ihc ppt for web

Hernani Cualing MD 1

Slide Based Cytometry of

Immunostained Tissue:

"Virtual Flow Cytometry" on a

Slide


Background

• The standard of clinical practice is to estimate the percentage of immunohistochemically (IHC) stained cells

• This practice is subjective and often gives a wide range of results that depends on the level of the microscopists’ skill. – limitation of our visual system

– difficulty in counting positive(or negative) cells • overlapped stained nuclei

• variability of immunostaining presence of other irrelevant objects

• Takes time to manually count these cells

• Wide intra- and interobserver results because of subjectivity

• Developing a cytometry tool that promises similar objectivity as flow cytometry, will only decrease the incidence of errors in mensuration and diagnosis.


Quantitative IHC Report-All iHC are

estimates

Above example is the semiquantitative

report by estimate- subjective, may be

inaccurate,may contribute to error

propagation in diagnosis and prognosis


Although the hue value of the neck

of the hourglass remains the same,

we do not see them as equal.

Although we excell in pattern recognition

our color visual system is influenced by

the saturation and intensity components of color;

we are also poor judge of intensity differences

Human perception of color is limited.

(hue = 0)


Computerized algorithms could separate color

into its components for analysis- left panel,magnified view

of immunohistologic stained cells- negative nuclei and positive nuclei, right panel are

the hue, saturation and intensity components of the pixels


Overlapping and variability of

staining and presence of other

cells of different class(type)

Positive

lymphoid cells

Negative

lymphoidcells

Non

lymphoid

negative

tumor cells

Irrelevant

objects


Stained and unstained cells of the same class, ie, lymphocytes

unstained


Flow cytometry, as a model for this virtual flow cytometry, classifies cells of

the same class as positive and negative on a defined set of marker/s


Flow cytometry uses fresh cell suspension to give objective results but the observer

loses visual confirmation. In contrast, immunohistochemistry using fixed tissue,

allows observer to see but loses the objectivity in the counting of these cells. The

flow cytometry also loses the location information-location cytomics is not

recovered.MARKERS

– IMMUNOPHENOTYPE

• flow

cytometry

• Immunohisto-

chemistry


Applications of virtual flow cytometry

• Research applications– Tissue culture biomarkers analysis; location cytomics

• Prognostic markers– tumor infiltrating lymphocytes (CD8 TILs in B-cell

lymphomas, sarcomas, carcinomas, and others)

• Diagnostic uses

– CD4/CD8 ratio

– Kappa Lambda ratio

– Proliferative Index ( Ki-67, Mib1)

• Therapeautic applications

– Monitoring targeted cell antigens pre- and post-therapy in PTCLs-AntiCD2,anti CD30, etc

– Monitoring CD20 post-Rituxan therapy


Challenges of Slide based virtual Flow Cytometry

1)Thick tissue sections with overlapping of cells,

2) Variability of technics and platforms in immunostaining of tissues,

3) Optimal sampling resolution for obtaining size and staining data per cell object ( objective magnification 10x vs 20x vs 40x),

4) Issues of optical output properties of image acquisition hardware/s,

5 ) Lack of cell based algorithms able to detect dual populations (positive immunostained cells and non-immunostained relevant cells of the same class) and,

6) Arriving at an automated and adaptive thresholding algorithm based on the characteristics of the objects and the background non objects,

7) Determining image specific parameters that optimize detection of percentage positive cells


Solutions• 1. Quality Histology ( 2 micron sections and good hematoxylin countestain)

• 2. Using an automated industry standard autostainer

• 3. At 20x magnification, sampling is optimal ; 10x too pixelated, 40x less cells per frame

• 4. Built in contrast enhancement provided by the three chip CCD blue, green and red channels

• 5. Enhanced image lends easily to “dual thresholding” of positive and negative cells of the same class; thresholding divided the image pixels into positive and negative cell objects; here we threshold for brown and then blue pixels

• 6. Adaptive automated histogram based thresholding algorithm combined with percentile thresholding; the latter to discard the darkest and brightest pixels

.

