Fully Automated Targeted Confocal and Single-Molecule Localization Microscopy

Part of the Methods in Molecular Biology book series (MIMB, volume 1663)


Single-molecule localization microscopy (SMLM) enables imaging of biological structures in the nanometre range. Long measurement times are the consequence of this kind of microscopy due to the need of acquiring thousands of images. We built a setup that automatically detects target structures using confocal microscopy and images them with SMLM. Utilizing the Konstanz Information Miner (KNIME), we were able to connect a confocal microscope with an SMLM unit for targeted screening. In this process, we developed KNIME plugins to communicate with the microscope components and combined them to a workflow. Thus, measuring biological nanometre-sized structures in a sufficient number to get statistical significance becomes feasible. For proof of principle HIV-1 assembly complexes in HeLa cells derived from transfection of replication deficient viral construct were imaged by a fully automated screen.

Key words

Super-resolution microscopy Single-molecule localization microscopy (SMLM) Konstanz Information Miner (KNIME) High content screening (HCS) Multiscale imaging Online feedback image analysis 



The authors thank Michael Berthold and the KNME/KNIP developers for their support with the creation of the KNIME nodes. This work was funded within the project CancerTelSys (grant number 01ZX1302) in the e:Med program, within the project RNA-Code (grant number 031A298) in the e:Bio program and within the project HD-HuB (grant number 031A537C), all of the German Federal Ministry of Education and Research (BMBF). The Advanced Biological Screening Facility is also supported by the CellNetworks-Cluster of Excellence (grant number EXC81).


  1. 1.
    Rust MJ, Bates M, Zhuang X (2006) Stochastic optical reconstruction microscopy (STORM) provides sub-diffraction-limit image resolution. Nat Methods 3(10):793–795CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Tischer C, Hilsenstein V, Hanson K, Pepperkok R (2014) Chapter 26 – Adaptive fluorescence microscopy by online feedback image analysis. In: Jennifer CW, Torsten W (eds) Methods in cell biology, vol 123. Academic Press, New York, pp 489–503Google Scholar
  3. 3.
    Conrad C, Wunsche A, Tan TH, Bulkescher J, Sieckmann F, Verissimo F, Edelstein A, Walter T, Liebel U, Pepperkok R, Ellenberg J (2011) Micropilot: automation of fluorescence microscopy-based imaging for systems biology. Nat Methods 8(3):246–249CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2008) KNIME: the Konstanz Information Miner. In: Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R (eds) Data analysis, machine learning and applications: proceedings of the 31st annual conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7–9, 2007. Springer, Berlin, Heidelberg, pp 319–326CrossRefGoogle Scholar
  5. 5.
    Muranyi W, Malkusch S, Müller B, Heilemann M, Kräusslich H-G (2013) Super-resolution microscopy reveals specific recruitment of HIV-1 envelope proteins to viral assembly sites dependent on the envelope C-terminal tail. PLoS Pathog 9(2):1–13CrossRefGoogle Scholar
  6. 6.
    Lampe M, Briggs JAG, Endress T, Glass B, Riegelsberger S, Kräusslich H-G, Lamb DC, Bräuchle C, Müller B (2007) Double-labelled HIV-1 particles for study of virus–cell interaction. Virology 360(1):92–104CrossRefPubMedGoogle Scholar
  7. 7.
    Trkola A, Purtscher M, Muster T, Ballaun C, Buchacher A, Sullivan N, Srinivasan K, Sodroski J, Moore JP, Katinger H (1996) Human monoclonal antibody 2G12 defines a distinctive neutralization epitope on the gp120 glycoprotein of human immunodeficiency virus type 1. J Virol 70(2):1100–1108PubMedPubMedCentralGoogle Scholar
  8. 8.
    Edelstein AD, Tsuchida MA, Amodaj N, Pinkard H, Vale RD, Stuurman N (2014) Advanced methods of microscope control using µManager software. J Biol Methods 1 (2):10Google Scholar
  9. 9.
    Schindelin J, Dietz C, Gunkel M (2015) Micro-manager integration for KNIME image processing. Accessed 20 Dec 2016
  10. 10.
    Dempsey GT, Bates M, Kowtoniuk WE, Liu DR, Tsien RY, Zhuang X (2009) Photoswitching mechanism of cyanine dyes. J Am Chem Soc 131(51):18192–18193CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Huang F, Hartwich TMP, Rivera-Molina FE, Lin Y, Duim WC, Long JJ, Uchil PD, Myers JR, Baird MA, Mothes W, Davidson MW, Toomre D, Bewersdorf J (2013) Video-rate nanoscopy using sCMOS camera-specific single-molecule localization algorithms. Nat Methods 10(7):653–658CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Ovesný M, Křížek P, Borkovec J, Švindrych Z, Hagen GM (2014) ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging. Bioinformatics 30(16):2389–2390CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD-96. AAAI Press, Portland, OR, pp 226–231Google Scholar
  14. 14.
    Micro-Manager (2011) Device support. Accessed 20 Dec 2016

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© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.High-Content Analysis of the Cell (HiCell) and Advanced Biological Screening FacilityBioQuant, Heidelberg UniversityHeidelbergGermany
  2. 2.Department of Infectious Diseases, VirologyUniversity Hospital HeidelbergHeidelbergGermany

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