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Quantitative Fluorescence Microscopy

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Quantitative Fluorescence Microscopy Ana Gonz lez Wusener Instituto de Investigaciones Biotecnol gicas IIB-INTECH Universidad Nacional de General San Mart n – PowerPoint PPT presentation

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Title: Quantitative Fluorescence Microscopy


1
Quantitative Fluorescence Microscopy
  • Ana González Wusener
  • Instituto de Investigaciones Biotecnológicas
    IIB-INTECH
  • Universidad Nacional de General San Martín

2
Protein functions are regulated in an integrated
network, which is the result of the integration
of protein transport, posttranslational
modifications and specific interactions, which
occurs in different subcellular compartments at
different time scales...
The spatial localization of a protein in the cell
is the first step to integrate it in the complex
cell network.
3
The light microscope has been used to document
the localization of fluorescent molecules in cell
biology research. With advances in digital
cameras and the discovery and development of
genetically encoded fluorophores, there has been
an increase in the use of fluorescence microscopy
to quantify spatial and temporal measurements of
fluorescent molecules in biological specimens.
What information is present in a fluorescence
microscopy digital image?
The intensity value of a pixel is related to the
number of fluorophores present at the
corresponding area in the specimen. We can use
digital images to extract two types of
information from fluorescence microscopy images
(1) spatial, which can be used to calculate
distances, areas, and velocities
(2) intensity, which can be used to determine the
local concentration of fluorophores in a specimen.
4
Signal, background, and noise
In quantitative fluorescence microscopy, we want
to measure the signal coming from the
fluorophores used to label the object of interest
in our specimen.
Background adds to the signal of interest
Intensity values in the digital image Signal
Background
!
To accurately and precisely measure the signal of
interest, background should be reduced as much as
possible, and must be subtracted from
measurements.
5
Noise causes variance in the intensity values
above and below the real intensity value of the
signal plus the background
To detect the presence of a signal, the signal
must be significantly higher than the noise level
of the digital image.
The precision of quantitative microscopy
measurements is limited by the signal-to-noise
ratio (SNR) of the digital image.
SNR affects spatial measurements and intensity
measurements
6
Maximizing signal The intensity of the signal in
digital fluorescence microscopy images is
affected by every step along the path to
quantitation, including
The specimen - Choose a brighter and more
photo-stable fluorophore - Fixed specimens
should be mounted in a glycerol-based mounting
medium that contains an anti-photobleaching
inhibitor
The microscope - Use illumination wavelengths
that will optimally excite the
fluorophore - The numerical aperture (NA) of
the objective lens is an important
determinant of the brightness of the optical
image - Spherical aberration is reduced by
mounting fixed specimens in a mounting
medium with a refractive index similar to that of
the immersion medium
7
The detector - Increasing the exposure time
allows the flux of photons coming from the
specimen to accumulate (as electrons) in the
detector, increasing the intensity values in
the image up to a point. Detectors have a
limited capacity to hold electrons if this
capacity is reached, the corresponding pixel
will be saturated and any photons reaching
the detector after saturation will not be
counted. The linearity of the detector is
lost.
Saturated images cannot be used for quantitation
of fluorescence intensity values
- Binning on the CCD chip increases the intensity
of the pixels without increasing readout noise,
resulting in a higher SNR digital image.
However, because the resulting pixels
represent a larger area of the specimen,
binning decreases the resolution of the digital
image.
8
Background fluorescence reduces dynamic range and
decreases SNR
Dynamic range of a CCD camera is defined as the
full well capacity of the photodiodes (i.e., the
number of photons that can be detected per pixel
before saturation) divided by the detector noise.
Photons from background sources fill the
detector, limiting the number of signal photons
that can be collected before the detector
saturates and effectively decreasing dynamic
range.
Detector noise
Thermal noise is caused by the stochastic
generation of thermal electrons within the
detector, and is largely eliminated by
cooling. Read noise is generated by the
amplifier circuitry used to measure the voltage
at each pixel, and is usually the dominant source
of noise in standard cooled CCD cameras designed
for quantitative imaging. Read noise is usually
expressed in the manufacturers technical
specifications as a number of electrons.
!
Noise is not a constant, so it cannot be
subtracted from a digital image.
9
Resolution In digital microscopy, spatial
resolution is defined by - the
microscope - the detector It limits our
ability to accurately and precisely locate an
object and distinguish close objects as separate
from one another
Resolution in the optical image. Distance by
which two objects must be separated in order to
distinguish them as separate from one another,
which is equal to the radius of the smallest
point source in the image (defined as the first
minimum of the airy disk) r (0.61)? NA
Resolution in the digital image. The resolution
of a digital image acquired with a CCD camera
depends on the physical size of the photodiodes
that make up the chip. The pixel size should be
at least two times smaller than the resolution
limit of the microscope optics, so that the
smallest possible object in the image (defined as
the diameter of the airy disk) will be sampled by
4 pixels.
10
  • Magnification decreases image intensity
  • Smaller pixels generally collect fewer photons

