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Xin Qi, PhD, Daniel J. Medina, PhD, Laura Zheng, MS,

Optimizing Specimen Preparation and Multispectral Imaging Protocols to Accommodate Automated Assessment of Expression in Non-Hodgkin’s Lymphoma. 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

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Xin Qi, PhD, Daniel J. Medina, PhD, Laura Zheng, MS,

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  1. Optimizing Specimen Preparation and Multispectral Imaging Protocols to Accommodate Automated Assessment of Expression in Non-Hodgkin’s Lymphoma 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 data ( i, j, λ) 620 nm 580nm 540nm 500nm 460nm 420nm λ- cube RGB image CIE standard observer color-matching functions 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.

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

  8. FDC+CD21 DAPI Raw Cube FDC(594) CD21(488) Composite after un-mixing Spectrum Library CD21 (488) + FDC (594) 10x; Frozen Tonsil FDC+CD21 (7-1-10 S8)

  9. CD21 + FDC RGB

  10. CD21 + FDC Composite

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

  12. Close up: RGB

  13. Close up: Composite

  14. FISH + CD138 RGB DAPI CD138 Unmix

  15. Quantum Dot • 20 times brighter • 200 times more stable • Single exitation range • Narrow emission spectrum

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

  17. Qdot CD21 + FDC RGB

  18. Qdot CD21 and FDC composite

  19. 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.

  20. 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.

  21. 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.

  22. 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.

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

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

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