Biology subjects

Mort, R. L.

Publications and source records attributed to Mort, R. L..

5 recordsLinked to original sources

bioRxiv2026

FluoroFate: A generalisable platform for time-resolved single-cell analysis of cell fate enables quantification of cell death dynamics

Fundamental cellular decisions of life and death are governed by intricate and tightly regulated intracellular signalling pathways that determine whether cells proliferate, enter quiescence, or undergo programmed cell death (apoptosis). Live-cell fluorescence imaging enables these processes to be observed in real time at single-cell resolution, but two problems limit their study. First, existing biosensors do not allow apoptotic status and cell cycle progression to be resolved in tandem within the same cell. Second, interpreting live-cell imaging data is challenging even where multiplex reporters exist, as the biological meaning of fluorescent signals depends on their temporal ordering, and large-scale imaging experiments generate complex, multidimensional data that are difficult to analyse systematically and at scale. Here we address both problems. We present FluoroFate, a generalisable and user-friendly graphical interface-driven tool for time-resolved single-cell analysis of multiplex live-cell imaging datasets, which integrates existing, robust deep learning-based segmentation, cell tracking, and temporal classification methods to quantify fluorescent reporter dynamics in individual cells across time without the need for specialist computational expertise. Alongside FluoroFate, we develop tricistronic Fluorescent Ubiquitination-based Cell Cycle Indicator (Fucci) and apoptosis biosensors, enabling simultaneous monitoring of cell cycle progression and caspase activation within the same cell. Applying FluoroFate, we resolve apoptotic and non-apoptotic cell death at the single-cell level based on the temporal ordering of Annexin V and propidium iodide signals, identifying distinct kinetic and phenotypic cell death profiles in response to pharmacological perturbation. We highlight divergent temporal dynamics and modes of cell death between birinapant and cycloheximide treatment, reflecting differences in how TNF/TNFR1 signalling is disrupted by these agents. At the single-cell level, we uncover parallel, independently regulated death programmes, demonstrating that loss of RIPK1 selectively impairs apoptotic cell death whilst leaving non-apoptotic death largely unaffected. We then use FluoroFate to analyse timelapse images of our combined Fucci-apoptosis reporters, resolving cell cycle progression and caspase activation within the same cell over time. Together, FluoroFate and our new cell cycle and apoptosis biosensors represent a broadly applicable platform for extracting mechanistic insight from live-cell imaging data.

cell biology↗

bioRxiv2025

FuChi: A cell cycle biosensor for investigating cell-cycle kinetics during avian development.

The ability to monitor the proliferative status of live cells both in vitro and in vivo over time has revolutionised our understanding of development, growth and disease. This was first made possible by fluorescent ubiquitination-based cell cycle indicator (Fucci) technology, which distinguishes specific cell cycle phases through the reciprocal degradation of fluorescently tagged, truncated forms of human CDT1 and GMNN proteins. Fucci genetic systems have been successfully implemented in transgenic mice, zebrafish, and axolotls. To date, no viable, stably expressing Fucci line has been developed in an avian species. Although a range of continuously improving Fucci constructs have been developed in recent years, existing in vivo Fucci models remain limited because they rely on older reporter technology that fails to distinguish cells in S, G2, and M phases or to label cells in early G1. As a result, these models can be challenging to interpret and their utility for continuous cell tracking and precise analysis of cell-cycle dynamics is limited. Here, we introduce FuChi, a multicistronic Fucci-expressing chicken line incorporating a newly optimised reporter construct composed of an mCerulean-tagged Histone H1.0 linker protein fused via a self-cleaving 2A peptide to the tandem Fucci(CA) cell cycle biosensor, with additional epitope tags included for detection in fixed tissues. We show that this system accurately discriminates and permits tracking of cells in G1, S, G2, and M phases both in vitro and in vivo, enabling faithful visualisation of cell cycle status in intact tissues and organs. Using FuChi embryos, we mapped proliferation dynamics across developing tissues, analysed cell cycle states of migrating cells, and performed live imaging of early embryos. These latter experiments revealed that transition from S phase may be a key morphogenetic event during gastrulation as mesendoderm cells egress from the primitive streak to form embryonic structures including the prechordal plate. Pairing this advanced reporter with the intrinsic experimental advantages of the chicken embryo positions FuChi as a premier in vivo system for studying cell-cycle kinetics in development, delivering clear technological improvements over current Fucci models. FuChi chickens provide a powerful new resource for studying embryonic development, organ growth, tissue homeostasis, disease processes, and infection responses.

developmental biology↗

bioRxiv2023

A novel ILK/STAT3 pathway controls plasticity in a neural stem cell model of glioblastoma.

