think outside the dot

Think outside the dot.

Feature Description and Application

AMNIS CORPORATION – Company Overview 04/19/23

IDEAS Image Analysis Software

• IDEAS is an image analysis application that performs high content morphometric analysis on tens of thousands of images.

• Features are what IDEAS uses to quantify cell morphology.• 162 features in the default template.• 23 features per channel and 6 channels per image

or 138 channel features.• 8 additional mask based features.• 16 System features.• Unlimited number of user defined features.• New features are continually being developed.


Which of the 162 features do I use?

Centroid X,Y

Centroid X,Y Intensity

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Feature Type

IDEAS calculates two types of features• Single features are automatically calculated by

IDEAS and are divided into two categories.• A) Mask based features, such as Area and Intensity.• B) System features, such as camera timer and flow

speed.• Combined Features are created by combining cell

based features and are defined by the user. Examples of combined features are radial delta centroid, and nuclear to cytoplasmic ratio.


Feature Hierarchy

1. Single Features (Require a Mask, 23 per channel, 31 overall) A. Mask Based Features

Size: Units in pixels (Area, Perimeter, Major Axis, Minor Axis, Major Axis Intensity, Minor Axis Intensity) Signal Strength: Units are in integrated pixel values (Intensity, Mean Intensity, Minimum Intensity, Peak Intensity)Location: Units in X,Y Coordinates from an origin in the upper left (Centroid X, Centroid Y, Centroid X Intensity, Centroid Y Intensity).Shape: Defines mask shape within the image (Aspect Ratio, Aspect Ratio Intensity,Object Rotation Angle, Object Rotation Angle Intensity, Compactness, Elongatedness,

Negative Curvature, Spot Count).Texture: Defines pixel or regional variation (Spot Small Total, Spot Medium Total, Gradient Max, Gradient RMS, Frequency)Correlation: Units in transformed Pearson’s Correlation values (Similarity, Similarity Bright Detail).

B. System Features (Do not Require a Mask, 16)Object Rate: (Flow Speed, Camera Timer, Camera Line Number)Channel background: (Background Mean Intensity 1-6, Background Standard Deviation 1-6)Object number

2. Combined Features (User defined):

(Radial Delta Centroid, Nuclear to Cytoplasmic ratio, Perimeter ^2/ Area, Peak/Mean Ratio)


Feature Mask Correlation

Single features require a mask• A feature quantifies cell morphology and intensity.• A mask defines a region of interest.• The feature is generated using the pixel values

within the mask from either the corrected image file (cif) or from a processed image such as the open residue image.

• User defined masks can identify subcellular regions and can enhance the resolution of the feature.

• Additional features can be calculated from any user defined mask or by combining features.


Types of Masks

There are three types of masks• The system mask is the default mask and is

designed to quantify total fluorescence. M1, M2 and the combined mask are system masks.

• A function mask requires user input and there are 5 types of function masks. Dilate, Erode, Fill, Morphology and Threshold.

• A combined mask uses Boolean logic to combine and subtract masks. An example is the cytoplasmic mask, created by taking the brightfield mask and not the morphology mask of the nucleus.


Function Masks

There are five types of function masks• Dilate: Adds pixels to the outside of the starting

mask.• Erode: Subtracts pixels in from the edge of the

starting mask.• Fill: Fills in closed gaps in the starting mask.• Morphology: Uses an algorithm to mask the fine

structure within the starting mask.• Threshold: Masks the brightest pixels within the

starting mask.


Pixelated Imagery

•6 Channel Images are collected using a 10 bit CCD camera operated in TDI (time delay integration) mode.

•Pixel values from a 10 bit detector range from 0 to 1023.

•Each image is a grey scale two dimensional representation of the cell.

•The color of the image is determined by the wavelengths of the channel it’s in.

•A mask is applied that determines the region of interest.

•Features are calculated based on the pixel values underneath the mask.


Pixelated Imagery

•6 Channel Images are collected using a 10 bit CCD camera operated in TDI (time delay integration) mode.

•Pixel values from a 10 bit detector range from 0 to 1023.

