Biology subjects

Barberis, A.

Publications and source records attributed to Barberis, A..

9 recordsLinked to original sources

bioRxiv2026

pysigscore: gene signatures scoring across bulk and single-cell transcriptomics

SummaryHigh-throughput transcriptomics has made gene signatures central to interpreting gene expression data, with applications in diagnosis, prognosis, and prediction. Quantifying signature activity and assessing its robustness remain challenging because scoring methods primarily rely on various assumptions, and no single approach is universally optimal. Here, we present pysigscore, a Python framework for gene set scoring in bulk and single-cell RNA-seq data. pysigscore integrates 18 built-in scoring methods with a fully customisable scorer, allowing users to define and benchmark new scoring functions. It also provides reliability analyses, including p-value estimation and leave-one-out experiments, to assess the significance of scores and gene-level contributions. We validated pysigscore on the CCLE, TCGA, and PBMC datasets, recovering the expected enrichment in liver, hypoxia, inflammatory, and cell-cycle signatures. Availability and ImplementationSource code is available at https://github.com/bioinformatics-hub/pysigscore. Contact: tommaso.giacomello@phd.unibocconi.it, francesca.buffa@unibocconi.it Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2025

Disentangling Stimulus & Population Dynamics in Mouse V1: Orthogonal Subspace Decomposition for Neural Representation

Understanding how the primary visual cortex of mice represents the external sensory input separately from the internal states is a fundamental challenge in systems neuroscience. Our work contributes to the problem of decoupling the stimulus-driven and internally generated components of neural activity in the primary visual cortex. Internally generated (or intrinsic) activity refers to neural dynamics that are not directly driven by sensory stimuli, reflecting the brains ongoing, endogenous processes. Neuronal activity encodes both external stimuli and internal cortical states. The internally modulated activity, though not directly observable, can be inferred from the shared structure in population responses, and thus, serves as a proxy for the internal cortical state. We developed a two-phase Partial Least Squares Regression (PLSR) framework that decomposes neural activity into two orthogonal low-dimensional subspaces: (1) a "population" sub-space capturing global variability shared across neurons, and (2) a "stimulus" subspace containing dimensions that discriminate between stimulus conditions while being linearly uncorrelated with the population subspace. We focus on the granular (L4) and supragranular (L2/3) layers of awake mice exposed to visual stimuli consisting of optical flow directions, using mesoscopic two-photon calcium imaging. In both L4 and L2/3 layers, many components individually yield above-chance decoding accuracy, yet a small low-dimensional subspace preserves nearly the full decoding performance of the high-dimensional population. Stimulus-driven components exhibit strong cross-mouse correlations, indicating a conserved coding scheme present in both L4 and L2/3. These components are stable across the entire recording session, reflecting robustness of the underlying representation over time. Removing the global modulation did not abolish stimulus discriminability in either layer, suggesting that information about stimulus direction is not dependent on this global signal. Both L4 and L2/3 stimulus components exhibit comparable decoding performance as well as similar tuning representations, suggesting common encoding of stimulus direction across layers.

neuroscience↗

bioRxiv2025

Mapping Dendritic Spines Using 2D Two-Photon Laser Scanning

Neurons transform complex spatiotemporal patterns of synaptic input into structured sequences of action potentials that relay meaningful information. Excitatory inputs converging onto dendrites engage or interact with local non-linear regenerative events, adding a computational layer that expands the variety and complexity of input-output transformations. In conjunction with non-linear conductance-mediated mechanisms, the spatial arrangement of active synapses along dendrites strategically shapes this local computation. Mapping synaptic input organization is thus critical to fully uncover neuronal input-output function. Spine calcium imaging offers a direct functional readout of the location of active contacts, but such mapping requires access to spines distributed on intricate three-dimensional dendritic trees. We present a modular software pipeline for targeted imaging and analysis of dendrites using sequential two-dimensional scanning on standard two-photon microscopes. Designed to work with conventional two-photon microscopy setups, the method is fully compatible with ScanImage. It includes a pre-acquisition tool (ROIpy) and a post-acquisition analysis suite (Spyne). ROIpy generates dendrite-aligned region-of-interests for scattered depth-specific acquisition of neuronal arborizations. Spyne includes deep-learning modules for spine identification (using DeepD3) and within-spine calcium events detection (via a custom-classifier). This method is compatible with a range of experimental designs, including simultaneous two-photon imaging and patch-clamp recordings, as well as fully optical setups. The acquisition pipeline supports plane-by-plane imaging of the whole-arbor, targeted to specific compartments or to user-defined branches of interest. Our work provides a versatile strategy for targeted dendritic imaging using two-dimensional scanning multiphoton microscopy. While ROIpy allows adaptation to diverse experimental goals beyond synaptic mapping, Spyne provides an analysis strategy for functional mapping of active synapses at single-cell resolution, offering a basis for modeling how the spatial organization of synaptic inputs shapes dendritic integration..

