Trajectories of cell-cycle progression from fixed cell populations

Gut, Gabriele; Tadmor, Michelle D.; Pe’er, Dana; Pelkmans, Lucas; Liberali, Prisca

doi:10.1038/nmeth.3545

Cited by 102 publications

(131 citation statements)

References 22 publications

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“…A series of studies 35,37,40,80–85 have now used these principles to infer dynamics from single cell profiles, while assuming linear 35,40,48,86 , cyclic 45,87 , or bifurcating 37,80–84 trajectories. For example, one early method, Wanderlast, used single cell multiplex protein measurements from mass cytometry (CyTOF) data to build a trajectory of B cell differentiation 35 .…”

Section: The Temporal Axis: Inferring Dynamicsmentioning

confidence: 99%

Scaling single-cell genomics from phenomenology to mechanism

Tanay

Regev

2017

Nature

689

560

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Three fundamental questions in biology are how do individual cells differentiate to form tissues, how do tissues function in a coordinated and flexible fashion, and which gene regulatory mechanisms support these processes. Single cell genomics open new ways to tackle these questions by combining the comprehensive nature of genomics with the microscopic resolution required to describe complex multi-cellular systems. Early single cell genomic studies provides us with remarkably rich phenomenology of heterogeneous cellular states, but transforming observational studies to models of dynamics and causal mechanism in tissues poses new challenges and require stronger integration between theoretical, computational and experimental frameworks.

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Section: The Temporal Axis: Inferring Dynamicsmentioning

confidence: 99%

Scaling single-cell genomics from phenomenology to mechanism

Tanay

Regev

2017

Nature

689

560

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“…In the cluster A (non-responding cells, Figure 3d), we have observed an enrichment for cells that just came out of mitosis. This could be explained by a high ERK activity present during this cell cycle stage 40 or by an inability of the cells to enrich the sensor in the nucleus at this phase of the cell cycle. In the second cluster (Cluster B, Figure 3e), a weak activation of ERK is followed by deactivation.…”

Section: Single Cell Analysis Of the Dynamics Of Erk Activitymentioning

confidence: 99%

Visualizing cellular heterogeneity by quantifying the dynamics of MAPK activity in live mammalian cells with synthetic fluorescent biosensors

Bordignon

Dotto

et al. 2019

Preprint

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Mitogen-Activated Protein Kinases (MAPKs) control a wide array of cellular functions by transducing extracellular information into defined biological responses. In order to understand how these pathways are regulated, dynamic single cell measurements are highly needed. Fluorescence microscopy is well suited to perform these measurements, however, more dynamic and sensitive biosensors that allow the quantification of signaling activity in living mammalian cells are required. We have engineered a synthetic fluorescent substrate for human MAPKs that relocates from the nucleus to the cytoplasm when phosphorylated by the kinase. We demonstrate that this reporter provides a better sensitivity relative to other similar biosensors and has allowed the monitoring of ERK MAPK activity pulses upon a single physiological EGF stimulation. In addition, we display its applicability to other MAPKs and in multiple cancer cell lines or primary cells as well as its application in vivo in developing tumors. Using our newly developed biosensors, dynamic single cell measurements with high temporal resolution can be obtained. These reporters can be widely applied to the analysis of molecular mechanisms of MAPK signaling in healthy and diseased state, in cell culture assays or in vivo.

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“…Of course, moving towards larger numbers of analytes would certainly provide for a deeper characterization. [67][68][69] We utilized computational and theoretical methods 32,33,36,37,[70][71][72][73] , integrated with additional cell biology experiments, to translate the SCBC kinetic series of snapshots in to classes of single cell trajectories.…”

Section: Figure 5 Differential Drug Sensitivity Of Cells Associated mentioning

confidence: 99%

Trajectories from Snapshots: Integrated proteomic and metabolic single-cell assays reveal multiple independent adaptive responses to drug tolerance in a BRAF-mutant melanoma cell line

et al. 2019

Preprint

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The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge, with relevance towards understanding biological changes ranging from cellular differentiation to epigenetic (adaptive) responses of diseased cells to drugging. We report on a combined experimental and theoretic method for determining the trajectories that specific highly plastic BRAF V600E mutant patient-derived melanoma cancer cells take between drug-naïve and drug-tolerant states. Recent studies have implicated non-genetic, fast-acting resistance mechanisms are activated in these cells following BRAF inhibition. While single-cell highly multiplex omics tools can yield snapshots of the cell state space landscape sampled at any given time point, individual cell trajectories must be inferred from a kinetic series of snapshots, and that inference can be confounded by stochastic cell state switching. Using a microfludic-based single-cell integrated proteomic and metabolic assay, we assayed for a panel of signaling, phenotypic, and metabolic regulators at four time points during the first five days of drug treatment. Dimensional reduction of the resultant data set, coupled with information theoretic analysis, uncovered a complex cell state landscape and identified two distinct paths connecting drug-naïve and drugtolerant states. Cells are shown to exclusively traverse one of the two pathways depending on the level of the lineage restricted transcription factor MITF in the drug-naïve cells. The two trajectories are associated with distinct signaling and metabolic susceptibilities, and are independently druggable. Our results update the paradigm of adaptive resistance development in an isogenic cell population and offer insight into the design of more effective combination therapies.

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Trajectories of cell-cycle progression from fixed cell populations

Cited by 102 publications

References 22 publications

Scaling single-cell genomics from phenomenology to mechanism

Scaling single-cell genomics from phenomenology to mechanism

Visualizing cellular heterogeneity by quantifying the dynamics of MAPK activity in live mammalian cells with synthetic fluorescent biosensors

Trajectories from Snapshots: Integrated proteomic and metabolic single-cell assays reveal multiple independent adaptive responses to drug tolerance in a BRAF-mutant melanoma cell line

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