English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Look@NanoSIMS - a tool for the analysis of nanoSIMS data in environmental microbiology

MPS-Authors
/persons/resource/persons210683

Polerecky,  L.
Permanent Research Group Microsensor, Max Planck Institute for Marine Microbiology, Max Planck Society;

/persons/resource/persons210216

Adam,  B.
Department of Biogeochemistry, Max Planck Institute for Marine Microbiology, Max Planck Society;

/persons/resource/persons210612

Milucka,  J.
Department of Biogeochemistry, Max Planck Institute for Marine Microbiology, Max Planck Society;

/persons/resource/persons210622

Musat,  N.
Department of Molecular Ecology, Max Planck Institute for Marine Microbiology, Max Planck Society;

/persons/resource/persons210827

Vagner,  T.
Department of Biogeochemistry, Max Planck Institute for Marine Microbiology, Max Planck Society;

/persons/resource/persons210556

Kuypers,  M. M. M.
Department of Biogeochemistry, Max Planck Institute for Marine Microbiology, Max Planck Society;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Polerecky, L., Adam, B., Milucka, J., Musat, N., Vagner, T., & Kuypers, M. M. M. (2012). Look@NanoSIMS - a tool for the analysis of nanoSIMS data in environmental microbiology. Environmental Microbiology, 14(4), 1009-1023.


Cite as: http://hdl.handle.net/21.11116/0000-0001-C851-2
Abstract
We describe an open‐source freeware programme for high throughput analysis of nanoSIMS (nanometre‐scale secondary ion mass spectrometry) data. The programme implements basic data processing and analytical functions, including display and drift‐corrected accumulation of scanned planes, interactive and semi‐automated definition of regions of interest (ROIs), and export of the ROIs' elemental and isotopic composition in graphical and text‐based formats. Additionally, the programme offers new functions that were custom‐designed to address the needs of environmental microbiologists. Specifically, it allows manual and automated classification of ROIs based on the information that is derived either from the nanoSIMS dataset itself (e.g. from labelling achieved by halogen in situ hybridization) or is provided externally (e.g. as a fluorescence in situ hybridization image). Moreover, by implementing post‐processing routines coupled to built‐in statistical tools, the programme allows rapid synthesis and comparative analysis of results from many different datasets. After validation of the programme, we illustrate how these new processing and analytical functions increase flexibility, efficiency and depth of the nanoSIMS data analysis. Through its custom‐made and open‐source design, the programme provides an efficient, reliable and easily expandable tool that can help a growing community of environmental microbiologists and researchers from other disciplines process and analyse their nanoSIMS data.