MMICWorkshopDec112015

MMICWorkshopDec112015

Light Microscopy and

Digital Imaging Workshop

Matthew S. Savoian

[email protected]

December 11, 2015

Purpose:

Provide a primer on different light microscopy imaging and analysis techniques -and their limitations- using MMIC-based equipment as practical examples

Programme

December 11, 2015 Science Tower D Room 1.03

Morning Session Begins 9:30AM

Introduction to Light Microscopy

 Basic Concepts: Magnification, Resolution, Depth of Field

 Different Transmitted Light Modalities

Break

Epi-Fluorescence Microscopy

 Mechanism of Fluorescence

 Widefield Epi-Fluorescence Microscope Components

 Fluorescent Probes/Stains (Fluorescent Proteins as Biosensors)

 Fundamentals of Digital Imaging

 Scanning Confocal Microscopy

Lunch Break

Afternoon Session Ends 4:00 PM

ImageJ as a Tool for Digital Image Analysis

 ImageJ Basics

Histograms, LUTs and Displays

 2D and 3D Spatial Measurements

 Semi-automated Particle Counting and Analysis

 Measuring Volumes

 Quantitation of Fluorescence Intensity

Quantifying Movement

Analysis of attendee data- as time permits

*Tea, coffee and nibbles will be available throughout the day*

Principles of Microscopy

Microscopy allows us to view processes that would not be visible to the naked eye

Object too small - we cannot see objects smaller than about

0.1mm or the thickness of a human hair)

Object lacks contrast (Stains/Phase-Contrast/DIC)

Process too slow (time-lapse) or not visible in nature (molecular dynamics or interactions-FRAP, FRET)

Every

microscope has limits

Poor sample preparation is a recipe for disappointment and poor imaging

Milestones in Microscopy

100- Romans use crystals as “magnifying” and

“burning” lenses

1595-Jensen makes first compound microscope

1665- Hooke publishes his

Micrographia” describing insects and coins the term “cell”

1670s- Van Leeuwenhoek observes “animalcules”

(protozoa), bacteria, RBCs, sperm, etc.,

1800s- Microscopes improved; theoretical limits of light microscopy determined

1931- Knoll and

Ruska produce first

Transmission Electron

Microscope (TEM)

1945- Porter et al., use

TEM to look at tissue culture cells

1967- Modern Epifluorescence microscope invented

1980s- Macromolecular

Reconstructions using

TEM and tomography

1965- First commercial

Scanning Electron

Microscope

1994- Chalfie et al., use

Green fluorescent protein

(GFP) as an in vivo marker

1987- Confocal microscope applied to cell biology

?

2000s- superresolution invented

Resolution of Different Microscopes

100s of nm nm

10s of nm

Common Light Microscope Imaging

Methods

Transmitted Light Modalities (absorption/phase shift)

• Bright Field

• Phase-Contrast

• Differential Interference Contrast (DIC)

Epi-Fluorescence Light Modalities (emission)

• Widefield

• Scanning Confocal

Upright Light Microscope Anatomy

Digital Camera

Epi-Fluorescence

Filter Cubes

Eyepieces

/Oculars

Stage

Condenser

Lamp

Condenser focusing knob

Optional Hg

Lamp for Epi-

Fluorescence

Mode

Objective lenses

Transmitted Light

Intensity control

Transmitted

Light source

Fine/Coarse focus knob

IMAGE FORMATION:

Attributes of Microscopes

 Magnification

 Resolution

Light is a wave and a particle

Refraction: Bending of light as wave changes speed when travelling through different materials (e.g., a straw looking bent in a glass of water)

Diffraction: Bending of light as wave encounters an object or edge

+

Waves IN Phase =

Constructive Interference

(Brighter Signal)

+

Waves OUT OF Phase =

Destructive Interference

(Darker Signal)

These processes are the core of microscope image formation

Magnification

How big something appears

Compound microscope used in conventional light microscopy utilises several lenses

 Objective lens (closest to specimen) – 2.5x-100x

Projection lens (eyepiece/other) – 10x, etc.,

Total magnification is the product of the magnification of the individual lenses

 Apparent Image Size can be misleading- size must be denoted using calibration or scale bars

But magnification can be “empty”

Resolution

What is resolution?

Smallest distance apart at which two points on a specimen can still be seen separately

This is directly related to the light collecting capability of the optical system

---The Objective Lens---

The Diffraction Pattern Defines the

Image Characteristics

The Airy Disk (2D diffraction pattern)

Glowing

Object

(50nm)

Using a self-luminous object as an example

Airy Disk

Diffraction

Through

Lens zero order

X- Axis

-1 order

Modified from http://zeiss-campus.magnet.fsu.edu

The Airy Disk (2D diffraction pattern) Dictates Object

Apparent Lateral Size

Using a self-luminous object as an example

Position on Linescan

Airy Disk

Glowing

Object

(50nm)

Diffraction

Through

Lens

X- Axis

D

D=Full Width Half Maximum (FWHM)

D x,y

=0.61

/ N.A.

=wavelength of emitted light

N.A.=Numerical Aperture of Objective Lens

(light collecting power of lens)

For Example:

A 50nm bead imaged with a 100x oil Immersion Lens (NA 1.4) emitting 520nm (green) light

D x,y

=0.61(520nm)/1.4

D x,y

=226nm

The minimum apparent lateral size of an object viewed at 520nm light is 226nm

The Airy Disk Dictates Resolvable Lateral Separation

Distance

Glowing Object

(50nm)

D x,y

D x,y

= Lateral Resolution

=0.61

/ N.A.

