Biology subjects

Bonnet, E.

Publications and source records attributed to Bonnet, E..

6 recordsLinked to original sources

bioRxiv2026

scROMA: batch-aware pathway-activity inference and a ground-truth simulation framework for single-cell transcriptomics

BackgroundPathway-activity analysis summarizes gene-level single-cell measurements into interpretable functional modules, but widely used methods lack an integrated significance framework, do not account for the batch effects that pervade multi-sample studies, and are not natively interoperable with Python-based workflows. The field also lacks simulation resources with ground-truth pathway activity for quantitative benchmarking. ResultsWe present scROMA, a singular-value-decomposition-based method that quantifies pathway activity as coordinated variation, with per-cell scores, per-gene contributions, and permutation-based significance, natively integrated with the Scanpy/AnnData ecosystem. Its batch-aware extension is, to our knowledge, the first to correct batch effects within the gene-set subspace rather than across the full transcriptome, isolating technical variation at the pathway level while preserving signal in other genes. We also release a generative simulation framework producing synthetic data with fully specified ground-truth activities. On simulated benchmarks scROMA is competitive across tasks, and under batch effects its batch-aware mode recovers ordinal pathway structure that full-transcriptome integration misses. Across cystic fibrosis airway, intestinal-organoid, breast cancer, and lung cancer datasets it recovers established biology while separating it from technical and inter-donor variation; in the intestinal-organoid atlas it reproducibly recovers an inflammatory program across donors, separates its sustained from transient components, and resolves cell-type-specific niche-factor targets. ConclusionsscROMA is open-source and released with the simulation framework and pre-generated benchmark datasets as a community resource, providing a scalable, statistically grounded, and batch-aware approach to pathway-level analysis in single-cell transcriptomics.

systems biology↗

bioRxiv2025

Hair follicle stem cell fate supports distinct clinical endotypes in Hidradenitis Suppurativa

Hidradenitis suppurativa (HS) is a severe skin disorder affecting 1% of the global population, with a complex and poorly understood pathogenesis involving aberrant keratinization and autoinflammation. It remains unclear whether autoinflammatory events precede or follow hyperkeratotic changes in hair follicle (HF) epithelia. Using single-cell RNA sequencing, we characterized HF cell populations in HS patients and investigated their role in disease pathogenesis. We uncovered two distinct differentiation trajectories of HF stem cells (HF-SCs): one leading to interfollicular epidermis (IFE) basal cells enriched in inflammatory pathways, and another giving rise to outer root sheath (ORS) cells associated with keratinization. In HS lesions, both populations displayed altered inflammatory phenotypes and were closely linked to immune cell infiltration, pointing to a role in disease heterogeneity. By integrating clinical features with HF cell composition from 49 HS patients, we identified three major endotypes: (i) an inflammatory subtype, marked by T cell infiltration and an expansion of IFE basal cells; (ii) a keratinizing subtype, characterized by ORS enrichment and minimal inflammation; and a mixed subtype, exhibiting features of follicular remodeling, fistula formation, and variable immune involvement. These findings provide novel insights into the epithelial-immune interactions that drive HS and support a stratified therapeutic approach tailored to the specific HF dysfunctions of each patient subgroup. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=49 SRC="FIGDIR/small/656362v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@4e8af9org.highwire.dtl.DTLVardef@85f61borg.highwire.dtl.DTLVardef@e085a7org.highwire.dtl.DTLVardef@66d804_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

The JAK2-STAT3 pathway controls a beneficial proteostasis response of reactive astrocytes in Huntington disease

Huntingtons disease is a fatal neurodegenerative disease characterized by striatal neurodegeneration, aggregation of mutant Huntingtin and the presence of reactive astrocytes. Astrocytes are important partners for neurons and engage in a specific reactive response in Huntingtons disease that involves morphological, molecular and functional changes. How reactive astrocytes contribute to Huntingtons disease is still an open question, especially because their reactive state is poorly reproduced in experimental mouse models. Here, we show that the JAK2-STAT3 pathway, a central cascade controlling astrocyte reactive response, is activated in the putamen of Huntingtons disease patients. Selective activation of this cascade in astrocytes through viral gene transfer reduces the number and size of mutant Huntingtin aggregates in neurons and improves neuronal defects in two complementary mouse models of Huntingtons disease. It also reduces striatal atrophy and increases glutamate levels, two central clinical outcomes measured by non-invasive magnetic resonance imaging. Moreover, astrocyte-specific transcriptomic analysis shows that activation of the JAK2-STAT3 pathway in astrocytes coordinates a transcriptional program that increases their intrinsic proteolytic capacity, through the lysosomal and ubiquitin-proteasome degradation systems. This pathway also enhances their production and exosomal release of the co-chaperone DNAJB1, which contributes to mutant Huntingtin clearance in neurons. Together, our results show that the JAK2-STAT3 pathway controls a beneficial proteostasis response in reactive astrocytes in Huntingtons disease, which involves bi-directional signalling with neurons to reduce mutant Huntingtin aggregation, eventually improving disease outcomes.

