Biology subjects

Le Devedec, S. E.

Publications and source records attributed to Le Devedec, S. E..

9 recordsLinked to original sources

bioRxiv2026

Functional Mammary Gland Organoids From Human Induced Pluripotent Stem Cells

The human mammary gland arises through sequential lineage restrictions from surface ectoderm to hormone-responsive bilayered epithelium, yet a tractable in vitro model that captures these events has remained elusive. Here, we established a stepwise differentiation protocol that directs human induced pluripotent stem cells (hiPSCs) into mammary organoids (MGOs) by sequential induction of non-neural ectoderm, mammary placodes, and lineage maturation in a defined medium. The resulting MGOs reproducibly formed polarized basal (KRT14+, TP63+) and luminal (KRT8+, GATA3+) layers, with ~25% of luminal cells expressing oestrogen receptor (ER). MGOs accumulated lipids, expressed {beta}-casein upon prolactin stimulation, and retained lineage fidelity after serial passaging. Upon transplantation into cleared mouse mammary fat pads, MGOs generated branched mammary ductal networks. Single-cell transcriptomics confirmed the emergence of basal, luminal hormone-sensing, luminal secretory, and stromal populations, transcriptionally aligned with adult human breast epithelium and responsive to endocrine disruption. These findings demonstrate that hiPSCs can be reprogrammed into functional mammary epithelium, providing a platform to dissect development, endocrine signalling, and breast cancer pathogenesis.

cell biology↗

bioRxiv2026

Multi-omics characterization of breast cancer metabolism identifies new metabolic targets

Breast cancer cells undergo metabolic reprogramming to support proliferation and metastasis, but these alterations are heterogeneous. To systematically assess this heterogeneity under controlled conditions, we profiled 31 intracellular polar metabolites and 50 amines, together with exchange rates of 57 amines, in a panel of 51 breast cancer cell lines spanning diverse phenotypes. These data were integrated with lipidomics and transcriptomics. Multi-omics factor analysis identified metabolic signatures linked to proliferation and to differences between luminal, HER2-positive, basal A, and basal B cell lines, overlapping with an epithelial-to-mesenchymal transition phenotype. Fast-proliferating, aggressive cell lines showed increased uptake of essential amino acids and altered nucleotide levels, while heterogeneity in glutamine metabolism was mainly driven the subtype. This was partially associated with heterogeneous expression of metabolite transporters. To test the functional relevance, 67 metabolic genes were silenced in Hs578T using siRNA, and effects on proliferation and migration were measured by a sulforhodamine B assay, live-cell imaging, and a random cell migration assay. This resulted in 34 knockdowns that reduced proliferation and 20 reduced migration, with strong effects for the glycosyltransferases EXT1 and EXT2, nucleotide metabolism genes GART and HPRT1, and metabolite transporters SLC7A1, SLC7A11, and SLC16A3. These findings highlight phenotype-specific metabolic dependencies and identify candidate drug targets in aggressive breast cancer.

cancer biology↗

bioRxiv2025

Data-driven dynamic modelling identifies polyploidisation as key process in cell cycle progression upon DNA damage

Chemotherapeutic agents often cause DNA damage in order to kill fast-dividing cancer cells or disrupt their proliferation. Therefore, understanding the interplay between DNA damage and cell cycle progression is highly relevant for understanding cancer cell behaviour. An important regulator is transcription factor p53, primarily known for its function to maintain genomic stability, regulate transient and permanent cell cycle arrest and apoptosis. Activated p53 transcriptionally regulates the expression of many proteins, among which are MDM2, p21 and BTG2. MDM2 functions as a direct inhibitor of p53 by targeting it for ubiquitination. The proteins p21 and BTG2 are known for their regulatory function in G1 and G2 cell cycle arrest. Using HepG2-FUCCI cells, we showed that exposure to cisplatin or etoposide caused a temporary G2 arrest. To study the link between protein expression and cell cycle arrest, we developed a mathematical model in which we integrated a previously established model for the protein expression dynamics of p53, MDM2, p21 and BTG2 with a cell cycle model. This allowed us to determine the importance of p21 and BTG2 in their stimulation of G1 and G2 cell cycle arrest. We found that the protein dynamics could predict the G2 cell cycle arrest in exposed cells, but only in combination with endoreplication, i.e., the alternation of S and G phases without mitosis, resulting in polyploid cells. Our model predicted that the majority of cells endoreplicate upon exposure to high concentrations of cisplatin and most concentrations of etoposide, which we validated with additional time-lapse imaging data in which we could track individual cells. In conclusion, polyploidisation is a generic response of HepG2 cells after treatment with DNA-damaging compounds.