•Each image is a grey scale two dimensional representation of the cell.

•The color of the image is determined by the wavelengths of the channel its in.

•A mask is applied that determines the region of interest.

•Features are calculated based on the pixel values underneath the mask.

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Pixelated Imagery

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•These are the pixel values for a single PE image.

•Light is quantified for each 0.5 um pixel in the image, and identifies both the intensity and location of the fluorescence.

•All 162 features are calculated from the digital image and include everything from total intensity to variation across the image.


Creating a New Mask and Feature

Select New Mask Select Function Mask

System Mask

Membrane Mask

Morphology Mask

Use the new mask to generate a feature

Hand tag a training set of cells to test the new feature

5_Intensity1e4 1e50

500

1e3

1.5e3

2e3

1e4

5_Intensity

5_S

pot S

mal

l Tot

al

R1, Mitotic

Training Cells

Or plot the new feature


Size Based Features

• Size based features are in pixel units.• Area• Major Axis• Minor Axis• Major Axis Intensity• Minor Axis Intensity• Perimeter


Area

Description:

Area measures the number of pixels in a mask and is expressed in pixels.

Applications:

•Quantify and compares cell size.

•Identify Single Cells.

•Calculate the radius, diameter and volume of the cell.

•Identify apoptosis using the Area of the 30% Threshold mask of a nuclear dye.

•Create a pseudo FSC vs. SSC plot for comparing with flow cytometry.

Brightfield

Channel 5 PI DNA

1 1 1 1 1

1

1 1 1 1 1 1 1

1 1 1 1

1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1

1 1 1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1

1 1 1 1 1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1

1 1 1 1 1

1

1

1 1

11 1

1

1

1

1


Major Axis and Minor Axis

Description:

Major Axis corresponds to the longest dimension of the ellipse of best fit.

Minor Axis is the narrowest dimension of the ellipse of best fit.

Applications:

•Quantify and compare cell width and height.

•Identify small, medium and large cells.

•Convert the radius and diameter to um.

•Compare particle diameters.

Major Axis

Minor Axis

Data collected in collaboration withMD. Steven SollottGRC NIH, Baltimore MD

Brightfield Fluo4


Major and Minor Axis Intensity

Description:

Major Axis Intensity is the longest dimension of the ellipse of best fit and is intensity weighted.

Minor Axis Intensity is the narrowest dimension of the ellipse of best fit and is intensity weighted.

Applications:

•Quantify and compare fluorescence width and height.

•Identify single cells.

Major Axis Intensity

Minor Axis Intensity

Data collected in collaboration withMD. Steven SollottGRC NIH, Baltimore MD

Brightfield Fluo4


Perimeter

Description:

The perimeter measures the boundary length of the mask in number of pixels.

Applications:

•Quantify and compare cell circumference.

•Identify cells with highly irregular surfaces from smooth cells.

•Perimeter of the morphology or threshold masks can identify cells with dendrites vs. those that don’t have them..

Brightfield

Channel 5 PI DNA


Signal Strength Features

• Signal Strength Features are measured in integrated pixel values. • Intensity• Mean Intensity• Minimum Intensity• Peak Intensity


Intensity

Description:

Intensity is the sum of the pixel values within the mask (total intensity) minus the background intensity and is calculated using the formula;

Intensity = Total Intensity – (Background Mean Intensity x Area)

Applications:

•Quantify relative levels of fluorescence between cells and within different regions of the same cell.

•Immunophenotyping.

•Cell cycle analysis.

•Protein expression.

•Protein activation.

Brightfield HLA FITC

Intensity_CD451e4 1e5 1e6

1e3

1e4

1e5

Baso

Mono

Lymph

Neutro

Eo

Intensity_CD45

Inte

nsity

_Dar

kfie

ld

Single Cells


Peak Intensity

Description:

Peak Intensity is the largest pixel value within the mask.

Applications:

•Measure the maximum pixel value within the mask.

•Identify cells that saturate the CCD.

•Peak to mean ratio identifies bright punctate staining vs. uniform staining.