neuroscience↗

bioRxiv2019

How do invasion syndromes evolve? An experimental evolution approach using the ladybug Harmonia axyridis

Experiments comparing native to introduced populations or distinct introduced populations to each other show that phenotypic evolution is common and often involves a suit of interacting phenotypic traits. We define such sets of traits that evolve in concert and contribute to the success of invasive populations as an invasion syndrome. The invasive Harlequin ladybird Harmonia axyridis displays such an invasion syndrome with, for instance, females from invasive populations being larger and heavier than individuals from native populations, allocating more resources to reproduction, and spreading reproduction over a longer lifespan. Invasion syndromes could emerge due to selection acting jointly and directly on a multitude of traits, or due to selection on one or a few key traits that drive correlated indirect responses in other traits. Here, we investigated the degree to which the H. axyridis invasion syndrome would emerge in response to artificial selection on either female body mass or on age at first reproduction, two traits involved in their invasion syndrome. To further explore the interaction between environmental context and evolutionary change in molding the phenotypic response, we phenotyped the individuals from the selection experiments in two environments, one with abundant food resources and one with limited resources. The two artificial selection experiments show that the number of traits showing a correlated response depends upon the trait undergoing direct selection. Artificial selection on female body mass resulted in few correlated responses and hence poorly reproduced the invasion syndrome. In contrast, artificial selection on age at first reproduction resulted in more widespread phenotypic changes, which nevertheless corresponded only partly to the invasion syndrome. The artificial selection experiments also revealed a large impact of diet on the traits, with effects dependent on the trait considered and the selection regime. Overall, our results indicate that direct selection on multiple traits was likely necessary in the evolution of the H. axyridis invasion syndrome. Furthermore, they show the strength of using artificial selection to identify the traits that are correlated in different selective contexts, which represents a crucial first step in understanding the evolution of complex phenotypic patterns, including invasion syndromes.

evolutionary biology↗

bioRxiv2019

Imitation as a model-free process in human reinforcement learning

While there is not doubt that social signals affect human reinforcement learning, there is still no consensus about their exact computational implementation. To address this issue, we compared three hypotheses about the algorithmic implementation of imitation in human reinforcement learning. A first hypothesis, decision biasing, postulates that imitation consists in transiently biasing the learners action selection without affecting her value function. According to the second hypothesis, model-based imitation, the learner infers the demonstrators value function through inverse reinforcement learning and uses it for action selection. Finally, according to the third hypothesis, value shaping, demonstrators actions directly affect the learners value function. We tested these three psychologically plausible hypotheses in two separate experiments (N = 24 and N = 44) featuring a new variant of a social reinforcement learning task, where we manipulated the quantity and the quality of the demonstrators choices. We show through model comparison that value shaping is favored, which provides a new perspective on how imitation is integrated into human reinforcement learning.

neuroscience↗

bioRxiv2019

Comprehensive analysis of RNA-seq kits for standard, low and ultra-low quantity samples

High-throughput RNA-sequencing has become the gold standard method for whole-transcriptome gene expression analysis, and is widely used in numerous applications to study cell and tissue transcriptomes. It is also being increasingly used in a number of clinical applications, including expression profiling for diagnostics and alternative transcript detection. However, despite its many advantages, RNA sequencing can be challenging in some situations, for instance in cases of low input amounts or degraded RNA samples. Several protocols have been proposed to overcome these challenges, and many are available as commercial kits. In this study, we comprehensively test three recent commercial technologies for RNA-seq library preparation (TruSeq, SMARTer and SMARTer Ultra-Low) on human reference tissue preparations, using standard (1g), low (100 and 10 ng) and ultra-low (< 1 ng) input amounts, and for mRNA and total RNA, stranded or unstranded. The results are analyzed using read quality and alignment metrics, gene detection and differential gene expression metrics. Overall, we show that the TruSeq kit performs well with an input amount of 100 ng, while the SMARTer kit shows degraded performance for inputs of 100 and 10 ng, and the SMARTer Ultra-Low kit performs relatively well for input amounts < 1 ng. All the results are discussed in detail, and we provide guidelines for biologists for the selection of a RNA-seq library preparation kit.

genomics↗