Spotlight publications

1. HUMAN CANCER: In this study, multiplex immunofluorescence was used to analyze the staining patterns of 61 antigens on colon cancer samples from 747 patients. Results revealed tumor heterogeneity and offered the ability to map the TOR and MAPK pathways at the single-cell level. This data has served as a source for the development of analysis algorithms currently in use.*

Gerdes MJ, Sevinsky CJ, Sood A, et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc Natl Acad Sci U S A. 2013 Jul 16;110(29):11982-7.

*Note that sophisticated analyses were not available at the time of publication. Single-cell segmentation then cluster analysis was performed. Visualization was done with in house

2. MOUSE STEM CELL: The Coffey lab at Vanderbilt University was an early adopter of Cell DIVE imaging in their work with mouse models to understand stem cell differentiation in the gut. This paper focuses on the tuft cell, a strange cell, which was mostly uncharacterized until this paper. Researchers used Cell DIVE to analyze tuft cell numbers in the small intestine and colon and found two new tuft cell markers. Then used physiological perturbations to characterize tuft cell phenotype.

McKinley ET, Sui Y, Al-Kofahi Y, et al. Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity. JCI Insight. 2017 Jun 2;2(11). pii: 93487.

3. HUMAN CANCER, GLIOMA: This study used different modes of analysis, including genomic sequencing, magnetic resonance imaging (MRI), and multiplexed immunofluorescence, to better understand IDH1 mutant gliomas. These results demonstrate the utility of Cell DIVE in being paired with other modes of study to characterize tumor heterogeneity. Better tumor characterization can lead to an increased understanding of treatment response in genetically different gliomas.

Berens ME, Sood A, Barnholtz-Sloan JS, et al. Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas. PLoS One. 2019 Dec 27;14(12):e0219724.

4. HUMAN METASTATIC CANCER: This paper examines the antitumor immune response, identifying homogenous and heterogenous cell patterns within the tumor microenvironment. This work resulted in the development of mathematic and spatial analysis tools with two algorithms to reveal cell interplay within the tumor microenvironment. A correlation between HLA-1 expression in the tumor and cytotoxic T lymphocyte infiltration into the tumor was observed. The use of Cell DIVE imaging in this study allowed for automated selection of regions of interest within the tumors for further analysis with other markers. Tumor microenvironment expression characteristics were found to be associated with patient outcomes. In the future this type of study is essential for the analysis of the response following immunotherapy.

Yan Y, Leontovich AA, Gerdes MJ, et al. Understanding heterogeneous tumor microenvironment in metastatic melanoma. PLoS One. 2019 Jun 5;14(6):e0216485.

5. HUMAN CANCER: This paper examines samples from relapsed thymoma and thymic carcinoma patients treated with avelumab. Two patients had a positive response and two had a partial response. All experienced adverse events when taking the drug. Cell DIVE imaging was used to characterize the tissues from patients with partial response (pre-treatment and post-treatment).

Rajan A, Heery CR, Thomas A, et al. Efficacy and tolerability of anti-programmed death-ligand 1 (PD-L1) antibody (Avelumab) treatment in advanced thymoma. J Immunother Cancer. 2019 Oct 21;7(1):269.