To compensate for loss of signal due to smaller
pixel size, longer camera exposure times or more
intense illumination may be necessary. If the
pixel size is too large, the optical image will
be under-sampled and detail will be lost in the
digital image.
11
Additional threats to accuracy and precision in
quantitative microscopy
Non-uniform illumination Uneven illumination can
be detrimental to quantitative measurements
because it may cause the intensity of an object
in one area of the field of view to measure
differently than the intensity of an object of
equal fluorophore concentration in another area
of the field of view.
To reduce uneven illumination, the wide-field
fluorescence microscope should be aligned.
12
Bleed-through Bleed-through of one fluorophores
emission through the filter set of another
fluorophore can occur when a specimen is labeled
with multiple fluorophores whose excitation and
emission spectra overlap.
Avoid bleed-through by carefully choosing
fluorophores and filter sets.
13
Photobleaching The rate of photobleaching is
specific to the fluorophore, its environment, and
the intensity of the illuminating light.
FITC phalloidin
AlexaFluor 488 phalloidin
Antiphotobleaching reagents can be added to the
mounting medium to reduce the rate of
photobleaching.
14
Image processing and storage Some types of image
processing and storage can change the relative
intensity values in a digital image, rendering
them unusable for quantitative measurements.
Analysis of pixel intensity values should be done
on raw images stored without further scaling or
processing, or on images that have been corrected
using methods that have been demonstrated to
preserve the linear relationship between photons
and image intensity values (e.g., flat-field
corrected 16-bit TIFF images are a good choice
for quantitation).
TIFF image
JPEG image
15
www.macbiophotonics.ca/downloads
16
Signal and background in an image LOD Limit of
Detection Pixels whose grey value is greater
than LOD are significantly different from
background (Bg) For fluorescent labeled cells,
background would be better measured in unlabeled
cells. If this is difficult to achieve, one can
measure background from an empty region.
LOD signal Bg average 3SD Bg
17
  1. File / Open
  2. Create an Area Selection in an empty region with
    the Rectangle Area Selection Tool
  3. Add the area selection to ROI (t)
  4. Plugins / ROI / BG Substraction from ROI

18
  1. Create a Line Selection
  2. Add the area selection to ROI (t)
  3. Analyze/ Plot Profile

Displays a two-dimensional graph of the
intensities of pixels along a line within the
image. The x-axis represents distance along the
line and the y-axis is the pixel intensity.
19
Threshold mask For quantitative analysis we must
only take significant pixels from the microscope
image. How can we select them Threshold mask
Defining a correct threshold is not an easy
issue. T.L. (Threshold level) Bg 3SDBg
Use this tool to set lower and upper threshold
values, segmenting the image into features of
interest and background. The thresholded features
are displayed in red and background is displayed
in grayscale. Image/Adjust/Threshold
20
  • Substract background
  • Create an Area Selection in an empty region with
    the Rectangle Area Selection Tool
  • Add the area selection to ROI (t)
  • Plugins / ROI / BG Substraction from ROI
  • Measure average fluorescence intensity of cell
    background
  • Create a Line Selection
  • Add the area selection to ROI (t)
  • Analyze/ Plot Profile
  • Copy and Paste in Microsoft Office Excel Book
  • Calculate average value
  • Threslhold Level Bg 3 x SDBg
  • Image/ Adjust / Threslhold
  • Edit / Selection / Create Selection
  • Add the created selection to ROI (t)

21
Colour Image processing Images can have colour
in three ways Pseudocolour A pseudo-coloured
image is a single channel, (i.e. grey) image that
has colour ascribed to it via a look up table
or LUT (palette, colour table). This is a table
of grey values (zero to 256 or 4095 whether 8-bit
or 12-bit grey) with accompanying red, green and
blue values. So instead of displaying a grey, the
image displays a pixel with a defined amount of
each colour. Differences in colour in the
pseudo-coloured image reflect differences in
intensity of the object rather than differences
in colour of the specimen that has been imaged.
Image/Lookup Tables/Green
22
24-bit RGB images The colours in RGB images
(24-bit, 8-bits for each of the red, green and
blue channels) are designed to reflect genuine
colours, i.e. the green in an RGB image reflects
green colour in the specimen, the differences in
intensity of the green reflects differences in
intensity of green in the specimen. Another
option would be to use Magenta rather than red in
red-green-blue merge.
RGB
Image/Colour/RGB Merge Plugins/Colour
Functions/Recolor RGB to MGB
MGB
23
  • Colour Composite Images
  • A colour composite handles colour images in
    'layers', which ImageJ calls "channels".
  • The advantages with this type of image over RGB
    images are
  • Each channel is kept separate from the others and
    can be turned on and off vial the 'Channels'
    dialog .
  • Each original channel can be kept as 16-bit.
  • More than 3 channels can be merged and kept
    separate.
  • The contrast and brightness of individual
    channels can be adjusted after merging.
  • Image/Colour/Make Composite

