Biology subjects

Antonello, P.

Publications and source records attributed to Antonello, P..

4 recordsLinked to original sources

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↗

bioRxiv2025

Unmmaped RNA-Seq reads from inoculated sugarcane reveals long non-coding RNAs related to retrotransposons and suggest microbiome modulation

BackgroundTranscriptome studies have contributed to the understanding of protein coding and non-coding gene expression of several organisms. They also provide knowledge into host responses to pathogen infections. Not common yet is to detect transcripts from multiple interacting organisms present in a given sample. In addition, transcriptome studies with complex polyploid hybrid genomes and not completely sequenced, such as the one of sugarcane, remains a challenge. In such studies, a considerable set of reads may not align on any reference genome but still hold significant relevance to the study, allowing to gather information beyond the complex organism itself. ResultsA complete transcriptome analysis of sugarcane inoculated with a pathogen, with and without the addition of a beneficial bacteria, generated a subset of reads that did not map to their respective genome references. Here, we report a pipeline based on the assembly of a collection of unmapped reads that potentially interfere with the gene expression of the associated microbiome, as well as the identification of long non-coding RNAs related to transposable elements present in a host-pathogen interaction. In addition, the detection of transcripts of naturally occurring microorganisms in sugarcane allowed the identification of its microbiome at the species level. Further studies support changes in microorganism transcription levels according to the biotic stress the plant was conditioned. ConclusionA quick and practical pipeline is proposed to study unmapped reads to infer relevant information that would remain otherwise unnoticed. New sequences from hybrid sugarcane transcriptome, such as long non-coding genes related to known retrotransposons are described. Also, changes in microbiome gene expression provides insights to the microbiome alterations and bring knowledge to the sugarcane "pathobiome".

bioinformatics↗

bioRxiv2022

ADeS: a deep learning based Apoptosis Detection System for live cell imaging.

Intravital microscopy has revolutionized live cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy time-lapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy time-lapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in vivo, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.

bioinformatics↗

bioRxiv2022

A pipeline to track unlabeled cells in wide migration chambers using pseudofluorescence

Cell migration is a pivotal biological process, whose dysregulation is found in many diseases including inflammation and cancer. Advances in microscopy technologies allow now to study cell migration in vitro, within microenvironments that resemble in vivo conditions. However, when cells are observed within large 3D migration chambers at low magnification and for extended periods of time, data analysis becomes difficult. Indeed, cell detection and tracking are hampered due to the large pixel size, the possible low signal-to-noise ratio and distortions in the cell shape due to changes in the z-axis position. Although fluorescent staining can be used to facilitate cell detection, it may alter cell behavior and suffer from fluorescence loss over time (photobleaching). Here we describe the application of an image analysis pipeline based on deep learning to convert the transmitted light signal from unlabeled lymphoma cells to pseudofluorescence. Such pipeline confers a significant improvement in tracking accuracy while not suffering from photobleaching. This is reflected in the possibility of tracking cells for three-fold longer periods of time.

cell biology↗