systems biology↗

bioRxiv2024

Normalization strategies for lipidome data in cell line panels

Sample collection can significantly affect measurements of relative lipid concentrations in cell line panels, hiding intrinsic biological properties of interest between cell lines. Most quality control steps in lipidomic data analysis focus on controlling technical variation. Correcting for the total amount of biological material remains an additional challenge for cell line panels. Here, we investigated how we can normalize lipidomic data acquired from multiple cell lines to correct for differences in sample biomass. We studied how commonly used data normalization and transformation steps during analysis influenced the resulting lipid data distributions. We compared normalization by biological properties such as cell count or total protein concentration, to statistical and data-based approaches, such as median, mean, or probabilistic quotient-based normalization and used intraclass correlation to estimate how similarity between replicates changed after normalization. Normalizing lipidomic data by cell count improved similarity between replicates, but only for a study with cell lines with similar morphological phenotypes. For cell line panels with multiple morphologies collected over a longer time, neither cell count nor protein concentration was sufficient to increase the similarity of lipid abundances between replicates of the same cell line. Data-based normalizations increased these similarities, but also created artifacts in the data caused by a bias towards the large and variable lipid class of triglycerides. This artifact was reduced by normalizing for the abundance of only structural lipids. We conclude that there is a delicate balance between improving the similarity between replicates and avoiding artifacts in lipidomic data and emphasize the importance of an appropriate normalization strategy in studying biological phenomena using lipidomics.

bioinformatics↗

bioRxiv2023

A Network-based Transcriptomic Landscape of HepG2 cells to Uncover Causal Gene Cytotoxicity Interactions Underlying Drug-Induced Liver Injury

Drug-induced liver injury (DILI) remains the main reason of drug development attritions largely due to poor mechanistic understanding. Toxicogenomics to interrogate the mechanism of DILI has been broadly performed. Gene network-based transcriptome analysis is a bioinformatics approach that potentially contributes to improving mechanistic interpretation of toxicogenomics data. In this current study, we performed an extensive concentration time course response-toxicogenomics study in the HepG2 cell line exposed to various DILI compounds, reference compounds for stress response pathways, cytokine receptors, and growth factor receptors. We established > 500 conditions subjected to whole transcriptome targeted RNA sequences and applied weighted gene co-regulated network analysis (WGCNA) to the transcriptomics data followed by identification of gene networks (modules) that were strongly modulated upon the exposure of DILI compounds. Preservation analysis on the module responses of HepG2 and PHH demonstrated highly preserved adaptive stress responses gene networks. We correlated gene network with cell death as the progressive cellular outcomes. Causality of the target genes of these modules was evaluated using RNA interference validation experiments. We identified that GTPBP2, HSPA1B, IRF1, SIRT1 and TSC22D3 exhibited strong causality towards cell death. Altogether, we demonstrate the application of large transcriptome datasets combined with network-based analysis and biological validation to uncover the candidate determinants of DILI.

pharmacology and toxicology↗

bioRxiv2023

Spatio-temporal transcriptomic analysis reveals distinct nephrotoxicity, DNA damage and regeneration response after cisplatin

