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.
Highlights
Assessing the quality of localisation microscopy images is highly challenging due to the difficulty in reliably detecting errors in experimental data
When image processing is an integral part of the technique, as in single-molecule localisation microscopy (SMLM) methods[1,2], it is important to verify that the image processing is not producing errors or biases
We exploit the accuracy and reliability of Haar wavelet kernel analysis (HAWK) to identify potential artefacts in a localisation microscopy image produced without HAWK, and to indicate where HAWK preprocessing has reduced localisation precision sufficiently that underlying fine structure could have been made unresolvable
Summary
Assessing the quality of localisation microscopy images is highly challenging due to the difficulty in reliably detecting errors in experimental data. 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. A number of different types of artefact can be introduced by SMLM image processing, including missing/biased structure, blurring and artificial sharpening These artefacts occur when emitter fluorescence profiles overlap in the raw data and are incorrectly localised towards their mutual centre, introducing a bias that is often substantial when compared with the estimated localisation precision[3,4,5,6,7]. SQUIRREL’s optimisation process assumes all variation in the reference image is reflected in the density of localisations in the reconstruction, meaning that nonlinearity will be reported as errors
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
