Which Elements to Build Co-localization Workflows? From Metrology to Analysis

  • Patrice Mascalchi
  • Fabrice P. CordelièresEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2040)


Co-localization analysis is one of the main interests of users entering a facility with slides in hands and nice analysis perspectives in mind. While being available through most, if not all, analysis software, co-localization tools are mainly perceived as black boxes, fed with images, that will, hopefully, return (the expected) numbers.

In this chapter, we will aim at deconstructing existing generic co-localization workflows, extracting elementary tools that may be reused and recombined to generate new workflows. By differentiating work cases, identifying co-localization reporters and the metrics others have been using, we aim at providing the audience with the elementary bricks and methods to build their really own co-localization workflows. A special emphasis is given on the preparatory phase where the acquisition system is assessed, using basic metrological tests.

Key words

Co-localization Co-expression Co-occurrence Correlation Co-distribution Elements Workflow Image processing Image analysis 



The Bordeaux Imaging Center is a service unit of the CNRS-INSERM and Bordeaux University, member of the national infrastructure France BioImaging supported by the French National Research Agency (ANR-10-INBS-04). FPC is a member of NEUBIAS (Network for European Bioimage Analysts), COST Action CA15124. Figures have been assembled using ImageJ’s FigureJ plugin [78].

Supplementary material


  1. 1.
    Malkusch S, Endesfelder U, Mondry J et al (2012) Coordinate-based colocalization analysis of single-molecule localization microscopy data. Histochem Cell Biol 137:1–10CrossRefGoogle Scholar
  2. 2.
    Malkusch S, Heilemann M (2016) Extracting quantitative information from single-molecule super-resolution imaging data with LAMA – LocAlization Microscopy Analyzer. Sci Rep 6:34486CrossRefGoogle Scholar
  3. 3.
    Bolte S, Cordelières FP (2006) A guided tour into subcellular colocalization analysis in light microscopy. J Microsc 224:213–232CrossRefGoogle Scholar
  4. 4.
    Cordelières FP, Bolte S (2008) JACoP v2.0: improving the user experience with co-localization studies. In: ImageJ User & Developer Conference. pp 174–181Google Scholar
  5. 5.
    Cordelières FP, Bolte S (2014) Experimenters’ guide to colocalization studies: finding a way through indicators and quantifiers, in practice. Methods Cell Biol 123:395–408CrossRefGoogle Scholar
  6. 6.
    Adler J, Pagakis SN, Parmryd I (2008) Replicate-based noise corrected correlation for accurate measurements of colocalization. J Microsc 230:121–133CrossRefGoogle Scholar
  7. 7.
    Adler J, Parmryd I (2010) Quantifying colocalization by correlation: the Pearson correlation coefficient is superior to the Mander’s overlap coefficient. Cytometry A 77:733–742CrossRefGoogle Scholar
  8. 8.
    Miura K, Tosi S (2017) Epilogue: a framework for bioimage analysis. In: Standard and super-resolution bioimaging data analysis. John Wiley and Sons Ltd, Hoboken, NJ, pp 269–284CrossRefGoogle Scholar
  9. 9.
    French AP, Mills S, Swarup R et al (2008) Colocalization of fluorescent markers in confocal microscope images of plant cells. Nat Protoc 3:619–628CrossRefGoogle Scholar
  10. 10.
    Zinchuk V, Zinchuk O (2008) Quantitative colocalization analysis of confocal fluorescence microscopy images. Curr Protoc Cell Biol Chapter 4:Unit 4.19PubMedGoogle Scholar
  11. 11.
    Adler J, Parmryd I (2012) Colocalization analysis in fluorescence microscopy. Methods Mol Biol 931:97–109CrossRefGoogle Scholar
  12. 12.
