During the past decade, localization microscopy (LM) has transformed into an accessible, commercially available technique for life sciences. However, data processing can be challenging to the non-specialist and care is still needed to produce meaningful results. PALMsiever has been developed to provide a user-friendly means of visualizing, filtering and analyzing LM data. It includes drift correction, clustering, intelligent line profiles, many rendering algorithms and 3D data visualization. It incorporates the main analysis and data processing modalities used by experts in the field, as well as several new features we developed, and makes them broadly accessible. It can easily be extended via plugins and is provided as free of charge open-source software. Contact: thomas.pengo@gmail.co

Holden, Seamus J.

Manley, Suliana

Pengo, Thomas

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Bioimage informaticsPALMsiever: a tool to turn raw data into resultsfor single-molecule localization microscopyThomas Pengo1,2,*,†, Seamus J. Holden3 and Suliana Manley31Design of Biological Systems, Systems Biology, Center for Genomic Regulation, Dr. Aiguader 88, 08003 Barcelona,Spain, 2Advanced Light Microscopy Unit, Center for Genomic Regulation, Dr. Aiguader 88, 08003 Barcelona, Spain,and 3Laboratory for Experimental Biophysics, Institute for Physics of Biological Systems, School of BasicSciences, Swiss Federal School of Technology, 1015 Lausanne, Switzerland*To whom correspondence should be addressed.†Part of this work was developed when TP was affiliated with EPFL3.Associate Editor: Robert F. MurphyReceived on March 31, 2014; revised on October 17, 2014; accepted on October 27, 2014AbstractDuring the past decade, localization microscopy (LM) has transformed into an accessible, commer-cially available technique for life sciences. However, data processing can be challenging tothe non-specialist and care is still needed to produce meaningful results. PALMsiever has beendeveloped to provide a user-friendly means of visualizing, filtering and analyzing LM data.It includes drift correction, clustering, intelligent line profiles, many rendering algorithms and 3Ddata visualization. It incorporates the main analysis and data processing modalities used byexperts in the field, as well as several new features we developed, and makes them broadly access-ible. It can easily be extended via plugins and is provided as free of charge open-source software.Contact: thomas.pengo@gmail.comEight years since its inception, single-molecule localization micros-copy (LM; Betzig et al., 2006; Rust et al., 2006) has transformedfrom a specialist technique in the hands of a few labs, to an access-ible, commercially available technique, widely used in the life sci-ences (Patterson et al., 2010). Although the instrumentationrequired for LM is now readily available, the data processing aspectsof the technique remain less well developed. Most recent develop-ments in LM data processing have focused on extracting single mol-ecule localizations from the raw data (ISBI LM Challenge 2012,http://bigwww.epfl.ch/palm/), or visualization of processed data(Baddeley et al., 2010). However, these represent just the first andlast steps of analysis. Equally important are the post-processingsteps required to convert raw localizations to meaningful data.Filtering out spurious localizations, grouping and tracking of mul-tiple localizations, correcting drift, choosing the appropriate render-ing and performing statistical analysis on the localizations are just afew equally important aspects for which little software supportexists (Wolter et al., 2012), leaving the non-specialist with theunnecessary burden of developing ad hoc solutions.PALMsiever was made publicly available in 2012 (Pengo andManley, 2012) to fill this void and has been under continuous devel-opment since then (ISBI LM Challenge 2012, http://bigwww.epfl.ch/palm/). While other complementary tools have since been intro-duced (El Beheiry and Dahan, 2013; Ovesny et al., 2014),PALMsiever provides a single user-friendly extensible platform forLM data processing, combining visualization, post-processing, filter-ing and analysis features. The user interface (Fig. 1a) encouragesdata exploration, by rendering the data on-the-fly, interactively re-sponding to re-centering and zooming. Spurious localizations can beinteractively eliminated by iteratively adjusting the display rangesfor each of the available parameters, either directly by inspecting itshistogram (Fig. 1ii), or automatically by removing the lower andupper 5% (Fig. 1a, arrow). Microscope drift during acquisition canbe corrected using the 3D drift correction plugins, either by autocor-relation analysis or using fiducial markers. Line profiles (a simpleright-click in the image) are automatically aligned to the underlyingstructure by principal components analysis and fitted to a Gaussianfunction. In the presence of curvilinear structures, the includedVC The Author 2014. Published by Oxford University Press. 