Cell Fate Decision as High-Dimensional Critical State Transition.

Cell fate choice and commitment of multipotent progenitor cells to a differentiated lineage requires broad changes of their gene expression profile. But how progenitor cells overcome the stability of their gene expression configuration (attractor) to exit the attractor in one direction remains elusive. Here we show that commitment of blood progenitor cells to the erythroid or myeloid lineage is preceded by the destabilization of their high-dimensional attractor state, such that differentiating cells undergo a critical state transition. Single-cell resolution analysis of gene expression in populations of differentiating cells affords a new quantitative index for predicting critical transitions in a high-dimensional state space based on decrease of correlation between cells and concomitant increase of correlation between genes as cells approach a tipping point. The detection of "rebellious cells" that enter the fate opposite to the one intended corroborates the model of preceding destabilization of a progenitor attractor. Thus, early warning signals associated with critical transitions can be detected in statistical ensembles of high-dimensional systems, offering a formal theory-based approach for analyzing single-cell molecular profiles that goes beyond current computational pattern recognition, does not require knowledge of specific pathways, and could be used to predict impending major shifts in development and disease.

Direct elicitation of template concentration from quantification cycle (Cq) distributions in digital PCR.

Digital PCR (dPCR) exploits limiting dilution of a template into an array of PCR reactions. From this array the number of reactions that contain at least one (as opposed to zero) initial template is determined, allowing inferring the original template concentration. Here we present a novel protocol to efficiently infer the concentration of a sample and its optimal dilution for dPCR from few targeted qPCR assays. By taking advantage of the real-time amplification feature of qPCR as opposed to relying on endpoint PCR assessment as in standard dPCR prior knowledge of template concentration is not necessary. This eliminates the need for serial dilutions in a separate titration and reduces the number of necessary reactions. We describe the theory underlying our approach and discuss experimental moments that contribute to uncertainty. We present data from a controlled experiment where the initial template concentration is known as proof of principle and apply our method on directly monitoring transcript level change during cell differentiation as well as gauging amplicon numbers in cDNA samples after pre-amplification.

Non-Darwinian dynamics in therapy-induced cancer drug resistance.

The development of drug resistance, the prime cause of failure in cancer therapy, is commonly explained by the selection of resistant mutant cancer cells. However, dynamic non-genetic heterogeneity of clonal cell populations continuously produces metastable phenotypic variants (persisters), some of which represent stem-like states that confer resistance. Even without genetic mutations, Darwinian selection can expand these resistant variants, which would explain the invariably rapid emergence of stem-like resistant cells. Here, by using quantitative measurements and modelling, we show that appearance of multidrug resistance in HL60 leukemic cells following treatment with vincristine is not explained by Darwinian selection but by Lamarckian induction. Single-cell longitudinal monitoring confirms the induction of multidrug resistance in individual cells. Associated transcriptome changes indicate a lasting stress response consistent with a drug-induced switch between high-dimensional cancer attractors. Resistance induction correlates with Wnt pathway upregulation and is suppressed by ß-catenin knockdown, revealing a new opportunity for early therapeutic intervention against the development of drug resistance.