24
Merging multi-channel images
RGB colour merging The ImageJ function
Image/Colour/RGB merge can be used to merge red,
green and/or blue channel images or Image
Stacks This reduces 16-bit images to 8-bits
(based on the current Brightness and Contrast
values) then generates a 24-bit RGB image. An
alternative to the normal Red-Green merge is to
merge the images based on Cyan and Magenta, or
Cyan-Yellow or any other colour
combination.  Plugins/Colour Functions/Colour
merge
25
Merging transmitted light and fluorescence images
Fluorescence and transmitted light brightfield
images can be merged with the function
Plugins/Colour Functions/RGB-Grey Merge
26
Splitting multi-channel Images
RGB 24-bit An RGB image or stack can be split to
the respective red, green and blue image
components using the menu command Image / Colour/
RGB split. Running this command with the Alt-key
down keeps the original RGB image/stack. The
plugin Plugins/Colour Functions/RGB to Montage
works with single slice RGB images. A new RGB
stack is created. Colour Composite The
composite can be reverted to a greyscale stack
via the menu command Image/Hyperstacks/Hyperstack
to Stack. The channels can be subsequently split
to individual images via the menu command
Image/Stacks/Stack to Images.
27
GFP-a actinina Vinculina (AlexaFluor 568)
28
GFP-a actinina Vinculina (AlexaFluor 568)
29
(No Transcript)
30
An adherent culture of Swiss mouse embryo cells
(3T3) was immunofluorescently labeled with
primary anti-vinculin mouse monoclonal antibodies
followed by goat anti-mouse Fab heavy and light
chain fragments conjugated to Cy3 (red emission).
In addition, the specimen was simultaneously
stained for DNA with the ultraviolet-absorbing
probe Hoechst 33342, and for the cytoskeletal
filamentous actin network with Alexa Fluor 488
conjugated to phalloidin.
The culture of A-10 myoblasts was
immunofluorescently labeled with anti-vinculin
mouse monoclonal primary antibodies followed by
goat anti-mouse IgG secondary antibodies
conjugated to Alexa Fluor 647 (pseudocolored
blue). In addition, the specimen was stained for
DNA with the ultraviolet-absorbing probe Hoechst
33342 (pseudocolored cyan), for the cytoskeletal
filamentous actin network with Alexa Fluor 488
conjugated to phalloidin, and for mitochondria
with MitoTracker Red CMXRos.
A culture of Indian Muntjac fibroblast cells was
transfected with a DsRed-Mitochondria plasmid
subcellular localization vector to target
cellular mitochondria. Stable transfectants were
isolated and grown into log phase before being
fixed, permeabilized, and labeled with DAPI and
Alexa Fluor 488 conjugated to phalloidin,
targeting DNA in the cell nucleus and the F-actin
cytoskeletal network, respectively.
The nuclei of embryonic Swiss mouse fibroblasts
in culture were targeted with the nucleic acid
probe DAPI. In addition, the cells were also
stained with Alexa Fluor 488 conjugated to
phalloidin (filamentous actin) and MitoTracker
Red CMXRos (mitochondria).
31
A culture of Swiss mouse embryo cells was
immunofluorescently labeled with primary
anti-vinculin mouse monoclonal antibodies
followed by goat anti-mouse Fab fragments
conjugated to Cy3 (yielding red emission). In
addition, the specimen was simultaneously stained
for DNA with the ultraviolet-absorbing probe
Hoechst 33342 (blue emission), and for the
cytoskeletal filamentous actin network with Alexa
Fluor 488 (green emission) conjugated to
phalloidin.
Immunofluorescence with mouse anti-alpha-tubulin
was employed to visualize distribution of the
microtubule network in a log phase monolayer
culture of African water mongoose skin cells. The
secondary antibody (goat anti-mouse IgG) was
conjugated to Alexa Fluor 568 and mixed with
Alexa Fluor 488 conjugated to phalloidin to
simultaneously image tubulin and the actin
cytoskeleton. Nuclei were counterstained with
Hoechst 33258.
Nuclei of 3T3 cells grown in culture were stained
with the fluorophore DAPI and imaged utilizing a
combination of fluorescence and phase contrast
illumination.
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