24
Resolution and Image Quality
Metrics
Candidate brightness: This is related to the number of photons per candidate molecule. If the dye
blinking density is high (non-sparse) a large tail or second peak can be seen at higher signal counts.
Localisations per Frame: In sparse blinking samples illuminated with constant laser power a gradual
decline in accepted localisations will be seen (as above). A sudden drop in localisation number can be a
result of focus drift or inappropriate change in illumination conditions, resulting in either many
overlapping signals (non-sparse) blinking or no blinking at all. A gradually increasing accepted
localisation number tends to indicate a transition from too dense to sparse blinking as more dyes are
bleached.
Pixel Widths (PSF Sigma): Represents the width of each candidate position in the raw image. Assuming a
pixel size of 160 nm and using Alexa 647 or similar this value should be at 1.3. Larger values are
indicative of spherical aberration (check the glass thickness, immersion oil and objective lens correction
collar), defocus (poor focusing and/or a sample with fluorophores at variable Z positions, or too high
blinking densities resulting in mislocalisations).
Thompson Localisation Precisions: Is generated from every localisation and is based on the emitted
photons (signal counts calibration). A large peak between 15-30 nm should be seen in good quality
datasets.
Greyed out regions show data that has been excluded from the reviewed super-resolution image. Large
differences in row and column directions are indicative of high density blinking (non-sparse) in samples
with orientation such as actin filaments or microtubules.
Rees et al. Opt. Nanoscopy 1(1), 12 (2012).
Alternative approaches to quantifying resolution are explained by Ram et al, PNAS, 2005,
doi10.1073pnas.0508047103 and Nieuwenhuizen et al., Nature Methods, 2013, doi:10.1038/nmeth.2448
27
Precision Limit: This is an estimate of the image resolution based on the signal counts calibrated to
photons from the raw data. In this case it is a guide line for the best case ability to discriminate two
objects as being separate. If certain parts of the raw images contained high background signal these
areas will have worse resolution than indicated. Areas with much higher contrast than the rest of the
image will have better resolution. This resolution number does not account for labelling size (in the
case of antibody labelling up to 15 nm of distance may be added between fluorophore and molecule of
interest). It also does not account for any drift during the image acquisition.
Mean Precision Estimate: This is based on the calculated Thompson localisation precisions.
Number of accepted localisations: The number of data points in the final image. Being more stringent
with quality control criteria will reduce the number of localisations in the final image and can lead to a
pointillist (dotty) image. In other words, it has been undersampled. 1 localisation – 1 blink. If that blink
is spread out across more than one frame it will get localised in each one, i.e. no time-based
assessments are made.
Overall image quality is dependent on:
Structures of interest being well labelled – i.e. there is fluorophore attached to most or all of the
molecules of interest
Accepted localisation number – a sufficient number of those labelled molecules have been imaged
Mean precision estimate – each of those molecules has been imaged well enough to be accurately
positioned (a function of photons against background and being in focus)
Drift – minimal or no movement of the sample in relation to the objective lens throughout the image
acquisition
Visualisation – an appropriate pixel size in simple histogram visualisation method (recommended to
use a value the same as the mean precision estimate value). This does not apply when using the
jittered histogram method, which is better in most cases than the simple histogram.
Resolution and Image Quality
Metrics
For more on this see Rees et al, Journal of Optical Nanoscopy, 2012
25
Comparison of Visualisation
methods
Jittered histogram
Simple histogram
(25 nm pixels)
Simple histogram
(40 nm pixels)
Simple histogram
(10 nm pixels)
Simple histogram
(25 nm pixels)
In localisation microscopy, a visualisation method is used to convert the localised positions into a
reconstructed image of the underlying specimen. Several different visualisation algorithms exist
[Baddeley, 2010, Microsc. Microanal. 16 64–72], and the best of these tend to be based on Density
Estimation Theory [Silverman, Density Estimation for Statistics and Data Analysis, CRC Press 1985].
