The document discusses fluorescence microscopy techniques including fluorescence fluctuation spectroscopy (FFS) and fluorescence lifetime imaging microscopy (FLIM). FFS techniques like fluorescence correlation spectroscopy (FCS) and photon counting histogram (PCH) analysis provide information about molecule dynamics and concentrations in samples. FLIM is used to measure parameters in cells like oxygen concentrations, ion levels, and biochemical reactions. FRET measurements with FLIM can detect molecular interactions on the nanoscale by measuring donor fluorescence lifetime changes.

2. Alba FFS /FLIM confocal microscope
Applications in Biology

3. Cell Images

4. Moving from Images to Spectroscopy

5. Confocal microscopy: FFS & FLIM
Alba confocal microscope and FFS/FLIM spectrometer

6. Alba, 4-channel unit schematics

7. What is
Fluorescence Fluctuations Spectroscopy (FFS)
 FCS (Magde, Elson and Webb, 1972)
(
 PCH (Chen et al., 1999)
(
 FIDA (Kask et al., 1999)
 FCA (Mueller, 2004)
(
 ICS (Peterson et al. 1993)
 RICS (Digman et al. 2005)
 N & B (Digman et al., 2008)

8. What can cause a fluctuation in the fluorescence?
Diffusion
• Number of fluorescent
molecules in the volume of Flow
observation
• Diffusion or binding
DNA Conformational
• Conformational Dynamics Fluctuation
• Rotational Motion
• Protein Folding Rotation
• Blinking
• And many more Chemical Reactions
Binding

9. The Signal
Role of the confocal volume
Detected Intensity (kcps)
19.8
19.6
19.4
t5 19.2
t3
19.0
18.8
t2 0 5 10 15 20 25 30 35
Time (s)
t4
t1

10. How to extract the information about the fluctuations and
their characteristic time?
Distribution of the duration of the fluctuations
Distribution of the amplitude of the fluctuations
To extract the distribution of the duration of the
fluctuations we use a math based on calculation of
the correlation function
To extract the distribution of the amplitude of the
fluctuations, we use a math based on the PCH distribution

11. The definition
δF(t)δF(t + τ )
δF (t ) = F (t ) − F (t ) G( τ) = 2
F(t)
3
26x10
Fluorescence
24
Photon Counts
22
20
18
16
14
time
12
0 5 10 15 20 25 30 35
Time
τ Average Fluorescence
t t+τ

12. Data Presentation and Analysis
Autocorrelation function: <N> and D
PCH parameters: <N> and ε
<N>: no. of particles in the observation
volume
D: diffusion coefficient
ε: brightness

13. Autocorrelation Adenylate Kinase -EGFP
Chimeric Protein in HeLa Cells
Fluorescence Intensity
Examples of different HeLa cells transfected with AK1-EGFP
Examples of different HeLa cells transfected with AK1b -EGFP
Qiao Qiao Ruan, Y. Chen, E. Gratton, M. Glaser & W. Mantulin; Biophys. J. 83 (2002)

14. Autocorrelation of EGFP & Adenylate Kinase -EGFP
EGFP-AK in the cytosol
D=13 µm2/
G(τ)
s
EGFP-AKβ in the cytosol
EGFPsolution
D=87 µm2/
s EGFPcell
Time (s)
Normalized autocorrelation curve of EGFP in solution (•), EGFP in the cell (• ), AK1-EGFP in
the cell(•), AK1b-EGFP in the cytoplasm of the cell(•).

15. Autocorrelation of Adenylate Kinase –EGFP
on the Membrane
Clearly more than one diffusion time
D1= 13 µm2/s
D2=0.23 µm2/s
A mixture of AK1ß-EGFP in the plasma membrane of the cell.