• 7. We discovered the parameters obtained from the image itself to automate the thresholding function using the entropy algorithm. We found a good correlation between the ratio of positive cell objects with negative cell objects and the value of this parameter entropy.


• Image segmentation- is the process of dividing an image into regions;

in digital images based on pixels (picture elements) properties into

objects and into background. Most use either thresholding of pixel

histogram or localizing object boundaries using edge detection.

• The simplest and most widely used segmentation method is

thresholding- where all the pixels below a certain darker threshold is

assigned the object and above, the background –grayscale and color

• Cellular logic operations- to further define objects

Image analysis components for slide based cytometry

1. Segmentation- need dual mode for population of positive and

negative

2. Feature extraction-size, perimeter, staining density, etc.

3. Classification- positive and negative, percent positive, stain

density of positive

Definitions


Edge vs Thresholding

Two major approaches in segmentation use the property of color and gray

level values of objects: discontinuity and similarity.

Objects with well-defined discontinuities usually benefit from edge-based

detection and

those with poorly defined discontinuities may benefit from similarity

approach such as thresholding. Accurate segmentation remains one

of the problems in pixel based image ratio with up to 15% error on both

falsely positive and falsely negative results (USPTO patent #

6,553,135).

Because of the complex, discontinuous, multicolor, large scale

images in immunohistochemically stained biologic cells, We

implemented cell-based thresholding algorithm.


The generic method in extracting information from an image by

segmentation is by thresholding the brightness of pixels-

divides the image into component objects or regions based on

their distribution in the brightness scale; Objects==below

threshold and background==above threshold, that is for gray

scale images. For colored images, the issue is more complex.


Approaches:

1) simple gray scale thresholding?


Raw color frame for manual interpretation

Tumor nuclei

CD8 positive

T-lymphocytes

CD8 negative

lymphocytes


corresponding computer generated

interpretation

Tumor nuclei

CD8 positive

T-lymphocytes

CD8 negative

lymphocytes


Strategy for reducing the theory of a slide

cytometer or virtual flow cytometer to practice

1. Count positive and negative cells of the

same cell type-phase I

2. Extract the size and color intensity

information and generate a two parameter

histogram distribution-phase II

3. Validate the results in relation to

manual count and flow cytometry results on a

defined cancer type-phase III.


Results of Phase I Study : CD8 Tumor

infiltrating Lymphocytes with Positive and

Negative Cells

Case x Immunostai

nDensity Lymphocyte Number

Lymphocytes (+) stain

Lymphocyte stain (+) (%)

1 4 58 23 39.655174 2 4 52 22 42.307693 3 4 57 43 75.438599 4 4 56 22 39.285713 5 4 61 44 72.131149 6 4 106 48 45.28302 7 4 54 25 46.296295 8 4 74 13 17.567568 9 4 43 33 76.744186

10 4 45 22 48.888889 11 4 36 18 50 12 4 73 8 10.958904 13 4 131 13 9.923664 14 4 86 24 27.906977 15 4 42 18 42.857143

Min 4 36 8 9.923664 Max 4 131 48 76.744186 Median 4 57 22 42.857143 Total 974 376 Average 4 64.933334 25.066668 38.603695


Phase II extraction of cell

objects data• The formula below was used to convert pixel area to cell diameter in

microns ( Y data), for a 20x magnified image:

Cell diameter = 2 * (sqrt of (Area in pixels / )) / 1.5

= sqrt of (Area in pixels * P) ;where diameter is in microns, is conversion factor (

0.56588424212) for 20x

• The optical density data per cell object was given by the sum of optical density

of the colored pixels comprising the previously labeled Cell objects ( X data).

• Each cell object then was labeled and contained size and density numbers

which were used for the dot plot display. The final resulting data then consisted

of an array of labeled objects with size and corresponding stain density.

• These two parameters defined the frequency distribution of the dual cell objects

in the two parameterl dot plot: a display commonly used in flow cytometry.


Flow cytometry immunophenotypic analysis is applied to cell suspension to

give the percentage values of stained and unstained cells based on their

fluorescence intensities.


iHCFlow ™ Tissue Cytometry

• A method and approach is developed-preservation of location cytomics.

