21st International Workshop on“Single Molecule Spectroscopy and Super-resolution Microscopy in the Life Sciences”

Filling the usability gap:
Bioinformatics solutions for Image-Scanning Microscopy,
Stochastic Optical Fluctuation Imaging, and Surface Single
Molecule Experiments
Dirk Hähnel
III. Institute of Physics – Biophysics
Georg-August-University Göttingen
21st International Workshop on
“Single Molecule Spectroscopy and Super-resolution
Microscopy in the Life Sciences”
Berlin 4th September 2015

2
Making it easy and user-friendly?
6. price < 10thd. USD
1. physicist
2. chemicist
3. artifacts
4. image stacks
5. dynamical biosystems
CSDISMRequirements
Palm
Storm
SIM
SSIM
Sted Tirf SOFI

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Image scanning microscopy
Schulz, O., Pieper, C., Enderlein, J. et.al. Resolution doubling in fluorescence microscopy with
confocal spinning-disk image scanning microscopy. PNAS vol.110 no. 52 21000-21005 (2013).
Müller, C. B. & Enderlein, J. Image Scanning Microscopy. Phys. Rev. Lett. 104, 1–4 (2010).

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Critical timing requirements
spin-disc
laser
camera

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CSDISM micromanager plugin
micromanager
• calibration
• acquisition parameter setting
real time trigger
• laser
• ccd

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Fast CSDISM
Imaging of dynamical biological systems:
acquisition time > image reconstruction
real time trigger
• laser
• ccd
• live image reconstruction
micromanager
• calibration
• acquisition parameter setting

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Calibration
nonlinear LVM fit
coordinates (z,y,x)
for
image reconstruction
few seconds later

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Fast image reconstruction routing
benchmark:
image
reconstruction
logic Circuit ~5MHz
theoretically up to
45 parallel cameras
possible
Or
frame n-1
frame n
frame n+1
intermediate image iteration with 90Mhz/Pixel
• no memory swapping
• virtual circuit routing
very low key
FPGA frame
grabber + logic
hardware
becomes

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Integration requirements
2015 coming soon:
• beta software
• setup recipe
• double resolution
• complexity blackbox

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Stochastic optical fluctuation imaging
Dertinger, T., Enderlein, J., et.al. Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI)
Vol. 106 no. 52 PNAS (2009).
physical challenges:
1. subpixel resolution
2. linearize brightness
implementation challenges
1. timing / speed
2. memory allocation
3. cumulants computation

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Physical: sub pixel resolution challenge
avg. of movie SOFI fSOFI (4x)
Rat hippoc. Neuron(Neuro-transmitter receptor subunit GABABR1) blinking QDot525
real space
fourier space real space

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Physical: brightness challenge
linearized fSOFI
deconvolution
2nd order fSOFI image
(4x pixel)
average
image
Rat hippoc. neuron, neuro-transmitter receptor subunits GABABR1 : QD525
(green), GABABR2: QD625(red)

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Implementation: timing challenge
acquisition
image reconstruction
final SOFI image
acquisition image reconstruction
Final SOFI Image
Imaging today: no dynamics Imaging dynamical biological processes
live imaging
real time reconstruction
𝑡 𝑎𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛 ≥ 𝑡 𝑟𝑒𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛

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Implementation: memory challenge
subpixel = more gates
subpixel = more time
cumulants => data swapping
linearization => very complex
start
input frame
SCMOS input:
3 Gpixel => SOFI image
1,5 GByte => SOFI image
SCMOS subpixel:
12 Gpixel => SOFI image
6 GByte => SOFI image
…
end

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Implementation: cumulants computation challenge
challenge:
memory space < data 3D stack
𝑛 𝑡ℎ
cumulants are 𝑛 𝑡ℎ
moments
corrected by lower order moments
Tremendous matrix operations

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Integration and development roadmap
camera link
FFT
cumulants
linearization
micromanager
integration
coming soon
integration
• standard FPGA card
• beta software coming soon
• setup recipe
• complexity blackbox

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Manually: single molecule measurements on surfaces
physical challenge:
• drift
• bleaching
implementation challenges:
• drift compensation
• photon economy
• repositioning
stage
coverslide
objective
lense
PMC AOTF Laser
APD 1
APD 3
APD2
APD4
Z(r) Y(r)
X(r)
position
signal
sample

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Objective: single molecule sample vs. urban environment
Copyright Berliner Zeitung

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Automated: single molecule measurements on surfaces
measurement:
• photon counting
• orientation
• imaging
• …
positioning:
autofocus
excitation:
• intensity
• polarization
• exposure
• …
TCSPC defocused image
multiple
events

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Implementation: drift challenge
time (minutes)
drift(um)
lateral
axial
max error:
lateral 6,7nm
axial 7,8 nm
mean:
lateral 0,6 nm
axial 0,1 nm

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Implementation: photon economy and repositioning challenge
reproducible measurements:
identical orientation pattern
on a coordinate for different
measurement events.
180nm 180nm
180nm 180nm
180nm 180nm 180nm
1…30

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Acknowledgements

21st International Workshop on“Single Molecule Spectroscopy and Super-resolution Microscopy in the Life Sciences”

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