D

=wavelength of emitted light

N.A.=Numerical Aperture (light collecting power of lens)

For Example:

A 50nm bead imaged with a 100x oil Immersion Lens (N.A. 1.4) with 520nm (green) light

D x,y

=0.61(520nm)/1.4

D x,y

=226nm

500nm

125nm

Two objects spaced closer than 226nm appear as one

Resolved

• Shorter wavelengths give higher resolution

• Higher N.A. gives higher resolution

Not Resolved

Magnification has no impact on lateral resolution

The Point Spread Function is the 3D

Diffraction pattern in your microscope

Object

(50nm)

D z

Axial Resolution D z

= λ η /( N.A.

) 2

Emitted light

(520nm)

Lens Numerical

Aperture (1.4)

Refractive index of mounting media (1.515)

D z

= 520nm ( 1.515

)/( 1.4

) 2

D z

= 401nm

The minimum apparent axial size and separation distance of an object emitting 520nm light is ~400nm

Axial (Z) resolution is ~ ½ of lateral (XY) resolution

Magnification has no impact on axial resolution

Images are comprised of Airy Disks/PSFs

How do we exceed the diffraction limit?

TEM Image

Alternative technologies

 Transmission Electron Microscopy (TEM)

Resolution: ~5nm (Atomic!)

 “Super-resolution” Light Microscopy

Resolution: ~70-150nm (depending on method)

Magnification

Microscope

Tube Focal

Length

∞ or 160mm

Deciphering the Objective Lens

Optimal coverslip thickness

Corrected Aberrations

• U- Can transmit UV

• Plan- Entire field in focus

• Sapo/Apo- All colours focus in same plane

Numerical Aperture (N.A.)

Immersion media Required

• Oil for immersion oil

• Gly for glycerine

• Water for water

FN- Field Number

(corresponds to diameter of ocular lens for best field of view)

Additional Details (e.g.)

• DIC/NIC-Differential Interference

Contrast

• PH- Phase-Contrast

Focal

Length

Objective Lens N.A. Determination

Objective Lens

N.A.= n sin(

)

Front Lens Element

 n n = Refractive Index between lens and sample air=1.0

water=1.33

oil=1.515

angle between optical axis and widest ray captured by lens

Focused Sample

Lower N.A. lenses collect less light; therefore images are less bright and lower

resolution

Highest possible N.A. in air is ~0.95 0.95= 1.0

(sin72)

Higher magnification lenses have a shorter focal length, greater

 and

commonly require oil to capture the light and achieve higher N.A.

!!!oil should never contact a dry lens!!!

**Addition of oil to a dry lens distorts light collecting pathway**

Depth of Field

Amount of a specimen in focus at the same time

Depth of field (DoF) decreases with increased magnification and N.A.

Objective Lens

Focal Plane

Low Mag/Low N.A. (40x/0.65)

0.4

m DoF

1.0

m DoF

High Mag/High N.A. (60x/0.85)

For the thinnest optical section use a high magnification and high N.A. lens

Table from www.olympusmicro.com/primer/anatomy/objectives.html

Contrast

or

Distinguishing detail relative to the background

Many samples have poor inherent contrast 

Bright Field image of Insect Cells

Without contrast, magnification and resolution are irrelevant

In Transmitted Light Microscopy contrast can be generated by:

 Altering the light absorption of a sample (e.g., stains)

Increasing the phase shift of light on a sample (special optics)

Transmitted Light Optical Contrasting

Techniques

Bright Field

Phase-Contrast

DIC/NIC (Differential Interference

Contrast/Nomarski Interference Contrast)

Transmitted Light Microscopy

Light from tungsten lamp focused on specimen by condenser lens and travels

through

sample

Inverted Microscope Upright Microscope

Detector

Projector

Lens

Objective

Lens

Mirror

Lamp

Condenser

Slide and Sample

Stage

Condenser

Mirror

Lamp

Objective

Lens

Detector

Projector

Lens

To achieve highest quality images it is essential that the sample is correctly illuminated

Köehler Illumination

August Köehler, of the Zeiss corporation invented Köehler illumination in 1893

Samples are uniformly illuminated

Glare and unwanted stray light minimised

Maximise resolution and contrast

Setting Up Köehler Illumination

A)Focus on sample with low power objective

Close condenser field diaphragm

Raise condenser up to highest position

B)Lower condenser until diaphragm image (octagon) is in focus

C)Centre using condenser centering screws

D)Open field diaphragm until just filling field of view

Adjust condenser aperture diaphragm

A

B

C

D

Transmitted Light Resolution (D) x,y

=1.22

 /N.A.

objective

+N.A.

condenser

The Condenser Diaphragm Balances System

CONTRAST and RESOLUTION

100% Open

80% Open

50% Open 20% Open

Extent of aperture diaphragm closure

Resolution

Contrast

80% open is optimal for most applications

Bright Field Microscopy

Image contrast produced by absorption of light (object vs. background)

Specimens commonly look coloured on white background (transmission of non-absorbed light waves)

May be due to natural pigments or introduced stains (e.g., histology)

Human Tissue (Stained)

Leaf

Plant Embryo (Stained)

Walther Flemming’s 1882 illustrations of “MITOSIS” (Greek for “thread”) using

non-specific

aniline dyes

Salamander Gill Cells

Chromosomes

Spindle

But stained samples are DEAD!!!

Artefacts? Dynamics?

Phase-Contrast Microscopy

Human eyes detect differential absorption-

If light is not absorbed by a sample you cannot see it

Phase-Contrast Microscopy:

Small changes in the phase of light are converted into visible contrast changes

No staining is required

Vertebrate Culture Cells

Vertebrate Mitotic Culture

Cell

Spindle

Chromosomes

. . . And that means you can study living samples!

