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A prevailing interpretation of Waddington’s landscape is that distinct cell types with distinct physiologies are produced and stabilized by dynamical attractors in gene expression space. This notion is often applied in the analysis of single-cell omics data, where cells are clustered into groups that represent cell types, prior to downstream analyses like differential gene expression. Until the advent of single-cell measurement technologies, however, it has been impossible to characterize the heterogeneity of cells in the neighborhoods of these attractor states. In this work, we apply graph theory to characterize the distribution of cells in epigenetic space, using data from various tissues and organisms as well as various single-cell omics technologies. Rather than finding distinct clusters of cells that map cell types to specific regions of epigenetic space, we found that cells of very distinct types and lineages occupy the same region of space. Further, we found that the density distribution of cells is approximately power-law, with most cells existing in low-density regions, very far from other cells. This highly heterogeneous density distribution is unexpected, as it is not consistent with the distributions we would expect to see in the neighborhood of an attractor. We found these two observations are universal in single-cell data on epigenetic state of multicellular organisms, regardless of the tissue, organism, measurement technique employed, or the approach used to select the subset of genes on which the analysis was performed. The fact that currently-available single-cell data is inconsistent with the predictions of Waddington’s landscape poses a challenge both for the robust analysis of these data and for our overall understanding of epigenesis in development.

mTORC1 senses nutrient and growth factor status and phosphorylates downstream targets, including the transcription factor TFEB, to coordinate metabolic supply and demand. The molecular mechanisms of mTORC1 activation are thought to enforce a strict requirement for simultaneous amino acid and growth factor stimuli, but this model has not been evaluated with quantitative or single-cell methods. Here, we develop a series of fluorescent protein-TFEB fusions and investigate how combinations of stimuli jointly regulate signaling from mTORC1 to TFEB at the single-cell level. Live-cell imaging of individual cells revealed that mTORC1-TFEB signaling responds with graded changes to individual amino acid and growth factor inputs, rather than behaving as a logical “AND” gate. We find that mTORC1 inputs can be sequentially sensed, with responses that vary between mTORC1 substrates and are amplified by input from other kinases, including GSK3β. In physiologically relevant concentrations of amino acids, we observe fluctuations in mTORC1-TFEB signaling that indicate continuous responsiveness to nutrient availability. Our results clarify how the molecular regulation of mTORC1 enables homeostatic processes at the cellular level and provide a more precise understanding of its behavior as an integrator of multiple inputs.

Single-cell RNA sequencing (scRNA-seq) aims to characterize how variation in gene expression is distributed across cells in tissues and organisms. Yet, effective comprehension of these extremely high-dimensional datasets remains a critical barrier to progress in biological research. In standard analyses of scRNA-seq data, feature selection steps aim to reduce the dimensionality of the data by focusing on a subset of genes that are the most biologically variable across a set of cells. Ideally, these features provide the genes that are the most informative for partitioning groups of transcriptionally distinct cells, each representing a different cell type or identity. In this work, we propose a simple feature selection model where a binomial sampling process for each mRNA species produces a null model of technical variation. To compare our model to existing methods, we use scRNA-seq data where cell identities have been established a priori for each cell, and characterize whether different feature sets retain biologically varying genes, distort neighborhood structures, and allow popular clustering algorithms to partition groups of cells into their established classes. We find that our model of biological variation, which we term “Differentially Distributed Genes” or DDGs, outperforms existing methods, and enables dimensionality reduction without loss of critical structure within the data set.

Cellular metabolism can be reconfigured to balance nutrient processing (catabolism) and cellular demands (anabolism). However, the kinetics of reconfiguration within individual mammalian cells, and heterogeneity between cells, have remained unexplored. Using live-cell imaging, we investigate the kinetics of bioenergetic adaptation in individual cells. In response to acute inhibition of oxidative phosphorylation, AMPK substrates are phosphorylated bimodally, identifying cells in different underlying states of anabolism and catabolism. Manipulation of glycolysis, insulin/Akt signaling, or protein synthesis shifts the distribution of these states. Long-term lineage analysis confirms that this cellular energy balance cycles over time within individual cells, independently of the cell division cycle. We further demonstrate that AMPK inhibits the ERK and mTOR cell growth signaling pathways specifically when anabolism is in excess. Our results reveal dynamic fluctuations of energetic balance, establish distinct time scales of cellular energetic control, and open opportunities for more precise prediction and control of cellular metabolic functions.

