Protein Counting in Single Cancer Cells.

The cell is the basic unit of biology and protein expression drives cellular function. Tracking protein expression in single cells enables the study of cellular pathways and behavior but requires methodologies sensitive enough to detect low numbers of protein molecules with a wide dynamic range to distinguish unique cells and quantify population distributions. This study presents an ultrasensitive and automated approach for quantifying phenotypic responses with single cell resolution using single molecule array (SiMoA) technology. We demonstrate how prostate specific antigen (PSA) expression varies over several orders of magnitude between single prostate cancer cells and how PSA expression shifts with genetic drift. Single cell SiMoA introduces a straightforward process that is capable of detecting both high and low protein expression levels. This technique could be useful for understanding fundamental biology and may eventually enable both earlier disease detection and targeted therapy.

Assessing the stochastic intermittency of single quantum dot luminescence for robust quantification of biomolecules.

Single molecule detection schemes promise that one has the ability to reach the ultimate limit of detection: one molecule. In this paper, we use the stochastic luminescence of single semiconductor nanocrystals (quantum dots, QDs) to detect and localize particles as digital counts. These digital counts can be correlated to the concentration of analytes in solution. Here, we use total internal reflection fluorescence (TIRF) microscopy to probe individual QDs immobilized on a functionalized substrate. QDs have found their niche in the bioanalytical community due to their remarkable brightness and stability. Despite their numerous outstanding photophysical properties, QDs at the single particle level display a pronounced intermittent luminescence, posing a challenge for the detection of individual particles. In this paper, we demonstrate a reliable method for detecting QDs that takes advantage of these signal fluctuations by comparing the variations in the QD's fluorescence signals against variations of the background signal. The quantitative methodology developed here results in signal-to-background ratios up to 90:1, which is at least 8-times higher than the ratios obtained using methodologies relying solely on signal integration. This enhanced signal-to-background ratio facilitates a robust thresholding process and results in femtomolar limits of detection.

Ultra-sensitive protein detection via Single Molecule Arrays towards early stage cancer monitoring.

The early diagnosis of cancers and continued monitoring of tumor growth would be greatly facilitated by the development of a blood-based, non-invasive, screening technique for early cancer detection. Current technologies for cancer screening and detection typically rely on imaging techniques or blood tests that are not accurate or sensitive enough to definitively diagnose cancer at its earliest stages or predict biologic outcomes. By utilizing Single Molecule Arrays (SiMoA), an ultra-sensitive enzyme-linked immunosorbent assay (ELISA) technique, we were able to measure increasing levels of prostate specific antigen (PSA) within murine serum over time, which we attribute to tumor development. The measured concentrations of PSA were well below the detectable limits of both a leading clinical diagnostic PSA ELISA assay as well as a commercial ultra-sensitive PSA assay. Our work benchmarks the role of SiMoA as a vital tool in monitoring previously non-detectable protein biomarkers in serum for early cancer detection and offers significant potential as a non-invasive platform for the monitoring of early stage cancer.