•Plotting peak intensity vs. area of a 30% threshold mask can identify antibody capping.

RTX FITC CD45 PE Brightfield

This image is saturated in the FITC channel (peak intensity = 1023) but not in the PE channel.

4_Peak Intensity0 200 400 600 800 1e3

0

200

400

600

800

1e3

4_Peak Intensity

3_P

eak

Inte

nsity

Raji Cells


Mean Intensity

Description:

Mean Intensity is the average pixel value within the mask and is calculated using the formula;

Mean Intensity = Total Intensity / Area

Applications:

•Quantifies relative levels of mean fluorescence between cells.

•Identify bright punctate spots by calculating the peak to mean ratio.

•Track internalization of surface bound antibodies.

Brightfield CD71 FITC

FITC Cell A Cell B

Mean Intensity 150 150

Peak Intensity 455 739

Total Intensity 240,000 200,000

A

B


Minimum Intensity

Description:

Minimum Intensity is the lowest pixel value within the selected mask.

Applications:

•Quantify spectral absorbance using the brightfield image.

•Identify over compensated images.

•Measure the level of malaria infection in RBCs.

Brightfield YoYo1 Brightfield YoYo1

5_Minimum Intensity60 90 120 150 180 210

0

1

2

3

4

5_Minimum Intensity

Nor

mal

ized

Fre

quen

cy %

Malaria Infected RBC


Location Features

• Location Features are in X,Y pixel coordinates from an origin in the upper left corner.

• Centroid X• Centroid Y• Centroid X Intensity• Centroid Y Intensity


Centroid X, and Centroid Y

Description:

Centroid X is the number of pixels from the first column of the image to the center of the mask.

Centroid Y is the number of pixels from the first row of the image to the center of the mask.

Applications:

•Identify the center of the mask.

•Used to calculate the Delta Centroid or the distance between two fluorescent markers.

•Used to calculate the Radial Delta Centroid.

Brightfield RTX AF488

Brightfield RTX AF488

(0,0)

54

32

(0,0)37

35

X

X

Y

Y


Centroid X and Y Intensity

Description:

Centroid X Intensity, is the intensity weighted X centroid and is shifted from the center of the mask toward the center of fluorescence.

Centroid Y Intensity, is the intensity weighted Y centroid.

Applications:

•Identify the center of peak fluorescence.

•Used to calculate the distance between two fluorescent markers.

•Used to calculate the intensity weighted Radial Delta Centroid.

Feature FITC PE

Centroid X 38.5 38.9

Centroid X Intensity 35.1 38.3

X Intensity Shift 3.4 0.6

Centroid Y 30.7 31.6

Centroid Y Intensity 23.9 30.4

Y Intensity Shift 6.8 1.2

Centroid X,Y



Centroid X,Y


Shape Features

• Shape Features define the mask shape and have units that vary with the feature.

• Aspect Ratio• Aspect Ratio Intensity• Object Rotation Angle• Object Rotation Angle Intensity• Compactness• Elongatedness• Negative Curvature• Spot Count


Aspect Ratio

Description:

Aspect Ratio is the minor axis divided by the major axis and describes how round or oblong a mask is.

Applications:

•Quantify the roundness of the mask.

•Identify single cells vs. doublets.

•Cell classification based on shape change.

•Identify recently divided cells in mitosis.

Aspect ratio=0.93

Aspect ratio=0.32

Brightfield Composite



Aspect Ratio Intensity

Description:

Aspect Ratio Intensity is the minor axis intensity divided by the major axis intensity.

Applications:

•Quantify the roundness of the fluorescent image.

•Better resolution for identifying single cells vs. doublets in experiments using a DNA dye.

•Cell classification based on fluorescent morphology.

Brightfield Draq5

Intensity_DRAQ50 1e5 2e5 3e5 4e5 5e5

0

.2

.4

.6

.8

1

1.2R1

Intensity_DRAQ5

Asp

ect

Rat

io In

ten

sity

_DR

AQ

5

All

Aspect Ratio Intensity Dq5= 0.31

Aspect Ratio Dq5= 0.61

Aspect Ratio Intensity Dq5= 0.31

Aspect Ratio Dq5= 0.61


Object Rotation Angle and Intensity

Description:

Object Rotation Angle is the angle of the major axis from a horizontal plane in radians.

