubMed
    • Google Scholar
37. 1. Hell SW
    2. Wichmann J

    (1994) Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy

    Optics Letters 19:780–782.

    https://doi.org/10.1364/ol.19.000780

    • PubMed
    • Google Scholar
38. 1. Holzer RG
    2. Park E-J
    3. Li N
    4. Tran H
    5. Chen M
    6. Choi C
    7. Solinas G
    8. Karin M

    (2011) Saturated fatty acids induce c-Src clustering within membrane subdomains, leading to JNK activation

    Cell 147:173–184.

    https://doi.org/10.1016/j.cell.2011.08.034

    • PubMed
    • Google Scholar
39. 1. Hosokawa T
    2. Liu P-W
    3. Cai Q
    4. Ferreira JS
    5. Levet F
    6. Butler C
    7. Sibarita J-B
    8. Choquet D
    9. Groc L
    10. Hosy E
    11. Zhang M
    12. Hayashi Y

    (2021) CaMKII activation persistently segregates postsynaptic proteins via liquid phase separation

    Nature Neuroscience 24:777–785.

    https://doi.org/10.1038/s41593-021-00843-3

    • Google Scholar
40. 1. Irannejad R
    2. Tomshine JC
    3. Tomshine JR
    4. Chevalier M
    5. Mahoney JP
    6. Steyaert J
    7. Rasmussen SGF
    8. Sunahara RK
    9. El-Samad H
    10. Huang B
    11. von Zastrow M

    (2013) Conformational biosensors reveal GPCR signalling from endosomes

    Nature 495:534–538.

    https://doi.org/10.1038/nature12000

    • PubMed
    • Google Scholar
41. 1. Jacobsen AV
    2. Murphy JM

    (2017) The secret life of kinases: insights into non-catalytic signalling functions from pseudokinases

    Biochemical Society Transactions 45:665–681.

    https://doi.org/10.1042/BST20160331

    • PubMed
    • Google Scholar
42. 1. Kanev GK
    2. de Graaf C
    3. de Esch IJP
    4. Leurs R
    5. Würdinger T
    6. Westerman BA
    7. Kooistra AJ

    (2019) The landscape of atypical and eukaryotic protein kinases

    Trends in Pharmacological Sciences 40:818–832.

    https://doi.org/10.1016/j.tips.2019.09.002

    • PubMed
    • Google Scholar
43. 1. Khater IM
    2. Nabi IR
    3. Hamarneh G

    (2020) A review of super-resolution single-molecule localization microscopy cluster analysis and quantification methods

    Patterns 1:100038.

    https://doi.org/10.1016/j.patter.2020.100038

    • PubMed
    • Google Scholar
44. 1. Kim S
    2. Yang X
    3. Li Q
    4. Wu M
    5. Costyn L
    6. Beharry Z
    7. Bartlett MG
    8. Cai H

    (2017) Myristoylation of Src kinase mediates Src-induced and high-fat diet-accelerated prostate tumor progression in mice

    The Journal of Biological Chemistry 292:18422–18433.

    https://doi.org/10.1074/jbc.M117.798827

    • PubMed
    • Google Scholar
45. 1. Kinoshita-Kikuta E
    2. Utsumi T
    3. Miyazaki A
    4. Tokumoto C
    5. Doi K
    6. Harada H
    7. Kinoshita E
    8. Koike T

    (2020) Protein-N-myristoylation-dependent phosphorylation of serine 13 of tyrosine kinase Lyn by casein kinase 1γ at the Golgi during intracellular protein traffic

    Scientific Reports 10:16273.

    https://doi.org/10.1038/s41598-020-73248-0

    • PubMed
    • Google Scholar
46. 1. Koegl M
    2. Zlatkine P
    3. Ley SC
    4. Courtneidge SA
    5. Magee AI

    (1994) Palmitoylation of multiple Src-family kinases at a homologous N-terminal motif

    The Biochemical Journal 303 (Pt 3):749–753.

    https://doi.org/10.1042/bj3030749

    • PubMed
    • Google Scholar
47. 1. Koester AM
    2. Tao K
    3. Szczepaniak M
    4. Rames MJ
    5. Nan X

    (2022) Nanoscopic spatial association between ras and phosphatidylserine on the cell membrane studied with multicolor super resolution microscopy

    Biomolecules 12:1033.

    https://doi.org/10.3390/biom12081033

    • PubMed
    • Google Scholar
48. 1. Kung JE
    2. Jura N

    (2016) Structural basis for the non-catalytic functions of protein kinases

    Structure 24:7–24.

    https://doi.org/10.1016/j.str.2015.10.020

    • PubMed
    • Google Scholar
49. 1. Kusumi A
    2. Tsunoyama TA
    3. Hirosawa KM
    4. Kasai RS
    5. Fujiwara TK

    (2014) Tracking single molecules at work in living cells

    Nature Chemical Biology 10:524–532.

