Transcript Quantitative Fluorescence Microscopy

Quantitative Fluorescence Microscopy
Ana González Wusener
Instituto de Investigaciones Biotecnológicas IIB-INTECH
Universidad Nacional de General San Martín
“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.
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.
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.
!
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
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
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.
No camera binning
2x2 camera binning
4x4 camera binning
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 manufacturer’s
technical specifications as a number of electrons.
Noise is not a constant, so it cannot be subtracted from a digital image.
!
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)λ
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.
Resolution
Signal intensity
 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.
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.
Bleed-through
Bleed-through of one fluorophore’s 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.
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.
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
www.macbiophotonics.ca/downloads
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
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
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1. Create a Line Selection
2. Add the area selection to ROI (“t”)
3. Analyze/ Plot Profile
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Distance (pixels)
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Distance (pixels)
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Gray Value
Gray Value
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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.
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
1. Substract background
a) Create an Area Selection in an
empty region
with the
Rectangle Area Selection Tool
b) Add the area selection to ROI
(“t”)
c) Plugins / ROI / BG Substraction
from ROI
2. Measure average fluorescence
intensity of cell background
a) Create a Line Selection
b) Add the area selection to ROI
(“t”)
c) Analyze/ Plot Profile
d) Copy and Paste in Microsoft
Office Excel Book
e) Calculate average value
f) Threslhold Level = Bg + 3 x SDBg
3. Image/ Adjust / Threslhold
4. Edit / Selection / Create Selection
5. Add the created selection to ROI
(“t”)
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
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.
Image/Colour/RGB Merge
Plugins/Colour Functions/Recolor RGB to MGB
RGB
MGB
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:
1. Each channel is kept separate from the
others and can be turned on and off
vial the 'Channels' dialog .
2. Each original channel can be kept as
16-bit.
3. More than 3 channels can be merged
and kept separate.
4. The contrast and brightness of
individual channels can be adjusted
after merging.
Image/Colour/Make Composite
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
Merging transmitted light and
fluorescence images
Fluorescence and transmitted light
brightfield images can be merged with
the function:
Plugins/Colour Functions/RGB-Grey Merge
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.
GFP-α actinina
Vinculina (AlexaFluor 568)
GFP-α actinina
Vinculina (AlexaFluor 568)
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).
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.