Object Rotation Angle Intensity is the angle of the major axis intensity from a horizontal plane in radians.

Applications:

•Identify the orientation of an image relative to the image frame.

Brightfield 7AAD Composite

Object Rotation Angle 7AAD = 1.2

Major Axis

Horizontal Plane

Object Rotation Angle

Major Axis

Object Rotation Angle

Horizontal Plane

Object Rotation Angle Intensity 7AAD = 1.2

Object Rotation Angle 7AAD = 0.83

Object Rotation Angle Intensity 7AAD = 0.81


Compactness

Description:

Compactness Compactness is computed as the deviation of the object contour from a circle with the same radius and center as the object. A perfect circle has compactness = 0, amoeboid shapes increase in compactness.

Applications:

•Quantify irregularities in the morphology mask.

•Cell classification for identifying cell types based on nuclear morphology.

•Discriminates small round shapes from amoeboid shapes.

Brightfield Draq5

Compactness_C60 .3

0

30

60

90

120

150

Compactness_C6

Fre

qu

en

cy

Draq5 stained PBMC

Lymphocytes

Neutrophils

0.07

0.11

0.16

0.18

Nuclear Compactness


Negative Curvatures

Description:

Negative Curvature Corresponds to the number of negative and positive slope changes along the contour of the morphology mask. One positive and one negative change equals a count of 1.

Applications:

•Enumerate the number of inward folds in the morphology mask.

•Identify cell types based on nuclear morphology.

•Discriminates small round shapes from amoeboid shapes.

Brightfield Draq5 Negative Curvatures

1

1

1

1

2

2

2

3

3

4

0

1

2

3

4


Elongatedness

Applications:

•Quantify the roundness of the morphology mask.

•Identify single cells vs. doublets.

•Cell classification based on shape change.

•Identifies recently divided cells in mitosis.

488 Elongatedness = 6.4

Brightfield Composite AF488



Description:

Elongatedness is the ratio of the maximum width to the minimum width of the bounding rectangle of the object.


Spot Count

Description:

Spot Count is an integer corresponding to the number of connected components within a mask.

Applications:

•Enumerate the number of fluorescent particle inside a cell.

•FISHIS chromosomal polysomy.

•Parasitic protozoan enumeration.

•Counting phagocytosed particles.

Brightfield Babesia YOYO1

Single Parasite

Two Parasites

Three Parasites

Data collected in collaboration withPhD Henry WortisTufts Dept. of Pathology, Boston MA


Texture Features

• Texture Features measure pixel or regional variation and indicate the granularity or complexity of the image.

• Spot Small Total• Spot Medium Total• Gradient Max• Gradient RMS• Frequency


Spot Small and Medium Total

Description:

Spot Small Total is the local background subtracted intensity of spots smaller then 7 pixels in diameter.

Spot Medium Total is the local background subtracted pixel intensity of spots smaller then 14 pixels in diameter

Applications:

•Quantify the amount of light in small spots.

•Identify cells with bright punctate staining.

•Used to distinguish apoptotic cells from live cells.

•Quantifies the total intensity of small FISH spots.

Transferrin PE

Transferrin Topo Map

Pixel values in blue are integrated into the Spot Small Total value.

Intensity

Line Profile


Description:

Gradient Max is the largest slope between any three by three adjacent pixels in the image.

Gradient RMS is the over all magnitude of all the gradient values in the image and reflects gradient quadratic mean.

Gradient Max and RMS

Applications:

•Quantify image crispness.

•Identify cells with high contrast

•Used to eliminate out of focus events.

•Identify apoptotic events, or cells with crisp bright staining.

Brightfield Draq 5

A histogram of a line that transects the image shows highGradients for in focus cells, and low gradients for out of focusCells.


Frequency

Description:

Frequency is the standard deviation of the pixel intensities under the mask, and is an indicator of texture.

Applications:

•Quantify light variation within a mask.