Cell-to-cell heterogeneity in metabolism plays an unknown role in physiology and pharmacology. To functionally characterize cellular variability in metabolism, we treated cells with inhibitors of oxidative phosphorylation (OXPHOS) and monitored their responses with live-cell reporters for ATP, ADP/ATP, or activity of the energy-sensing kinase AMPK. Across multiple OXPHOS inhibitors and cell types, we identified a subpopulation of cells resistant to activation of AMPK and reduction of ADP/ATP ratio. This resistant state persists transiently for at least several hours and can be inherited during cell divisions. OXPHOS inhibition suppresses the mTORC1 and ERK growth signaling pathways in sensitive cells, but not in resistant cells. Resistance is linked to a multi-factorial combination of increased glucose uptake, reduced protein biosynthesis, and G0/G1 cell-cycle status. Our results reveal dynamic fluctuations in cellular energetic balance and provide a basis for measuring and predicting the distribution of cellular responses to OXPHOS inhibition.

Receptor tyrosine kinases transduce signals through ERK and drive distinct cellular behaviors, but it is not clear how dynamic patterns of ERK activity are generated. To approach this question quantitatively, we have generated an epithelial cell line expressing a tunable version of the canonical nerve growth factor (NGF) receptor TrkA. Upon NGF stimulation, we visualized the dynamics of ERK activity in live cells using an improved fluorescent reporter for ERK activity, EKAR3. Examination of the NGF receptor pathway in isolation revealed that stimulation with NGF promotes a prolonged ERK activity pulse that is amplitude-modulated by the concentration of the stimulus, in contrast to the frequency modulation observed in the epidermal growth factor (EGF)-RAS-ERK pathway. This result implies that the primary determinants of ERK kinetics are the unique set of signaling proteins recruited to the activating receptor. To dissect the context in which ERK is activated in respect to parallel signaling pathways, we have engineered mutant NGF receptors that cannot be phosphorylated at one or more of the three adaptor binding sites and characterized how each signaling pathway contributes to the pattern of dynamic ERK activity. Furthermore, because our NGF receptor expression system allows the tunable and quantifiable expression of receptor levels, we have evaluated the effect of receptor number on ERK kinetics in single cells. Our findings indicate that the dynamics of ERK activity are in fact decided at the receptor-level and may explain how different receptor tyrosine kinase profiles in cancer cells modulate distinct patterns of ERK activity. Citation Format: Breanne Sparta, Michael Pargett, Kevin Distor, Marta Minguet, John G. Albeck. Receptor-level feedback mechanisms distinguish frequency- and amplitude-modulated ERK activation. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-30.

In both physiological and cell culture systems, EGF-stimulated ERK activity occurs in discrete pulses within individual cells. Many feedback loops are present in the EGF receptor (EGFR)-ERK network, but the mechanisms driving pulsatile ERK kinetics are unknown. Here, we find that in cells that respond to EGF with frequency-modulated pulsatile ERK activity, stimulation through a heterologous TrkA receptor system results in non-pulsatile, amplitude-modulated activation of ERK. We further dissect the kinetics of pulse activity using a combination of FRET- and translocation-based reporters and find that EGFR activity is required to maintain ERK activity throughout the 10-20-minute lifetime of pulses. Together, these data indicate that feedbacks operating within the core Ras-Raf-MEK-ERK cascade are insufficient to drive discrete pulses of ERK activity and instead implicate mechanisms acting at the level of EGFR.

Single-cell analysis of the mitogen-activated protein kinase (MAPK) extracellular signal regulated kinase (ERK) activity provides a means to perform highly detailed kinetic studies, assess heterogeneity between cells, and distinguish the subcellular localization of ERK activity. We describe here the methods needed to perform such measurements in a cell type of the investigator’s choosing. We discuss the selection of appropriate reporters, and provide detailed methods for stably introducing reporters, collecting live-cell data, and automatically extracting quantitative information from individual cells.