Phase-Contrast Microscopy

In Phase-Contrast microscopy the optical path of the microscope is modified so that it converts phase changes into an image

Light from lamp emerges as a hollow cone

Light is refracted by the sample

But

not

the background

A phase ring at the focal plane of the objective exaggerates phase differences between refracted and un-refracted light

These appear as intensity differences in recombined image www.olympusmicro.com/primer/techniques/ phase contrast/phase.html

Differential Interference Contrast (DIC)

Microscopy

Contrast based on exaggerating differences in Refractive Index of object and surrounding medium

Objects have a‘relief’ like appearance

**DOES NOT PROVIDE TOPOLOGICAL INFORMATION**

Surface analysis requires alternative techniques: e.g.,

Scanning Electron Microscopy (SEM)

Mitotically Dividing Neuroblast

Stem Cell

Generates the highest resolution image of any transmitted light method

Generates the thinnest optical section of any transmitted light method

Well suited for high resolution live cell studies

5

Polariser 2

4

3

Wollaston Prism 2

Objective Lens

Sample

2

Wollaston Prism 1

How Does DIC work?

Detector

1) Light emitted from Lamp is polarised by

Polariser 1

2) Polarised light passes through

Wollaston Prism 1 , is split into Ordinary

(

O

) and Extraordinary ( E ) rays separated by diffraction limit

3)

O

and E differentially interact with sample-

O

(passes/refracts through nucleus)-pathway longer than E

4) Objective Lens focuses

O

and E into

Wollaston Prism 2 for recombination

1

Polariser 1

Lamp

5) Combined ray passes through

Polariser 2 and then into detector for viewing

Comparing Transmitted Light Optical Contrasting

Techniques

Phase contrast

DIC

Modified from www.olympusmicro.com/primer/techniques/dic/dicphasecomparison.html

Epi-Fluorescence Microscopy:

A Tool for Molecule-Specific Imaging

Indirect Immunofluorescence Staining

( Microtubules , Centromeres and DNA )

Bright Field

(Dye Stained)

Fluorescent Dye Stained

( Proteins and Lipids )

Dividing Vertebrate Cells (Salamander and Human)

Dairy product-based Emulsion

Epi-Fluorescence Microscopy

Common Applications

 Co-localisation

 Dynamics

 Protein-Protein Interactions

 Protein Post-translational

Modifications

Epi-Fluorescence Microscope Configurations

 Widefield (classic fluorescence microscope)

 Scanning Confocal

FluorescenceThe process whereby a molecule emits radiation following bombardment by incident radiation

What is Fluorescence and How Does it Work?

Fluorescence energy diagram

Excitation Light

e-

Vibrational

Relaxation e-

Fluorophore electrons

Fluorophore

Emitted Light

Alexa 488 Green

Dye ee-

GFP

The emitted wavelength is

ALWAYS LONGER and Lower

Energy -

Stoke’s shift

Input Output

Short wavelength/High energy

Long wavelength/Low energy

Fluorophores Have Unique Fluorescence Spectra

Fluorescence Spectrum of Alexa 488

Max Excitation

(490nm)

Max Emission

(525nm)

Excitation

(Absorption)

Emission

GAUSSIAN Absorption and Emission Profiles

Peak values listed by manufacturers

Prolonged excitation damages fluorophore and prevents emission

**PHOTOBLEACHING**

Epi-Fluorescence Microscope Light Path

(Basic Widefield Setup)

Illumination Sources

Hg Lamp - spectrum of excitation light wavelengths

(350-600nm)

Projection lens

Emission filter

Fluorescence

Illumination

Source

Objective

In epi-fluorescence microscopy the objective lens acts as the condenser

Modified from Lodish 6th Fig 9.10a

Lasers - Discreet wavelength per laser

(e.g., 405nm, 488nm, 561nm,

633nm)

Alternatives:

Light Emitting Diodes (LEDs) discreet wavelength per LED

Metal Halide Lamp (e.g., Xenon; broad spectrum of visible wavelengths

Epi-fluorescence Microscopes Require Filters

3 Component System

3) Emission

Filter

1) Excitation

Filter

Hg Lamp

2) Dichroic

Mirror

Alexa488 filterset

Bandpass Filter – blocks wavelengths outside of selected interval ( e.g., AT480/30x; only 465-

495nm transmitted )

Longpass Filter - blocks wavelength transmission below some value ( e.g., AT515LP;

≥515nm transmitted )

Shortpass Filter - attenuates longer wavelengths and transmits

(passes) shorter wavelengths

Dichroic mirror - reflects excitation beam and transmits emitted ( e.g.,

AT505DC; ≥505nm transmitted )

3 Classes of Fluorescent Probes Provide Specific

Labelling

1) Dye-small organic molecule conjugates that directly bind their targets

Target Species Probe Function Example Probe

Various Ions

Lipids

Proteins

Actin

Microtubules

Nucleic Acid

Mitochondria

ER

Lysosomes

Golgi pH/Ion Concentration

Localisation

Localisation

Localisation

Localisation

Localisation

Localisation

Localisation

Localisation

Localisation pHRhodo/Fura-2

Nile Red

Fast Green

Phallodin-alexa dye conjugate

Taxol-alexa dye conjugate

Hoecsht33342, SYTO dyes

MitoTracker

ER-tracker

LysoTracker

Ceramide-BODIPY conjugate

All are cell membrane permeable and can be used on living samples

2) Dye-antibody conjugate labelling

Direct

Immunofluorescence

• Antibody from host animal has fluorescent probe covalently attached

• Antibody-Probe binds to target epitope

Indirect

Immunofluorescence

• Antibody from host animal 1 binds to target epitope

• Probe-conjugated antibody from animal 2 binds antibody 1

Pros:

Signal amplified

Cons:

Second antibody may non-specifically bind to sample resulting in “dirty” staining

Both require samples to be fixed and permeabilised with detergents

3) Dye-free genetically encoded labels

The Fluorescent Protein (FP)

Revolution

Green Fluorescent Protein (GFP)

3º Structure

β-Barrel confers stability

Aequorea victoria

2º Structure

11

β-sheets

4

α-helices

Chromophore

(Ser65-Tyr66-Gly67)