    https://doi.org/10.1038/nchembio.1558

    • PubMed
    • Google Scholar
50. 1. Lagerholm BC
    2. Thompson NL

    (1998) Theory for ligand rebinding at cell membrane surfaces

    Biophysical Journal 74:1215–1228.

    https://doi.org/10.1016/S0006-3495(98)77836-1

    • Google Scholar
51. 1. Lelek M
    2. Gyparaki MT
    3. Beliu G
    4. Schueder F
    5. Griffié J
    6. Manley S
    7. Jungmann R
    8. Sauer M
    9. Lakadamyali M
    10. Zimmer C

    (2021) Single-molecule localization microscopy

    Nature Reviews. Methods Primers 1:39.

    https://doi.org/10.1038/s43586-021-00038-x

    • PubMed
    • Google Scholar
52. 1. Li G-M
    2. Zhao J
    3. Li B
    4. Zhang X-F
    5. Ma J-X
    6. Ma X-L
    7. Liu J

    (2018) The anti-inflammatory effects of statins on patients with rheumatoid arthritis: a systemic review and meta-analysis of 15 randomized controlled trials

    Autoimmunity Reviews 17:215–225.

    https://doi.org/10.1016/j.autrev.2017.10.013

    • PubMed
    • Google Scholar
53. 1. Liang SI
    2. van Lengerich B
    3. Eichel K
    4. Cha M
    5. Patterson DM
    6. Yoon T-Y
    7. von Zastrow M
    8. Jura N
    9. Gartner ZJ

    (2018) Phosphorylated EGFR dimers are not sufficient to activate ras

    Cell Reports 22:2593–2600.

    https://doi.org/10.1016/j.celrep.2018.02.031

    • PubMed
    • Google Scholar
54. 1. Liang L-Y
    2. Roy M
    3. Horne CR
    4. Sandow JJ
    5. Surudoi M
    6. Dagley LF
    7. Young SN
    8. Dite T
    9. Babon JJ
    10. Janes PW
    11. Patel O
    12. Murphy JM
    13. Lucet IS

    (2021) The intracellular domains of the EphB6 and EphA10 receptor tyrosine pseudokinases function as dynamic signalling hubs

    The Biochemical Journal 478:3351–3371.

    https://doi.org/10.1042/BCJ20210572

    • PubMed
    • Google Scholar
55. 1. Lin CY
    2. Huang JY
    3. Lo LW

    (2016) Exploring in vivo cholesterol-mediated interactions between activated EGF receptors in plasma membrane with single-molecule optical tracking

    BMC Biophysics 9:6.

    https://doi.org/10.1186/s13628-016-0030-5

    • PubMed
    • Google Scholar
56. 1. Liu W
    2. Kawahara M
    3. Ueda H
    4. Nagamune T

    (2009) The Influence of domain structures on the signal transduction of chimeric receptors derived from the erythropoietin receptor

    The Journal of Biochemistry 145:575–584.

    https://doi.org/10.1093/jb/mvp013

    • Google Scholar
57. 1. Longfield SF
    2. Mollazade M
    3. Wallis TP
    4. Gormal RS
    5. Joensuu M
    6. Wark JR
    7. van Waardenberg AJ
    8. Small C
    9. Graham ME
    10. Meunier FA
    11. Martínez-Mármol R

    (2023) Tau forms synaptic nano-biomolecular condensates controlling the dynamic clustering of recycling synaptic vesicles

    Nature Communications 14:7277.

    https://doi.org/10.1038/s41467-023-43130-4

    • Google Scholar
58. 1. Lu HE
    2. MacGillavry HD
    3. Frost NA
    4. Blanpied TA

    (2014) Multiple spatial and kinetic subpopulations of CaMKII in spines and dendrites as resolved by single-molecule tracking PALM

    The Journal of Neuroscience 34:7600–7610.

    https://doi.org/10.1523/JNEUROSCI.4364-13.2014

    • PubMed
    • Google Scholar
59. 1. Mace PD
    2. Murphy JM

    (2021) There’s more to death than life: noncatalytic functions in kinase and pseudokinase signaling

    The Journal of Biological Chemistry 296:100705.

    https://doi.org/10.1016/j.jbc.2021.100705

    • PubMed
    • Google Scholar
60. 1. Mahecic D
    2. Testa I
    3. Griffié J
    4. Manley S

    (2019) Strategies for increasing the throughput of super-resolution microscopies

    Current Opinion in Chemical Biology 51:84–91.

    https://doi.org/10.1016/j.cbpa.2019.05.012

    • PubMed
    • Google Scholar
61. 1. Manley S
    2. Gillette JM
    3. Patterson GH
    4. Shroff H
    5. Hess HF
    6. Betzig E
    7. Lippincott-Schwartz J

    (2008) High-density mapping of single-molecule trajectories with photoactivated localization microscopy

    Nature Methods 5:155–157.