    Royon A, Papon G (2013) Calibration of fluorescence microscopes. Imag Microsc 3:41–43Google Scholar
  13. 13.
    Royon A, Converset N (2017) Quality control of fluorescence imaging systems. Imag Microsc 12:22–25Google Scholar
  14. 14.
    Zwier JM, Van Rooij GJ, Hofstraat JW et al (2004) Image calibration in fluorescence microscopy. J Microsc 216:15–24CrossRefGoogle Scholar
  15. 15.
    Brakenhoff GJ, Wurpel GWH, Jalink K et al (2005) Characterization of sectioning fluorescence microscopy with thin uniform fluorescent layers: sectioned Imaging Property or SIPcharts. J Microsc 219:122–132CrossRefGoogle Scholar
  16. 16.
    Model M (2014) Intensity calibration and flat-field correction for fluorescence microscopes. In: Current protocols in cytometry. John Wiley & Sons, Inc., Hoboken, NJGoogle Scholar
  17. 17.
    Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682CrossRefGoogle Scholar
  18. 18.
    De Chaumont F, Dallongeville S, Chenouard N et al (2012) Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods 9:690–696CrossRefGoogle Scholar
  19. 19.
    Matthews C, Cordelières FP (2010) MetroloJ: an ImageJ plugin to help monitor microscopes’ health. In: ImageJ User & Developer ConferenceGoogle Scholar
  20. 20.
    Matthews C, Cordelières FP (2017) MetroloJ plugin.
  21. 21.
  22. 22.
    Ollion J, Cochennec J, Loll F et al (2013) TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization. Bioinformatics 29:1840–1841CrossRefGoogle Scholar
  23. 23.
  24. 24.
  25. 25.
    Gammon S (2006) Spectral unmixing of bioluminescence signals.
  26. 26.
    Neher R, Theis F, Zeug A (2009) PoissonNMF – blind source separation of fluorescence microscopy data.
  27. 27.
    Peng T, Thorn K, Schroeder T et al (2017) A BaSiC tool for background and shading correction of optical microscopy images. Nat Commun 8:14836CrossRefGoogle Scholar
  28. 28.
    Kirshner H, Aguet F, Sage D et al (2013) 3-D PSF fitting for fluorescence microscopy: implementation and localization application. J Microsc 249:13–25CrossRefGoogle Scholar
  29. 29.
    Kirshner H, Sage D (2017) PSF generator plugin.
  30. 30.
    Sage D, Donati L, Soulez F et al (2017) DeconvolutionLab2: An open-source software for deconvolution microscopy. Methods 115:28–41CrossRefGoogle Scholar
  31. 31.
    Vonesch C, Cristofani RT, Schmit G (2016) DeconvolutionLab1 plugin,
  32. 32.
    Sage D, Vonesch C, Schmit G, et al (2017) DeconvolutionLab2 plugin,
  33. 33.
    Zucker RM, Price OT (2001) Evaluation of confocal microscopy system performance. Methods Mol Biol 44:295–308Google Scholar
  34. 34.
    Zucker R (2004) Confocal microscopy system performance: axial resolution. Microscopy Today 12:38–40CrossRefGoogle Scholar
  35. 35.
    Zucker R (2006) Quality assessment of confocal microscopy slide-based systems: instability. Cytometry A 69:677CrossRefGoogle Scholar
  36. 36.
    Cole RW, Jinadasa T, Brown CM (2011) Measuring and interpreting point spread functions to determine confocal microscope resolution and ensure quality control. Nat Protoc 6:1929–1941CrossRefGoogle Scholar
  37. 37.
    Hng KI, Dormann D (2013) ConfocalCheck – a software tool for the automated monitoring of confocal microscope performance. PLoS One 8:e79879CrossRefGoogle Scholar
  38. 38.
    Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675CrossRefGoogle Scholar
  39. 39.
    Pearson H (2005) Image manipulation: CSI: cell biology. Nature 434:952–953CrossRefGoogle Scholar
  40. 40.
    Pearson H (2007) The good, the bad and the ugly. Nature 447:138–140CrossRefGoogle Scholar
  41. 41.
    Zimmermann T (2005) Spectral imaging and linear unmixing in light microscopy. Adv Biochem Eng Biotechnol 95:245–265PubMedGoogle Scholar
  42. 42.
