ScatterJ: An ImageJ plugin for the evaluation of analytical microscopy datasets

We present ScatterJ, an ImageJ plugin that allows for extracting qualitative as well as quantitative information from analytical microscopy datasets. A large variety of analytical microscopy methods are used to obtain spatially resolved chemical information. The resulting datasets are often large and complex, and can contain information that is not obvious or directly accessible. ScatterJ extends and complements existing methods to extract information on correlation and colocalization from pairs of species‐specific or element‐specific maps. We demonstrate the possibilities to extract information using example datasets from biogeochemical studies, although the plugin is not restricted to this type of research. The information that we could extract from our existing data helped to further our understanding of biogeochemical processes such as mineral formation or heavy metal sorption. ScatterJ can be used for a variety of different two‐dimensional (2D) and three‐dimensional (3D) datasets such as energy‐dispersive X‐ray spectroscopy maps, 3D confocal laser scanning microscopy maps, and 2D scanning transmission X‐ray microscopy maps.

[1]  Y. Stierhof,et al.  3‐D analysis of bacterial cell‐(iron)mineral aggregates formed during Fe(II) oxidation by the nitrate‐reducing Acidovorax sp. strain BoFeN1 using complementary microscopy tomography approaches , 2014, Geobiology.

[2]  Michel Herbin,et al.  Advances in the segmentation of multi-component microanalytical images. , 2005, Ultramicroscopy.

[3]  F. Cordelières,et al.  A guided tour into subcellular colocalization analysis in light microscopy , 2006, Journal of microscopy.

[4]  D. F. Ogletree,et al.  Soft X-ray Microscopy and Spectroscopy at the Molecular Environmental Science Beamline at the Advanced Light Source , 2006 .

[5]  Michael D. Abràmoff,et al.  Image processing with ImageJ , 2004 .

[6]  J. Edward Jackson,et al.  Principal Components and Factor Analysis: Part I - Principal Components , 1980 .

[7]  G. Wei,et al.  Tuning with pH: The selectivity of a new rhodamine B derivative chemosensor for Fe3+ and Cu2+ , 2011 .

[8]  J. M. V. Rayner,et al.  Linear relations in biomechanics: the statistics of scaling functions , 2009 .

[9]  Michel Herbin,et al.  Rapid and Brief Communication A 'no-threshold' histogram-based image segmentation method , 2002 .

[10]  A. Kappler,et al.  Organic carbon and reducing conditions lead to cadmium immobilization by secondary Fe mineral formation in a pH-neutral soil. , 2013, Environmental science & technology.

[11]  Noël Bonnet,et al.  Artificial intelligence and pattern recognition techniques in microscope image processing and analysis , 2000 .

[12]  A. Kappler,et al.  Abiotic oxidation of Fe(II) by reactive nitrogen species in cultures of the nitrate‐reducing Fe(II) oxidizer Acidovorax sp. BoFeN1 – questioning the existence of enzymatic Fe(II) oxidation , 2013, Geobiology.

[13]  Bertram Manz,et al.  Advanced imaging techniques for assessment of structure, composition and function in biofilm systems. , 2010, FEMS microbiology ecology.

[14]  J. Aten,et al.  Measurement of co‐localization of objects in dual‐colour confocal images , 1993, Journal of microscopy.

[15]  Noël Bonnet,et al.  Preliminary investigation of two methods for the automatic handling of multivariate maps in microanalysis , 1995 .

[16]  F. Guyot,et al.  Extracellular Iron Biomineralization by Photoautotrophic Iron-Oxidizing Bacteria , 2009, Applied and Environmental Microbiology.

[17]  Steven Mills,et al.  Colocalization of fluorescent markers in confocal microscope images of plant cells , 2008, Nature Protocols.

[19]  Paul Heinz Müller,et al.  Tafeln der mathematischen Statistik , 1973 .

[20]  F. James Rohlf,et al.  Biometry: The Principles and Practice of Statistics in Biological Research , 1969 .

[21]  T. Tyliszczak,et al.  Fate of Cd during microbial Fe(III) mineral reduction by a novel and Cd-tolerant Geobacter species. , 2013, Environmental science & technology.

[22]  F. Fujiyama,et al.  Transiently increased colocalization of vesicular glutamate transporters 1 and 2 at single axon terminals during postnatal development of mouse neocortex: a quantitative analysis with correlation coefficient , 2007, The European journal of neuroscience.

[23]  N. Boisset,et al.  Preservation of protein globules and peptidoglycan in the mineralized cell wall of nitrate‐reducing, iron(II)‐oxidizing bacteria: a cryo‐electron microscopy study , 2011, Geobiology.

[24]  J. McDonald,et al.  Statistical tests for measures of colocalization in biological microscopy , 2013, Journal of microscopy.

[25]  David B. Williams,et al.  Transmission Electron Microscopy: A Textbook for Materials Science , 1996 .

[26]  A. Kappler,et al.  Fe(III) mineral formation and cell encrustation by the nitrate‐dependent Fe(II)‐oxidizer strain BoFeN1 , 2005 .

[27]  A. Hitchcock,et al.  Soft X‐ray spectro‐tomography study of cyanobacterial biomineral nucleation , 2009, Geobiology.

[28]  G. Lawes,et al.  Scanning Electron Microscopy and X-Ray Microanalysis , 1987 .

[29]  Andreas Kappler,et al.  Linking environmental processes to the in situ functioning of microorganisms by high-resolution secondary ion mass spectrometry (NanoSIMS) and scanning transmission X-ray microscopy (STXM). , 2012, Environmental microbiology.

[30]  M Lerotic,et al.  Cluster analysis of soft X-ray spectromicroscopy data. , 2003, Ultramicroscopy.

[31]  Gary Chinga,et al.  Quantification of paper mass distributions within local picking areas , 2007 .

[32]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[33]  Benjamin Schmid,et al.  A high-level 3D visualization API for Java and ImageJ , 2010, BMC Bioinformatics.

[34]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

[35]  Sylvain V Costes,et al.  Automatic and quantitative measurement of protein-protein colocalization in live cells. , 2004, Biophysical journal.

[36]  Ullrich Köthe,et al.  Ilastik: Interactive learning and segmentation toolkit , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[37]  C. Jacobsen,et al.  Rapid and Accurate Analysis of an X-Ray Fluorescence Microscopy Data Set through Gaussian Mixture-Based Soft Clustering Methods , 2013, Microscopy and Microanalysis.

[38]  Michel Herbin,et al.  A 'no-threshold' histogram-based image segmentation method , 2002, Pattern Recognit..