• The conversion of immunohistochemistry (IHC) data to a flow cytometry-like two parameter dot-plot display, is calledIHCFLOW technic and because the dot plot is obtained from a slide using single cell analysis of the same population; is also called a virtual flow cytometry.


How we differ from previous studies

– Other systems use multispectral instrumentation and adaptive wavelength filters

– confocal immunofluorescent microscopy to obtain separation of positive stain from nonstained pixels.

– few hybrid microscope-flow cytometry were developed to obtain single cell information to mimic flow cytometry.

– Immunofluorescent technics were applied on slide substrate to obtain cell populations using laser scanning instrumentation

• The IHCFLOW technic does not need complex, fluorescent and expensive instrumentation. It needs a 3 chip RGB CCD equipped microscope

• Some studies looked at counting immunohistochemistry stained slides, but none converted the results to a flow cytometry paradigm.


Virtual Flow cytometry


Segmentation and Result


Results of Flow Cytometry

and Virtual Flow Cytometry

6 % CD8 +5.08 % CD8

+


CD3

CD3 = 24%


BCL-1

85%


Ki-67

28%


CD5

55%


Slide Based image cytometry

Slide based cytometry promises to be a tool to complement flow cytometry

and enhance objectivity in histologic analysis

Niche: For tissue and cell-based quantitative analysis of

immunohistochemistry stained cells

Other applications:

Quantitative tissue microarrays

Stromal marker studies

Quantitative biomarker profile of epithelia

Tumor infiltrating lymphocytes in mantle cell lymphomas

Tumor infiltrating lymphocytes in colonic carcinomas


Tissue MoAb Microarray ( bcl-1 in mantle cell

lymphoma)


Tissue Microarray Slide Based Flow Cytometry


Area X Y Perim

.

Angle Circ. Feret IntDe

n

Skew %Are

a

XStart YStart

1 51 253 6 25 0 -1 9 0 0 100 250 2

2 118 503 7 48 174 1 18 0 0 100 505 2

3 173 468 10 47 172 1 17 10455 1 76 464 3

4 73 56 12 32 56 1 13 0 0 100 55 7

5 167 220 22 49 66 1 19 1530 5 96 221 13

6 299 128 29 68 69 1 24 0 0 100 123 19

7 54 145 26 66 20 0 16 0 0 100 145 20

8 830 191 38 129 33 1 50 85425 0 60 193 21

9 227 233 30 57 74 1 20 16065 1 72 230 21


Ki67 in tonsilar epithelium- 31 %


Ki67 in tonsilar epithelium- 18 %


VALIDATION OR PHASE III: PROJECT on

MANTLE CELL LYMPHOMAS AND Tumor

Infiltrating lymphocytes ( TIL’S)


DESIGN AND RESULTS

• Segmentation of a 512 x 474 RGB image and tabular display of statistical results table took 2-3 seconds using proprietary developed algorithms ( by IHCFLOW, Inc).

• We used a panel of 7 antibodies for validation on 14 cases of mantle cell lymphoma giving percentage positive, total lymphocytes, and staining density. A total of 2,027 image frames with 810,800 cell objects (COBs) were evaluated. Antibodies to CD3, CD4, CD8, Bcl-1, Ki-67, CD20, CD5 were subjected to virtual flow cytometry on tissue. The results of Tissue Cytometry were compared with manual counts of expert observers and with the results of flow cytometric immunophenotyping of the same specimen.

• The correlation coefficient and 95 % confidence interval by linear regression analysis yielded a high concordance between manual human results (M), flow cytometry results (FC), and Tissue Cytometry (TC) results per antibody, (r =0.9365 manual vs TC, r =0.9537 FC vs TC). The technical issues were resolved and the solutions and results were evaluated and presented

• Reference: Cualing HD,Moscinski LC, Zhong E. “Virtual Flow Cytometry” of Immunostained Lymphocytes on Microscopic Tissue Slides, Clinical Cytometry Jan 15, 2007.


