Biology subjects

Castellani, G.

Publications and source records attributed to Castellani, G..

10 recordsLinked to original sources

bioRxiv2026

Disruption of the Brain-Spleen Axis Impairs Monocyte-Microglia Communication and Accelerates Disease Progression in a Model of Amyloidosis

Alzheimers disease (AD) is characterized by a prolonged asymptomatic phase before cognitive decline emerges, yet the mechanisms driving symptom onset remain unclear. Here, we hypothesized that the transition from asymptomatic to symptomatic disease is linked to dysfunction of brain-immune communication. Retrograde neuronal tracing in the 5xFAD mouse model of amyloidosis revealed reduced brain-spleen connectivity at advanced disease stages. To probe the functional role of the brain-spleen axis in coping with disease, we denervated the splenic nerve at an early presymptomatic stage. This intervention accelerated cognitive decline, impaired splenic hematopoiesis, diminished monocyte recruitment to the brain, disrupted monocyte-microglia signaling networks, and reduced the transition of microglia from a homeostatic to the disease-associated (DAM) state. Conversely, enhancing splenic noradrenergic input increased hematopoiesis, restored monocyte homing to the brain, and delayed cognitive impairment. The protective role of splenic monocytes was independently validated in a retinal cytotoxic injury model, in which splenic denervation impaired post-insult retinal ganglion cell survival. Together, these findings identify an active brain-spleen circuit in regulating monocyte recruitment and establish peripheral monocytes as key drivers of microglial state transitions and disease progression.

neuroscience↗

bioRxiv2025

The normal human lymph node cell classification and landscape defined by high-dimensional spatial proteomic.

Lymph nodes (LN) are key secondary lymphoid organs (SLO) for a coordinated immune response. They have been extensively characterized by numerous investigative techniques chiefly as single cell suspensions because they are composed of vagile yet crowded hematolymphoid elements, unfriendly to spatial tissue organization-saving techniques. We comprehensively classify in situ all cells of 19 human LN free of pathology with a 78-marker antibody panel, an hyperplexed cyclic staining method, MILAN, and an analytical bioinformatic pipeline, BRAQUE. A total of 77 cell types were classified, encompassing T, B, innate immune and stromal cells. CD4 and CD8 T-cells were classified into 27 unique subsets by leveraging the expression profiles of TCF7, the presence of co-inhibitory receptors and the spatial distribution. CD5 and TCF7 expression defined novel B-cell types. CD27+ mature B-cells occupied previously unrecognized nodal spaces non-overlapping with the cortex and the plasma-cell rich medullary cords. Type 2 conventional dendritic cells were located in nodular paracortical aggregates. Statistically controlled pairwise neighborhood analysis showed sparse cell-cell interactions, known and new neighbors, established and novel LN landscape niches. A high-dimensional proteomic interrogation of the normal human LN provides spatial allocation of known cell types, novel interactions and the landscape organization.

immunology↗

bioRxiv2025

Alzheimer's disease causes bone marrow myelopoiesis dysfunction

Accumulating evidence suggests that both innate and adaptive immunity play crucial roles in combating Alzheimers disease (AD). Specifically, enhancing the homing of monocyte-derived macrophages to the affected brain has been shown to reduce local inflammation, decrease proteinopathy, rescue neurons, and mitigate cognitive decline. However, the factors limiting their spontaneous recruitment remain unclear. Using multi-omics techniques, we identified impaired myelopoiesis and monocyte development in both mice and AD patients. While not the primary cause of the disease, this impairment is associated with disease progression. In the 5xFAD mouse model, monocyte differentiation was found to be disrupted due to a maladaptive bone marrow (BM) response, driven by type I interferon (IFN-I) signaling. A similar phenotype was found in circulating monocytes from AD patients compared to healthy controls. Blocking IFN-I with monoclonal antibodies or using chimeric AD mice with BM from mice lacking the IFN-I receptor (IFNAR1) alleviated myelopoiesis dysfunction, normalized monocyte phenotypes, and reduced cognitive impairment. These improvements in myeloid function were accompanied by an increased homing of monocyte-derived macrophages in the AD brain. Our results reveal an unexpected dysfunction in BM myelopoiesis in the context of neurodegeneration and support the emerging concept that neurodegenerative diseases are not solely brain-centric.

neuroscience↗

bioRxiv2024

Seeing or believing in hyperplexed spatial proteomics via antibodies. New and old biases for an image-based technology.

