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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:

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Presentation on theme: "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:"— Presentation transcript:

1 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 Boston, September 20, 2010

2 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.

3 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

4 Challenges Sample preparation protocols (FICTION) Multispectral Imaging protocols Unmixing Signals Data processing

5 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

6 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

7 CD21 single CD21: Cell Marque, rabbit mono, 1:200 2 nd antibody: donkey anti-rabbit, Alexa fluorescence at 488 nm Counter stained by DAPI; 10x frozen tonsil (7-1-10 S1) Auto-fluorescenceDAPI CD21 Composite after un-mixing Raw Cube Spectrum Library

8 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

9 CD21 + FDC RGB

10 CD21 + FDC Composite

11 FDC + FISH t(11;14) 488nm FDC 524nm FISH 588nm FISH

12 Close up: RGB

13 Close up: Composite

14 FISH + CD138 RGBDAPI CD138Unmix

15

16

17 Quantum Dot 20 times brighter 200 times more stable Single exitation range Narrow emission spectrum

18 Qdot FDC CD21 Deep Red (663) CD21(525) Composite after un-mixing Raw Cube FDC (605) (FDC, Cell Marque, rabbit mono, 1:50 + CD21, 1:200 2 nd 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

19 Qdot CD21 + FDC RGB

20 Qdot CD21 and FDC composite

21 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.

22 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.

23 Acknowledgement David J. Foran, Ph. D Daniel J. Medina, Ph. D Wenjin Chen, Ph.D.Laura Zheng, M.S. Lauri Goodell, M.DLei Cong, M.S. Lin Yang, Ph.DHana Aviv, Ph.D Fuyong Xing, M.S. Roger K. Strair, Ph.D., M.D. Will Cukierski Bekah Gensure Vicky Chu, M.S.

24 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.

25 Thank you! Further Questions –Clinical Aspects: Lauri Goodell, MD goodell@umdnj.edu goodell@umdnj.edu –Molecular Biology: Daniel Medina, PhD medinadj@umdnj.edu medinadj@umdnj.edu –Multispectural Imaging: Xin Qi, PhD qixi@umdnj.edu qixi@umdnj.edu

26 Qdot FDC (FDC, Abcam, mouse mono, 1:400 2 nd Antibody: goat anti-mouse, Q-dot at 605nm, 1:50 Counter stain: Qnuclear Deep Red [640; 663] 9-9-10 S3) Auto-fluorescence Deep Red (663) FDC(605nm) Composite after un-mixing Raw Cube Spectrum Library

27 Qdot CD21 (CD21, Cell Marque, rabbit mono, 1: 200 2 nd Antibody: goat anti-mouse, Q-dot at 525nm, 1:50 9-9-10 S1) Auto-fluorescence Deep Red (663) CD21 (525nm) Composite after un-mixing Raw Cube Spectrum Library

28 FDC single Auto-fluorescenceDAPI FDC(594nm) Composite after un-mixing Raw Cube Spectrum Library FDC: Cell Marque, mouse mono, 1:100 2 nd antibody: donkey anti-mouse, Alexa fluorescence at 594 nm Counterstained by DAPI; 10x frozen tonsil (7-1-10 S3)


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