    https://doi.org/10.1038/nmeth.1176

    • PubMed
    • Google Scholar
62. 1. Martínez-Mármol R
    2. Chai Y
    3. Conroy JN
    4. Khan Z
    5. Hong SM
    6. Kim SB
    7. Gormal RS
    8. Lee DH
    9. Lee JK
    10. Coulson EJ
    11. Lee MK
    12. Kim SY
    13. Meunier FA

    (2023a) Hericerin derivatives activates a pan-neurotrophic pathway in central hippocampal neurons converging to ERK1/2 signaling enhancing spatial memory

    Journal of Neurochemistry 165:791–808.

    https://doi.org/10.1111/jnc.15767

    • PubMed
    • Google Scholar
63. 1. Martínez-Mármol R
    2. Small C
    3. Jiang A
    4. Palliyaguru T
    5. Wallis TP
    6. Gormal RS
    7. Sibarita JB
    8. Götz J
    9. Meunier FA

    (2023b) Fyn nanoclustering requires switching to an open conformation and is enhanced by FTLD-Tau biomolecular condensates

    Molecular Psychiatry 28:946–962.

    https://doi.org/10.1038/s41380-022-01825-y

    • PubMed
    • Google Scholar
64. 1. Martinsen JH
    2. Saar D
    3. Fernandes CB
    4. Schuler B
    5. Bugge K
    6. Kragelund BB

    (2022) Structure, dynamics, and stability of the globular domain of human linker histone H1.0 and the role of positive charges

    Protein Science 31:918–932.

    https://doi.org/10.1002/pro.4281

    • PubMed
    • Google Scholar
65. 1. McCaw L
    2. Shi Y
    3. Wang G
    4. Li YJ
    5. Spaner DE

    (2017) Low density lipoproteins amplify cytokine-signaling in chronic lymphocytic leukemia cells

    EBioMedicine 15:24–35.

    https://doi.org/10.1016/j.ebiom.2016.11.033

    • PubMed
    • Google Scholar
66. 1. McGraw KL
    2. Basiorka AA
    3. Johnson JO
    4. Clark J
    5. Caceres G
    6. Padron E
    7. Heaton R
    8. Ozawa Y
    9. Wei S
    10. Sokol L
    11. List AF

    (2014) Lenalidomide induces lipid raft assembly to enhance erythropoietin receptor signaling in myelodysplastic syndrome progenitors

    PLOS ONE 9:e114249.

    https://doi.org/10.1371/journal.pone.0114249

    • PubMed
    • Google Scholar
67. 1. Mill P
    2. Lee AWS
    3. Fukata Y
    4. Tsutsumi R
    5. Fukata M
    6. Keighren M
    7. Porter RM
    8. McKie L
    9. Smyth I
    10. Jackson IJ

    (2009) Palmitoylation regulates epidermal homeostasis and hair follicle differentiation

    PLOS Genetics 5:e1000748.

    https://doi.org/10.1371/journal.pgen.1000748

    • PubMed
    • Google Scholar
68. 1. Modi V
    2. Dunbrack RL

    (2019) Defining a new nomenclature for the structures of active and inactive kinases

    PNAS 116:6818–6827.

    https://doi.org/10.1073/pnas.1814279116

    • PubMed
    • Google Scholar
69. 1. Moraga I
    2. Wernig G
    3. Wilmes S
    4. Gryshkova V
    5. Richter CP
    6. Hong W-J
    7. Sinha R
    8. Guo F
    9. Fabionar H
    10. Wehrman TS
    11. Krutzik P
    12. Demharter S
    13. Plo I
    14. Weissman IL
    15. Minary P
    16. Majeti R
    17. Constantinescu SN
    18. Piehler J
    19. Garcia KC

    (2015) Tuning cytokine receptor signaling by re-orienting dimer geometry with surrogate ligands

    Cell 160:1196–1208.

    https://doi.org/10.1016/j.cell.2015.02.011

    • PubMed
    • Google Scholar
70. 1. Murphy JE
    2. Padilla BE
    3. Hasdemir B
    4. Cottrell GS
    5. Bunnett NW

    (2009) Endosomes: a legitimate platform for the signaling train

    PNAS 106:17615–17622.

    https://doi.org/10.1073/pnas.0906541106

    • PubMed
    • Google Scholar
71. 1. Murphy AJ
    2. Bijl N
    3. Yvan-Charvet L
    4. Welch CB
    5. Bhagwat N
    6. Reheman A
    7. Wang Y
    8. Shaw JA
    9. Levine RL
    10. Ni H
    11. Tall AR
    12. Wang N

    (2013) Cholesterol efflux in megakaryocyte progenitors suppresses platelet production and thrombocytosis

    Nature Medicine 19:586–594.

    https://doi.org/10.1038/nm.3150

    • PubMed
    • Google Scholar
72. 1. Oakhill JS
    2. Chen Z-P
    3. Scott JW
    4. Steel R
    5. Castelli LA
    6. Ling N
    7. Macaulay SL
    8. Kemp BE

    (2010) β-Subunit myristoylation is the gatekeeper for initiating metabolic stress sensing by AMP-activated protein kinase (AMPK)