Protein first isolated and studied in 1962 in “squeezates” by Shimomura

Gene cloned in 1992 by Prasher et al.,

Used as an in vivo marker by Chalfie and co-workers in 1994

GFP and Fluorescent Protein Technology have provided unparalleled insights into biological processes

GFP Glows WITHOUT Additional Cofactors or

Agents

 238 a.a. long

 ~27 kDa

Stable at physiological range of Temperatures and pHs

 Rapid folding (and glowing)

GFP is NON-TOXIC, uses conserved codons and can be fused to genes of interest from any organism

N-term fusion

C-term fusion

Promoter GFP gene + linker Gene of interest

Promoter Gene of interest Linker + GFP gene

Protein localisation without antibodies

Monitor organelle and structure movements in living preps

Fusion of GFP to different promoters identifies periods/areas of unique gene activity

Observe rapid protein redistributions and dynamics

Biosensors to study molecular interactions in vivo

The Fluorescent Protein Revolution

1000

PubMed results for “Fluorescent

Protein” and “GFP”

800

600

400

200

0

Year

The Fluorescent Protein (FP) Palette

FPs engineered/isolated from other organisms with variants covering the spectrum

Chromophore differs but all have β-Barrel

In vivo Molecular Specificity

Tubulin::EGFP Histone:mCherry

Modified from Shaner et al., 2007

Many suffer from forming dimers/tetramers– can lead to artefacts

Mitotic Neuroblast

The Fluorescent Protein (FP) Palette

FP experiment considerations:

1) Does FP interfere with protein function?

 Is placement better on N or C term?

 Does tag form multimers?

2) Is FP bright and photostable enough for experiment?

3) Are FPs spectrally distinct?

EGFP and mCherry

EGFP and EYFP

Vs.

Well defined Extreme overlap-hard to resolve

Fluorescent Proteins as Optical

Highliters

Fluorescent Proteins as Highliters

Some Fluorescent Proteins can be differentially controlled by light

504nm

PA-EGFP

Photoactivatable

(on with UV light)

 PA-GFP (ex. 504nm; em. green )

PA-mCherry1 (ex. 564nm; em. red )

405nm

504nm

X

Photoswitchable

(on/off)

 Dronpa

 rsEGFP2

 Dreiklang

 rsCherry

(em. green )

(em. green )

(em. green/yellow )

(em. red )

Excite Inactivate Activate

(nm) (nm) (nm)

503

478

511

572

503

503

405

450

400

408

365

550

503nm

Dronpa

503nm

503nm

400nm

Dronpa

*

503nm

Fluorescent Proteins as Highliters

Photoconvertible

Conversion Wavelength (nm)

 PS-CFP2 cyan -togreen

 Dendra2 green -tored

 PCDronpa2 green -tored

 mEOS2 green -tored

Kaede green -tored

 psmOrange2 orange -tofar red

405

480

405

405

405

489

Fluorescent Proteins can serve as timers

mCherry Derivatives

Fast-FT

Medium-FT

Slow-FT

Blue -toRed Fluorescence Conversion Time (Hours)

~4

~7

~28

DsRed derivatives

- all tetrameric

DSRed-E5 green -tored ~18 hours

Image Acquisition: Digital Imaging

Object Microscope Detector A/D Converter Computer

Digital Imaging

Easy work flow from microscope to presentation (seminars, publications, etc.,)

 Software allows data manipulation and analysis at your desk

Storage footprint and expense minimal

The Pathway of Digital Image Formation

Object Microscope

Detector

A/D Converter

Emits Photons

Transmits Photons

Captures Photons

And Turns them into VOLTs

Turns Volts into Pixels

(x,y and grey value data)

Computer

Controls Acquisition and allows

Visualisation/Analysis of

Photons in Quantitative

Way

The Pathway of Digital Image Formation

Object Microscope Detector A/D Converter Computer

Detectors

Photosensitive devices that transduce incoming photons into PROPORTIONATE AND

SPATIALLY ORGANISED voltage distributions

In other words. . .

The Pathway of Digital Image Formation

It makes a map!

Each map unit is a pixel: x,y information and brightness information

X-Axis

X-Axis

X-Axis

A/D

Conversion

X-Axis

The Pathway of Digital Image Formation:

Detectors

Digital Camera

Charge Coupled Device (CCD)

 Complementary Metal-Oxide Superconductor (CMOS)

Photomultiplier Tube (PMT)

Camera PMT

Entire image formed simultaneously from arrays of physically subdivided detectors (pixels)

Image formed spot by spot

(raster scanning)

The Pathway of Digital Image Formation:

Detector Characteristics

Physical Pixel Size: Not so important- apparent size is (see next)

Pixel Number: Not so important– most CCDs <2MPx (1400x1080)

Dynamic Range: Total range of shades

8bit= 2 8 =256

12bit= 2 12 =4095

16bit= 2 16 =65,535

Quantum Efficiency: Efficiency of electron production per photon collision

CCD/CMOS 60-90%

PMT 15-30%

Noise: Non-signal-based contributors to the image

 Shot/Photon Noise- Random emission of photons from sample

Thermal Noise- random e- due to thermal fluctuation in detector

 Electronic Noise- when signal transmitted from detector to A/D converter

Detector Characteristics: Pixel Size

(Spatial Information)

Pixel size should be matched to system resolution

Each pixel should appear 1/3 to 1/2 the size of the Airy

Disk

Detector Detector

Detector

“Undersampled”

Detail Lost

Optimal “Oversampled”

 Empty Magnification

 Signal Intensity Lost

Detector Characteristics: Pixel Size

Pixel Size Limits Image Information

0.5µm beads imaged using different pixel sizes

240nm pixel 96nm pixel

48nm pixel

Corresponding linescans

“Undersampled” Optimal “Oversampled”

Oversampling offers little spatial improvement but may decrease image brightness or increase scan time

Detector Characteristics: Pixel Size

To bin or not to bin (CCD/CMOS only)

Binning or “Super-Pixel” Formation

1) Photon hits 2) charge read from chip and turned into grey scale (A/D conversion)

16 pixel detector (4x4)

Bin 1x1

4 pixel detector (2x2)

Bin 2x2

Bin

1x1

2x2

4x4

8x8

Resolution

(pixel)

2752x2208

1376x1104

688x552

344x276

Apparent pixel size with 60x NA

1.4 lens (nm)

75.67

151.33

302.67

605.33

A/D convert charge on each pixel

A/D convert each superpixel (1/4 as many)

For Qimaging Retiga6000 monochrome

• Loss of resolution- Does it matter?

