Method for co-cluster analysis in multichannel single-molecule localisation data

Jeremie Rossy; Edward Cohen; Katharina Gaus; Dylan M. Owen

doi:10.1007/s00418-014-1208-z

Method for co-cluster analysis in multichannel single-molecule localisation data

Jeremie Rossy, Edward Cohen, Katharina Gaus^*, Dylan M. Owen

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

59 Citations (Scopus)

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Abstract

We demonstrate a combined univariate and bivariate Getis and Franklin's local point pattern analysis method to investigate the co-clustering of membrane proteins in two-dimensional single-molecule localisation data. This method assesses the degree of clustering of each molecule relative to its own species and relative to a second species. Using simulated data, we show that this approach can quantify the degree of cluster overlap in multichannel point patterns. The method is validated using photo-activated localisation microscopy and direct stochastic optical reconstruction microscopy data of the proteins Lck and CD45 at the T cell immunological synapse. Analysing co-clustering in this manner is generalizable to higher numbers of fluorescent species and to three-dimensional or live cell data sets.

Original language	English
Pages (from-to)	605-612
Number of pages	8
Journal	Histochemistry and Cell Biology
Volume	141
Issue number	6
Early online date	19 Mar 2014
DOIs	https://doi.org/10.1007/s00418-014-1208-z
Publication status	Published - Jun 2014

Keywords

Cluster analysis
Super-resolution
Co-localisation
PALM
STORM
OPTICAL RECONSTRUCTION MICROSCOPY
PAIR-CORRELATION-ANALYSIS
COLOCALIZATION ANALYSIS
PROTEIN HETEROGENEITY
FLUORESCENT-PROBES
DIFFRACTION-LIMIT
PATTERNS

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10.1007/s00418-014-1208-z

http://link.springer.com/article/10.1007%2Fs00418-014-1208-z

Cite this

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title = "Method for co-cluster analysis in multichannel single-molecule localisation data",

abstract = "We demonstrate a combined univariate and bivariate Getis and Franklin's local point pattern analysis method to investigate the co-clustering of membrane proteins in two-dimensional single-molecule localisation data. This method assesses the degree of clustering of each molecule relative to its own species and relative to a second species. Using simulated data, we show that this approach can quantify the degree of cluster overlap in multichannel point patterns. The method is validated using photo-activated localisation microscopy and direct stochastic optical reconstruction microscopy data of the proteins Lck and CD45 at the T cell immunological synapse. Analysing co-clustering in this manner is generalizable to higher numbers of fluorescent species and to three-dimensional or live cell data sets.",

keywords = "Cluster analysis, Super-resolution, Co-localisation, PALM, STORM, OPTICAL RECONSTRUCTION MICROSCOPY, PAIR-CORRELATION-ANALYSIS, COLOCALIZATION ANALYSIS, PROTEIN HETEROGENEITY, FLUORESCENT-PROBES, DIFFRACTION-LIMIT, PATTERNS",

author = "Jeremie Rossy and Edward Cohen and Katharina Gaus and Owen, {Dylan M.}",

year = "2014",

month = jun,

doi = "10.1007/s00418-014-1208-z",

language = "English",

volume = "141",

pages = "605--612",

journal = "Histochemistry and Cell Biology",

issn = "0948-6143",

publisher = "Springer Verlag",

number = "6",

}

TY - JOUR

T1 - Method for co-cluster analysis in multichannel single-molecule localisation data

AU - Rossy, Jeremie

AU - Cohen, Edward

AU - Gaus, Katharina

AU - Owen, Dylan M.

PY - 2014/6

Y1 - 2014/6

N2 - We demonstrate a combined univariate and bivariate Getis and Franklin's local point pattern analysis method to investigate the co-clustering of membrane proteins in two-dimensional single-molecule localisation data. This method assesses the degree of clustering of each molecule relative to its own species and relative to a second species. Using simulated data, we show that this approach can quantify the degree of cluster overlap in multichannel point patterns. The method is validated using photo-activated localisation microscopy and direct stochastic optical reconstruction microscopy data of the proteins Lck and CD45 at the T cell immunological synapse. Analysing co-clustering in this manner is generalizable to higher numbers of fluorescent species and to three-dimensional or live cell data sets.

AB - We demonstrate a combined univariate and bivariate Getis and Franklin's local point pattern analysis method to investigate the co-clustering of membrane proteins in two-dimensional single-molecule localisation data. This method assesses the degree of clustering of each molecule relative to its own species and relative to a second species. Using simulated data, we show that this approach can quantify the degree of cluster overlap in multichannel point patterns. The method is validated using photo-activated localisation microscopy and direct stochastic optical reconstruction microscopy data of the proteins Lck and CD45 at the T cell immunological synapse. Analysing co-clustering in this manner is generalizable to higher numbers of fluorescent species and to three-dimensional or live cell data sets.

KW - Cluster analysis

KW - Super-resolution

KW - Co-localisation

KW - PALM

KW - STORM

KW - OPTICAL RECONSTRUCTION MICROSCOPY

KW - PAIR-CORRELATION-ANALYSIS

KW - COLOCALIZATION ANALYSIS

KW - PROTEIN HETEROGENEITY

KW - FLUORESCENT-PROBES

KW - DIFFRACTION-LIMIT

KW - PATTERNS

U2 - 10.1007/s00418-014-1208-z

DO - 10.1007/s00418-014-1208-z

M3 - Article

SN - 0948-6143

VL - 141

SP - 605

EP - 612

JO - Histochemistry and Cell Biology

JF - Histochemistry and Cell Biology

IS - 6

ER -

Method for co-cluster analysis in multichannel single-molecule localisation data

Abstract

Keywords

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