    PNAS 107:19237–19241.

    https://doi.org/10.1073/pnas.1009705107

    • PubMed
    • Google Scholar
73. 1. Oneyama C
    2. Iino T
    3. Saito K
    4. Suzuki K
    5. Ogawa A
    6. Okada M

    (2009) Transforming potential of Src family kinases is limited by the cholesterol-enriched membrane microdomain

    Molecular and Cellular Biology 29:6462–6472.

    https://doi.org/10.1128/MCB.00941-09

    • PubMed
    • Google Scholar
74. 1. Ortiz MA
    2. Mikhailova T
    3. Li X
    4. Porter BA
    5. Bah A
    6. Kotula L

    (2021) Src family kinases, adaptor proteins and the actin cytoskeleton in epithelial-to-mesenchymal transition

    Cell Communication and Signaling 19:67.

    https://doi.org/10.1186/s12964-021-00750-x

    • PubMed
    • Google Scholar
75. 1. Owen DM
    2. Rentero C
    3. Rossy J
    4. Magenau A
    5. Williamson D
    6. Rodriguez M
    7. Gaus K

    (2010) PALM imaging and cluster analysis of protein heterogeneity at the cell surface

    Journal of Biophotonics 3:446–454.

    https://doi.org/10.1002/jbio.200900089

    • PubMed
    • Google Scholar
76. 1. Padmanabhan P
    2. Martínez-Mármol R
    3. Xia D
    4. Götz J
    5. Meunier FA

    (2019) Frontotemporal dementia mutant Tau promotes aberrant Fyn nanoclustering in hippocampal dendritic spines

    eLife 8:e45040.

    https://doi.org/10.7554/eLife.45040

    • PubMed
    • Google Scholar
77. 1. Papadopulos A
    2. Tomatis VM
    3. Kasula R
    4. Meunier FA

    (2013) The cortical acto-Myosin network: from diffusion barrier to functional gateway in the transport of neurosecretory vesicles to the plasma membrane

    Frontiers in Endocrinology 4:153.

    https://doi.org/10.3389/fendo.2013.00153

    • PubMed
    • Google Scholar
78. 1. Park YJ
    2. Cho CS
    3. Emery P
    4. Kim WU

    (2013) LDL cholesterolemia as a novel risk factor for radiographic progression of rheumatoid arthritis: a single-center prospective study

    PLOS ONE 8:e68975.

    https://doi.org/10.1371/journal.pone.0068975

    • PubMed
    • Google Scholar
79. 1. Patel O
    2. Roy MJ
    3. Murphy JM
    4. Lucet IS

    (2020) The PEAK family of pseudokinases, their role in cell signalling and cancer

    The FEBS Journal 287:4183–4197.

    https://doi.org/10.1111/febs.15087

    • PubMed
    • Google Scholar
80. 1. Patwardhan P
    2. Resh MD

    (2010) Myristoylation and membrane binding regulate c-Src stability and kinase activity

    Molecular and Cellular Biology 30:4094–4107.

    https://doi.org/10.1128/MCB.00246-10

    • PubMed
    • Google Scholar
81. 1. Paul MD
    2. Torosyan H
    3. Jura N

    (2022) Piquing our interest: Insights into the role of PEAK3 in signaling and disease

    Science Signaling 15:eabm9396.

    https://doi.org/10.1126/scisignal.abm9396

    • PubMed
    • Google Scholar
82. 1. Rahbek-Clemmensen T
    2. Lycas MD
    3. Erlendsson S
    4. Eriksen J
    5. Apuschkin M
    6. Vilhardt F
    7. Jørgensen TN
    8. Hansen FH
    9. Gether U

    (2017) Super-resolution microscopy reveals functional organization of dopamine transporters into cholesterol and neuronal activity-dependent nanodomains

    Nature Communications 8:740.

    https://doi.org/10.1038/s41467-017-00790-3

    • PubMed
    • Google Scholar
83. 1. Rana MS
    2. Lee CJ
    3. Banerjee A

    (2019) The molecular mechanism of DHHC protein acyltransferases

    Biochemical Society Transactions 47:157–167.

    https://doi.org/10.1042/BST20180429

    • PubMed
    • Google Scholar
84. 1. Resh MD

    (1994) Myristylation and palmitylation of Src family members: the fats of the matter

    Cell 76:411–413.

    https://doi.org/10.1016/0092-8674(94)90104-x

    • PubMed
    • Google Scholar
85. 1. Roberts SK
    2. Hirsch M
    3. McStea A
    4. Zanetti-Domingues LC
    5. Clarke DT
    6. Claus J
    7. Parker PJ
    8. Wang L
    9. Martin-Fernandez AML

    (2018) Cluster analysis of endogenous HER2 and HER3 receptors in SKBR3 cells

    Bio-Protocol 8:e3096.