    Gammon ST, Leevy WM, Gross S et al (2006) Spectral unmixing of multicolored bioluminescence emitted from heterogeneous biological sources. Anal Chem 78:1520–1527CrossRefGoogle Scholar
  43. 43.
    Zimmermann T, Marrison J, Hogg K et al (2014) Clearing up the signal: spectral imaging and linear unmixing in fluorescence microscopy. In: Paddock SW (ed) Confocal microscopy: methods and protocols. Springer New York, New York, NY, pp 129–148CrossRefGoogle Scholar
  44. 44.
    Wallace W, Schaefer LH, Swedlow JR (2001) A workingperson’s guide to deconvolution in light microscopy. Biotechniques 31:1076–1078. 1080, 1082 passimCrossRefGoogle Scholar
  45. 45.
    Sibarita JB (2005) Deconvolution microscopy. Adv Biochem Eng Biotechnol 95:201–243PubMedGoogle Scholar
  46. 46.
    Landmann L (2002) Deconvolution improves colocalization analysis of multiple fluorochromes in 3D confocal data sets more than filtering techniques. J Microsc 208:134–147CrossRefGoogle Scholar
  47. 47.
    Landmann L, Marbet P (2004) Colocalization analysis yields superior results after image restoration. Microsc Res Tech 64:103–112CrossRefGoogle Scholar
  48. 48.
    Richardson W (1972) Bayesian-based iterative method of image restoration. J Opt Soc Am 62:55–59CrossRefGoogle Scholar
  49. 49.
    Lucy LB (1974) An iterative technique for the rectification of observed distributions. Astronomical J 79:745CrossRefGoogle Scholar
  50. 50.
    Frigo M, Johnson SG (1998) FFTW: an adaptive software architecture for the FFT. In: Proceedings of 1998 IEEE International Conference Acoustics speech and signal processing. pp 1381–1384 IEEEGoogle Scholar
  51. 51.
    Frigo M, Johnson SG (2005) The design and implementation of FFTW3. Proc IEEE 93:216–231CrossRefGoogle Scholar
  52. 52.
    Weigert M, Schmidt U, Boothe T et al (2018) Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat Methods 15:1090–1097CrossRefGoogle Scholar
  53. 53.
    Luisier F, Vonesch C, Blu T et al (2010) Fast interscale wavelet denoising of poisson-corrupted images. Signal Processing 90:415–427CrossRefGoogle Scholar
  54. 54.
    Luisier F (2017) PureDenoise plugin.
  55. 55.
    Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. Electron Imaging 13:146–165CrossRefGoogle Scholar
  56. 56.
    Sommer C, Gerlich DW (2013) Machine learning in cell biology – teaching computers to recognize phenotypes. J Cell Sci 126:5529–5539CrossRefGoogle Scholar
  57. 57.
    Sadanandan SK, Ranefall P, Le Guyader S, et al (2017) Automated training of deep convolutional neural networks for cell segmentation Sci Rep 7:1–7Google Scholar
  58. 58.
    Arganda-Carreras I, Kaynig V, Rueden C et al (2017) Trainable weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33:2424–2426CrossRefGoogle Scholar
  59. 59.
    Arganda-Carreras I, Kaynig V, Rueden C, et al (2016) Trainable segmentation: release v3.1.2.
  60. 60.
    Carpenter AE, Jones TR, Lamprecht MR et al (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7:R100CrossRefGoogle Scholar
  61. 61.
    Sommer C, Straehle C, Köthe U, et al (2011) Ilastik: interactive learning and segmentation toolkit. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. pp 230–233Google Scholar
  62. 62.
    Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philos Mag 2:559–572CrossRefGoogle Scholar
  63. 63.
    Manders EM, Stap J, Brakenhoff GJ et al (1992) Dynamics of three-dimensional replication patterns during the S-phase, analysed by double labelling of DNA and confocal microscopy. J Cell Sci 103(Pt 3):857–862Google Scholar
  64. 64.
    Manders EMM, Verbeek FJ, Ate JA (1993) Measurement of co-localisation of objects in dual-colour confocal images. J Microsc 169:375–382CrossRefGoogle Scholar
  65. 65.
