Sub-diffraction error mapping for localisation microscopy images

Richard J. Marsh, Ishan Costello, Mark-Alexander Gorey, Donghan Ma, Fang Huang, Mathias Gautel, Maddy Parsons and Susan Cox ()
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Richard J. Marsh: King’s College London
Ishan Costello: King’s College London
Mark-Alexander Gorey: King’s College London
Donghan Ma: Purdue University
Fang Huang: Purdue University
Mathias Gautel: King’s College London
Maddy Parsons: King’s College London
Susan Cox: King’s College London

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract Assessing the quality of localisation microscopy images is highly challenging due to the difficulty in reliably detecting errors in experimental data. The most common failure modes are the biases and errors produced by the localisation algorithm when there is emitter overlap. Also known as the high density or crowded field condition, significant emitter overlap is normally unavoidable in live cell imaging. Here we use Haar wavelet kernel analysis (HAWK), a localisation microscopy data analysis method which is known to produce results without bias, to generate a reference image. This enables mapping and quantification of reconstruction bias and artefacts common in all but low emitter density data. By avoiding comparisons involving intensity information, we can map structural artefacts in a way that is not adversely influenced by nonlinearity in the localisation algorithm. The HAWK Method for the Assessment of Nanoscopy (HAWKMAN) is a general approach which allows for the reliability of localisation information to be assessed.

Date: 2021
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DOI: 10.1038/s41467-021-25812-z

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