Abstract
The distribution of single-cell properties across a population of cells can be measured using diverse tools, but no technology directly quantifies the biochemical stimulation events regulating these properties. Here we report digital counting of growth factors in single cells using fluorescent quantum dots and calibrated three-dimensional deconvolution microscopy (QDC-3DM) to reveal physiologically relevant cell stimulation distributions. We calibrate the fluorescence intensities of individual compact quantum dots labeled with epidermal growth factor (EGF) and demonstrate the necessity of near-infrared emission to overcome intrinsic cellular autofluoresence at the single-molecule level. When applied to human triple-negative breast cancer cells, we observe proportionality between stimulation and both receptor internalization and inhibitor response, reflecting stimulation heterogeneity contributions to intrinsic variability. We anticipate that QDC-3DM can be applied to analyze any peptidic ligand to reveal single-cell correlations between external stimulation and phenotypic variability, cell fate, and drug response.
Highlights
The distribution of single-cell properties across a population of cells can be measured using diverse tools, but no technology directly quantifies the biochemical stimulation events regulating these properties
As described in detail in Methods, a three-step process is applied to count epidermal growth factor (EGF) molecules per cell: (1) Single quantum dots (QDs)-EGF spots are identified in videos by distinctive time-course intensity traces, I(t), for which two discrete intensities are present in a b
Blinking can impact each QDC-3DM step depicted in Fig. 1b, so the analytical performance can depend on both the QD photophysical properties and the image acquisition conditions
Summary
The distribution of single-cell properties across a population of cells can be measured using diverse tools, but no technology directly quantifies the biochemical stimulation events regulating these properties. Applied together with systems biology models and information theory, it is becoming clear that any population of genetically identical cells naturally exhibits substantial cell-to-cell variability that is integral to the emergence of ensemble biological functions[3] This heterogeneity has important consequences, as rare cells, rather than cells near the ensemble mean, often dominate clinically meaningful pathogenic processes and drug resistance[4,5,6]. Input factors are typically applied at stimulation extremes (zero and near saturation)[9], whereas physiologically relevant tissue concentrations are in intermediate regimes (c ~ 1–100 pM)[10,11] over which cells exhibit sensitive and heterogeneous dose–response relationships (EC50 ~ 1–100 pM)[12,13]. Accurate quantification of initiating signals is very challenging[17] and requires single-molecule sensitivity
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