Biology subjects

Poli, F.

Publications and source records attributed to Poli, F..

6 recordsLinked to original sources

bioRxiv2026

VesiScope: A stand-alone tool for the automatic detection, quantification and size analysis of GUVs from phase contrast microscopy images

Fluorescence microscopy is routinely used in the quantitative analysis of Giant Unilamellar Vesicles (GUVs) because it makes GUVs visible and easy to detect. Nonetheless, it requires fluorescence-equipped microscopes and, especially, the addition of tagged molecules that may introduce undesired and unaccounted variations compared to the pristine formulation. Alternatively, phase-contrast microscopy can be used to observe GUVs without labelling, yet the automatic detection of GUVs in phase-contrast images by standard image processing techniques is difficult. To overcome this limitation, we developed a framework for the automated detection and size characterization of label-free GUVs based on YOLOv11 model, a state-of-art lightweight Convolutional Neural Network (CNN) for object detection. Trained on 164 phase-contrast microscope images containing 4666 manually annotated vesicles, the model enables robust detection under low contrast-to-noise ratio and heterogeneous illumination. The trained model is implemented in a stand-alone desktop application supporting multiple analysis in a single run, pixel-to-micrometer calibration, and automated size distribution analysis. This approach achieves high detection accuracy and competitive cross-microscope generalization using YOLOv11 nano on greyscale image. Retraining scripts compatible with standard hardware are also provided to enhance adaptability. This tool enables fast and reliable analysis of GUVs without altering their composition, supporting experimental reproducibility.

synthetic biology↗

bioRxiv2026

How Similar Are Two Brains? A Comprehensive Benchmark of Brain Network Similarity Measures

A central goal of neuroscience is to establish how similar (or different) brain networks are across individuals, development, psychiatric conditions, and even between biological and artificial brains. Whenever one such comparison is made, it relies on the assumption that we have a measure that produces an accurate and plausible similarity score. Yet, the field lacks one such measure, and the wide variety of existing measures often produces conflicting results. To address this problem, we perform a systematic comparison of 16 established similarity measures. First, we show that different measures consistently disagree on which brain networks are most similar. Second, we rank all measures according to multiple criteria such as biological plausibility, computational efficiency, and sensitivity. We show that DeltaCon is the most accurate measure in terms of parameter recovery, and that it ranks among the fastest and most noise-tolerant. For this reason, we conclude that DeltaCon is the best general-purpose similarity measure. However, no measure ranks best on more than two of the five criteria we assessed, and different measures should be preferred depending on the exact research question. Measure selection can now be made on evidence and should be a crucial decision in any study that compares brain networks.

neuroscience↗

bioRxiv2026

Curiosity shapes brain-like architectures and functions

How does complex cognition emerge from simpler underlying processes? We show that two components are sufficient: infant-like curiosity and brain-like biophysical constraints jointly drive the emergence of complex neuronal architectures and cognitive abilities. We first tested curiosity-driven exploration in 275 8-to 15-month-old infants. We then implemented these mechanisms in artificial recurrent neural networks, letting them actively sample 20 cognitive tasks during training, while forcing the networks to operate under brain-like biophysical constraints. These two components were sufficient for a range of biological and cognitive phenomena to emerge: The resulting networks captured human synaptic development, the adult brains architecture, and displayed compositional generalisation, a hallmark of human cognition. Curiosity, long viewed as a downstream consequence of complex brains, is also a driver of their complexity.

