Lecture notes

2012-11-12

The Wählby lecture format

Geometric properties

     

 Microscopy

Geometric properties Spectral properties Temporal properties Access Quantitative aspects

Focus: Industrial/applied science

R&D | Innovative Medicines | CVGI Mölndal | Dept. of Cell and Molecular Pharmacology

     

 Epi-fluorescent, bright field, (confocal, multi-photon, electron)

Densitometric properties R

G

B

 Non-optical methods  plasmons, gratings, electrodes, mass-spec

 Sample properties     

(auto) fluorescence, refractive index, thickness, polarizing Uneven distribution, cell cultures, tissues (volumes, stereology) Artifacts: folds, tears, scratches, dust Variable thickness movement

 Live cell imaging, 3D imaging, 4D imaging


Epi-fluorescent systems • Automated, robotic • Slides – 1536 well • 2 – 60x • Up to about 0.9 NA • Mostly air objectives • + robotic arms

Geometric properties Densitometric properties Spectral properties Temporal properties

Brightfield systems • Live cell analysis • Slide scanning systems

Access Quantitative aspects




1

2012-11-12

Plasmons Gratings (Bind Scanner)

Sample stability:  Fixatives

Cellular dielectric Spectroscopy (CellKey)

Multi electrode arrays

 formaldehydes, glutaraldehydes, alcohols, parafin

 Fixation will affect your sample  Stability  N2 atmosphere  Antigen presentation – different for different fixatives

 Antigen retrieval – the magic solution  Temperature, pH, enzymatic digestion Mass spec imaging



Sample stability: RNA imaging

DNA

GAPDH mRNA

GAPDH protein

    

Optical thickness of sample holder Phase contrast in narrow wells Polarisation Refractive index (plastics, wells with fluids) Oil and water objectives problematic

 Tears, folds, scratches, dust  Uneven sample distribution overlay


 In tissue (Stereology)  During preparation  On slide


2

2012-11-12

Sample distribution: density affects expression Low density Hoechst

High density Drebrin

Hoechst

Drebrin

Normalised cell density

Cell density vs. Cell number Log(cell density) vs cell number

Sample distribution

 Spherical objects  Neurospheres  Pancreatic islets

Cell number (nucleii) Cell density ≠ cell number. Density increases exponentially with linear increases in cell number. R&D | Innovative Medicines | CVGI Mölndal | Dept. of Cell and Molecular Pharmacology


Sample distribution: small moving objects

PCS

Ampho

granules [Aβ] -6

Z

Time

Y X

-11

Image at multiple time points in each well

3 Z positions/site 4 sites/well = 12 images/ time point/well

density

>46000 images for one 384-well plate

Time

[Aβ]

A

[Aβ]

Antibiotic choice does affect cellular density. Density affects amyloid binding. Does density affect the drebrin response to amyloid? R&D | Innovative Medicines | CVGI Mölndal | Dept. of Cell and Molecular Pharmacology

Z

Deconvoluted images created from each z-stack Using nearest neighbours method or ”remove haze”

B

C

D

E

F

B-A

C-B

D-C

E-D

F-E

”Difference” images created (temporal colocalisation) Fading, Dial in sensitivity by selecting interval, Normalisation


3

2012-11-12

Densitometric properties  Good samples, good images  Variable? -> Many images/samples

 Fluorescence vs. reflected light (brightfield)  Signal enhancement, pre-detector  Washing, TSA, Duo-Link, dye efficiency  Autofluorescence (sample/mounting material)

 Signal enhancement @ detector  Detectors (bit depth, pixel size/binning, cooling)

 Signal enhancement post detector  Multishot techniques, Increase resolution, reduce noise

 Image compression


     

Geometric properties Densitometric properties Spectral properties


”Sample preparation. Sample preparation. Sample preparation.”

Temporal properties Access Quantitative aspects




4

2012-11-12

Dealing with 2D spatial variation Low density Hoechst

Signal enhancement Pre-detector

High density Drebrin

Hoechst

Drebrin

 Sample prep sample prep sample prep  Wash thoroughly – robotics  Reduce non-specific binding  Reduce autofluorescence  Thin substrate important  Long wavelenths

 Don’t wash at all  Extra cellular quenchers  Dyes and Pre-dyes

 Chemical enhancers

 Acquire many images  Dynamic imaging

 TSA, PLA (next slide)  Permeabilisation

 Images are aquired until appropriate number of pre-defined events occurs

 Methanol, acetone – dissolve membranes (lipids)  Saponin – removes cholesterol  Triton X-100, tween-20 – can extract proteins

 Pre-scan at low mag, rescan regions of interest (ROI) at high mag R&D | Innovative Medicines | CVGI Mölndal | Dept. of Cell and Molecular Pharmacology


1 bit (2)

Single molecules: (Trk A, NGF) and PLA 2 bit (4)

4 bit (16) Typical dynamic ranges for: Fluorescence

 10, 12, 16 bit  (4096, 16384, 65536 levels) Brightfield

8 bit (256)