    https://doi.org/10.21769/BioProtoc.3096

    • PubMed
    • Google Scholar
86. 1. Rocks O
    2. Peyker A
    3. Kahms M
    4. Verveer PJ
    5. Koerner C
    6. Lumbierres M
    7. Kuhlmann J
    8. Waldmann H
    9. Wittinghofer A
    10. Bastiaens PIH

    (2005) An acylation cycle regulates localization and activity of palmitoylated Ras isoforms

    Science 307:1746–1752.

    https://doi.org/10.1126/science.1105654

    • PubMed
    • Google Scholar
87. 1. Rocks O
    2. Gerauer M
    3. Vartak N
    4. Koch S
    5. Huang Z-P
    6. Pechlivanis M
    7. Kuhlmann J
    8. Brunsveld L
    9. Chandra A
    10. Ellinger B
    11. Waldmann H
    12. Bastiaens PIH

    (2010) The palmitoylation machinery is a spatially organizing system for peripheral membrane proteins

    Cell 141:458–471.

    https://doi.org/10.1016/j.cell.2010.04.007

    • PubMed
    • Google Scholar
88. 1. Rowlinson SW
    2. Yoshizato H
    3. Barclay JL
    4. Brooks AJ
    5. Behncken SN
    6. Kerr LM
    7. Millard K
    8. Palethorpe K
    9. Nielsen K
    10. Clyde-Smith J
    11. Hancock JF
    12. Waters MJ

    (2008) An agonist-induced conformational change in the growth hormone receptor determines the choice of signalling pathway

    Nature Cell Biology 10:740–747.

    https://doi.org/10.1038/ncb1737

    • Google Scholar
89. 1. Roy MJ
    2. Surudoi MG
    3. Kropp A
    4. Hou J
    5. Dai W
    6. Hardy JM
    7. Liang L-Y
    8. Cotton TR
    9. Lechtenberg BC
    10. Dite TA
    11. Ma X
    12. Daly RJ
    13. Patel O
    14. Lucet IS

    (2023) Structural mapping of PEAK pseudokinase interactions identifies 14-3-3 as a molecular switch for PEAK3 signaling

    Nature Communications 14:3542.

    https://doi.org/10.1038/s41467-023-38869-9

    • PubMed
    • Google Scholar
90. 1. Rust MJ
    2. Bates M
    3. Zhuang X

    (2006) Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)

    Nature Methods 3:793–795.

    https://doi.org/10.1038/nmeth929

    • PubMed
    • Google Scholar
91. 1. Sakamoto A
    2. Tsukamoto T
    3. Furutani Y
    4. Sudo Y
    5. Shimada K
    6. Tomita A
    7. Kiyoi H
    8. Kato T
    9. Funatsu T

    (2016) Live-cell single-molecule imaging of the cytokine receptor MPL for analysis of dynamic dimerization

    Journal of Molecular Cell Biology 8:553–555.

    https://doi.org/10.1093/jmcb/mjw027

    • PubMed
    • Google Scholar
92. 1. Salavessa L
    2. Lagache T
    3. Malardé V
    4. Grassart A
    5. Olivo-Marin J-C
    6. Canette A
    7. Trichet M
    8. Sansonetti PJ
    9. Sauvonnet N

    (2021) Cytokine receptor cluster size impacts its endocytosis and signaling

    PNAS 118:e2024893118.

    https://doi.org/10.1073/pnas.2024893118

    • PubMed
    • Google Scholar
93. 1. Sandilands E
    2. Frame MC

    (2008) Endosomal trafficking of Src tyrosine kinase

    Trends in Cell Biology 18:322–329.

    https://doi.org/10.1016/j.tcb.2008.05.004

    • PubMed
    • Google Scholar
94. 1. Sandoval-Usme MC
    2. Umaña-Pérez A
    3. Guerra B
    4. Hernández-Perera O
    5. García-Castellano JM
    6. Fernández-Pérez L
    7. Sánchez-Gómez M

    (2014) Simvastatin impairs growth hormone-activated signal transducer and activator of transcription (STAT) signaling pathway in UMR-106 osteosarcoma cells

    PLOS ONE 9:e87769.

    https://doi.org/10.1371/journal.pone.0087769

    • PubMed
    • Google Scholar
95. 1. Sato I
    2. Obata Y
    3. Kasahara K
    4. Nakayama Y
    5. Fukumoto Y
    6. Yamasaki T
    7. Yokoyama KK
    8. Saito T
    9. Yamaguchi N

    (2009) Differential trafficking of Src, Lyn, Yes and Fyn is specified by the state of palmitoylation in the SH4 domain

    Journal of Cell Science 122:965–975.

    https://doi.org/10.1242/jcs.034843

    • PubMed
    • Google Scholar
96. 1. Selvakumar P
    2. Lakshmikuttyamma A
    3. Charavaryamath C
    4. Singh B
    5. Tuchek J
    6. Sharma RK

    (2005) Expression of myristoyltransferase and its interacting proteins in epilepsy

    Biochemical and Biophysical Research Communications 335:1132–1139.