Glioblastomas (GBM) are driven by malignant neural stem-like cells that display extensive heterogeneity and phenotypic plasticity, which drives tumour progression and therapeutic resistance. Here we show that the nodal extracellular matrix-cell adhesion protein integrin-linked kinase (ILK; a pseudokinase), is a key determinant of phenotypic plasticity and the mesenchymal-like, invasive cell state in mouse GBM stem cells. We found that a novel ILK-STAT3 signalling pathway is required for plasticity that enables the transition of GBM stem cells to an astrocyte-like state both in vitro and in vivo. GBM cells genetically depleted of ILK become predominantly stabilised in a transcriptionally-defined progenitor-like state that is characterised by lack of response to differentiation cues and constitutive proliferation. Loss of ILK or interference with STAT3 impairs differentiation potential, reducing phenotypic plasticity of tumour cell populations; additionally, ILK loss causes a mesenchymal- to epithelial-like morphological transition and suppression of malignancy-associated features. Our work defines ILK as a central regulator of multiple GBM phenotypes including phenotypic plasticity and mesenchymal state.

cancer biology↗

bioRxiv2023

Newly born mesenchymal cells disperse through a rapid mechanosensitive migration

Embryonic mesenchymal cells are dispersed within an extracellular matrix but can coalesce to form condensates with key developmental roles. Cells within condensates undergo fate and morphological changes, and induce cell fate changes in nearby epithelia to produce structures including hair follicles, feathers or intestinal villi. Here, by imaging of mouse and chicken embryonic skin, we find that mesenchymal cells undergo much of their dispersal in early interphase, in a stereotyped process of displacement driven by three hours of rapid and persistent migration, followed by a long period of low motility. The cell division plane and the elevated migration speed and persistence of newly born mesenchymal cells are mechanosensitive, aligning with tension in the tissue. This early G1 migratory behaviour disperses mesenchymal cells and allows the daughters of recent divisions to travel long distances to enter dermal condensates, demonstrating an unanticipated effect of a cell cycle sub-phase on core mesenchymal behaviour. HighlightsO_LIAfter mesenchymal cell division the speed and persistence of daughter cell migration is elevated for 180 minutes C_LIO_LIMesenchymal cell division and migration are directed by tissue tension C_LIO_LINewly born mesenchymal cells are uniquely responsive to tissue strain C_LIO_LINewly born mesenchymal cells are preferentially recruited to dermal condensates C_LIO_LIIncreased dispersal of newly born cells enables long distance travel to dermal condensates C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/525849v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@c69b4dorg.highwire.dtl.DTLVardef@fe0295org.highwire.dtl.DTLVardef@5b9f61org.highwire.dtl.DTLVardef@13ca1aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2017

A Multi-Stage Representation Of Cell Proliferation As A Markov Process

The stochastic simulation algorithm commonly known as Gillespies algorithm (originally derived for modelling well-mixed systems of chemical reactions) is now used ubiquitously in the modelling of biological processes in which stochastic effects play an important role. In well mixed scenarios at the sub-cellular level it is often reasonable to assume that times between successive reaction/interaction events are exponentially distributed and can be appropriately modelled as a Markov process and hence simulated by the Gillespie algorithm. However, Gillespies algorithm is routinely applied to model biological systems for which it was never intended. In particular, processes in which cell proliferation is important (e.g. embryonic development, cancer formation) should not be simulated naively using the Gillespie algorithm since the history-dependent nature of the cell cycle breaks the Markov process. The variance in experimentally measured cell cycle times is far less than in an exponential cell cycle time distribution with the same mean.\n\nHere we suggest a method of modelling the cell cycle that restores the memoryless property to the system and is therefore consistent with simulation via the Gillespie algorithm. By breaking the cell cycle into a number of independent exponentially distributed stages we can restore the Markov property at the same time as more accurately approximating the appropriate cell cycle time distributions. The consequences of our revised mathematical model are explored analytically as far as possible. We demonstrate the importance of employing the correct cell cycle time distribution by recapitulating the results from two models incorporating cellular proliferation (one spatial and one non-spatial) and demonstrating that changing the cell cycle time distribution makes quantitative and qualitative differences to the outcome of the models. Our adaptation will allow modellers and experimentalists alike to appropriately represent cellular proliferation - vital to the accurate modelling of many biological processes - whilst still being able to take advantage of the power and efficiency of the popular Gillespie algorithm.

cell biology↗