neuroscience↗

bioRxiv2025

Right time, right place: Heterochronicity shapes brain network formation

Brain network formation unfolds on a non-uniform developmental timetable, with different cortical regions generating connections at different developmental phases. Generative network models (GNMs) aim to uncover the principles underpinning the organisation of connectomes by creating synthetic networks according to simple computational rules. These models capture the connectomes topology, operationalised here as the overall distributions of network metrics (e.g., modularity, small-worldness, rich-club structure). However, they typically ignore the differential timing of connectivity formation. By omitting this temporal programme, GNMs often misplace topological features in physical space. Here, we add a heterochronous growth term to GNMs and use a new model fitness function that weighs topology and topography equally. Topography refers to the spatial embedding of the network, the actual anatomical positions of tracts. With these advances, we can generate synthetic networks that more faithfully reproduce the spatial layout of diffusion-MRI connectomes from two independent adult cohorts. Compared with classical, temporally agnostic models, heterochronous simulations improve model fit, accurately locate cortical hubs and modules, and converge on a single caudal-to-rostral gradient of brain maturation. Integrating heterochronicity makes GNMs more faithful to brain development, setting the stage for using them to explain and ultimately predict individual differences in network formation.

neuroscience↗

bioRxiv2025

Single cell transcriptomics in a treatment-segregated cohort exposes a STAT3-regulated therapeutic gap in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic pulmonary disease of unknown etiology. Since approved IPF drugs only slow disease progression, novel therapeutics are required that improve clinical outcomes. Here we report a single cell lung RNA-Seq and gene regulatory network analysis of the largest IPF cohort assembled to date. Segregating this cohort based on status of treatment with approved first-generation IPF antifibrotics (untreated, nintedanib- and pirfenidone-treated), we describe for the first time the transcriptional landscape of untreated IPF across 40 lung cell types, and the elements of this program that are impacted by these antifibrotics. On average, nearly 60% of the untreated IPF-dysregulated transcriptome is refractory to treatment with these drugs, a transcriptional deficit we refer to as the IPF therapeutic gap. Gene regulatory network analysis indicated a dominant functional footprint for the transcription factor STAT3 in both untreated IPF and the IPF therapeutic gap. Validating our analysis in a translational precision cut lung slice platform that recapitulates IPF explants, pharmacological inhibition of STAT3 reduced the IPF therapeutic gap in numerous lung cell types. Finally, we resolved a STAT3-anchored master regulatory network comprising numerous profibrotic transcription factors in IPF alveolar fibroblasts, a critical fibrotic lineage. Our study represents a comprehensive resource for translational lung fibrosis research and introduces a strategy for drug discovery that is adaptable to human disease more broadly.

cell biology↗

bioRxiv2021

Single Cell RNA-seq and Mass Cytometry Reveals a Novel and a Targetable Population of Macrophages in Idiopathic Pulmonary Fibrosis

In this study, we leveraged a combination of single cell RNAseq, cytometry by time of flight (CyTOF), and flow cytometry to study the biology of a unique macrophage population in pulmonary fibrosis. Using the profiling data from 312,928 cells derived from 32 idiopathic pulmonary fibrosis (IPF), 29 healthy control and 18 chronic obstructive pulmonary disease (COPD) lungs, we identified an expanded population of macrophages in IPF that have a unique transcriptional profile associated with pro-fibrotic signature. These macrophages attain a hybrid transitional state between alveolar and interstitial macrophages, are enriched with biological processes of pro-fibrotic immune cells, and express novel surface markers and genes that have not been previously reported. We then applied single cell CyTOF to simultaneously measure 37 markers to precisely phenotype the uniquely expanded macrophage subset in IPF lungs. The SPADE algorithm independently identified an expanded macrophage cluster, and validated CD84 and CD36 as novel surface markers that highly label this cluster. Using a separate validation cohort, we confirmed an increase in CD84++CD36++ macrophage population in IPF compared to control and COPD lungs by flow cytometry. Further, using the signature from the IPF-specific macrophages and the LINCS drug database, we predicted small molecules that could reverse the signature of IPF-specific macrophages, and validated two molecules, CRT and Cucur, using THP-1 derived human macrophages and precision-cut lung slices (PCLS) from IPF patients. Utilizing a multi-dimensional translational approach, our work identified a novel and targetable population of macrophages found in end-stage pulmonary fibrosis. One Sentence SummarySingle cell RNAseq, CyTOF, and flow cytometry reveal the presence of an aberrant macrophage population in pulmonary fibrosis

cell biology↗