Biology subjects

Phan, M.-S.

Publications and source records attributed to Phan, M.-S..

5 recordsLinked to original sources

bioRxiv2026

EpiCure (Epithelial Curation): a versatile and handy tool for curation of epithelial segmentation

Investigating single-cell dynamics and morphology in tissues and embryos requires highly accurate quantitative analysis of microscopy images. Despite significant advances in the field of bioimage analysis, even the most sophisticated segmentation and tracking algorithms inevitably produce errors (e.g. : over segmentation, missing objects, miss-connected objects). Although error rate may be small, their propagation throughout a time-lapse sequence has catastrophic effects on the accuracy of tracking and extraction of single cell parameters. Extracting single cell temporal information in the context of tissue/embryo requires thus expert curation to identify and correct segmentation errors. In the movies commonly used in developmental biology and stem cell research, both the number of imaged cells and the duration of recording are large, making this manual correction task extremely time-consuming. This has now become a major bottleneck in the fields of development, stem cell biology and bioimage analysis. We present here EpiCure (Epithelial Curation), a versatile tool designed to streamline and accelerate manual curation of segmentation and tracking in 2D movies of large epithelial tissues. EpiCure uses temporal information and morphometric parameters to automatically identify segmentation and tracking errors and provides user-friendly tools to correct them. It focuses on ergonomics and offers several visualization options to help navigating in movies of tissue covering a large number of cells, speeding up the detection of errors and their curation. EpiCure is highly interoperable and supports input from a wide range of segmentation tools. It also includes multiple export filters, enabling seamless integration with downstream analysis pipelines. In this paper, using movies from several animal models, we highlight the importance of curating cell segmentation and tracking for accurate downstream analysis, and demonstrate how EpiCure helps the curation process for extracting accurate single cell dynamics and cellular events detection, making it faster and amenable on large dataset.

developmental biology↗

bioRxiv2026

Oscillatory Hedgehog signaling and temporal coordination of atonal dynamics at the eye differentiation front in Drosophila

Pattern formation in the Drosophila eye involves the pulsatile expression of the proneural gene atonal (ato) and the periodic activation of Notch. Oscillatory expression of the ato gene results from the synchronous activation of two distinct ato enhancers that are active in two adjacent rows of cell clusters along the front of differentiation. How synchronous enhancer activation is achieved along the front is not known. Hedgehog (Hh) is a diffusive signal produced just posterior to the front which contributes to regulate ato gene expression. Here, we find that both ato enhancers are regulated by Hh and that two Hh target genes, patched (ptc) and decapentaplegic (dpp), are expressed at the front of differentiation in an oscillatory manner downstream of a constant Hh signal. Consistent with a role of Hh in the temporal coordination of ato gene expression along the front, lowering Hh activity was associated with pattern irregularities along the front. We propose a model whereby the periodic expression of the Hh receptor Ptc under the control of pulsatile Ato and/or Notch dynamics produces rhythmic changes in extracellular Hh which would in turn coordinate ato dynamics locally. In this model, oscillatory Hh would provide a temporal cue to coordinate patterning dynamics along the front of differentiation in the developing eye.

developmental biology↗

bioRxiv2024

Pulsatile dynamics propagate crystalline order in the developing Drosophila eye

Pattern formation in developing tissues often involves self-organization guided by positional information. In most tissues, however, its dynamics, and therefore the underlying logic, remain unknown. Examining self-organized patterning of the fly eye, we combine experiments and modeling to elucidate how rows of light-receiving units emerge in the wake of a traveling differentiation front to form a crystal-like array. Live imaging of the proneural factor Atonal reveals unanticipated oscillations at the front, which are produced by the successive activation of two distinct enhancers and associated with pulsatile Notch signaling. Our observations are inconsistent with current models of eye patterning, whereby each row of differentiating cells provides a negative template for the next. Instead, they inform a new relay model in which transient Notch signaling from differentiating cells provides a positive template for the onset of differentiation two rows ahead, conveying both temporal and spatial information to propagate oscillations and crystal-like order.

developmental biology↗

bioRxiv2021

Bringing TrackMate in the era of machine-learning and deep-learning.

TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments.

bioinformatics↗

bioRxiv2020

A scale-space approach for 3D neuronal traces analysis

AO_SCPLOWBSTRACTC_SCPLOWTridimensional microscopy and algorithms for automated segmentation and tracing are revolutionizing neuroscience through the generation of growing libraries of neuron reconstructions. Innovative computational methods are needed to analyze these neural traces. In particular, means to analyse the geometric properties of traced neurites along their trajectory have been lacking. Here, we propose a local tridimensional (3D) scale metric derived from differential geometry, which is the distance in micrometers along which a curve is fully 3D as opposed to being embedded in a 2D plane or 1D line. We apply this metric to various neuronal traces ranging from single neurons to whole brain data. By providing a local readout of the geometric complexity, it offers a new mean of describing and comparing axonal and dendritic arbors from individual neurons or the behavior of axonal projections in different brain regions. This broadly applicable approach termed nAdder is available through the GeNePy3D open-source Python quantitative geometry library.

neuroscience↗