Hyperplexed in-situ targeted proteomics via antibody immunodetection (i.e. > 15 markers) is changing how we classify cells and tissues. Differently from other high-dimensional single-cell assays (flow cytometry, single cell RNA sequencing), the human eye is a necessary component in multiple procedural steps: image segmentation, signal thresholding, antibody validation and iconographic rendering. Established methods complement the human image evaluation, but may carry undisclosed biases in such a new context, therefore we re-evaluate all the steps in hyperplexed proteomics. We found that the human eye can discriminate less than 64 out of 256 gray levels and has limitations in discriminating luminance levels in conventional histology images. Furthermore, only images containing visible signals are selected and eye-guided digital thresholding separates signal from noise. BRAQUE, a hyperplexed proteomic tool, can extract, in a marker-agnostic fashion, granular information from markers which have a very low signal-to-noise ratio and therefore are not visualized by traditional visual rendering. By analyzing a public human lymph node dataset, we also found unpredicted staining results by validated antibodies, which highlight the need to upgrade the definition of antibody specificity in hyperplexed immunostaining. Spatially hyperplexed methods upgrade and supplant traditional image-based analysis of tissue immunostaining, beyond the human eye contribution. IMPACT STATEMENTStaining with multiple biomarkers a single tissue section, multiplex staining, is changing how we examine in-situ normalcy, pathology and the interrelationship of good and bad biological actors. Bioinformatic pipelines developed to deal with high-dimensional datasets such as single cell RNA sequencing or multiparameter flow-cytometry have been adapted to analogous types of data derived from tissue, and co-exist with conventional image analysis tools and human eye-guided image evaluation. We wanted to evaluate if multiplex staining with more than 15 markers (hyperplexed) has comparable sensitivity to conventional image analysis and if these latter analysis tools carry undisclosed biases and should not be used together with hyperplexed staining. We found that the human eye has a reduced discriminative power for grayscale and luminance levels compared to the 8-bit available spectrum, affecting positive signal recognition above the noise. We also found that the granular analytical power of recent bioinformatic pipelines can extract information from images which defy human eye perception and deliver information unattainable with existing image analysis tools for single-stain images.

systems biology↗

bioRxiv2023

Covering Hierarchical Dirichlet Mixture Models on binary data to enhance genomic stratifications in Onco-Hematology

Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genetically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In particular, Dirichlet processes have become the preferred method to approach the fit of mixture models. Usually, the multinomial distribution is at the core of such models. However, despite their advanced statistical formalism, these processes are not to be considered black box techniques and a better understanding of their working mechanisms enables to improve their employment and explainability. Focused on genomic data in Acute Myeloid Leukemia, this work unfolds the driving factors and rationale of the Hierarchical Dirichlet Mixture Models of multinomials on binary data. In addition, we introduce a novel approach to perform accurate patients clustering via multinomials based on statistical considerations. The newly reported adoption of the Multivariate Fishers Non-Central Hypergeometric distributions reveals promising results and outperformed the multinomials in clustering both on simulated and real onco-hematological data. Author summaryExplainable models are particularly attractive nowadays since they have the advantage to convince clinicians and patients. In this work we show that a deeper understanding of the Hierarchical Dirichlet Mixture Model, a non-black box method, can lead to better data modelling. In onco-hematology Hierarchical Dirichlet Mixture Models typically help to cluster molecular alterations rather than patients. Here, an intuitive statistical approach is presented to tackle patient classification based on the Hierarchical Dirichlet Mixture Models outcome. Additionally, molecular alterations are usually modelled by Hierarchical Dirichlet Mixture Models as a mixture of multinomial distributions. This work highlights that the alternative Fishers Non-Central Hypergeometric distribution can provide even better results and can give a higher priority to rare molecular alterations for patient classification.

cancer biology↗

bioRxiv2022

CometAnalyser: a user-friendly, open-source deep-learning microscopy tool for quantitative comet assay analysis.