 8, 10, 12 bit/channel Phase contrast

 8, 10 bit R&D | Innovative Medicines | CVGI Mölndal | Dept. of Cell and Molecular Pharmacology


5

2012-11-12

Signal enhancement At the detector    

Bit depth Binning/bigger sensor Gain Cooling  Decreased noise

Signal enhancement Post detector

     

Geometric properties Densitometric properties Spectral properties Temporal properties Access Quantitative aspects

 Multishot techniques:     

Average – removes noise Median – removes moving objects

Focus:

Sum/integration – enhance signals

Industrial/applied science

Resolution enhancement Deconvolution



Spectral properties  Microscopy  Epi-fluorescent, confocal, multi-photon, bright field, electron

 Illumination  Emission spectra: mercury, xenon, diodes, lasers  Vigneting/uneven illumination, illumination changes over time

 Systems can be spectrally calibrated/characterised  Intensity, alignment, PSF

 Sample standards do not exist (yet)  Variable parameters  Humidity, temperature, pressure, osmolarity, viscosity, refractive index, fluid meniscus, possibly sample container dimensions + thickness

 Filter bleed R&D | Innovative Medicines | CVGI Mölndal | Dept. of Cell and Molecular Pharmacology


6

2012-11-12

Diodes  Very stable over time  Long lifespan  Faster wavelength switching

 Much less heat



Illumination artifacts

Calibration

 Can be detected statistically during automated QC  Obvious when you have many images Original

Arc burn on reflector


 Technical standards  PSF, spherical aberation

 No biological standards

Vignetting


7

2012-11-12

Optimisation of live-cell experiments

Quantifying filter bleed

Different operating temperatures

 Can be performed in every plate for each Various speeds and run times Four types of tips Injection height, speed, position

Different magnifications

Four types of plate

Five different Ca-sensitivedyes

experiment (384, possibly 96)

   

Image all wells with 4 colours Control wells contain only 1 colour Signals in irrelevant channels quantified Subtract appropriate fraction of each channel from other channels

 Similar to ”compensation” in flow

Blue Green Yellow

cytometry

 Time consuming  Not usually necessary

Red

Different buffer systems

Three types of foil

Reproducibility Stability R&D | Innovative Medicines | CVGI Mölndal | Dept. of Cell and Molecular Pharmacology


Physical factors and spectral characteristics  Temperature  Cold plate in warm machine = condensation

 Evaporation  Changes osmolarity, density, miniscus level  Not uniform: edge effects

 Biological coatings  Poly-D-lysine, collagen, laminen, finger prints  Variable optical thickness – focusing challenge

     





8

2012-11-12

Normalisation to positive control at pixel level = good assay

Temporal properties

14000000

IX-background

Mean intensity

12000000

 Experimental design is important  Live cell 3 and 4D imaging – images + time!  Sampling frequency

FLIPRator

10000000

IX-cell specific

8000000 6000000 4000000 2000000 0 0

50

 Hardware mods: filter wheels, spinning disks, line scanners  Higher frequency, lower signal

2000000

Z´= - 0.96

1500000

Mean raw intensities

1000000

 Sample stability

500000 0

 Viability, osmolarity, atmosphere, dye distribution/stability  Reagent stability, mixing

min

120 100 80 60 40 20 0

16 sec.

Mean normalised intensities

max


Automated Image Analysis of live cell responses 64 sec.

max

Z´= 0.8

min R&D | Innovative Medicines | CVGI Mölndal | Dept. of Cell and Molecular Pharmacology

16 sec.

100

Time (sec)

Functional population segmentation using agonists

16 sec.

KCl-sensitive cells represent about 3.2% of the total population (4.7% beta-III tubulin). Baseline acquisition

  

First injection (e.g. trypsin)

Second injection (+ve control)

Third injection (+ve control)

About 10% of these respond to capsaicin (0.3% of total population).

(Frame alignment) Baseline images averaged Subtract average baseline (pixel level background subtraction)

Capsaicin

KCl

A23187

overlay

100

   

Positive control(s) used to identify image ROIs

80

Measure only in ROIs at each timepoint

60

Disregard cells that move or disappear

40



Cellular or image level

Normalised data and statistics generated for each timepoint (Z´= 0.8)

Baseline

120

20

Segmentation

0 0

20


40

60

80

100

120

Four populations


9

2012-11-12

Identify subpopulations with morphology

Responses to 25 nM trypsin in three cellular populations

Measure responses

60

fuzzy cells

50

buffer

small cells

Fuzzy/Glial/ Schwann trypsin

neurons

40 Average intensity 30 Fold change from baseline 20

Small/Fibro- buffer blasts

trypsin

10

buffer

neurons

trypsin

0 0

20

40 60 Time (sec)