    https://doi.org/10.1016/j.bbrc.2005.07.190

    • PubMed
    • Google Scholar
97. 1. Selvakumar P
    2. Lakshmikuttyamma A
    3. Shrivastav A
    4. Das SB
    5. Dimmock JR
    6. Sharma RK

    (2007) Potential role of N-myristoyltransferase in cancer

    Progress in Lipid Research 46:1–36.

    https://doi.org/10.1016/j.plipres.2006.05.002

    • Google Scholar
98. 1. Shaw AS
    2. Kornev AP
    3. Hu J
    4. Ahuja LG
    5. Taylor SS

    (2014) Kinases and pseudokinases: lessons from RAF

    Molecular and Cellular Biology 34:1538–1546.

    https://doi.org/10.1128/MCB.00057-14

    • PubMed
    • Google Scholar
99. 1. Sieber JJ
    2. Willig KI
    3. Heintzmann R
    4. Hell SW
    5. Lang T

    (2006) The SNARE motif is essential for the formation of syntaxin clusters in the plasma membrane

    Biophysical Journal 90:2843–2851.

    https://doi.org/10.1529/biophysj.105.079574

    • PubMed
    • Google Scholar
100. Preprint

     1. Small C
     2. Martínez-Mármol R
     3. Wallis TP
     4. Gormal RS
     5. Götz J
     6. Meunier FA

     (2020) Frontotemporal dementia mutant tau (P301L) locks fyn in an open, active conformation conducive to nanoclustering

     bioRxiv.

     https://doi.org/10.1101/2020.09.04.282855

     • Google Scholar
101. 1. Sohn HW
     2. Tolar P
     3. Pierce SK

     (2008) Membrane heterogeneities in the formation of B cell receptor-Lyn kinase microclusters and the immune synapse

     The Journal of Cell Biology 182:367–379.

     https://doi.org/10.1083/jcb.200802007

     • PubMed
     • Google Scholar
102. 1. Stone MB
     2. Shelby SA
     3. Núñez MF
     4. Wisser K
     5. Veatch SL

     (2017) Protein sorting by lipid phase-like domains supports emergent signaling function in B lymphocyte plasma membranes

     eLife 6:e19891.

     https://doi.org/10.7554/eLife.19891

     • PubMed
     • Google Scholar
103. 1. Su R
     2. Han ZY
     3. Fan JP
     4. Zhang YL

     (2010) A possible role of myristoylated alanine-rich C kinase substrate in endocytic pathway of Alzheimer’s disease

     Neuroscience Bulletin 26:338–344.

     https://doi.org/10.1007/s12264-010-0131-0

     • Google Scholar
104. 1. Suzuki KGN
     2. Fujiwara TK
     3. Sanematsu F
     4. Iino R
     5. Edidin M
     6. Kusumi A

     (2007) GPI-anchored receptor clusters transiently recruit Lyn and G alpha for temporary cluster immobilization and Lyn activation: single-molecule tracking study 1

     The Journal of Cell Biology 177:717–730.

     https://doi.org/10.1083/jcb.200609174

     • PubMed
     • Google Scholar
105. 1. Tamarit B
     2. Bugault F
     3. Pillet A-H
     4. Lavergne V
     5. Bochet P
     6. Garin N
     7. Schwarz U
     8. Thèze J
     9. Rose T

     (2013) Membrane microdomains and cytoskeleton organization shape and regulate the IL-7 receptor signalosome in human CD4 T-cells

     Journal of Biological Chemistry 288:8691–8701.

     https://doi.org/10.1074/jbc.M113.449918

     • Google Scholar
106. 1. Tang A-H
     2. Chen H
     3. Li TP
     4. Metzbower SR
     5. MacGillavry HD
     6. Blanpied TA

     (2016) A trans-synaptic nanocolumn aligns neurotransmitter release to receptors

     Nature 536:210–214.

     https://doi.org/10.1038/nature19058

     • PubMed
     • Google Scholar
107. 1. Taylor SS
     2. Kornev AP

     (2011) Protein kinases: evolution of dynamic regulatory proteins

     Trends in Biochemical Sciences 36:65–77.

     https://doi.org/10.1016/j.tibs.2010.09.006

     • PubMed
     • Google Scholar
108. 1. Tomasselli AG
     2. Mast E
     3. Janes W
     4. Schiltz E

     (1986) The complete amino acid sequence of adenylate kinase from baker’s yeast

     European Journal of Biochemistry 155:111–119.

     https://doi.org/10.1111/j.1432-1033.1986.tb09465.x

     • PubMed
     • Google Scholar
109. 1. Tomatis VM
     2. Papadopulos A
     3. Malintan NT
     4. Martin S
     5. Wallis T
     6. Gormal RS
     7. Kendrick-Jones J
     8. Buss F
     9. Meunier FA

     (2013) Myosin VI small insert isoform maintains exocytosis by tethering secretory granules to the cortical actin

     The Journal of Cell Biology 200:301–320.

