Current models of stem cell biology assume that normal and neoplastic stem cells reside at the apices of hierarchies and differentiate into nonstem progeny in a unidirectional manner. Here we identify a subpopulation of basal-like human mammary epithelial cells that departs from that assumption, spontaneously dedifferentiating into stem-like cells. Moreover, oncogenic transformation enhances the spontaneous conversion, so that nonstem cancer cells give rise to cancer stem cell (CSC)-like cells in vitro and in vivo. We further show that the differentiation state of normal cells-of-origin is a strong determinant of posttransformation behavior. These findings demonstrate that normal and CSC-like cells can arise de novo from more differentiated cell types and that hierarchical models of mammary stem cell biology should encompass bidirectional interconversions between stem and nonstem compartments. The observed plasticity may allow derivation of patient-specific adult stem cells without genetic manipulation and holds important implications for therapeutic strategies to eradicate cancer.breast cancer | dedifferentiation

SummaryCells receive time-varying signals from the environment and generate functional responses by secreting their own signaling molecules. Characterizing dynamic input-output relationships in single cells is crucial for understanding and modeling cellular systems. We developed an automated microfluidic system that delivers precisely defined dynamical inputs to individual living cells and simultaneously measures key immune parameters dynamically. Our system combines nanoliter immunoassays, microfluidic input generation, and time-lapse microscopy, enabling study of previously untestable aspects of immunity by measuring time-dependent cytokine secretion and transcription factor activity from single cells stimulated with dynamic inflammatory inputs. Employing this system to analyze macrophage signal processing under pathogen inputs, we found that the dynamics of TNF secretion are highly heterogeneous and surprisingly uncorrelated with the dynamics of NF-κB, the transcription factor controlling TNF production. Computational modeling of the LPS/TLR4 pathway shows that post-transcriptional regulation by TRIF is a key determinant of noisy and uncorrelated TNF secretion dynamics in single macrophages.

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We developed an automated microfluidic chip that can measure dynamic cytokine secretion and transcription factor activation from cells responding to time-varying stimuli. Our chip patterns antibodies, exposes cells to time-varying inputs, measures cell secretion dynamics, and quantifies secretion all in the same platform. Secretion dynamics are measured using micrometer-sized immunoassays patterned directly inside the chip. All processes are automated, so that no user input is needed for conducting a complete cycle of device preparation, cell experiments, and secretion quantification. Using this system, we simulated an immune response by exposing cells to stimuli indicative of chronic and increasing inflammation. Specifically, we quantified how macrophages respond to changing levels of the bacterial ligand LPS, in terms of transcription factor NF-κB activity and TNF cytokine secretion. The integration of assay preparation with experimental automation of our system simplifies protocols for measuring cell responses to dynamic and physiologically relevant conditions and enables simpler and more error free means of microfluidic cellular investigations.