We formulated a computational model for a MAPK signaling cascade downstream of the EGF receptor to investigate how interlinked positive and negative feedback loops process EGF signals into ERK pulses of constant amplitude but dose-dependent duration and frequency. A positive feedback loop involving RAS and SOS, which leads to bistability and allows for switch-like responses to inputs, is nested within a negative feedback loop that encompasses RAS and RAF, MEK, and ERK that inhibits SOS via phosphorylation. This negative feedback, operating on a longer time scale, changes switch-like behavior into oscillations having a period of 1 hour or longer. Two auxiliary negative feedback loops, from ERK to MEK and RAF, placed downstream of the positive feedback, shape the temporal ERK activity profile but are dispensable for oscillations. Thus, the positive feedback introduces a hierarchy among negative feedback loops, such that the effect of a negative feedback depends on its position with respect to the positive feedback loop. Furthermore, a combination of the fast positive feedback involving slow-diffusing membrane components with slower negative feedbacks involving faster diffusing cytoplasmic components leads to local excitation/global inhibition dynamics, which allows the MAPK cascade to transmit paracrine EGF signals into spatially non-uniform ERK activity pulses.

Purpose: PI3K/AKT/mTOR network adaptation through the upregulation of receptor tyrosine kinases signaling and subsequent re-activation of AKT is a common resistance mechanism to chemotherapies and small molecule inhibitors of mammalian target of rapamycin (mTOR). In order to block this feedback loop, MM-141, a tetravalent bispecific antibody directed at IGF-1R and ErbB3, was co-administered in combination with everolimus in various preclinical models of cancer. As everolimus is an approved therapy for women with advanced hormone receptor-positive breast cancer after failure of treatment with aromatase inhibitor, we have specifically focused on the analysis of a MM-141/everolimus combination in a hormone refractory breast cancer mouse model. Experimental Procedures: Estrogen-supplemented mice bearing BT-474-M3 ER+/PR+ breast cancer xenografts were implanted with tamoxifen-releasing pellets. Following the development of resistance to tamoxifen, these mice were randomized into pharmacodynamics and efficacy assessment sub-groups, and treated with everolimus, MM-141 or the combination. In pharmacodynamics assessment subgroups, the tumors were harvested 24 hours following the last MM-141 dose and profiled for a panel of molecular markers of activation of PI3K/AKT/mTOR pathway. In addition we have assessed the activity of MM-141, everolimus or the combination in cell culture on a broad panel of breast cancer cell lines using CellTiter-Glo® Luminescent Cell Viability Assay. Data Summary: We have demonstrated that MM-141 has significant anti-tumor activity in the ER+/PR+ BT-474-M3 breast cancer xenograft model and strongly potentiates the activity of everolimus in a tamoxifen-resistant setting. Pharmacodynamic profiling illustrates that the combination treatment achieves sustained inhibition of PI3K/AKT/mTOR signaling. MM-141 mediates the resistance to everolimus by inhibiting IGF-1R, ErbB3, and IRS-1 and controlling the re-activation of AKT. Further, MM-141 is able to inhibit the proliferation of a panel of breast cancer cell lines in vitro. This inhibition is increased upon co-treatment of the cells with MM-141 and everolimus. Conclusions: Our in vivo preclinical studies show that MM-141 combines synergistically with the mTOR inhibitor everolimus and this combination is active in reversing acquired resistance to the anti-hormonal therapy tamoxifen. This potentiation of everolimus by MM-141 is tied to its ability to reverse the re-activation of AKT signaling induced by this mTOR blocker. We demonstrate that this potentiation phenomenon is common in a wide panel of breast cancer cell lines. Overall, our results suggest that MM-141 could be a potentially valuable addition to the breast cancer treatment regimens comprising an mTOR inhibitor and an anti-hormonal therapy. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):C169. Citation Format: Sharlene Adams, Jason Baum, Breanne Sparta, Victoria Rimkunas, Jian Tang, Emily Pace, Shoshana Rosenthal, Adam Camblin, Sergio Iadevaia, Akos Czibere, Ulrik Nielsen, Alexey Lugovskoy. MM-141, a bispecific antibody inhibitor of PI3K/AKT/mTOR, attenuates tumor growth and potentiates everolimus in mouse models of anti-hormonal therapy-resistant ER/PR+ breast cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr C169.