    Dunn KW, Kamocka MM, McDonald JH (2011) A practical guide to evaluating colocalization in biological microscopy. Am J Physiol Cell Physiol 300:C723–C742CrossRefGoogle Scholar
  66. 66.
    Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15:72–101CrossRefGoogle Scholar
  67. 67.
    McDonald JH, Dunn KW (2013) Statistical tests for measures of colocalization in biological microscopy. J Microsc 252:295–302CrossRefGoogle Scholar
  68. 68.
    Costes SV, Daelemans D, Cho EH et al (2004) Automatic and quantitative measurement of protein-protein colocalization in live cells. Biophys J 86:3993–4003CrossRefGoogle Scholar
  69. 69.
    Lachmanovich E, Shvartsman DE, Malka Y et al (2003) Co-localization analysis of complex formation among membrane proteins by computerized fluorescence microscopy: application to immunofluorescence co-patching studies. J Microsc 212:122–131CrossRefGoogle Scholar
  70. 70.
    Neubias Cost Action CA15124 (2018), BISE bio imaging search engine: a bio image information index.
  71. 71.
    R Core Team (2018) R: a language and environment for statistical computing.
  72. 72.
    Berthold MR, Cebron N, Dill F et al (2009) KNIME – the konstanz information miner: version 2.0 and beyond. ACM 11:26–31Google Scholar
  73. 73.
    Allan C, Burel JM, Moore J et al (2012) OMERO: flexible, model-driven data management for experimental biology, vol 9, pp 245–253Google Scholar
  74. 74.
    Gavrilovic M, Wählby C (2009) Quantification of colocalization and cross-talk based on spectral angles. J Microsc 234:311–324CrossRefGoogle Scholar
  75. 75.
    van Steensel B, van Binnendijk E, Hornsby C et al (1996) Partial colocalization of glucocorticoid and mineralocorticoid receptors in discrete compartments in nuclei of rat hippocampus neurons. J Cell Sci 109:787–792PubMedGoogle Scholar
  76. 76.
    Lagache T, Lang G, Sauvonnet N et al (2013) Analysis of the spatial organization of molecules with robust statistics. PLoS One 8:e80914CrossRefGoogle Scholar
  77. 77.
    Lagache T, Sauvonnet N, Danglot L et al (2015) Statistical analysis of molecule colocalization in bioimaging. Cytometry A 87:568–579CrossRefGoogle Scholar
  78. 78.
    Mutterer J, Zinck E (2013) Quick-and-clean article figures with FigureJ. J Microsc 252:89–91CrossRefGoogle Scholar
  79. 79.
    Li Q, Ledoux-Rak I, Lai ND (2015) Influence of incident beam polarization on intensity and polarization distributions of tight focusing spot. Adv Device Mater 1:4–10CrossRefGoogle Scholar
  80. 80.
    Schindelin J, Eglinger J, Guizzetti L, et al (2018) Coloc2.\_2
  81. 81.
    Cordelières FP, Bolte S (2018) JaCoP, just another co-localization plugin v2.\_2.0:just\_another\_colocalization\_plugin:start
  82. 82.
    Gilles JF, Dos Santos M, Boudier T et al (2017) DiAna, an ImageJ tool for object-based 3D co-localization and distance analysis. Methods 115:55–64CrossRefGoogle Scholar
  83. 83.
    Rizk A, Paul G, Incardona P et al (2014) Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh. Nat Protoc 9:586–596CrossRefGoogle Scholar
  84. 84.
    Lavancier F, Pécot T, Zengzhen L, et al (2018) GcoPS.
  85. 85.
    Ovesný M, Křížek P, Borkovec J et al (2014) ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging. Bioinformatics 30:2389–2390CrossRefGoogle Scholar
  86. 86.
    De Chaumont F (2018) Colocalizer.
  87. 87.
    Lagache T, Grassart A, Dallongeville S et al (2018) Mapping molecular assemblies with fluorescence microscopy and object-based spatial statistics. Nat Commun 9:698CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Bordeaux Imaging CenterUMS 3420 CNRS—Université de Bordeaux—US4 INSERM, Pôle d’imagerie photonique, Centre Broca Nouvelle-AquitaineBordeauxFrance

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