• (Airy disk is 226nm (NA 1.4; lambda=520nm), Bin 2x2 pixel is 151nm; therefore acceptable)

• Signal:Noise improved 4x! (A/D conversion of ¼ pixels)

• Image acquisition speed improved

Always consider binning as an option

Detector Characteristics: Dynamic Range

(Intensity Information)

Most monochrome images are 8 bit (2 8 =256 shades)

Displayed as a pseudo-coloured LOOK UP TABLE (LUT)

R G B colour images are 24 bit ( Red 8bit+ Green 8bit+ Blue 8bit data)

Detector

Each pixel is like a bucket

As photons strike detector, electric charge builds (fills the bucket)

“Full”

255

The bucket’s depth defines dynamic range

“Empty”

0

Dynamic Range (Intensity Information)

As photons strike, electric charge

PROPORTIONATELY

accumulates

(fills the bucket)

Object

“Full”

255 e‐ e‐ e‐

“Empty”

Captured Image

0

0

0

0

0

0

80

0 0

200 80

0

0

200 255 200 0

80 200 80 0

0 0 0 0 0

Grey Value

Numerical Distribution

Dynamic Range (Intensity Information)

As photons strike, electric charge

PROPORTIONATELY

accumulates

(fills the bucket)

“Full”

255

“bucket full”

e‐ e‐ e‐

“Empty”

0

Pixel SATURATED

ADDITIONAL PHOTONS NOT RECORDED

Adjacent pixels may acquire additional charge and saturate

0 0 0 0 0

0 255 255 255 0

0 255 255 255 0

Object

Captured Image

0 255 255 255 0

0 0 0 0 0

Grey Value

Numerical Distribution

Dynamic Range (Intensity Information)

Grey Scale LUT

255

0

“Good”

Information Missing

Excessive “white” areas– spatial and intensity detail not visible

Loss of information due to saturation?

No data lost- monitor screen too bright?

Dynamic Range (Intensity Information)

Look Up Tables can reveal saturation/underexposure

255

“Proper” Histogram

Intensity Value

0

“HiLo” LUT

Image Saturated

INFORMATION PERMANETLY LOST

Dynamic Range (Intensity Information)

As photons strike, electric charge PROPORTIONATELY accumulates

(fills the bucket)

Below saturation, fluorescence intensity is proportional to collected photons and can be quantified as a metric of molecular concentrations

(Which we will explore later)

Scanning Confocal Microscopy (SCM)

A Hardware Approach to Improving Epi-

Fluorescence Image Quality

Scanning Confocal Microscopy Provides Thin

Optical Sections

Drosophila

cells stained for Microtubules and DNA

Focal Plane

Imaged Volume

Background fluorescence is collected from above and below focal plane

Collected fluorescence limited to focal plane

SCM: The Confocal Principle

The sharpened image is due to the “pinhole”

An excitation laser is scanned across the sample

Pinhole located in front of detector blocks emitted light not originating from the focal plane

Dichroic Mirror/Beam Splitter

Detector Pinhole

SCM: The Pinhole Dictates Optical Section thickness

Pinhole size 1.0 Airy Units

(Default)

Pinhole size 2.0 Airy Units

Images of Microtubules in Drosophila cells

Intensity (Arbitrary Units)

Intensity (Arbitrary Units)

Opening the pinhole increases image blur

SCM: The Pinhole Size Determines Image

Brightness

Images of Drosophila cells imaged with identical settings

EXCEPT

for the pinhole diameter

( Microtubules DNA )

1.0 Airy Units (Default) 2.0 Airy Units 0.5 Airy Units

A larger pinhole creates a thicker optical section and allows more light to be captured

Pinholes < 1 Airy Unit reduce signal intensity but DO NOT significantly improve image quality

SCM: 3D Reconstructions

Any automated epi-fluorescence microscope can collect optical sections

Scanning Confocal Microscopy EXCELS with THICK specimens

Fruit fly Brain (52 sections, 2µm steps)

Pollen Grain (52 sections, 0.4µm steps)

Scanning Confocal Microscopy vs.

Widefield Epi-Fluorescence Microscopy

Pros:

 Thinner optical section

 Superior signal:background

3D reconstructions from optical slices

Better for imaging into thick specimens (5

m vs 50m)

 Ability to bleach/activate in fixed area of virtually any shape (FRAP/FRET)

 The ability to magnify without loss of intensity

Cons:

Substantial loss of emitted sample signal (<90%)

 Excitation lasers may rapidly photobleach sample

 SLOW scan speed so not ideal for studying living/fast events

In other words, experimental needs dictate the technique

More than “pretty pictures”:

Light Microscopy As A

Quantitative Tool

Measuring Protein Dynamics:

Fluorescence Recovery After Photobleaching (FRAP)

1) Pre-bleach: GFP-tagged molecules dynamically associate with structure

2) Bleach: HIGH ENERGY LIGHT IRREVERSIBLY damages targeted chromophores preventing further fluorescence

3) Recovery: Fluorescence returns to the structure as unbleached molecules exchange with and “dilute out” bleached ones

FRAP at work: Kinetochore Protein Dynamics

Drosophila

mitotic cell expressing GFP tagged

Klp67A

FRAP reveals:

% of protein pool that is dynamically exchanging

 Rate of mobility

Bleach event

A

Pre-bleach fluorescence intensity

B

Post-bleach intensity plateau

Difference between A-B reveals non-dynamic population

A

B

C

C

Slope identifies mobility rate

Steeper is more rapid

T

1/2

~6 sec

Studying Protein-Protein Interactions:

Bimolecular Fluorescence Complementation

(BiFC)

 Fluorescent Protein cloned as two separate halves

(e.g., YFP; N-term a.a. 1-154 + C-term 155-238) fused to candidate interactors (A, B)

Blue

Blue

Blue

A

B

 Neither fragment glows

A B

Yellow

 A-B interact and YFP halves come together;

YFP fluoresces

Quantify fluorescence intensity of each to reveal efficiency of binding

 A and B need to be within ~10nm

 Binding irreversible- not good for dissociation kinetics

UV

Studying Protein-Protein Interactions:

Förster Resonance Energy Transfer (FRET)

CFP

CFP Spectrum

YFP

YFP Spectrum

Blue

A

Blue B

Yellow

DONOR -

ACCEPTOR

CFP/YFP Spectrum

CFP

Emission

YFP

Excitation

UV

A B

Yellow

Proteins A and B interact

Measure fluorescence intensity to reveal efficiency of binding

Donor Emission must OVERLAP Acceptor Excitation

Chromophores are

10nm apart

FRET as a Quantitative Biosensor

Sites and durations of Mechanical Tension

Blue

UV

UV

A B

Tension LOW:

A contacts B;

FRET

Yellow

A B

Tension HIGH :

A and B separated

FRET LOST

Protein Modifications e.g., Local kinase activity

Phospho-amino acid

Binding Domain (PBD)

Kinase

Activity

P

Kinase Substrate

P

Kinase Substrate

(Phosphorylated)

1. Default State

NO FRET

2. Phosphorylation of

Substrate

NO FRET

3. Intramolecular binding

P-Substrate Binds PBD

FRET

BiFC and FRET:

Further Considerations

Blue

Yellow

UV

A B

Yellow

A B

Chromophore interaction is a function of DISTANCE and

ORIENTATION

N-terminal fragment fused at the N-terminal protein A + C-terminal fragment fused at the N-terminal protein B

N-terminal fragment fused at the N-terminal protein A + C-terminal fragment fused at the C-terminal protein B

N-terminal fragment fused at the C-terminal protein A + C-terminal fragment fused at the N-terminal protein B

N-terminal fragment fused at the C-terminal protein A + C-terminal fragment fused at the C-terminal protein B

C-terminal fragment fused at the N-terminal protein A + N-terminal fragment fused at the N-terminal protein B

C-terminal fragment fused at the N-terminal protein A + N-terminal fragment fused at the C-terminal protein B

C-terminal fragment fused at the C-terminal protein A + N-terminal fragment fused at the N-terminal protein B

C-terminal fragment fused at the C-terminal protein A + N-terminal fragment fused at the C-terminal protein B

And don’t forget, the linker needs to be long and flexible enough to permit interactions as well!

It’s Alive!!!!!!!

Dealing with Living Material

 What is physiological temperature?

 How metabolically active is it? Do waste products induce immediate insult? Is gas required?

RADIATION

Excitation light induces photobleaching and phototoxicity

Shorter

  higher energy  higher resolution  more phototoxic

 Longer

 less phototoxic but poorer resolution

 Limit exposure time/laser excitation power  but this means a weaker signal

 Limit z-series  but this means less spatial information

 Limit sampling (framing) rate  but this means poorer temporal resolution

Compromise based on EMPIRICAL DETERMINATION BALANCING WANTS vs NEEDS

Useful Online References and Primers: http://www.microscopyu.com/ http://zeiss-campus.magnet.fsu.edu/index.html

http://www.olympusmicro.com/index.html

Online spectra comparison http://www.chroma.com/spectra-viewer

Questions?

LUNCH TIME!

ImageJ: A Free to Use Image

Analysis Programme

By

Wayne Rasband http://imagej.nih.gov/ij/

There are multiple routes to analysing data

If you have questions. . . ASK!

MENUS

OPTIONS

Getting Around ImageJ: Layout

Function-specific

“sub-programmes”

Rectangle

Tool

Circle

Tool

Line

Tool

Freeform

Shape Tool

Polygon

Tool

Tools for Defining

Region of Interest (ROI)

Zoom In/Out

(shift +/-)

Move Image within window

(when zoomed)

Getting Around ImageJ: Loading Data Sets

ImageJ can open just about any data format. . .

(e.g., .Lif, .avi, .tif)

Open “SpindlePicture” image from

“WorkshopDec2015DataSets” folder

“Drag and Drop” Data Set onto

ImageJ Programme Bar

OR

SpindlePicture.tif

Click “Open”

Getting Around ImageJ: Histograms, LUTs &

Displays

Image Size Bit Depth= # Shades

Cursor Coordinates

Pixel Intensity at Cursor

Histogram: Distribution of Shades in an Image

Getting Around ImageJ: Histograms, LUTs &

Displays

LOOK UP TABLES (LUTs) change image displays but not their intensity values

Image->Adjust->Brightness/Contrast: changes display but not image data

Getting Around ImageJ: Histograms, LUTs & Displays

An RGB colour image is 3 intensity channels with 3 different LUTs

 Open “RGBMitosis” image from “WorkshopDec2015DataSets” folder

 Look at Values with cursor, Try to alter LUT

 Image->Color->Split Channels

 Image->Color->Merge Channels

Channel1= Red =Kinetochores

Channel2= Green =Microtubules

Channel3= Blue =DNA

Make a Composite Image

Composite=Colour Image with

Separate LUTs

Note: Channel #

 Manipulate LUTs and Brightness/Contrast for each Channel

Save altered LUT choices as RGB image

Image->Type->RGB Color

 File->Save As->Tiff

Getting Around ImageJ: Histograms, LUTs & Displays

 Open “RGBMitosis3D” image from “WorkshopDec2015DataSets” folder z-plane information

3D data sets are called “Stacks”

 Move through the volume- different information lay in different sections

Stacks can be manipulated

 Image->Stacks z-plane slider

To further view the 3D Information:

 Image->Stacks->Orthogonal Views

Move through the volume by dragging the crosshair

ANY image can be saved by selecting it and going to:

 File->Save As->Tiff->. . .