80

100

0

60

fuzzy cells

small cells neurons

40 Average intensity 30 Fold change from baseline 20

50

1000

fuzzy cells

50

10 20 30 40 average fold change from baseline

small cells

neurons

100 Average Number of cells analysed

10

10 1

0 110

11

1.1

0.11

110

trypsin buffer


1.1

0.11

trypsin buffer


Temporally separated agonist responses in

Temporal properties

functional populations identified using morphology Original

11

Trypsin inhibitor ug/ml

Trypsin inhibitor ug/ml

Baseline subtracted

120 100 80 60

 Experimental design is important  Live cell 3 and 4D imaging  Sampling frequency

40 20 0 0

20

40

60

80

100

120

 Hardware mods: filter wheels, spinning disks, line scanners  Higher frequency, lower signal Incremental change

Morphometric populations: glial/Schwann fibroblasts

 Sample stability = lots of quality control  Viability, osmolarity, atmosphere, dye distribution/stability  Reagent stability, mixing

neurons



10

2012-11-12

1

2

3

4

5

6

7

8

9

10

11

     

12

A B C D

buffer 50 nM tryp

E F G H

50 nM tryp buffer

buffer 50 nM tryp

50 nM tryp buffer

buffer 50 nM tryp

50 nM tryp

buffer 50 nM tryp

buffer

50 nM tryp

buffer 50 nM tryp

buffer

50 nM tryp

buffer 50 nM tryp

buffer


50 nM tryp buffer




Responses are fairly stable over an entire plate 150 125

Time (min) buffer

100

*

75

125

10

15

Time (sec)

trypsin - buffer

0 24 48

75

72

50

96

25

120

0 -50

144 168

100

-25 0

75

120 5

-50 150

100

48 96

25 0 -25 0

125

24 72

50

5

10

15

144 168

Time (sec)

150

0

Access

Time (min) 0

trypsin

24 48 72

50

96

25

**

120

0 0

-25 -50

5

10

144

15

168

Time (sec)

sum

200 100 0 -100 -200 -300

Time (min)

consumables, training and education, per well cost (multiplexing)

 Fairly common  Standard high-throughput methods in industrial and major academic settings

400 300

 Cost – large initial investment,

**

 Set-up time, appropriate method  Limited expertise

*

Time (min)



11

2012-11-12

Software      

Geometric properties Densitometric properties Spectral properties

 A lot of (expensive) proprietry software  Often unique to each platform

 General image analysis packages:  MetaXpress (high throughput MetaMorph variant)  SQL-based, high level but powerful scripting language  Extensive hardware control  Fairly open, reads anything  Cellomics BioApplications  Predefined (but very fast) analysis algorithms  Very closed  Visiopharm, BioPix, Definiens for morphology  Definiens, MatLab  Very competent, extreme learning curves  Spotfire, R (open source), DeNovo FCS image  Multidimensional data analysis

Temporal properties Access Quantitative aspects



Quantitative aspects    

Computing resources in the industrial setting (hard/software) Image processing: common methods, but automated Multidimensional data analysis: Spotfire, R and FCS Image Data presentation/storage  The best way to present complex data  Buy yourself a nice TV: good for data, good for movie nights


Open source/freeware:

   

Fiji/imageJ Cell profiler/analyst Phenoripper Volocity


Data management and hardware  SQL/Oracle/proprietry databases  NAS or SAN drives, (n) Tb  Backup?

    

High speed, low latency network Clusters of cores for analytical work (blade servers) Parallel processing Pipelin Pilot Lakshmanan


12

2012-11-12

Functional population segmentation using agonists

Image processing

KCl-sensitive cells represent about 3.2% of the total population (4.7% beta-III tubulin).

Common to microscopy:  QC (focus, artfacts)  Illumination correction (generally not used, low dignals only)  Deconvolution, thresholding, feature extraction  Local background detection  Textures, phenotypes  Controls: Technical, biological

About 10% of these respond to capsaicin (0.3% of total population).

Typical examples:  Expression (protein, RNA), phenotyping  Binding  Translocation, internalisation, transport (vessicles, organelles)  Apoptosis, proliferation, micronucleation  Membrane shape, cell movement  Morphology (phenotypic screening)  Functional responses (shape, signaling, secretion...)


Translocation

Baseline

Capsaicin

KCl

A23187

overlay

Segmentation Four populations


Binding

So, now you have your data...      

Toxicity

Neurite outgrowth

Proteolysis

No‐Doxycycline

  4 sites/well  4 colors/site  300 cells/image  20 measurements/cell  15 plates

15

384 wells/plate

5760 23040 92160 27648000 552960000 data points

Doxycycline 5µg/ml

Induced expression R&D | Innovative Medicines | CVGI Mölndal | Dept. of Cell and Molecular Pharmacology


13

2012-11-12

Thanks for your attention!

Quantitative aspects    

Software in the industrial setting Image processing... Multidimensional data analysis: Spotfire, R and FCS Image Data presentation/storage  The best way to present complex data (simple?)  Buy yourself a nice TV: good for data, good for movie nights The High Content Imaging Unit AstraZeneca, CVGI Cell and Molecular Phamacology Mölndal, Sweden [email protected]


     





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14

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