neuroscience↗

bioRxiv2025

Biological subtyping of autism via cross-species fMRI

It is frequently assumed that the phenotypic heterogeneity in autism spectrum disorder reflects underlying pathobiological variation. However, direct evidence in support of this hypothesis is lacking. Here, we leverage cross-species functional neuroimaging to examine whether variability in brain functional connectivity reflects distinct biological mechanisms. We find that fMRI connectivity alterations in 20 distinct mouse models of autism (n=549 individual mice) can be clustered into two prominent hypo- and hyperconnectivity subtypes. We show that these connectivity profiles are linked to distinct signaling pathways, with hypoconnectivity being associated with synaptic dysfunction, and hyperconnectivity reflecting transcriptional and immune-related alterations. Extending these findings to humans, we identify analogous hypo- and hyperconnectivity subtypes in a large, multicenter resting state fMRI dataset of n=940 autistic and n=1036 neurotypical individuals. Remarkably, hypo- and hyperconnectivity autism subtypes are replicable across independent cohorts (accounting for 25.1% of all autism data), exhibit distinct functional network architecture, are behaviorally dissociable, and recapitulate synaptic and immune mechanisms identified in corresponding mouse subtypes. Our cross-species investigation, thus, decodes the heterogeneity of fMRI connectivity in autism into distinct pathway-specific etiologies, offering a new empirical framework for targeted subtyping of autism.

neuroscience↗

bioRxiv2024

IGFs regulate cancer cell immune evasion in prostate cancer

Insulin-like growth factor-1 (IGF-1) promotes prostate cancer (PCa) development and lethality, with immunosuppressive properties in other disease models. These studies investigated the tumor-intrinsic immune effects of IGF-1 in PCa to understand mechanisms underlying its poor immunotherapy response. Transcriptional profiling of human (DU145, 22Rv1) and murine (Myc-CaP) PCa cells revealed, through pathway enrichment, that cytokine signalling, antigen processing and presentation, and other immune regulatory pathways, were most suppressed by IGF-1. We went on to investigate changes in the expression of components of two key pathways responsible for cancer cell recognition by immune cells and immune evasion: antigen processing and presentation, and PD-L1 checkpoint expression. These pathways are crucial for determining immunotherapy response. IGF-1 downregulated transporters associated with antigen processing (TAPs), endoplasmic reticulum aminopeptidase-1 (ERAP-1), and Class I component {beta}2-microglobulin, without major changes in Class I allele expression. These effects were associated with reduced presentation of Class I complexes on the Myc-CaP cell surface suggesting altered peptide transport, processing, and/or presentation. In contrast, IGF-1 upregulated immune checkpoint CD274 (PD-L1) via IGF receptor/AKT/ERK-dependent signaling. Analysis of public data (TCGA Firehose Legacy PCa) revealed increased CD274 expression in PCa with high endogenous IGF1 and IGFBP5, markers of high IGF axis activity. Additionally, in primary PCa (n=32), multiplex immunofluorescence revealed higher PD-L1 expression in the central tumor of men with high serum IGF-1 promoting cancer cell immune evasion in PCa. These findings indicate a novel mechanism by which IGF-1 mediates immunosuppressive effects in malignant prostate epithelium, potentially explaining the poor responsiveness of PCa to immunotherapy and highlighting the complex interplay between IGF signaling and immune evasion.

cancer biology↗

bioRxiv2022

Reconstructing the functional effect of TP53 somatic mutations on its regulon using causal signalling network modelling

AO_SCPLOWBSTRACTC_SCPLOWThe gene encoding tumor protein p53 (TP53) is the most frequently mutated gene in human cancer. Mutations in both coding and non-coding regions of TP53 can disrupt the regulatory function of the transcription factor, but the functional impact of different somatic mutations on the global TP53 regulon is complex and poorly understood. To address this, we first proceed with a machine learning (ML) approach, and then propose an integrated computational network modelling approach that reconstructs signalling networks using a comprehensive collection of experimental and predicted regulons, and compares their topology. We evaluate both these approaches in a scrutinized pan-cancer analysis of matched genomics and transcriptomics data from 1,457 cell lines (22 cancer types) and 12,531 clinical samples (54 cancer sub-types). Using a ML approach based on penalized generalized linear regression we were able to predict TP53 mutation, but failed to resolve different mutation types. Thus, to infer the impact of different TP53 mutations we compared the topological characteristics of the optimized and reconstructed (upwards of twenty thousand) gene networks and extracted gene signatures for each mutation type using network analysis. We demonstrate that by accounting for TP53 mutation characteristics such as i) mutation type (e.g. missense, nonsense), ii) deleterious consequences of the mutation, or iii) mapping to previously identified hotspots, we can infer a much richer understanding of gene expression regulation, than when simply grouping samples based on their mutation/wild type or gene expression status. Our study highlights a powerful strategy exploiting signalling networks to systematically characterize the functional impact of the full spectrum of somatic mutations. This approach can be applied in general to genetic variation, with clear implications for, but not limited to, the biomedical domain and precision medicine.

