On the relationship between cell cycle analysis with ergodic principles and age-structured cell population models

Kuritz, Karsten; Stöhr, Daniela; Pollak, Nadine; Allgöwer, Frank

doi:10.1016/j.jtbi.2016.11.024

Cited by 18 publications

(27 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To this end, we used a static flow cytometric measurement of DNA and mAG-hGeminin (1-110), a fluorescent ubiquitinationbased cell cycle indicator ( Fig. 2 A) (Sakaue-Sawano et al, 2008;Kuritz et al, 2017), in unperturbed NCI-H460/geminin cells to reconstruct the kinetics of geminin. The pseudotime obtained with the markers was mapped to the temporal scale on which the the cell cycle progresses, namely the age of a cell (time since cytokinesis).…”

Section: Resultsmentioning

confidence: 99%

“…The pseudotime obtained with the markers was mapped to the temporal scale on which the the cell cycle progresses, namely the age of a cell (time since cytokinesis). To achieve this, MAPiT derives real-time trajectories from the steady state age distribution (Powell, 1956;Kafri et al, 2013;Kuritz et al, 2017) Multicellular spheroids grown from cancer cells are widely used as avascular tumor models (Vörsmann et al, 2013;Jabs et al, 2017;Lin and Chang, 2008). As a consequence of nutrient and oxygen deprivation within the spheroids, proliferative cells begin to enclose inner layers of quiescent and necrotic cells, resembling a zonation found in solid tumours (Freyer and Sutherland, 1986;LaBarbera et al, 2012).…”

Section: Resultsmentioning

confidence: 99%

“…The human colon carcinoma cell line HCT116 was obtained from the Banca Biologica e Cell Factory of the IRCCS Azienda Ospedaliera Universitaria San Martino in Genoa (ICLC HTL95025). The geminin expressing non-small-cell lung cancer NCI-H460 cells have been described previously (Kuritz et al, 2017). NCI-H460/geminin cells were maintained in RPMI 1640 medium (Gibco, 21875034) supplemented with 5% heat-inactivated fetal calf serum (FCS, Pan -Biotech GmbH; P303309) and HCT116 cells were cultured in RPMI 1640 medium with 10% heat-inactivated FCS at 37 • C in a humidified incubator with 5% CO 2 .…”

Section: Cell Culturementioning

confidence: 99%

“…Alternative approaches attempting to transfer pseudotime to real-time analysis are technically restricted, e.g. limited to cell cycle analysis (Kafri et al, 2013;Kuritz et al, 2017), require specific source data, such as single-cell RNA-seq data (La Manno et al, 2018), or require computationally expensive estimations of non-indentifiable functions (Weinreb et al, 2018;Fischer et al, 2019). A more detailed discussion on attempts to transform pseudotime scales is provided in the Supplementary Information.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reconstructing temporal and spatial dynamics in single-cell experiments

Kuritz

Stöhr

Maichl

et al. 2019

Preprint

View full text Add to dashboard Cite

Modern cytometry methods allow collecting complex, multi-dimensional data sets from heterogeneous cell populations at single-cell resolution. While methods exist to describe the progression and order of cellular processes from snapshots of such populations, these descriptions are limited to arbitrary pseudotime scales. Here we describe MAPiT, an universal transformation method that recovers real-time dynamics of cellular processes from pseudotime scales. As use cases, we applied MAPiT to two prominent problems in the flow-cytometric analysis of heterogeneous cell populations: (1) recovering the kinetics of cell cycle progression in unsynchronized and thus unperturbed cell populations, and (2) recovering the spatial arrangement of cells within multi-cellular spheroids prior to spheroid dissociation for cytometric analysis. Since MAPiT provides a theoretic basis for the relation of pseudotime values to real temporal and spatial scales, it can be used broadly in the analysis of cellular processes with snapshot data from heterogeneous cell populations.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Cell Culturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reconstructing temporal and spatial dynamics in single-cell experiments