Publications and presentations

  1. Gerdes MJ, Sevinsky CJ, Sood A, et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Histopathology. 2013 Jul 16;110(29):11982-7.
  2. Clarke GM, Zubovits JT, Shaikh KA, et al. A novel, automated technology for multiplex biomarker imaging and application to breast cancer. Proc Natl Acad Sci U S A. 2014 Jan;64(2):242-55.
  3. Surrette C, Shoudy D, Corwin A, et al. Microfluidic Tissue Mesodissection in Molecular Cancer Diagnostics. SLAS Technol. 2017 Aug;22(4):425-430.
  4. Can A, Bello MO, Cline HE, et al. Multi-modal imaging of histological tissue sections. Published in: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 1008; 288-291.
  5. Kumar VS, Williams JW, Aggour KS, et al. Collaborative Analysis of High-Content Image Data. In NIST BioImage Informatics Conference. 2015.
  6. Larriera A, et al. MultiOmyx characterization of embryonic stem cells. Paper presented at: Am. Soc. Cell Biology. December 2014; Philadelphia, PA.
  7. Dinn S, et al. Measurement of multiple biomarkers using a novel fluorescence immunohistochemistry multiplexing technique. In Drug Discovery & Development of Innovative Therapeutics World Congress. 2014; 4–7.
  8. Lazare M. Novel image processing technique allows immunofluorescence signals to resemble traditional H&E stain and enable multiple biomarker visualizations on a single slide. Paper presented at: US & Canadian Academy of Pathology Annual Meeting (USCAP). Mar 2–8 2013; Baltimore, MD.
  9. McCulloch C, et al. System variability study of multiplexed immunofluorescence using cell line microarrays. Paper presented at: Joint Statistical Meetings (JSM). Jul 28–Aug 2 2013; San Diego, CA.
  10. Li Q. et al. Proof of principle: Colocalization of pan cytokeratins (AE1/AE3), pan cytokeratin (PCK26), and cytokeratin 8/18 using an Integrated Sequential Staining and Imaging Device. Paper presented at: International Academy of Digital Pathology (IADP). Aug 3–5, 2011; Quebec City, Canada.
  11. Zubovits J, et al. Novel multiplexing technology in tissue-based biomarker discovery. Paper presented at: Florida Oncology Symposium: Clinical Pathways to Personalized Medicine. May 5–7 2011; Naples, FL.
  12. Rittscher J, et al. (2011). Methods and algorithms for extracting high-content signatures from cells, tissues, and model organisms. Paper presented at: IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Mar 30–Apr 2 2011; Chicago, IL.
  13. Corwin AD, et al. An automated system for performing multiplexed immunohistochemistry. Paper presented at: Materials Research Society (MRS) Fall Meeting. Nov 29–Dec 3 2010; Boston, MA.
  14. Sevinsky C, et al. Multiplexed immunofluorescence in formalin- fixed paraffin-embedded prostate cancer. In Clin Cancer Res 16, (Suppl 2; A28). Paper presented at: AACR International Conference on Translational Cancer Medicine. Jul 11–14 2010; San Francisco, CA.
  15. Manhard CT, et al. Profiling active protein expression in salivary gland using multiplex methodsPaper presented at: American Association for Dental Research (AADR) Annual Meeting. Mar 3–6 2010; Washington, DC.
  16. Li Q, et al. Visualization the heterogeneity of cancer tissue at the molecular level in situ. Paper presented at: EORTC-NCIASCO Annual Meeting on ‘Molecular Markers in Cancer’. Oct 15–17 2009; Brussels, Belgium.
  17. Barash E, et al. Quantitative analysis of protein multiplexes in tissues using multispectral fluorescence imaging. Paper presented at: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Sep 20–24 2009; London, UK.
  18. Dinn S, et al. Measurement of multiple biomarkers using a novel fluorescence immunohistochemistry multiplexing technique. Paper presented at: Annual World Congress on Drug Discovery & Development of Innovative Therapeutics. Aug 4–7 2008; Boston, MA.
