Abstract—In this paper, we present a simple method for the processing and quantification of multiplexed Quantum Dot (QD) labeled images of clinical cancer tissue samples. QDs provide several features which make them ideal for reliable quantification, including long-term signal stability, high signal- to-noise ratios, as well as narrow emission bandwidths. Deconvolution of QD spectra is accomplished in a batch mode in which unmixing parameters are preserved across samples to allow for quantitative and reproducible comparisons. After unmixing the QD images, we segment each one to exclude acellular regions. We use a simple average intensity to quantify the level of QD staining for each image. We illustrate the viability of this approach by testing it on 28 tissue samples using a tissue microarray. We show that using as few as two QD protein targets (MDM-2, and B-actin), the Renal Cell Carcinoma (RCC) samples are distinguishable from adjacent normal tissue samples. A simple linear discriminant results in 100% classification of 25 RCC samples and 3 normal samples. This suggests that multiplexed QDs can be used to properly diagnose RCC from otherwise healthy tissue. We expect to apply this work to larger panels of more robust QD biomarker targets to aid in clinical decision-making for the diagnosis and prognosis of diseases, such as cancer. I. INTRODUCTION ORE than 50,000 men and women in the United States are estimated to be diagnosed with kidney cancer in 2008 . Renal cell carcinoma (RCC) exists in a variety of subtypes each of which has a different prognosis, progression, and recommended treatment strategy [1]. Recent advances in high-throughput genomics, largely due to Manuscript received April 16, 2008. This work was supported in part by the National Institutes of Health (Bioengineering Research Partnership R01CA108468, P20GM072069, Center for Cancer Nanotechnology Excellence U54CA119338), Georgia Cancer Coalition (Distinguished Cancer Scholar Award to MW) Microsoft Research, National Science Foundation (GRFP Fellowship to RM), and a Georgia Tech Institute of Bioscience and Bioengineering Seed Grant. M. L. Caldwell is a BS candidate at the Georgia Institute of Technology, Atlanta, GA 30318 USA (e-mail: mcaldwell@gatech.edu). R. A. Moffitt is a Ph.D. candidate at the Georgia Institute of Technology and Emory University, Atlanta, GA 30322 USA (e-mail: r.moffitt@gatech.edu). J. Liu is a Postdoctoral fellow at Emory University (e-mail: jliu44@emory.edu). R. M. Parry is a Postdoctoral fellow at Georgia Institute of Technology (e-mail: parry@bme.gatech.edu). Y. Sharma is a Ph.D. candidate at the Georgia Institute of Technology (e-mail: ysharma3@mail.gatech.edu). M. D. Wang is an Assistant Professor at the Georgia Institute of Technology, and Emory University (corresponding author; phone: 404-385- 5059; e-mail: maywang@bme.gatech.edu). the maturation of RNA microarray technology, have provided insights into the unique molecular profiles exhibited by these renal cancer subtypes [2]. This in turn has caused a shift in emphasis away from generalized cancer therapies and towards personalized and subtype specific treatments [1]. Current methods of RCC molecular subtyping rely almost entirely on the use of traditional organic dyes to label relevant biomarkers. However, traditional dyes present a number of obstacles to accurate quantification. First, many common dyes fluoresce over a broad range of wavelengths. This property makes it difficult to multiplex biomarkers as the emission spectra of different dyes often overlap with each other as well as excitation sources, and are difficult to unmix spectrally. In addition, most organic dyes are susceptible to photobleaching, which limits their usefulness as a quantitative tool as the intensity of the fluorescence degrades quickly with repeated measurements. The lack of multiplexing ability for organic dyes has led to a focus on more nonspecific and qualitative methods of cancer image classification, mainly analyzing morphology using hematoxylin and eosin (H&E) staining. Computational methods to automatically assess such images have had some success [3] but have not yet been widely implemented. Multiplexed bioconjugated QD staining is an emerging technology which has the potential to greatly increase the accuracy of cancer diagnosis and subtyping through molecular profiling. This technology has several properties which help overcome the known issues with current methods based on organic dyes. Quantum dots are nano-scale semiconductor crystals with broad excitation spectra and relatively narrow emission spectra which are controlled by the size of the crystals [4]-[9]. These properties allow the simultaneous excitation of multiple QDs in the same image with one light source as well as the simultaneous and separable observation of 5 or more different QDs in the visible and infrared range [5,8]. Furthermore, QDs are significantly brighter than most organic dyes and extremely resistant to photobleaching [4]-[9]. This combination of properties gives QDs the potential to be a robust and easily quantifiable tool for molecular profiling. Despite the promise of QDs as a quantitative pathology tool, significant obstacles still remain to their use in a clinical setting. One issue is the a priori identification of the relevant regions in the tissue sample before quantification, a task that Simple Quantification of Multiplexed Quantum Dot Staining in Clinical Tissue Samples Matthew L. Caldwell Student Member, IEEE, Richard A. Moffitt, Graduate Student Member, IEEE, Jian Liu, R. Mitchell Parry, Yachna Sharma, and May D. Wang, Member, IEEE M 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20-24, 2008 978-1-4244-1815-2/08/$25.00 ©2008 IEEE. 1907