16. Using PCH
Using PCH
20
Fluorescent
15
Monomer:
k (counts)
10
Intensity = 115,000 cps
5
0
0 0.1 0.2 0.3 0.4 0.5
Time (sec)
20
15
Aggregate:
k (counts)
10
5
Intensity = 111,000 cps
0
0 0.1 0.2 0.3 0.4 0.5
Time (sec)

17. Photon Count Histogram(PCH)
1.E+05 1.E+05 1.E+05
log(occurences)
1.E+04 1.E+04 1.E+04
log(occurences)
log(occurences)
1.E+03 1.E+03 1.E+03
1.E+02 1.E+02 1.E+02
1.E+01 1.E+01 1.E+01
1.E+00 1.E+00 1.E+00
0 3 6 9 12 15 18 21 24 0 3 6 9 12 15 18 21 24 0 3 6 9 12 15 18 21 24
k (counts) k (counts) k (counts)
Can we quantitate this?
What contributes to the distribution of intensities?

18. Complementary information obtained with the same
measurement
Using Autocorrelation Using Photon Counting
function (FCS): Histogram (PCH) Analysis:
 Diffusion Coefficient  Brightness
(diffusion times)
 No. of particles in observation
 No. of particles in observation volume
volume

19. What about … ?
• In a cell we may want to locate the areas where we have clusters
of molecules versus single molecules.
• In cells, both the concentration and the clustering of proteins can
differ in various locations and change during biological processes.
• Number & Brightness (N&B) measures the average number of
molecules and brightness in each pixel of an image.

20. TheThe Number & Brightness(N&B) analysis
Number and Brightness (N&B) analysis
Purpose: Provide a pixel resolution map of molecular number and
aggregation in cells
Method: First and second moment of the fluorescence intensity
distribution at each pixel
Source: Raster scanned image obtained with laser scanning microscopes
Output: The N and B maps, B vs intensity 2D histogram

21. How to distinguish pixels with many dim molecules from
pixels with few bright molecules?
at pixel k at pixel j
∑k
19.8 19.8
Intensity (kcps)
Intensity (kcps)
Average i
19.6
19.4
k
19.6
19.4
(first moment) < k >= i 19.2 19.2
K 19.0
18.8
0 5 10 15 20 25 30 35
19.0
18.8
0 5 10 15 20 25 30 35
Time (s) Time (s)
Variance
∑ ( k − < k >) 2
Frequency
Frequency
(second moment) i
σ2 = i
σ
K
19.4 khz 19.4 khz
• Given two series of equal average, the larger is the variance, the less molecules
contribute to the average. The ratio of the square of the average intensity (<k>2) to
the variance (s2) is proportional to the average number of particles <N>.
* Originally developed by Qian and Elson (1990) for solution measurements.

22. Calculating protein aggregates from images
This analysis provides a map of <N> and brightness (B) for every pixel in the image. The
units of brightness are related to the pixel dwell time and they are “counts/dwell
time/molecule”.
∑k i ∑ ( k − < k >)
i
2
< k >= i
σ2 = i
K K
<k > σ 2
B= =
<N > <k >
< k >2
< N >=
σ2 tim
e
s2 = Variance
<k>= Average counts
N = Apparent number of molecules
B = Apparent molecular brightness
K = # of frames analyzed

23. Selecting the dwell time
To increase the apparent brightness we could increase the dwell time, since the
brightness is measured in counts/dwell time/molecule.
Increasing the dwell time decreases the amplitude of the fluctuation.
1 µs dwell time 1 ms dwell time

24. What contributes to the variance?
Variance due to particle number fluctuations σ =ε n
2
n
2
Variance due to detector shot noise σ d = εn
2
The measured variance contains two terms, the variance due to the particle number
fluctuation and the variance due to the detector count statistics noise.
These two terms have different dependence on
the molecular brightness:
σ 2 = σ2 + σ2
n d
σ n = ε 2n
2
σ d = εn
2
(for the photon counting detector)
Both depend on the intrinsic brightness and the number of molecules.
We can invert the equations and obtain n and e
n is the true number of molecules
e is the true molecular brightness

