3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse

Griffié, Juliette; Shlomovich, Leigh; Williamson, David J.; Shannon, Michael J.; Aaron, Jesse; Khuon, Satya; Burn, Garth L.; Boelen, Lies; Peters, Ruby; Cope, Andrew P.; Cohen, Edward; Rubin-Delanchy, Patrick; Owen, Dylan M.

doi:10.1038/s41598-017-04450-w

Cited by 39 publications

(41 citation statements)

References 39 publications

(58 reference statements)

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“…Whilst Bayesian approaches (12) to parameter setting are very powerful when the underlying nano-structure can be modeled in advance, it is not suitable for exploratory applications where the nano-structure is not known a priori. The core concepts in this work could also be transferred to other clustering approaches which could be extended to threshold on persistence (14,15). For example Voronoï tesselation could be used to calculate the density estimate.…”

Section: Discussionmentioning

confidence: 99%

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Topological data analysis quantifies biological nano-structure from single molecule localization microscopy

Pike

Khan

Pallini

et al. 2018

Preprint

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The study of complex molecular organisation and nanostructure by localization based microscopy is limited by the available analysis tools. We present a segmentation protocol which, through the application of persistence based clustering, is capable of probing densely packed structures which vary in scale. An increase in segmentation performance over state-of-the-art methods is demonstrated. Moreover we employ persistent homology to move beyond clustering, and quantify the topological structure within data. This provides new information about the preserved shapes formed by molecular architecture. Our methods are flexible and we demonstrate this by applying them to receptor clustering in platelets, nuclear pore components and endocytic proteins. Both 2D and 3D implementations are provided within RSMLM, an R package for pointillist based analysis and batch processing of localization microscopy data. Topological data analysis | Persistent Homology | Single Molecule Localization Microscopy| Super-Resolution Microscopy | Clustering Correspondence: j.a.pike@bham.ac.uk or i.b.styles@bham.ac.uk Pike et al. | bioRχiv | November 13, 2018 | 1-15

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Section: Discussionmentioning

confidence: 99%

“…An alternative approach to density thresholding is to identify clusters based on persistence, or topographic prominence (13). This strategy has shown promise for SMLM datasets in the context of Ripley's K based clustering (14,15).…”

Section: Introductionmentioning

confidence: 99%

Topological data analysis quantifies biological nano-structure from single molecule localization microscopy

Pike

Khan

Pallini

et al. 2018

Preprint

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“…Non-specific binding events are detected as non-clustered localizations. Thus, after identifying the clusters of singlemolecule localizations using a well-established model-based Bayesian cluster analysis method 24,25 (colored outlined points in Fig. 1c-e, middle), we used k-means clustering to partition the localizations in each detected cluster into their corresponding protein positions ( Fig.…”

Section: Dna-qpaintmentioning

confidence: 99%

“…First, we randomly selected thirty non-overlapping 2 µm × 2 µm regions of interest (ROIs) for the analysis of each timecourse point experiment (before and following 2-and 8-min TCR stimulation), with a maximum of three ROIs per cell. 24,25 To avoid suboptimal clustering results; ROIs were selected such that they do not intersect with cell boundaries. To identify sets of localizations arising from a real docking strand target, we analyzed the data using a robust cluster analysis technique based on Ripley's K function, Bayesian statistics and topographical prominence.…”

Section: Reconstruction Of Proteins Maps and Data Analysismentioning

confidence: 99%

Multi-colour DNA-qPAINT reveals how Csk nano-clusters regulate T-cell receptor signalling

Simoncelli

Griffié

et al. 2019

Preprint

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Phosphorylation of the negative regulatory element of the tyrosine kinase Lck by Csk downmodulates T-cell receptor induced signalling. Being constitutively active, Csk spatial organization is responsible for regulating this signalling interaction. Here, we used stoichiometrically accurate, multiplexed, single-molecule super-resolution microscopy (DNA-qPAINT) to image the nanoscale spatial architecture of Csk and two binding partners implicated in its membrane association -PAG and TRAF3. Combined with a newly developed co-clustering analysis framework, we provide a powerful resource for dissecting signalling pathways regulated by spatio-temporal organisation. We found that Csk forms nanoscale clusters proximal to the plasma membrane that are lost post-stimulation and rerecruited at later time points. Unexpectedly, these clusters do not directly co-localise with PAG at the membrane, but instead provide a ready pool of monomers to down-regulate signalling. By generating CRISPR/Cas9 knock-out T-cells, our data also identify that protein tyrosine phosphatase non-receptor type 22 (PTPN22) is essential for Csk nanocluster rerecruitment and for maintenance of the synaptic PAG population.

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“…Here, we define R =100 nm, as molecular clusters in cellular membranes have been shown to be typically below the diffraction limit of light (< 200nm)[12,35-37]. L R is a mathematically well-defined quantity, considered as an accurate measure of local density and routinely used for spatial point pattern (SPP) cluster analysis [12,38-40]. For a CSR distribution, the average L R value equals R (in our case L 100 ∼ 100 , translating into 7 molecules encircled within 100 nm of each molecule).…”

Section: Modelmentioning

confidence: 99%

An agent-based model of molecular aggregation at the cell membrane

Griffié

Peters

Owen

2019

Preprint

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Molecular clustering at the plasma membrane has long been identified as a key process and is associated with regulating signalling pathways across cell types. Recent advances in microscopy, in particular the rise of super-resolution, have allowed the experimental observation of nanoscale molecular clusters in the plasma membrane. However, modelling approaches capable of recapitulating these observations are in their infancy, partly because of the extremely complex array of biophysical factors which influence molecular distributions and dynamics in the plasma membrane. We propose here a highly abstracted approach: an agent-based model dedicated to the study of molecular aggregation at the plasma membrane. We show that when molecules are modelled as though they can act (diffuse) in a manner which is influenced by their molecular neighbourhood, many of the distributions observed in cells can be recapitulated, even though such sensing and response is not possible for real membrane molecules. As such, agent-based offers a unique platform which may lead to a new understanding of how molecular clustering in extremely complex molecular environments can be abstracted, simulated and interpreted using simple rules.Author summaryMolecular aggregation in cell membranes is a key component of cellular machinery, involved across cell types in inter-cellular communication and signalling pathway initiation. As such, understanding the underlying mechanisms and molecule cluster characteristics at a more theoretical level is a pre-requisite. Complete descriptive molecular models have proven impossible to realise due to the overall complexity of the processes involved, highlighting the need for novel approaches. While conceptual models have been shown to be powerful tools and are routinely used in other fields with high level of complexity such as social sciences or economics, they are overall lacking from the literature when it comes to cell studies. We suggest in this work that the same principle applies to cell biology and in particular, the study of molecular clustering. We propose here a general model, independent of cell types or signalling pathways: an agent-based model dedicated to molecular clustering in the plasma membrane. We show we are able to recapitulate molecular aggregation similar to observations in cells while new properties are highlighted by our model, for instance, clustering is a digitised process.

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3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse

Cited by 39 publications

References 39 publications

Topological data analysis quantifies biological nano-structure from single molecule localization microscopy

Topological data analysis quantifies biological nano-structure from single molecule localization microscopy

Multi-colour DNA-qPAINT reveals how Csk nano-clusters regulate T-cell receptor signalling

An agent-based model of molecular aggregation at the cell membrane

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