  19. Sevinsky CJ, et al. Measurement of multiple biomarkers using a novel fluorescence immunohistochemistry multiplexing technique. Paper presented at: New York State Histotechnological Society (NYSHS) Annual Meeting. Mar 7–8 2008; Saratoga Springs, NY.
  20. Barnhardt NE, et al. Antibody validation: how do we determine speci city? Paper presented at: National Society for Histotechnology. Oct 26–31, 2007; Denver, CO.
  1. Pang, Z., et al. Autofluorescence removal using a customized filter set. Micros Res Techniq76(10), 1007–1015 (2013).
  2. Pang, Z., et al. Dark pixel intensity determination and its applications in normalizing different exposure time and autofluorescence removal. J Microsc-Oxford246 (1), 1–10 (2012).
  3. Woolfe, F.,et al. Autofluorescence removal by non-negative matrix factorization. IEEE T Image Process20(4), 1085–1093 (2011). DOI: 10.1109/TIP.2010.2079810
  4. Rittscher, J. Characterization of biological processes through automated image analysis. Annu Rev Biomed Eng 12, 315–344 (2010).
  5. Can, A. et al. Techniques for Cellular and Tissue-Based Image Quantitation of Protein Biomarkers. Microscopic Image Analysis for Lifescience Applications,. 1–8 (2008).
  6. Nelson, D.A. et al. Quantitative single cell analysis of cell population dynamics during submandibular salivary gland development and differentiation. Biology Open, 2(5), 439–447 (2013).
  7. Rittscher, J. and Santamaria-Pang, A. (2014, April). Mapping for tissue-based cytometry. In Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium (pp. 278–281). IEEE. DOI:10.1109/ISBI.2014.6867863.
  8. Santamaria-Pang, A. et al. (2013, April)Cell segmentation and classification via unsupervised shape ranking. In Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium (pp.406–409). IEEE. DOI:10.1109/ISBI.2013.6556498.
  9. Margolis, D. et al. (2012) Tissue segmentation and classification using graph-based unsupervised clustering. In Biomedical Imaging (ISBI), 2012, 9th IEEE International Symposium (pp. 162–165). IEEE. DOI:10.1109/ISBI.2012.6235509
  10. Santamaria-Pang, A. et al. (2012) Epithelial Cell Segmentation via Shape Ranking.In Shape Analysis in Medical Image Analysis Springer International Publishing, New York, p. 315–338 (2014).
  11. Rittscher, J. et al. (2011, March). Methods and algorithms for extracting high-content signatures from cells, tissues, and model organisms. In Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium (p. 1712–1716). IEEE.DOI: 10.1109/ISBI.2011.5872734
  12. Manhardt C. et al. (2011) Analyzing signaling dynamics in developing salivary glands using a multiplexing approach. Paper presented at Gordon Research Conference: Salivary Glands & Exocrine Biology. Feb 6–11.
  1. Graf, J.F. and Zavodszky, M.I. Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures. PloS one, 12 (11),e0188878 (2017).
  2. Spagnolo D.M. et al. Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images. Cancer Res 77 (21), 71–4. (2017).
  3. McKinley, E.T. et al. Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity. JCI insight, 2(11) (2017).
  4. Spagnolo, D.M. et al. Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers. J Pathol Inform,7(2016)
  5. Gerdes, M.J. et al. Emerging understanding of multiscale tumor heterogeneity. Frontiers in oncology,4, 366 (2014).
  1. Santamaria-Pang A, Padmanabhan R, Sood A, et al. Robust single cell quantification of immune cell subtypes in histological samples. 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). 2017; 121-124; Orlando, FL.
  2. Uhlik MT, Liu J, Falcon BL, et al. Stromal-Based Signatures for the Classification of Gastric Cancer. Cancer Res. 2016 May 1;76(9):2573-86.
  3. Al-Kofahi Y, Sevinsky C, Santamaria-Pang A, et al. Multi-channel algorithm for segmentation of tumor blood vessels using multiplexed image data. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). 2016; 213-216; Prague.