Kuritz

Stöhr

Maichl

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…However, pseudotemporal orderings are static descriptions which do not include dynamic information which can be obtained from time series experiments or steady state systems for example. Steadystate problems were previously addressed in the context of cell cycle transitions 7,8 based on discretized population structures. The problem of developmental trajectory estimation from time series data is not a steadystate problem which was recently addressed via an optimal transport framework for discrete transitions 9 ( Fig.…”

Section: (Introductory Paragraph)mentioning

confidence: 99%

Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series

Fischer

Fiedler

Kernfeld

et al. 2017

Preprint

View full text Add to dashboard Cite

(Abstract)Cellular development has traditionally been described as a series of transitions between discrete cell states, such as the sequence of double negative, double positive and single positive stages in Tcell development. Recent advances in single cell transcriptomics suggest an alternative description of development, in which cells follow continuous transcriptomic trajectories. A cell's state along such a trajectory can be captured with pseudotemporal ordering, which however is not able to predict development of the system in real time. We present pseudodynamics, a mathematical framework that integrates timeseries and genetic knockout information with such transcriptomebased descriptions in order to describe and analyze the realtime evolution of the system. Pseudodynamics models the distribution of a cell population across a continuous cell state coordinate over time based on a stochastic differential equation along developmental trajectories and random switching between trajectories in branching regions. To illustrate feasibility, we use pseudodynamics to estimate cellstatedependent growth and differentiation of thymic Tcell development. The model approximates a developmental potential function (Waddington's landscape) and suggests that thymic Tcell development is biphasic and not strictly deterministic before betaselection. Pseudodynamics generalizes classical discrete population models to continuous states and thus opens possibilities such as probabilistic model selection to single cell genomics.not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. (Fig. 1b). Here, we present pseudodynamics (Fig. 1a), a mathematical framework tailored to developmental trajectories which accounts both for the continuous population structure and the nonsteady state dynamics of the system to understand population growth and differentiation characteristics along trajectories.As an example, we establish an unbiased view of Tcell development based on a branching pseudotemporal ordering of cells observed with singlecell RNAseq data and validate it with previous findings. Secondly, we show that the population growth rate can be fit as a transcriptomic state dependent function which maps out selection pressure during Tcell maturation on specific transcriptomic states. Thirdly, we show how pseudodynamics facilitates the integration of wildtype and mutant data to annotate developmental trajectories with developmental checkpoints using the example of developmental arrest of Tcells at betaselection in Rag1 and Rag2 knockout mice. Our model extends previous efforts on modelling gene expression distributions in time 10 by population size dynamics and by the notion of developmental trajectories in transcriptome space. Pseudodynamics is independent of the method with which the pseudotemporal ordering is generated. In summary, pseudodynamics adds the following layers of information to a lineage trajectory:(1) population growth dynamics such as population bursts and selection, (2) an approxima...

show abstract

Stabilization of age‐structured chemostat hyperbolic PDE with actuator dynamics

Haacker,

Karafyllis,

Krstić

et al. 2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

For population systems modeled by age‐structured hyperbolic partial differential equations (PDEs), we redesign the existing feedback laws, designed under the assumption that the dilution input is directly actuated, to the more realistic case where dilution is governed by actuation dynamics (modeled simply by an integrator). In addition to the standard constraint that the population density must remain positive, the dilution dynamics introduce constraints of not only positivity of dilution, but possibly of given positive lower and upper bounds on dilution. We present several designs, of varying complexity, and with various measurement requirements, which not only ensure global asymptotic (and local exponential) stabilization of a desired positive population density profile from all positive initial conditions, but do so without violating the constraints on the dilution state. To develop the results, we exploit the relation between first‐order hyperbolic PDEs and an equivalent representation in which a scalar input‐driven mode is decoupled from input‐free infinite‐dimensional internal dynamics represented by an integral delay system.

show abstract

On the relationship between cell cycle analysis with ergodic principles and age-structured cell population models

Cited by 18 publications

References 33 publications

Reconstructing temporal and spatial dynamics in single-cell experiments

Reconstructing temporal and spatial dynamics in single-cell experiments

Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series

Stabilization of age‐structured chemostat hyperbolic PDE with actuator dynamics

Contact Info

Product

Resources

About