     https://doi.org/10.1083/jcb.201204092

     • PubMed
     • Google Scholar
110. 1. Tompa P

     (2014) Multisteric regulation by structural disorder in modular signaling proteins: an extension of the concept of allostery

     Chemical Reviews 114:6715–6732.

     https://doi.org/10.1021/cr4005082

     • PubMed
     • Google Scholar
111. 1. Ubersax JA
     2. Ferrell JE

     (2007) Mechanisms of specificity in protein phosphorylation

     Nature Reviews. Molecular Cell Biology 8:530–541.

     https://doi.org/10.1038/nrm2203

     • PubMed
     • Google Scholar
112. 1. Vaandrager AB
     2. Ehlert EM
     3. Jarchau T
     4. Lohmann SM
     5. de Jonge HR

     (1996) N-terminal myristoylation is required for membrane localization of cGMP-dependent protein kinase type II

     The Journal of Biological Chemistry 271:7025–7029.

     https://doi.org/10.1074/jbc.271.12.7025

     • PubMed
     • Google Scholar
113. 1. Valli J
     2. Garcia-Burgos A
     3. Rooney LM
     4. Vale de Melo E Oliveira B
     5. Duncan RR
     6. Rickman C

     (2021) Seeing beyond the limit: a guide to choosing the right super-resolution microscopy technique

     The Journal of Biological Chemistry 297:100791.

     https://doi.org/10.1016/j.jbc.2021.100791

     • PubMed
     • Google Scholar
114. 1. Van QN
     2. Prakash P
     3. Shrestha R
     4. Balius TE
     5. Turbyville TJ
     6. Stephen AG

     (2021) RAS Nanoclusters: dynamic signaling platforms amenable to therapeutic intervention

     Biomolecules 11:377.

     https://doi.org/10.3390/biom11030377

     • Google Scholar
115. 1. Vauquelin G
     2. Charlton SJ

     (2010) Long‐lasting target binding and rebinding as mechanisms to prolong in vivo drug action

     British Journal of Pharmacology 161:488–508.

     https://doi.org/10.1111/j.1476-5381.2010.00936.x

     • Google Scholar
116. 1. Vicidomini G
     2. Bianchini P
     3. Diaspro A

     (2018) STED super-resolved microscopy

     Nature Methods 15:173–182.

     https://doi.org/10.1038/nmeth.4593

     • PubMed
     • Google Scholar
117. 1. Vilas GL
     2. Corvi MM
     3. Plummer GJ
     4. Seime AM
     5. Lambkin GR
     6. Berthiaume LG

     (2006) Posttranslational myristoylation of caspase-activated p21-activated protein kinase 2 (PAK2) potentiates late apoptotic events

     PNAS 103:6542–6547.

     https://doi.org/10.1073/pnas.0600824103

     • PubMed
     • Google Scholar
118. 1. Villaseñor R
     2. Nonaka H
     3. Del Conte-Zerial P
     4. Kalaidzidis Y
     5. Zerial M

     (2015) Regulation of EGFR signal transduction by analogue-to-digital conversion in endosomes

     eLife 4:e06156.

     https://doi.org/10.7554/eLife.06156

     • PubMed
     • Google Scholar
119. 1. Wallis TP
     2. Jiang A
     3. Young K
     4. Hou H
     5. Kudo K
     6. McCann AJ
     7. Durisic N
     8. Joensuu M
     9. Oelz D
     10. Nguyen H
     11. Gormal RS
     12. Meunier FA

     (2023) Super-resolved trajectory-derived nanoclustering analysis using spatiotemporal indexing

     Nature Communications 14:3353.

     https://doi.org/10.1038/s41467-023-38866-y

     • PubMed
     • Google Scholar
120. 1. Wang X
     2. Mader MM
     3. Toth JE
     4. Yu X
     5. Jin N
     6. Campbell RM
     7. Smallwood JK
     8. Christe ME
     9. Chatterjee A
     10. Goodson T Jr
     11. Vlahos CJ
     12. Matter WF
     13. Bloem LJ

     (2005) Complete inhibition of anisomycin and UV radiation but not cytokine induced JNK and p38 activation by an aryl-substituted dihydropyrrolopyrazole quinoline and mixed lineage kinase 7 small interfering RNA

     The Journal of Biological Chemistry 280:19298–19305.

     https://doi.org/10.1074/jbc.M413059200

     • PubMed
     • Google Scholar
121. 1. Wang Y
     2. Gao J
     3. Guo X
     4. Tong T
     5. Shi X
     6. Li L
     7. Qi M
     8. Wang Y
     9. Cai M
     10. Jiang J
     11. Xu C
     12. Ji H
     13. Wang H

     (2014a) Regulation of EGFR nanocluster formation by ionic protein-lipid interaction

     Cell Research 24:959–976.

     https://doi.org/10.1038/cr.2014.89

     • PubMed
     • Google Scholar
122. 1. Wang C
     2. Weerapana E
     3. Blewett MM
     4. Cravatt BF

     (2014b) A chemoproteomic platform to quantitatively map targets of lipid-derived electrophiles