    10.1109/ ISBI.2016.7493247.
  4. 4. Yan Y, Leontovich A, Flotte T, et al. Tumor HLA Class I expression influences immune cells infiltration in metastatic melanoma tumor microenvironment. Cancer Immunol Res. March, 1 2017; (5) (3 Supplement) A71.
  5. . Yan Y, et al. Novel algorithms for spatial modelling of cellular interactions in the tumor microenvironment. Paper presented at AACR Annual Meeting. 2017, April; Washington, DC.
  6. Yan Y, et al. Local immunological response to invasive melanoma is associated with MHC-1 expression by tumor cells. Paper presented at the AACR Special Conference on Immunotherapy. 2016, October; Boston, MA.
  7. Badve SS, et al. Impact of heterogeneity of DCIS on immune cell infiltrations. Paper presented at San Antonio Breast Cancer Symposium. 2016, November; San Antonio, TX.
  8. Sevinsky C, Santamaria-Pang A, Sood A, et al. Multiplexed immunofluorescence quantitation and validation of multiple immune cell types in colon cancer epithelium and stroma. Cancer Res. July 2016 (76) (14 Supplement) 1467.
  9. Mansfield AS, et al. Multiplexed immunofluorescence demonstrates tumor infiltrating lymphocytes localize to periphery of primary melanomas but not metastases. Paper presented at: American Soc. Clinical Oncology. May 29–June 2 2015; Chicago, IL.
  10. Sevinsky C, Santamaria-Pang A, Zhang J, et al. Quantification of biologically relevant vascular phenotypes in human prostate cancer: automated image analysis using hyperplexed immunofluorescence. Cancer Res. August 2015 (75) (15 Supplement) 1709.
  11. . Adams A, et al. MultiOmy: A Novel Chemistry and Visualization Tools To Aid Resolution Of Discrepant Hodgkin Lymphoma Cases-A Single Slide Multiplex Assay For The Evaluation Of Classical Hodgkin Lymphoma. In Blood, 122(21). Paper presented at American Society of Hematology (ASH). Abstract #2994; 2013, December; New Orleans, LA.
  12. . Bordwell A., et al. (2013, November). MultiOmyx™: A novel multiplex methodology for the evaluation of Hodgkin Lymphoma. Paper presented at Association for Molecular Pathology (AMP). Abstract #295618. 2013, November; Phoenix, AZ.
  1. Simmons AJ, Banerjee A, McKinley ET, et al. Cytometry‐based single‐cell analysis of intact epithelial signaling reveals MAPK activation divergent from TNF‐α‐induced apoptosis in vivo. Mol Syst Bio. 2015 Oct; 11(10).
  2. Gerdes MJ, et al. Capture-stabilize approach for membrane protein SPR assays. Single-cell MultiOmyx analysis of normal and cancerous colon tissues. In Electronic Meeting Booklet. Paper presented at Annual Single Cell Analysis Investigators Meeting. 2013, April; Bethesda, MD.
  3. Sevinsky CJ, et al. Multiplexed immunofluorescence imaging and single-cell analysis of MAPK and mTOR signaling in a large cohort of colorectal cancer subjects. Paper presented at Ninth AACR-JCA Joint Conference: Breakthroughs in Basic and Translational Cancer Research. 2013, Feb; Maui, HI.
  4. Sevinsky C, et al. Multiplexed immunofluorescence analysis of hypoxia and tumor microenvironment in localized colorectal cancer. Paper presented at Keystone Symposia Conference, Advances in Hypoxic Signaling: From Bench to Bedside. 2012, Feb. Alberta Canada.
  5. Ginty F, Adak S, Can A, et al. Automated c-Met membrane to cytoplasm translocation score predicts shorter survival time in stage I and II colon cancer patients. The FASEB Journal. 2007 21:6: LB65.
  1. Li C, Ma H, Wang Y, et al. Excess PLAC8 promotes an unconventional ERK2-dependent EMT in colon cancer. 2014; 124(5): 2172-2187.
  2. Ginty F, Adak S, Can A, et al. The relative distribution of membranous and cytoplasmic met is a prognostic indicator in stage I and II colon cancer. Clin Cancer Res. 2008 Jun 15;14(12):3814-22.