Nephrotoxicity caused by drug or chemical exposure involves different mechanisms and nephron segments as well as a complex temporal integration of injury and repair responses. Distinct cellular transcriptional programs regulate the time-dependent tissue injury and regeneration responses. Whole kidney transcriptome analysis cannot dissect the complex the nephron segment spatio- temporal injury and regeneration responses. Here, we used laser capture microdissection of formalin- fixed paraffin embedded sections followed by whole genome targeted RNA-sequencing-TempO-Seq and co-expression gene-network (module) analysis to determine the spatial-temporal responses in rat kidney glomeruli (GM), cortical proximal tubules (CPT) and outer-medulla proximal tubules (OMPT) comparison with whole kidney, after a single dose of the nephrotoxicant cisplatin. We demonstrate that cisplatin induced early onset of DNA damage in both CPT and OMPT, but not GM. Sustained DNA damage response was strongest in OMPT coinciding with OMPT specific inflammatory signaling, actin cytoskeletal remodeling and increased glycolytic metabolism coincident with suppression of mitochondrial activity. Later responses reflected regeneration-related cell cycle pathway activation and ribosomal biogenesis in the injured OMPT regions. Activation of modules containing kidney injury biomarkers was strongest in the OMPT, with OMPT Clu expression best correlating with urinary clusterin biomarker measurements compared the correlation of Kim1. Our findings also showed that whole kidney responses were less sensitive than OMPT. In conclusion, our LCM-TempO-Seq method reveals a detailed spatial mechanistic understanding of renal injury/regeneration after nephrotoxicant exposure and identifies the most representative mechanism-based nephron segment specific renal injury biomarkers.

pharmacology and toxicology↗

bioRxiv2022

User-friendly high-content imaging analysis on a single desktop: R package H5CellProfiler

Technological development has led to ever-increasing amounts of data in high-content screening. For utilizing such data in an efficient, thorough, and user-friendly manner we developed the R package H5CellProfiler. H5CellProfiler is based on R packages data.table, ggvis, ggplot2 and shiny. H5CellProfiler launches a browser that allows scientists to analyze large single-cell datasets and make statistical summaries and graphs on their local desktop in a fast and memory-efficient manner. In addition, single-cell track labels are calculated and broken tracks are re-connected based on user-defined thresholds resulting in unique sets of annotated tracks.

bioinformatics↗

bioRxiv2021

Density-dependent migration characteristics of cancer cells driven by pseudopod coordination

The ability of cancer cells to invade neighboring tissue from primary tumors is an important determinant of metastatic behavior. Quantification of cell migration characteristics such as migration speed and persistence helps to understand the requirements for such invasiveness. One factor that may influence invasion is how local tumor cell density shapes cell migration characteristics, which we here investigate with a combined experimental and computational modeling approach. First, we generated and analyzed time-lapse imaging data on two aggressive Triple-Negative Breast Cancer (TNBC) cell lines, HCC38 and Hs578T, during 2D migration assays at various cell densities. HCC38 cells exhibited a counter-intuitive increase in speed and persistence with increasing density, whereas Hs578T did not exhibit such an increase. Moreover, HCC38 cells exhibited strong cluster formation with active pseudopod-driven migration, especially at low densities, whereas Hs578T cells maintained a dispersed positioning. In order to obtain a mechanistic understanding of the density-dependent cell migration characteristics and cluster formation, we developed realistic spatial simulations using a Cellular Potts Model (CPM) with an explicit description of pseudopod dynamics. Model analysis demonstrated that strong coordination between pseudopods within single cells could explain the experimentally observed increase in speed and persistence with increasing density in HCC38 cells. Thus, the density-dependent migratory behavior could be an emergent property of single-cell characteristics without the need for additional mechanisms. This implies that coordination amongst pseudopods may play a role in the aggressive nature of cancers through mediating dispersal.

systems biology↗

bioRxiv2019

Community Standards for Open Cell Migration Data

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited due to the diversity of experimental protocols and non-standardised output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardised data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardisation efforts by the Cell Migration Standardisation Organization, CMSO, an open community-driven organisation to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools, and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.

bioinformatics↗