•Identify images with a high degree of variation.

•Apoptotic cells may have very different scatter frequencies then live cells.

•Granular cells may have higher scatter frequency.

Brightfield Scatter AnxnV_7AAD

Apoptotic Cells with high scatter frequency

Live Cells with low scatter frequency


Correlation Features

• Correlation Features compare two channel images using a log transformed Pearson’s correlation coefficient.

• Similarity• Similarity Bright detail


Similarity

7-AAD Pixel Intensity

NF

-kB

Pix

el I

nte

nsi

ty

7-AAD Pixel Intensity

NF

-kB

Pix

el I

nte

nsi

ty

Untranslocated Translocated

NF-kB image

7-AAD image

7-AAD image

Description:

Similarity is the log transformed Pearson’s Correlation Coefficient.

Applications:

•Quantify translocation.

•Identify copolarization of two probes.

7-AAD image


Similarity Bright Detail

Non-specifically Localized Cell

y = 0.1203x + 31.495

R2 = 0.0326

0

25

50

75

100

0 50 100 150 200 250

AF 488 RTX

PE

CD

45

Co-localized Cell

y = 0.6498x - 0.1575

R2 = 0.9493

0

50

100

150

200

250

0 50 100 150 200 250 300 350

AF 488 RTX

PE

an

ti-R

TX

(H

B4

3)

Description:

Similarity Bright Detail is the log transformed Pearson’s correlation coefficient that is non mean normalized, and is applied to the open residue image.

Applications:

•Quantify the degree of colocalization between two probes.

•Used to track internalization and intracellular trafficking of antibody drug conjugates to either the endosomes or the lysosomes.

•Colocalization of Rituxan and compliment C3b.

Endosomes image

Endosomes image

ADC Image

ADC Image


SBD Open Residue Image

• To remove the contribution of background, an image processing step called the “opening residue” is performed on each image of the image pair prior to calculation of SBD.

• First, bright details are eroded with a 7 pixel-wide structuring element followed by a dilation to create the ‘Detail Eroded’ images.

Original Image Detail Eroded Image Bright Detail Image

• Next, the detail eroded images are subtracted from the originals to produce the ‘Bright Detail’ images.

• SBD measures the correlation of this final image pair.


System Features

• System Features do not require a mask, and tend to deal with system wide metrics.

• Object Number• Flow Speed• Camera Line number• Camera Timer• Background Mean Intensity 1-6• Background Standard Deviation 1-6


Camera Timer and Flow Speed

Description:

Camera Timer is the camera clock reading that starts with data acquisition and ends when the file is written. Multiplying by 0.00182 converts the units to seconds.

Flow Speed is the velocity of the core stream in mm/sec.

Applications:

•Track kinetic changes in the sample during data acquisition.

•Track changes in fluorescent intensities over time.

•Flow speed vs. Camera Timer can track velocity changes over time.

Cells with large areas tapered off over the runWhile small cells remained at a constant concentration.

Camera Timer0 1e5 2e5 3e5 4e5 5e5

3.9e4

4e4

4.1e4

4.2e4

4.3e4

4.4e4

4.5e4

Camera Timer

Flo

w S

pe

ed

All

Camera Timer0 3e5 6e5 9e5

0

5e3

1e4

1.5e4

2e4

2.5e4

Camera Timer

Are

a_D

ilate

(Ero

de

(M5

,18

),1

3)

All

Flow Speed vs. Camera Timer shows theperiodic variation in the flow speed that gets corrected out when data is analyzed.

23


Camera Line Number and Object NumberDescription:

Camera Line Number counts the number of pixel rows that is written off the camera during acquisition.

Object number is the order in which the images are collected

Applications:

•Track kinetic changes in the sample during data acquisition.

•Track changes in sample concentration over time.

Subtle changes in the sample concentration can beobserved by plotting the Object number vs. the Camera Line Number and is indicated by the slight variation in this line..