  1. Hollman-Hewgley D, Lazare M, Bordwell A, et al. A single slide multiplex assay for the evaluation of classical Hodgkin lymphoma. Am J Surg Pathol. 2014;38(9):1193–1202.
  2. Adams A, et al. MultiOmyx: Novel chemistry and visualization tools to aid resolution of discrepant Hodgkin Lymphoma cases. Paper presented at the International Symposium on Hodgkin Lymphoma (ISHL). Abstract #P072; Oct 12─15 2013; Cologne, Germany.
  3. Weiss L, Bordwell Alexander, Corwin A, et al. (2013). Multiplexed IHC analysis to enable Hodgkin lymphoma differential diagnosis on a single slide. J Clin Oncol. 2013; 31. e19536-e19536.
  1. Gerdes MJ, Gökmen-Polar Y, Sui Y, et al. Single-cell heterogeneity in ductal carcinoma in situ of breast. Mod Pathol. 2018;31(3):406–417.
  2. Sood A, Miller AM, Brogi E, et al. Multiplexed immunofluorescence delineated proteomic cancer cell states associated with metabolism. JCI Insight. 2016;1(6):e87030.
  3. Santagata S, Thakkar A, Ergonul A, et al. Taxonomy of breast cancer based on normal cell phenotype predicts outcome. J Clin Invest. 2014;124(2):859–870.
  4. LaPlante, N. et al. MultiOmyx screening for prognostic indicators of breast cancer. Paper presented at: San Antonio Breast Cancer Symposium (SABCS). Abstract P4-05-12. Dec 10–14, 2013, San Antonio, TX.
  5. Lazare M, et al. New visualization technique enables HER2 immuno uorescence staining to resemble traditional chromogenic DAB stain. Paper presented at: US & Canadian Academy of Pathology Annual Meeting (USCAP). 2013, Mar; Baltimore, MD.
  6. Al-Kofahi Y, et al. An automated algorithm for cell-level FISH dot counting. Paper presented at: SPIE Medical Imaging. 2013, Feb; Orlando, FL.
  7. T Ha, A Seppo, F Ginty, et al. HER2 Expression and Gene copy analysis by Immunofluorescence and Fluorescence in situ Hybridization, on a Single formalin-fixed paraffin-embedded tissue section. Cancer Res. December 15 2012; (72) (24 Supplement) P3-05-05.
  8. Sood, A. et al. Multiplexing technology for in situ biomarker profiling of Non-Small Cell Lung Cancer (NSCLC). Paper presented at: World Conference on Lung Cancer (WCLC). Oct 27–30 2013; Sydney Australia: S453–S453.
  9. Pang Z, et al. Co-localization of Her2/neu, ER, PR, Ki67, and cytokeratin on a triple positive breast cancer patient. Paper presented at: Biomedical Engineering Society (BMES) Annual Meeting. Oct 12–15 2011, Hartford, CT.
  1. Sood, A. et al. Multiplexing technology for in situ biomarker profiling of Non-Small Cell Lung Cancer (NSCLC). Paper presented at: World Conference on Lung Cancer (WCLC). Oct 27–30 2013; Sydney Australia: S453–S453.
  2. Kaanumalle, L. S. et al. Multiplexed analysis of lung cancer for distinguishing adenocarcinoma from squamous cell carcinoma. Paper presented at: World Conference on Lung Cancer (WCLC). Oct 27–30 2013; Sydney Australia. J Thorac Oncol; 8, S453–S454.
  1. Sevinsky C. et al. In situ phenotyping of prostate cancer tissue using multiplexed immunouorescent staining and quantitation technology. Paper presented at: WIN Symposium. Abstract P4.02. 2013, July; Paris, France.
  1. Zhang J., et al. Characterization of glioblastoma (GBM) vasculature and protein expression of surrounding tumor cells on single FFPE sections with a multi-cycle multiplexed in situ immunouorescent staining technology. Paper presented at: American Society of Clinical Oncology (ASCO) Annual Meeting. May 31–Jun 4 2013: Chicago, IL . J Clin Oncol; Abstract 2097.
  2. Zhang J. et al. (2012)Multi-cycle multiplexed in situ immuno uorescence analysis of 18 biomarkers on a single FFPE glioblastoma tissue section. Paper presented at: Society for Neuro-Oncology (SNO) Annual Meeting. Nov 15–18 2012: Washington, DC. Neuro-Oncology; 14, Suppl 6; CB-18, vi1011.

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