Getting Around ImageJ: Histograms, LUTs & Displays

To collapse the volume into a single 2D projection:

 Image->Stacks->Z Project

 Set top and bottom limits (exclude “empty” sections)

Choose “Max Intensity”

Maximum Intensity

Projection

Section 1

10 100

0 10

Vs.

Section 2

20 0

0 50

20

Result

100

0 50

Result looks good but not fully inclusive of intensities

Getting Around ImageJ: Histograms, LUTs & Displays

To collapse the volume into a single 2D projection:

 Image->Stacks->Z Project

 Set top and bottom limits (exclude “empty” sections)

Choose “Sum Slices”

Summed Intensity

Projection

Section 1

10 100

0 10

+

Section 2

20 0

0 50

30

Result

100

0 60

Less distinct as image includes intensities from all sections

Getting Around ImageJ: Measurements

Spatial Analyses Require Image Calibration

Image->Properties. . .

Image Properties

(commonly in file header)

# channels

# z-steps

# time points length units

apparent

pixel dimensions z-step size

Time between frames

3D pixel = voxel z x y

Apply properties values to all open images

If not in the file header ask/determine empirically

Getting Around ImageJ: Measurements

To add a Scale Bar

 Analyze->Tools->Scale Bar. . .

Bar Length

Bar Thickness

Label Visible/Hidden

Getting Around ImageJ: 2D Distance Measurements

 Open “3DMeasureRGB” from “WorkshopDec2015DataSets” folder

 Collapse to Max. Int. Proj

 Use Line Tool to draw line between centrosomes

Different line options are accessed by Right

Click

Measure Line By:

 Analyze->Measure

OR

 Ctrl + M

Copy and Paste Results in

Spreadsheet

(i.e., Excel)

Getting Around ImageJ: 3D Distance Measurements

 Open “3DMeasureRGB” from “WorkshopDec2015DataSets” folder

 Install Macro “3D-Distance-Tool”

(http://imagej.nih.gov/ij/macros/tools/3D_Distance_Tool.txt)

OR

Drag and drop “3D-Distance-Tool” on Toolbar

 Plugins->Macros-> “3D-Distance-Tool Options”

Marker size (pixels)

Numbered tag

 Alt + Left click to position second marker in different z-plane

Run Macro

 Left click to position first marker

 Distance Listed

Separation distance in x,y,z is greater than in x,y

2D projections may be misrepresentations of separations and distances

Getting Around ImageJ: Object Counting/Analysis

 Open “FieldofCells2015” image from “WorkshopDec2015DataSets” folder

How many nuclei are in the field? How large are they?

Semi-Automated Analysis: 1)Segmentation and 2)Quantitation

Segmentation: Defining objects of interest from the background and one another

1) Decrease image noise

 Process->Smooth

Removes spurious bright pixels

(alternatively use Gaussian Blur)

Getting Around ImageJ: Object Counting/Analysis

Segmentation: Defining objects of interest from the background and one another

2) Determine Background

 Use Freeform tool to define background (more area is better)

 Measure and Determine Mean Intensity

2) Subtract Background

 Process->Math->Subtract

Corrected Resultant Image

Preview Result

Getting Around ImageJ: Object Counting/Analysis

Thresholding and Automated Analysis

 Image->Adjust->Threshold

Corrected image

Thresholding includes/excludes intensity ranges

Set lower limit

Set upper limit

Only intensities between 36-255 will be recognised

Data is NOT altered unless “Apply” is selected

Corrected image

Vs.

Non-corrected image

What happens when we raise the lower limit?

Getting Around ImageJ: Object Counting/Analysis

Thresholding and Automated Analysis

Area, Deviation and

Intensity Boundaries

Summation of intensity values

Only thresholded objects analysed

 Analyze->Set Measurements

Define Parameters to be Measured

Summation of all intensity values/total # of pixels

Most frequent intensity value

Perimeter

Getting Around ImageJ: Object Counting/Analysis

Thresholding and Automated Analysis

Analyze->Analyze Particles

Particle size range

(real units or

pixels

)

Circle=1.00

Do not analyse particles touching edge of screen

Outlines of

Thresholded/

Analysed

Particles

OUTPUT

Summary of Results Table

Total

Particle

#

Total

Area

(

m

2 )

Avg Area

(

m

2 )

Intensity

Data

% image Avg.

Avg. Int. Den

(Mean Int.

*Area)

area thresholded

Perim

(

m)

Individual Results

Table

Getting Around ImageJ: Object Counting

Thresholding and Automated Analysis

BUT COMPUTERS ARE IMPERFECT!

Review Original image as 8bit

Grey scale image

Adjacent nuclei counted as one

Poor signal: nucleus excluded

Partial nuclei counted

Incorrect estimate of nuclear number and size

 Analyze->Analyze Particles->Exclude on edges

Getting Around ImageJ: Volume Measurements

(Demonstration Only)

3D Object Counter v2.0

By Fabrice P. Cordelieres http://fiji.sc/3D_Objects_Counter

1. Load Data 2. Define Measurements

SPATIAL

INTENSITY

3. Define Object(s) in 3D Space

Threshold object ( Segmentation )

Z-slice position

Object size limits ( in voxels )

CENTRE

Z-stack of pollen grain

(x,y,z calibrated)

(Previously filtered)

4. Output

Object map Surface map

Threshold determines object volume/area

Double check quality with: Image->Stacks->Orthogonal Views

Getting Around ImageJ: Comparing and Quantifying

Fluorescence

Linescans reveal intensity distributions

How does the distribution of

Klp67A vary?