Object Number0 100 200 300 400 500 600

0

5e5

1e6

1.5e6

2e6

2.5e6

Object Number

Cam

era

Line

Num

ber

All

Surge in cell concentration

Drop in cell concentration


Background Mean Intensity and Standard DeviationDescription:

Background Mean Intensity is calculated by adding the intensities in the top 4 and bottom 4 rows of the image and dividing by the number of pixels.

Background Standard Deviation is the square root of the variance in the top and bottom 4 rows.

Applications:

•Measure detector variation in the background of the image.

•Used to subtract background from features that calculate intensity.

BG Mean = 30.2

BG Stnd Dv = 1.0

30.1 191.730.030.1 30.3

0.96 1.1 1.3 1.7 1.5


Combined Features

• Combined features are user defined and are generated by combining base features and mathematical functions.

• Radial Delta Centroid• Nuclear to Cytoplasmic Ratio• Peak Intensity / Mean Intensity• Perimeter ^2/ Area


Radial Delta Centroid

Description:

Radial Delta Centroid is the radial distance from the center of one mask to another measured in pixels.

Applications:

•Quantifies the spatial relationship between two fluorescent probes.

•Identify false apoptotic positive cells with the TUNEL and Annexin V assay.

•Quantify shape change

0.3

0.0

0.3

44.3

46 46

44

C

entr

oid

Y P

ixel

s

C

entr

oid

Y P

ixel

s

Delta X and Y Centroid in Pixels

8.3

21

.8

23.3

56.3

48.2

56.5

34.5

Centroid X Pixels Centroid X Pixels

C

entr

oid

Y P

ixel

s

C

entr

oid

Y P

ixel

s

C=√(Delta Centroid X)2+(Delta Centroid Y)2


Nuclear To Cytoplasmic Ratio

Description:

Nuclear to Cytoplasmic Ratio is the area of the nuclear morphology mask divided by the area of the brightfield mask eroded 3 pixels and not the nuclear mask (cytoplasmic mask).

Applications:

•Compare the nuclear area to the cytoplasmic area.

•Identify cells in metaphase.

•Quantify changes in cell volume relative to nuclear volume over time.


Brightfield CompositeBrightfield Composite

Metaphase cells have lower nuclear to cytoplasmic ratios with small nuclear area and large cytoplasmic area.

5_Intensity

.5

1

1.5

2

2.5

5_Intensity

Nuc

lear

to C

ytop

lasm

ic R

atio

R1, Metaphase


Peak to Mean Ratio

Description:

Peak to Mean Ratio is the background subtracted Peak intensity divided by the background subtracted mean intensity

Applications:

•Identify bright punctate staining from diffuse staining.

•Quantify cells with incomplete capping.

•Identify endosomal internalization.

•Identify LC3 clustering at the autophagolysosome.

Brightfield GFP Composite

Area_Threshold(M3, GFP, 30%)0 50 100 150 200 250

0

2

4

6

8

10

12

Area_Threshold(M3, GFP, 30%)

Pea

k to

Mea

n

R5 , R7

Peak to Mean = (Peak Intensity – Background Mean) / (Mean Intensity – Background Mean)


Internalization

Description:

Internalization uses a complex mask to calculate the ratio of the brightest 50% of the pixels in the cytoplasm, divided by the brightest 50% of the fluorescence everywhere in the cell. When the brightest fluorescence is inside the cell the ratio is 1.

Applications:

•Quantify the fluorescence internalization.

•Internalization of EGF at 37 over time.

Brightfield EGF

Area_Threshold(M3, 3_EGF, 30%)0 50 100 150 200

0

.2

.4

.6

.8

1

Internalized EGF

Area_Threshold(M3, 3_EGF, 30%)

%In

tern

aliz

atio

n

R1

Intensity (Th50% and Center Mask) / Intensity Th50%

Brightfield EGF Brightfield EGF


Summary

• Pixelated imagery allows for the statistical analysis of cell morphology using mathematical functions called features.

• Creating dot plots of single and combined features allows for an easy to understand graphical display of large data sets.

• Collecting tens of thousands of images in conjunction with feature based algorithms allows for morphological discrimination and statistical analysis of unique cell types in a heterogeneous mixture.

think outside the dot

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