797This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/),which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contactjournals.permissions@oup.comBioinformatics, 31(5), 2015, 797–798doi: 10.1093/bioinformatics/btu720Advance Access Publication Date: 31 October 2014Applications Notetracing tool can automatically follow the structure (Fig. 1b) and gen-erate a fit from the projected profile (Fig. 1c). To compensate for thepresence of multiple detections of the same molecule in successiveframes, PALMsiever also includes a grouping algorithm, where de-tections are grouped into single molecules according to spatio-tem-poral proximity. Cluster analysis of interacting biomolecules isenabled by a plugin for the density-based cluster analysis DBSCAN(Endesfelder et al., 2013; Ester, 1996), which also facilitates noiseremoval (Fig. 1d, showing clustering of GaG-mEos2 data). Manyrendering methods are available such as scatterplot, 2D histogram,blurred 2D histogram, automatic kernel density estimation (Botev,2010), Delaunay triangulation, jittered 2D histogram, hue-encodeddepth and a novel occluding hue-encoded depth (Fig. 1e, in the sameorder). Thanks to 3D data support across the software, 3D volumescan be easily exported to a TIFF stack, visualized as a 3D isosurface(Fig. 1f), explored using slicing, or rendered using either the classicalhue-based depth coding, or a novel rendering that correctly accountsfor overlapping structures. As an alternative, data can also be ex-ported for visualization with ViSP (El Beheiry and Dahan, 2013).PALMsiever has been designed with software interoperability inmind, so a number of predefined input file formats have been pro-vided, e.g. QuickPALM (Henriques et al., 2010), RapidSTORM(Wolter et al., 2012), Leica SR GSD (Leica Microsystems, Wetzlar,Germany), PeakSelector (courtesy of H.Hess, HHMI), uTrack(Jaqaman et al., 2008), Vutara (Vutara, Salt Lake City, Utah, USA)and Octane (Niu and Yu, 2008). It can also easily be extended to im-port other file formats by adding new import filters or incorporatenew analyses by writing plugins using MATLAB, and renderings canalso conveniently be made available to the system clipboard or savedas TIFFs for inclusion in presentations or publications. The softwareis open-source and can be downloaded (together with example data-sets) at http://code.google.com/p/palm-siever.AcknowledgementsThe authors thank N. Olivier for providing the 3D microtubule data (Olivieret al., 2013) and J. Gunzenha¨user for the Gag-mEos2 data (Gunzenhauseret al., 2012).FundingTP was supported by the Brazilian Swiss Joint Research Programme of theEPFL and the SystemTB Collaborative Project (Ref. 241587), funded by EUFP7. SH and SM were supported by ERC Starting Grant 243016. SH was sup-ported by a Marie Curie Intra-European Fellowship, PIEF-GA-2011-297918.Conflict of Interest: none declared.ReferencesBaddeley,D. et al. (2010) Visualization of localization microscopy data,Microsc. Microanal. Off. J. Microsc. Soc. Am. Microbeam Anal. Soc.Microsc. Soc. Can., 16, 64–72.Betzig,E. et al. (2006) Imaging intracellular fluorescent proteins at nanometerresolution. Science, 313, 1642–1645.Botev,Z.I. et al. (2010) Kernel density estimation via diffusion. Ann. Stat.2010, 38, 2916–2957.El Beheiry,M. and Dahan,M. (2013) ViSP: representing single-particle local-izations in three dimensions. Nat. Methods, 10, 689–690.Endesfelder,U. et al. (2013) Multiscale spatial organization of RNA polymer-ase in Escherichia coli. Biophys. J., 105, 172–181.Ester,M.K., et al. (1996) A density-based algorithm for discovering clustersin large spatial databases with noise. In Proceedings of the SecondInternational Conference on Knowledge Discovery and Data Mining, AAAIPress.Gunzenhauser,J. et al. (2012) Quantitative super-resolution imaging revealsprotein stoichiometry and nanoscale morphology of assembling HIV-Gagvirions. Nano Lett., 12, 4705–4710.Henriques,R. et al. (2010) QuickPALM: 3D real-time photoactivation nano-scopy image processing in ImageJ. Nat. Methods, 7, 339–340.Jaqaman,K. et al. (2008) Robust single-particle tracking in live-cell time-lapsesequences. Nat. Methods, 5, 695–702.Niu,L. and Yu,J. (2008) Investigating intracellular dynamics of FtsZ cytoskel-eton with photoactivation single-molecule tracking. Biophys. J., 95,2009–2016.Olivier,N. et al. (2013) Resolution doubling in 3D-STORM imaging throughimproved buffers. PLoS One, 8, e69004.Ovesny,M. et al. (2014) ThunderSTORM: a comprehensive ImageJ plug-infor PALM and STORM data analysis and super-resolution imaging.Bioinformatics, 30, 2389–2390.Patterson,G. et al. (2010) Superresolution imaging using single-molecule local-ization. Annu. Rev. Phys. Chem., 61, 345–367.Pengo,T. and Manley,S. (2012) Software for the visualization and analysis ofsuper-resolution localization microscopy data. In SSMLMS 2012, Aug29–31 2012, Lausanne, Swizterland.Rust,M.J. et al. (2006) Sub-diffraction-limit imaging by stochastic optical re-construction microscopy (STORM). Nat. Methods, 3, 793–795.Wolter,S. et al. (2012) rapidSTORM: accurate, fast open-source softwarefor localization microscopy. Nat. Methods, 9, 1040–1041.−100 −50 0 50 1000100200300mu = −0.45   sigma = 11.38w = 28.47   Dist. from trace center [nm]# Points(a) (e)(b) (f)(d)Fig. 1. Single-molecule localization microscopy images, analyzed and ren-dered with PALMsiever. (a) The user interface encourages exploration of thedata. The histogram (ii) of each parameter can be analyzed and automaticallytrimmed to exclude the upper and lower 5% (arrow). (b) A curvilinear seg-ment of a microtubule stained with alexa fluor 647 is traced and (c) the corres-ponding histogram and double gaussian fit generated. (d) gag-meos2clusters, analyzed using a density-based clustering algorithm, which alsoallows to identify potential false localizations, i.e. Noise. (e) A microtubulesection is rendered using the many rendering algorithms available. In orderfrom 1 to 8: scatterplot, 2D histogram, blurred 2D histogram, kernel densityestimation (a novel automatic bandwidth estimation rendering), delaunaytriangulation, jittered histogram, hue-encoded depth, a novel occluding hue-encoded depth. (f) A 3D isosurface reconstruction of the microtubule sectionmicrotubule presented in e798 T.Pengo et al.

PALMsiever: a tool to turn raw data into results for single-molecule localization microscopy

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PALMsiever: a tool to turn raw data into results for single-molecule localization microscopy

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