Correlation

r = 0.95

r = 0.93


Mantle cell lymphoma and TIL’s


S04-A Y a S04-c y c

TC manual flow TC manual flow TC

bcl1 94 95 81 85 79

cd3 36 30 34 24 25 29 31

cd4 19 20 16 19 20 21 25

cd5 86 50 89 80 85 75 99

cd8 14 20 15 15 9 8 7

cd20 96 90 86 84 90 76 94

ki67 8 10 10 12 56


CD8 and CD20 tumor infiltrating lymphocytes (TILs)

and prognosis of adenocarcinoma of the colon

CD8 CD20

carcinoma


Quantitative microscopy redefined

Quantitative microscopy is increasingly used in both pathology and biomedical research applications

Applications needing morphometry values from digital images of microscopic sections of tissue-”Tissue Cytomics or Tissomics”

Where morphometry is defined beyond the commonly used pixel area of the target objects per image frame(“pixel based analysis”) ;

But that of quantitative information obtained per cell-“Cell-based analysis or Cytomics”

Delineation of % of cells carrying the targeted biomarker in a population of the same class of cells “ Class targeted cytomics”


Tissomics

• Combining diverse data streams across

different levels of observations-such as

molecular, cellular, clinical data-is a system

wide approach

• Tissue cytomics –high throughput high

content phenotyping methodology provides

data rich profile of cellular heterogeneity in

tissue enabling correlative statistics over

biologics targets


Class-targeted Cytomics

• Need for slide based:

– Non consumptive analysis= re-analysis

– Allows visual validation

– Cell parameters could be correlated

– Fixed tissue

– Amenable to routine and experimental

setup


Virtual flow cytometry applied to an

immunostained tissue analysed on a slide

matrix

• Reduced to practice, patent pending

• Not the same as antigen quantitation

• Has real world applications in daily

pathology practice

• CPT code ready- ie.88361- computer

assisted software analysis of IHC

• Similar to flow cytometry but performed

on paraffin fixed histologic sections


Cytomics tool

• The iHCFlow ™ Tissue Cytometry fulfills the major Cytomics criteria of

• 1) relating multiple parameters to each other,

• 2) within large population of cells,

• 3) on a single-cell basis,

• 4) on a quantitative and observer-independent manner.

• But unlike the other cytomics systems which use immunofluorescent tissue stains, the system differs in using routine immunohistochemistry on tissue generating flow cytometry-like results.

• Ecker R C,Steiner GE. Microscopy-based multicolor tissue cytometry at the single-cell level. Cytometry A

2004;59:182-190.


summarypopulation statistics and dot plot two-dimensional histogram results show a

reliable, robust, accurate reporting similar to that of a flow cytometer.

We differ from the current methods of image analysis systems by focusing on cell-based population statistics instead of pixel-area data.

The correlation between each case run in flow cytometry and estimated by experts and by the advanced image Tissue Cytometry is high and suggest a valid approach to objectively quantifying immunostaining

REFERENCES

Cualing HD,Moscinski LC, Zhong E. “Virtual Flow Cytometry” of Immunostained Lymphocytes on Microscopic Tissue Slides, Clinical Cytometry Jan 15,

2007.

Khalil F*, Heekin J, Lili Miles, Cualing H.The rate and morphometric pattern of bone marrow recovery post-myelosuppressive therapy for acute leukemia: a

quantitative study-submited to Archives of Pathology and Lab Medicine Aug 2007

Cualing H. Automated Analysis in Flow Cytometry. Cytometry, 2000 , 42:p.110-113.

Young IT, Quantitative Microscopy. IEEE Engineering in Medicine and Biology, 1996, 15(1): p.59-66.

Ridler TW ,Calvard S. Picture Thresholding Using Iterative Selection Method. IEEE Trans. On Systems, Man, and Cybernetics, 1978. SMC-8(8):p 630-632.

Cualing H. Kothari R, Balachander T. Immunophenotypic Diagnosis of Acute Leukemia Using Decision Tree Induction. Lab Investigation, 1999, 79:p.205-212.


Collaborators

• IHCFLOW, Inc

• GreenGreat, Inc.

• E Zhong

• Marshall Kadin

• L Moscinski

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