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Xin Qi, PhD, Daniel J. Medina, PhD, Laura Zheng, MS, Lei Cong, MS, Hana Aviv, PhD, Lauri A. Goodell, MD, Roger K. Strair,MD PhD, David J. Foran, PhD Presentor: Wenjin Chen, Ph.D. Center for Biomedical Imaging and Informatics The Cancer Institute of New Jersey Pathology Informatics 2010 Scientific Section

Xin Qi, PhD, Daniel J. Medina, PhD, Laura Zheng, MS, Lei Cong, MS, Hana Aviv, PhD, Lauri A. Goodell, MD, Roger K. Strair,MD PhD, David J. Foran, PhD Presentor:

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Xin Qi, PhD, Daniel J. Medina, PhD, Laura Zheng, MS, Lei Cong, MS, Hana Aviv, PhD, Lauri A. Goodell, MD,

Roger K. Strair,MD PhD, David J. Foran, PhD

Presentor: Wenjin Chen, Ph.D.Center for Biomedical Imaging and Informatics

The Cancer Institute of New Jersey

Pathology Informatics 2010 Scientific SectionBoston, September 20, 2010

Background Non-Hodgkin lymphomas (NHLs) are a diverse group of blood

cancers that include any kind of lymphoma except Hodgkin's lymphomas. Types of NHL vary significantly in their severity, from indolent to very aggressive.

Lymphomas are types of cancer derived from lymphocytes, a type of white blood cell. Lymphomas are treated by combinations of chemotherapy monoclonal antibodies, immunotherapy, radiation, and hematopoietic stem cell transplantation.

Our team previously reported the development of tools for performing grid-enabled, content-based image retrieval and caBIG compliant data management.

We have since begun to investigate the potential use of these tools for evaluating non-Hodgkin’s lymphoma histology specimens to determine the staining and expression patterns of markers CD20, CD21, CD138, and FDC.

Motivation Enhance our understanding of biology of non-

Hodgekin’s lymphomas Simultaneously visualize protein expressions

and chromosomal changes from tissue sections

Multispectual imaging potencially allows for analysing multiple proteins/DNA probes on single cells or on sub-cellular compartments

Preserve tissue resources

Challenges

Sample preparation protocols (FICTION)

Multispectral Imaging protocols

Unmixing Signals

Data processing

Our Imaging System

Nuance VIS-FLEX multispectral imaging workstation (Cambridge Research & Instrumentation, Inc.) integrated with a Nikon 90i microscope (Micron Optics, Inc.).

Specifications– Resolution: 1392x1040x31– Wavelength: 420nm – 720 nm

Experiment Setup

Multispectral images were acquired, after removing auto-fluorescence, at resolution of 1392 x 1042 x 31, where the 31 spectral bands cover wavelengths 420-720 nm. Composite images were created and the expression pattern of each biomarker was digitized and stored.

420nm

460nm

500nm

540nm

580nm

620 nm

Multispectral data ( i, j, λ)

CIE standard observer color-matching functions

λ- cube RGB image

CD21 single

CD21: Cell Marque, rabbit mono, 1:2002nd antibody: donkey anti-rabbit, Alexa fluorescence at 488 nm Counter stained by DAPI; 10x frozen tonsil (7-1-10 S1)

Auto-fluorescence DAPI

CD21 Composite after un-mixing

Raw Cube

Spectrum Library

FDC+CD21

FDC+CD21 (7-1-10 S8)

DAPI

CD21(488) Composite after un-mixing

Raw Cube FDC(594)

Spectrum Library

CD21 (488) + FDC (594)10x; Frozen Tonsil

CD21 + FDC RGB

CD21 + FDC Composite

FDC + FISH t(11;14)

488nmFDC

524nmFISH

588nmFISH

Close up: RGB

Close up: Composite

FISH + CD138

RGB DAPI

CD138 Unmix

Quantum Dot

20 times brighter 200 times more

stable Single exitation

range Narrow emission

spectrum

Qdot FDC CD21

Deep Red (663)

CD21(525) Composite after un-mixing

Raw Cube

FDC (605)

(FDC, Cell Marque, rabbit mono, 1:50 + CD21, 1:2002nd Antibody: goat anti-rabbit, Q-dot at 525nm, 1:50 + goat anti-mouse, Q-dot at 605 nm, 1:50 20x magnification 9-9-10 S4)

Spectrum Library

Qdot CD21 + FDC RGB

Qdot CD21 and FDC composite

Conclusion

Both the chromosome FISH probes and the surface expression of CD20, CD21, CD138 and FDA could be strongly detected using MSI even though the signals were faint using standard fluorescent imaging.

Future Work

Continue to improve sample preparation protocol (Qdot with FISH)

Compare the accuracy of the computational algorithms in assessing expression levels and biomarker localization in Non-Hodgkin’s lymphoma MSI data sets.

Acknowledgement

David J. Foran, Ph. D Daniel J. Medina, Ph. D

Wenjin Chen, Ph.D. Laura Zheng, M.S.

Lauri Goodell, M.D Lei Cong, M.S.

Lin Yang, Ph.D Hana Aviv, Ph.D

Fuyong Xing, M.S. Roger K. Strair, Ph.D., M.D.

Will Cukierski Bekah Gensure

Vicky Chu, M.S.

Acknowledgement This research was funded, in part, by grants from the National

Institutes of Health through contract 5R01EB003587-04 from the National Institute of Biomedical Imaging and Bioengineering and contracts 5R01LM009239-03 and 3R01LM009239-03S2 from the National Library of Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

Additional funds were provided by IBM through a Shared University Research Award. UMDNJ also wants to thank and acknowledge IBM for providing free computational power and technical support for this research through World Community Grid.

Thank you!

Further Questions– Clinical Aspects: Lauri Goodell, MD

[email protected]– Molecular Biology: Daniel Medina, PhD

[email protected]– Multispectural Imaging: Xin Qi, PhD

[email protected]

FDC single

Auto-fluorescence DAPI

FDC(594nm) Composite after un-mixing

Raw Cube

Spectrum Library

FDC: Cell Marque, mouse mono, 1:1002nd antibody: donkey anti-mouse, Alexa fluorescence at 594 nmCounterstained by DAPI; 10x frozen tonsil (7-1-10 S3)