Simple quantification of multiplexed quantum dot staining in clinical tissue samples.
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.
Caldwell
Matthew L
ML
Georgia Institute of Technology, Atlanta, GA 30318 USA. mcaldwell@gatech.edu
Moffitt
Richard A
RA
Liu
Jian
J
Parry
R Mitchell
RM
Sharma
Yachna
Y
Wang
May D
MD
D001698:Biomedical Engineering; D002292:Carcinoma, Renal Cell / Q000145:classification / Q000175:diagnosis / Q000378:metabolism; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007680:Kidney Neoplasms / Q000145:classification / Q000175:diagnosis / Q000378:metabolism; D045663:Quantum Dots; D012680:Sensitivity and Specificity; D013194:Staining and Labeling