     Nature Methods 11:79–85.

     https://doi.org/10.1038/nmeth.2759

     • PubMed
     • Google Scholar
123. 1. Wang T
     2. Martin S
     3. Nguyen TH
     4. Harper CB
     5. Gormal RS
     6. Martínez-Mármol R
     7. Karunanithi S
     8. Coulson EJ
     9. Glass NR
     10. Cooper-White JJ
     11. van Swinderen B
     12. Meunier FA

     (2016a) Flux of signalling endosomes undergoing axonal retrograde transport is encoded by presynaptic activity and TrkB

     Nature Communications 7:12976.

     https://doi.org/10.1038/ncomms12976

     • Google Scholar
124. 1. Wang W
     2. Tang Y
     3. Wang Y
     4. Tascau L
     5. Balcerek J
     6. Tong W
     7. Levine RL
     8. Welch C
     9. Tall AR
     10. Wang N

     (2016b) LNK/SH2B3 loss of function promotes atherosclerosis and thrombosis

     Circulation Research 119:e91–e103.

     https://doi.org/10.1161/CIRCRESAHA.116.308955

     • PubMed
     • Google Scholar
125. 1. Willems J
     2. de Jong APH
     3. Scheefhals N
     4. Mertens E
     5. Catsburg LAE
     6. Poorthuis RB
     7. de Winter F
     8. Verhaagen J
     9. Meye FJ
     10. MacGillavry HD

     (2020) ORANGE: a CRISPR/Cas9-based genome editing toolbox for epitope tagging of endogenous proteins in neurons

     PLOS Biology 18:e3000665.

     https://doi.org/10.1371/journal.pbio.3000665

     • PubMed
     • Google Scholar
126. 1. Wilmes S
     2. Hafer M
     3. Vuorio J
     4. Tucker JA
     5. Winkelmann H
     6. Löchte S
     7. Stanly TA
     8. Pulgar Prieto KD
     9. Poojari C
     10. Sharma V
     11. Richter CP
     12. Kurre R
     13. Hubbard SR
     14. Garcia KC
     15. Moraga I
     16. Vattulainen I
     17. Hitchcock IS
     18. Piehler J

     (2020) Mechanism of homodimeric cytokine receptor activation and dysregulation by oncogenic mutations

     Science 367:643–652.

     https://doi.org/10.1126/science.aaw3242

     • PubMed
     • Google Scholar
127. 1. Wong J
     2. Smith LB
     3. Magun EA
     4. Engstrom T
     5. Kelley-Howard K
     6. Jandhyala DM
     7. Thorpe CM
     8. Magun BE
     9. Wood LJ

     (2013) Small molecule kinase inhibitors block the ZAK-dependent inflammatory effects of doxorubicin

     Cancer Biology & Therapy 14:56–63.

     https://doi.org/10.4161/cbt.22628

     • PubMed
     • Google Scholar
128. 1. Xia D
     2. Götz J

     (2014) Premature lethality, hyperactivity, and aberrant phosphorylation in transgenic mice expressing a constitutively active form of Fyn

     Frontiers in Molecular Neuroscience 7:40.

     https://doi.org/10.3389/fnmol.2014.00040

     • PubMed
     • Google Scholar
129. 1. Yang N
     2. Jiang J
     3. Deng L
     4. Waters MJ
     5. Wang X
     6. Frank SJ

     (2010a) Growth hormone receptor targeting to lipid rafts requires extracellular subdomain 2

     Biochemical and Biophysical Research Communications 391:414–418.

     https://doi.org/10.1016/j.bbrc.2009.11.072

     • PubMed
     • Google Scholar
130. 1. Yang JJ
     2. Lee YJ
     3. Hung HH
     4. Tseng WP
     5. Tu CC
     6. Lee H
     7. Wu WJ

     (2010b) ZAK inhibits human lung cancer cell growth via ERK and JNK activation in an AP-1-dependent manner

     Cancer Science 101:1374–1381.

     https://doi.org/10.1111/j.1349-7006.2010.01537.x

     • PubMed
     • Google Scholar
131. 1. Young RM
     2. Holowka D
     3. Baird B

     (2003) A lipid raft environment enhances Lyn kinase activity by protecting the active site tyrosine from dephosphorylation

     The Journal of Biological Chemistry 278:20746–20752.

     https://doi.org/10.1074/jbc.M211402200

     • PubMed
     • Google Scholar
132. 1. Zhang C
     2. Cherifi I
     3. Nygaard M
     4. Haxholm GW
     5. Bogorad RL
     6. Bernadet M
     7. England P
     8. Broutin I
     9. Kragelund BB
     10. Guidotti J-E
     11. Goffin V

     (2015) Residue 146 regulates prolactin receptor folding, basal activity and ligand-responsiveness: potential implications in breast tumorigenesis

     Molecular and Cellular Endocrinology 401:173–188.

     https://doi.org/10.1016/j.mce.2014.12.006

     • Google Scholar

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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