Microtubules

Klp67A::EGFP DNA

Microtubules

Klp67A::EGFP DNA

Getting Around ImageJ: Comparing and Quantifying

Fluorescence

Linescans compare intensity distributions

 Open “FluorQuantRGB” image from “WorkshopDec2015DataSets” folder

Microtubules

Ndc80 CID

 Use line tool to draw line ROI across structures/features of interest

Use multi-segment line since object is not straight

 Plugins->Colour Functions->RGB Profiler

Distance in PIXELS

Intensity in Arbitrary Units

Changing line width or orientation affects profile

On Line Tool->Double left click

To save plot:

 File->Save As->Tiff

Getting Around ImageJ: Comparing and

Quantifying Fluorescence

Quantifying 3D Intensity Data: Which Projection Type?

Projections of 11 slice stacks

Maximum Intensity Projection

Section 1 Section 2

10 100 20 0

Vs.

0 10 0 50

Result

20 100

0 50

Summed Intensity Projection

Section 1

Section 2

10 100 20 0

+

0 10 0 50

Result

30 100

0 60

Summation of Intensities

Types of summed intensity values

1) IntDen: mean intensity * area

2) RawIntDen: summation of all pixel values

Intensity data excluded in maximum Intensity projection

Quantify summed values when data comes from multiple sections

Getting Around ImageJ: Comparing and

Quantifying Fluorescence

Quantifying Discreet (Subcellular) Intensities

How do we quantify the discreet accumulations of the protein shown in

RED ?

Microtubules Ndc80 CID

Getting Around ImageJ: Comparing and Quantifying

Fluorescence

 Open “FluorQuantRGB” image from “WorkshopDec2015DataSets” folder

But any intensity data is R + G + B

We want Red Channel Intensity only

Need to isolate red channel

 Image->Color->Split Channels

Red Green

Blue

Three individual channels

Getting Around ImageJ: Comparing and Quantifying

Fluorescence

Remember: Signal Intensity = Signal of Interest + Background

This varies within the image so can’t globally subtract it

Red Channel

 Draw ROI encompassing

Object

 Measure Intensity (Ctrl + M)

Move ROI to

appropriate BACKGROUND

 Measure Intensity (Ctrl + M)

Signal

Background

Copy and Paste Results in Spreadsheet (i.e., Excel)

Use Equation:

Intensity

Corrected

= (Intensity

Signal

– Intensity

Background

)/Intensity

Background

Intensity

Corrected

=(5947-5213)/5213

0.14 Arbitrary Units

Getting Around ImageJ: Comparing and

Quantifying Fluorescence

What is “appropriate” Background and why does if matter?

Empty: Bkgd Low

Structure: Bkgd High

Background MUST reflect measured object’s local environment

Intensity

Corrected

= (Int.

Signal

– Int.

Background

)/Int.

Background

Background too high=Intensity

Corrected

Background too low= Intensity

Corrected too low too high

Local Bkgd

To compare data between samples/slides,

imaging conditions should be constant

Structure: Bkgd High

This means that exposure/laser power/gain/etc., must be determined for brightest saturation) sample first (to avoid

Getting Around ImageJ: Quantifying Movement

(Demonstration Only)

Centromeres labelled with EGFP

DIC

How fast do the chromosomes move during division?

Dividing fly cells

Fluorescence and Transmitted Light data can be tracked

Useful data requires adequate SPATIAL and Temporal resolution

(~3 pixels movement per time point)

Getting Around ImageJ: Quantifying Movement

Object “automatic tracking” plugins for ImageJ:

 Difference Tracker

 MTrackJ2

 MultiTracker

 ObjectTracker

SpeckleTrackerJ

 SpotTracker

TrackMate

All based on segmentation

CID::EGFP

EB1::EGFP

Requires:

 Thresholding

(defining object vs. background)

 Defining object/particle size

 Objects MUST remain distinct to be followed with confidence

Getting Around ImageJ: Quantifying Movement

Semi-Automated Tracking

MTrackJ

By Erik Meijering http://www.imagescience.org/meijering/software/mtrackj

/

(2) Initiate new set of measurements

(1) Define reference (R) for movements

(3) Calculate displacement and velocity

(4) Overlay user defined path on data

Each mouse click positions data point and advances to next frame

(double click to terminate)

Summary of Results Table

Copy/export data for further analysis

Getting Around ImageJ: Quantifying Movement

Kymographs: Time/Space Plots e.g., Kbi Kymograph, Kymograph, MultipleKymograph

Kbi Kymograph (Kbi Tools Plugins)

By Natsumaro Kutsuna http://hasezawa.ib.k.u-tokyo.ac.jp/zp/Kbi/ImageJKbiPlugins

What is a kymograph?

T1 T2 T3

Because pixels are calibrated in space and time

SLOPE=VELOCITY

0

Time Displacement

(Time units)

Getting Around ImageJ: Quantifying Movement

Kymographs: Time/Space Plots

Basic procedure illustrated with Kbi Kymograph

Open data set

Make Max. Int. projection to reveal object movement pathway

Draw line along object pathway

Duplicate line on original data set

Edit->Selection->Restore Selection

Make kymograph

 Plugins->Kbi_Kymograph

Analyse kymograph to get slope/velocity

 Draw line along object edge

 Plugins->Kbi_KymoMeasure

Calibrate

 Copy/Export velocity

https://royalsociety.org/collections/micrographia/

Questions?

http://micro.magnet.fsu.edu/primer/techniques/flu orescence/gallery/fleaslarge.html

Thank you!

Jordan Taylor (TEM) [email protected]

Niki Murray (SEM) [email protected]

Remember, MMIC is free for

Massey-affiliated Work!

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