Quantitative imaging of living biological samples by Peak Force Tapping atomic force microscopy
Alexandre Berquand, Bruker Nano, August 17 2011
Why force measurements are essential in biology?
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• Mechanical properties of cells are determined by the dynamic behavior of their cytoskeleton.
• Alterations of the mechanical phenotype of the cell can lead to severe malfunctions or disease (cancer, malaria, neurodegeneration).
• Cancer cells are known to be softer than their normal homologues.
• AFM is the tool of choice to measure cells mechanical properties ex vivo and to correlate a change in mechanical properties with:
• Drug treatment
• Aging
• Pathology
AFM under physiological conditions
• Different types of perfusion systems to keep cells alive for a non-limited period of time:
Regular fluid cell
Perfusing Stage Incubator
Tapping Mode and Phase imaging
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• The phase shift just reflects the energy dissipated but is a contribution of several factors and is not quantitative
depends on AFM
parameters, surface and volume properties
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Force Spectroscopy
• Main drawbacks: slow, poor resolution and lack of information
distance (nm)
forc
e (
nN
)
0
1
2
-1
5000
Single force
Force volume
Stiffness (Young’s
modulus)
Adhesion
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Peak Force Tapping - principle
• Works with most standard AFM probes in the standard AFM cantilever holders.
• Z piezo is driven with sinusoidal waveform (not a triangle as in force-distance curves).
• Z drive frequency is 2 kHz (Catalyst 1 kHz). That’s far below the cantilever’s resonance.
• Z drive amplitude is fixed at typical value of 150 nm (300 nm peak-to-peak)
• Vertical motion of probe produces force-distance plots as it taps on the sample.
• Imaging feedback is based on the Peak Force of the force-distance curve.
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Peak Force Tapping - features
• SCANASYST:
• uses automatic image optimization technology
• simplifies and speeds up expert-level image acquisition
• PEAKFORCE QNM:
• generates quantitative maps of nanoscale material properties
• does this simultaneously during imaging at consistently low force and high resolution
• Data extraction:
PeakForce QNM - Calibration
• Relative method
• Calculate the defl. Sens.
• Calculate the spring constant
• Image a ref. sample and adjust the tip radius
• Adjust the deformation
• Absolute method
• Calculate the defl. Sens.
• Calculate the spring constant
• Image a tip check sample and measure the tip radius
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PeakForce QNM - Modulus measurement
Choose probe type according to range of expected modulus
Requirements:
Probe needs to deform sample (minimum: a few nm)
Probe needs to be deflected by sample (minimum a few nm)
2: Elasticity
3: Adhesion
• PeakForce QNM works in both air and liquid
• Relevant and quantitative contrast on all the channels
• Applications in liquids have not been as thoroughly explored:
• DNA, most of polymers: OK
• Cells?
Typical example: DNA
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Simon
Scheuring,
Physico-Chimie
Institut Curie ,
ScanAsyst lever, 0.4 N/m)
Scale bar
10 nm
Scheuring et al
Eur Biophys J
(2002)
Any compromise between measurement of mechanical properties and resolution?
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Sea water samples: imaging of frustules
• 1st time that such sample is imaged by AFM
• Very detailed contrast in Young’s modulus and deformation
• First image of living diatoms with PFT and PFQNM.
• YM of different parts:
• Fibulae ~200 MPa
• Silica stripes ~44 MPa
• Core matrix ~21 MPa
• …
Under press (Journal of Phycology)
Sea water samples: imaging of diatoms
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Imaging of E. coli K12
• Strain very hard to image by AFM because they move very fast when under stress
• b: 3d-height (10x10m) image of a necklace of living k12 acquired in 20 min.
• DMT modulus image of the same bacteria. Average Young’s modulus = 183 kPa
PFQNM study on human glioblastoma
U251-MG cells
(invasive)
1st site-specific
recombination:
Empty vector + GFP as
integration site
Selection of cells having
integrated the vector
2nd site-specific recombination:
Integration of expression vector
which carries the gene of interest,
inside the GFP site
Test with TP53 and PTEN
Possibly have ≠ mechanical properties
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PFQNM High Resolution images on glioblastoma - display 2 channels simultaneously
40x40 µm PF error image 3d-height + deformation skin
Topography (z: 0-250 pN) Elasticity (z: 0-1.2 MPa) Adhesion (z: 0-800 pN)
Deformation (z: 0-250 nm)
• 128x128 images (5 min per image): averaging on a high number of images
• Highly quantitative
• No damage of the sample
PFQNM Low Resolution images on glioblastoma - statistics
Elasticity (kPa)
0
20
40
60
80
100
120
140
Ctrl IND Ctrl non-
IND
tp53 non-
IND
tp53 IND pTEN non-
IND
pTEN IND
Deformation (nm)
0
50
100
150
200
250
Ctrl IND Ctrl non-IND tp53 non-
IND
tp53 IND pTEN non-
IND
pTEN IND
Young’s modulus (kPa)
Deformation (nm)
TP53 and PTEN induced are
significantly stiffer and less
deformable than the other
cell types
Results & Conclusion
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Imaging of living HaCaT and effect of Glyphosate
Cell under stress:
retracting &
synthesizing stress
fibers
[Glyphosate]
increase of YM by
factor 3
Adhesion much
higher between the
cells than on the
cells
Average dissipation
= 1.3 keV = 2.10-16 J
MIRO: Overlay optical and AFM data in a few clicks
3) Overlay optical and AFM
images1) Import optical image into
Nanoscope
2) Target a location for the
AFM scan
Hela HaCaT
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Combining MIRO and PFQNM
• a: overlay of fluorescence (nucleus + actin) and AFM (PF error + YM) images.
• b: PF error channel: 0-450 pN
• c: YM channel: 0-4 MPa
• d: deformation channel: 0-250 nm
• Offers nice perspectives in biology: correlate fluorescence and AFM signals simultaneously in response to drug treatment
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Typical samples and corresponding probes - Summary
Calibration of Young’s Modulus by Gelatin or Agarose: ~1 to 100 kPa
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Conclusions
• Since its development, Peak Force Tapping and PeakForce QNM have greatly improved to extend the range on biological samples
• Though it’s still not 100% quantitative for the softest samples, a very wide range of applications can be covered
• We are still working on expanding the range…
• Promising possibilities for recognition mapping with functionalized probes (still confidential)
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New Application Note released…
Acknowledgements (sample providers)
• Vesna Svetlicic, Tea Radic and Galja Pletikapic (Rudjer Boskovic Institute, Zagreb, Croatia)
• Gregory Francius (LCPME, Nancy, France)
• Andreas Holloschi, Leslie Ponce, Ina Schaeffer, Hella-Monika Kuhn, Petra Kioshis and Mathias Hafner (University of Applied Sciences, Mannheim, Germany)
• Laurence Nicod, Celine Caille and Celine Heu (Institut FEMTO-ST, Besancon, France)
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