A ‘Simple Histogram’ visualisation reconstructs an image in which the brightness of each of its pixels is
proportional to the number of localisations that fall within it. This benefits from simplicity, but requires
an optimal choice of pixel size to be made by the user, and also suffers from arbitrary variation due to
pixel edge-position.
The method of Gaussian Rendering (Baddeley) plots each localisation as a smooth Gaussian ‘bump’ of
density – the width of each bump may be optimally scaled (by ‘Adaptive Kernel Density Estimation’) to
suit the precision and density of localisations.
The ‘Jittered Histogram’ visualisation [Kricek Opt. Express 19 3226–35] is effectively a digitised form of
Gaussian rendering, and in rainSTORM the Jittered Histogram visualisation does employ a form of
adaptive width smoothing.
15
Box Tracking and Fiduciary Drift
Correction : Detecting Drift
(1) Process the data review and save
images as described
(2) Using the zoom tool identify a region of
interest with distinctive structure (in this
case what looks like a thick actin filament)
(3) Press Box Tracking & highlight an ROI
with the cross hairs on the reviewed image
(4) Wait a few seconds and a Boxed
Positions image will appear, with
localisations colour coded as a function of
frame number (ie. time).
For more see Metcalf et al, JoVE, 2013
A displacement of the different colours is an indication that there may be drift present in the image
21
Box Tracking and Fiduciary Drift
Correction: Drift Correction
(2) Using the zoom tool identify a fiducial marker
(3) Press “ ox Tracking” & highlight the
bead with cross hairs
(4) Wait a few seconds and a “ oxed
Positions” image will appear, with
localisations colour-coded as a
function of frame number (i.e. time).
(5) Press “Set Anchor”
(6) Press “Subtract Drift”
(7) Run Reviewer to generate a new drift
corrected image (without the bead included)
(8) To remove other beads from the image use
box tracking to highlight a bead then press
“Delete oxed” to remove it. Repeat for
additional beads and then run reviewer a final
time.
For more see Metcalf et al, JoVE, 2013
(1) Process the data review and save images as
described
32
Optical Offset Evaluation and
Correction
(3) Process channel 1 (eg. red), review and save
image.
(1) Add Tetraspeck beads (or similar) with dyes of
appropriate wavelengths using the same type of
glass as the sample.
(2) Take a single image at each relevant
wavelength (with appropriate laser lines and
filters) for example 640 nm excitation for a ‘red’
channel and 561 nm for a ‘green’ channel
(4) Press “ apture Ch1”
(7) Press “Eval Ch2” offset
(8) Capture the red channel from the sample, process, review and save data.
(9) Capture the green channel from the sample, process and review.
(10) Press “Subt Ch2 offset”, run reviewer and save data (this channel has now been corrected with
respect to Ch1).
This chromatic offset
correction can be
applied to all images
acquired at the same
wavelengths so long as
there are no changes to
detection path of the
microscope. For more
see Erdelyi et al., Optics
Express, 2013
(6) Press “ apture Ch2”
(5) Process channel 2 (eg. green), review and save image.
Tetraspeck beads before and after Offset Correction
Before
After
11
Batch Processing
Only available in the MATLAB (not compiled) version of rainSTORM
(1) Process a file, review and save as normal. All subsequent batch processed files
will be processed using the same settings as this one.
(2) In MATLAB, open file and select rainSTORM_extras_batch process.m
(3) In the editor window click run and then navigate to a folder containing tif files
to be batch processed. Select a file.
(4) rainSTORM will now process all of the files in that folder. If not parallel
processing a waitbar will be displayed for each image. Make sure the computer
doesn’t go into screen saver or auto-logout modes as the histogram and on screen
images will not be properly saved.
6
Particle Tracking
In development
(1) Process a file, review and save as normal.
(2) In MATLAB, open file and select rainSTORM_extras_TrajectoryFitting.m
(3) In the editor window click run and trajectory images will be generated
Only available in the MATLAB (not compiled) version of rainSTORM
2
3D Astigmatism
In development
Documents you may be interested
Documents you may be interested