systems biology↗

bioRxiv2021

Cooled SPAD array detector for low light-dose fluorescence laser scanning microscopy

The single-photon timing and sensitivity performance and the imaging ability of asynchronous-readout single-photon avalanche diode (SPAD) array detectors have opened up enormous perspectives in fluorescence (lifetime) laser scanning microscopy (FLSM), such as super-resolution image scanning microscopy and high-information content fluorescence fluctuation spectroscopy (FFS). However, the strengths of these FLSM techniques depend on the many different characteristics of the detector, such as dark-noise, photon-detection efficiency, after-pulsing probability, and optical-cross talk, whose overall optimization is typically a trade-off between these characteristics. To mitigate this trade-off, we present a novel SPAD array detector with an active cooling system, which substantially reduces the dark-noise without significantly deteriorating any other detector characteristics. In particular, we show that lowering the temperature of the sensor to -15{degrees}C significantly improves the signal-to-noise ratio due to a 10-fold decrease in the dark-count rate compared to room temperature. As a result, for imaging, the laser power can be decreased by more than a factor of three, which is particularly beneficial for live-cell super-resolution imaging, as demonstrated in fixed and living cells expressing GFP-tagged proteins. For FFS, together with the benefit of the reduced laser power, we show that cooling the detector is necessary to remove artifacts in the correlation function, such as spurious negative correlations observed in the hot elements of the detector, i.e., elements whose dark-noise is substantially higher than the median value. Overall, this detector represents a further step towards the integration of SPAD array detectors in any FLSM system. SIGNIFICANCESingle-photon avalanche diode (SPAD) array detectors are revolutionizing fluorescence laser-scanning microscopy (FLSM). Thanks to their single-photon timing and sensitivity ability and their imaging faculty, a SPAD array detector transforms any FLSM into a super-resolution microscope, and opens a whole range of possibilities for the study of sample dynamics by means of fluorescence fluctuation spectroscopy (FFS). However, dark-noise can be a severe problem for both imaging and FFS. For imaging, the signal overcomes noise only for a relatively high illumination intensity, which can be detrimental for live-cell experiments. For FFS, the noise leads to artifacts in the correlation curves, potentially leading to wrong conclusions about the sample. We show that lowering the temperature of the detector to -15{degrees}C solves both problems

biophysics↗

bioRxiv2021

Spatial regulation of coordinated excitatory and inhibitory synaptic plasticity at dendritic synapses

The induction of synaptic plasticity at an individual dendritic glutamatergic spine can affect neighboring spines. This local modulation generates dendritic plasticity microdomains believed to expand the neuronal computational capacity. Here, we investigate whether local modulation of plasticity can also occur between glutamatergic synapses and adjacent GABAergic synapses. Using MNI-glutamate and DPNI-GABA double uncaging combined with electrophysiology, live-cell imaging and single-particle tracking, we find that the induction of LTP at an individual glutamatergic spine causes the depression of nearby GABAergic inhibitory synapses (within 3 microns), whereas more distant ones are potentiated. Notably, L-type calcium channels and calpain are required for this plasticity spreading. Overall, our data support a model whereby input-specific glutamatergic postsynaptic potentiation induces a spatially-regulated rearrangement of inhibitory synaptic strength in the surrounding area through short-range heterosynaptic interactions. Such local coordination of excitatory and inhibitory synaptic plasticity is expected to profoundly influence dendritic information processing and integration.

neuroscience↗

bioRxiv2017

sigQC: A procedural approach for standardising the evaluation of gene signatures

With the increase in next generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools, poised to make a large impact on the diagnosis, management and prognosis for a number of diseases. Increasingly, it is becoming necessary to determine whether a gene expression signature may apply to a dataset, but no standard quality control methodology exists. In this work, we introduce the first protocol, implemented in an R package sigQC, enabling a streamlined methodological and standardised approach for the quality control validation of gene signatures on independent data sets. The emphasis in this work is in showing the critical quality control steps involved in the generation of a clinically and biologically useful, transportable gene signature, including ensuring sufficient expression, variability, and autocorrelation of a signature. We demonstrate the application of the protocol in this work, showing how the outputs created from sigQC may be used for the evaluation of gene signatures on large-scale gene expression data in cancer.

bioinformatics↗