Comet assay provides an easy solution to estimate DNA damage in single cells through microscopy assessment. It is widely used in the analysis of genotoxic damages induced by radiotherapy or chemotherapeutic agents. DNA damage is quantified at the single-cell level by computing the displacement between the genetic material within the nucleus, typically called "comet head", and the genetic material in the surrounding part of the cell, considered as the "comet tail". Today, the number of works based on Comet Assay analyses is really impressive. In this work, besides revising the solutions available to obtain reproducible and reliable quantitative data, we developed an easy-to-use tool named CometAnalyser. It is designed for the analysis of both fluorescent and silver-stained wide-field microscopy images and allows to automatically segment and classify the comets, besides extracting Tail Moment and several other intensity/morphological features for performing statistical analysis. CometAnalyser is an open-source deep-learning tool. It works with Windows, Macintosh, and UNIX-based systems. Source code, standalone versions, user manual, sample images, video tutorial and further documentation are freely available at: https://sourceforge.net/p/cometanalyser. HIGHLIGHTSO_LIComet assay provides an easy solution to estimate DNA damage in single cells. C_LIO_LIToday, an impressive number of works are based on Comet Assay analyses, especially in the field of cancer research. C_LIO_LIComet assay was originally performed as a qualitative analysis. C_LIO_LINone of the free tools today available work on both fluorescent- and silver-stained images. C_LIO_LIWe developed CometAnalyser, an open-source deep-learning tool designed for easy segmentation and classification of comets in fluorescent- and silver-stained images. C_LI

bioengineering↗

bioRxiv2020

Prediction of vascular ageing based on smartphone acquired PPG signals

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing including detrending, demodulating and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) - the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking - was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.

biophysics↗

bioRxiv2019

DNetPRO: A network approach for low-dimensional signatures from high-throughput data

The objective of many high-throughput \"omics\" studies is to obtain a relatively low-dimensional set of observables - signature - for sample classification purposes (diagnosis, prognosis, stratification). We propose DNetPRO, Discriminant Analysis with Network PROcessing, a supervised signature identification method based on a bottom-up combinatorial approach that exploits the discriminant power of all variable pairs. The algorithm is easily scalable allowing efficient computing even for high number of observables (104 - 105). We show applications on real high-throughput genomic datasets in which our method outperforms existing results, or compares to them but with a smaller number of selected variables. Moreover the linearity of DNetPRO allows a clearer interpretation of the obtained signatures in comparison to non linear classification models

bioinformatics↗

bioRxiv2019

Ecological modeling of Long Interspersed elements reveals footprints of evolution and a role of chromatin in shaping their genome landscape in mammals.

Transposons are genome components that account for the majority of genome size in many organisms, behaving as parasitic entities and interfering with the translation mechanism. Chromatin structure influences the activity of transposons, by coordinating genome accessibility for the expression and insertion of these sequences. As a case study, we show evidences of an evolutionary response of the chromatin structure to a variation in the activity of Long Interspersed Elements (LINEs) during mammals evolution, with focus on the murine radiation and primate evolution. LINEs activity was measured using a biophysical approach for modeling LINEs as an ecosystem, where different strains of transposons might reproduce, die and compete for access to the translational machinery of the host. The model, based on the discrete stochastic processes of amplification and deactivation of LINEs copies, has been adapted to the data using Bayesian statistics to estimate its main parameters: rate of growth of transposons copy number and rate of past competition between transposons variants. This approach allows to estimate the activity of ancient LINE strains still present in the genome as deactivated components, and the possible competition among different strains. We leverage these results to highlight how the change in the chromatin structure of the murine species seems to be following an increase of LINEs activity during the appearance of the murine specific strain Lx. On the contrary, a similar response is absent in primates evolution, which follows a decrease of LINEs activity during the amplication of primate specific LIMA/LPB strains.

biophysics↗

bioRxiv2017

Network integration of multi-tumour omics data suggests novel targeting strategies

We characterize different tumour types in the search for multi-tumour drug targets, in particular aiming for drug repurposing or novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allowed us to define three cluster-specific signatures, with genes belonging to NF-B signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we defined new pharmacological strategies validated by in vitro experiments that showed inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways with the potential to be used, singularly or in combination, for the design of novel treatment strategies.

systems biology↗