25. How to Calculate n and ε
σ2 σn σd ε 2n σ d
2 2 2
B= = + = + = ε +1
< k > < k > < k > εn < k >
This ratio identifies pixels of different brightness due to mobile particles.
The “true” number of molecules n and the “true” molecular brightness for
mobile particles can be obtained from
< k >2 σ 2− < k >
n= 2 ε=
σ −<k > <k >
If there are regions of immobile particles, n cannot be calculated because for the
immobile fraction the variance is = <k>. For this reason, several plots are offered
to help the operator to identify regions of mobile and immobile particles.
Particularly useful is the plot of NvsB.

26. The effect of the immobile part: with photon counting
detectors (Fluorescent beads in a sea of 100nM Fluorescein).
Intensity B map Monomer-octamer series x= 2.00000 y= 2.00000 #pixels= 0 in= 0
4
3.5
3
Variance/ntensity
2.5
2
4 1.5
3
Selecting 1 Immobile
fluorescein
2
0.5
B
1
0
0
0 1 2 3 4
100 200 300 intensity
intensity
4
3 Selecting
2
beads
B
1
0
100 200 300
intensity

27. Paxillin assembles as monomers and disassembles as
aggregates as large as 8-12
x= 1.90057 y= 1.03400 #pixels= 5702 in= 662
Average intensity Molecular Brightness
1.3
Variance/ntensity
1.2
1.1
1
0.9
1 2 3
intensity
x= 1.88378 y= 1.27400 #pixels= 716 in= 1
1.3
Variance/ntensity
1.2
1.1
1
0.9
1 2 3
intensity
Selected Monomers Selected >5mers
(Digman, M.A., et al, Biophys. J. 2008 Mar 15;94(6):2320-32)

28. Moment Analysis Provides Measurements of Oligomerization
(Courtesy of Dr. Joel Schwartz)

29. What about Fluorescence Lifetime Imaging?
FLIM is extensively used for the measurement of parameters in
cells; for instance:
• O2 concentrations in cells or in tissues
• Intracellular mapping of ion concentration and pH imaging
• Biochemical reactions (oxidation/reduction) processes
NAD and NADH
• FRET – Dynamic interactions of molecular species on the
nm scale

30. Perrin-Jablonski diagram

31. Applications of FRET
FRET strongly depends on the distance between the donor and acceptor:
Förster calculated the rate of transfer to be
6
1  R0 
kT =  R
τD  
tD is the lifetime of the donor in the absence of the acceptor.
R is the distance between the two groups
Ro is called the Förster distance
τD
E =1−
τA

32. FRET
Images of opossum kidney cells
expressing a CFP (left column) and
CFP/YFP tandem protein (right
column).
Top: intensity
Bottom: phase lifetime
Left: 2.1 ns; Right: 1.5 ns
Images obtained in frequency domain: Ti-Sapphire
– 2-photon exc. at 850 nm
The images in channel 1 were collected through a
520 – 560 nm YFP filter and in channel 2 through a
460 – 500 nm CFP filter.
Courtesy of Dr. Moshe Levi

33. Applications to Cerulean-Venus pairs FRET
Cerulean Cerulean 32-venus Cerulean 17-venus
τ = 3.06 ns τ = 2.63 ns τ = 2.2 ns
E = 14% E = 27%
Courtesy of Dr. A. Periasamy

34. Polar Plots Analysis
C17v
2.2 ns
C32v
2.6 ns

35. Summary FFS
• FCS provides the dynamics and the <N> in the observation
volume.
• PCH provides the brightness and the <N> in the
observation volume.
• N&B distinguishes between number of molecules and
molecular brightness in the same pixel. The immobile
fraction can be separated since it has a brightness value =1.

36. Summary FLIM
• Lifetime measurements provide a powerful tool for the
characterization of several processes in materials and life sciences
• The new DFD approach makes FLIM in cells faster and more
sensitive
• The Polar Plot approach greatly simplifies the data analysis