Biology subjects

Costantino, S.

Publications and source records attributed to Costantino, S..

4 recordsLinked to original sources

bioRxiv2025

DNAi: an open-source AI tool for unbiased DNA fiber analysis

DNA fiber assays are powerful tools for investigating replication dynamics at the single-molecule level. However, their application and widespread adoption has been hampered by the labor-intensive and tedious nature of manual analysis of large numbers of images. Quantification of labeled DNA fibers typically depends on subjective examination, selection, and annotation of individual fibers from fluorescence microscopy images reducing inter-user consistency, reproducibility, and experimental throughput. To address these issues, we developed DNAi, a computer vision tool based on deep learning allowing automated detection and quantification of labeled DNA fiber length. DNAi was trained on a large and diverse dataset of manually annotated images of DNA fibers and matches human performance and accuracy in segmentation and length measurement across a wide range of experimental conditions. The open-source tool includes a user-friendly interface, which permits visual validation and manual selection of segmented fibers. Overall, DNAi enables robust, rapid, and reproducible DNA fiber analysis, and is freely available. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/679603v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1c8fd78org.highwire.dtl.DTLVardef@78f40corg.highwire.dtl.DTLVardef@de60e5org.highwire.dtl.DTLVardef@1ab7db5_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2025

A new approach for high-content traction force microscopy to characterize large cell ensembles

1.1Accurate measurements of cellular forces are important for understanding a wide range of biological processes where traction plays a major role. The characterization of mechanical properties is needed to unravel complex phenomena like migration, morphogenesis, mechanotransduction, or shape regulation, but accurate data on large numbers of single cells remain scarce and challenging. The capacity to measure forces in populations of cells and to identify subsets within heterogeneous ensembles would enable to reveal and manipulate their intrinsic complexity. Traction Force Microscopy (TFM) is a technology that can quantify the contractile forces exerted by cells via measuring the displacement of fluorescent beads embedded on the surface of a soft substrate with precisely defined mechanical properties. However, conventional numerical approaches for measuring cellular forces using TFM are labor-intensive and can yield significant artifacts, making them ill-suited for high-throughput analysis. In this work, we propose using the Demons algorithm instead, leading to significant improvements in both computational efficiency and accuracy. Based computer simulations, we show that in some situations this methodology outperforms conventional approaches in terms of speed, it is less sensitive to the blur induced by out-of-focus images and improves the accuracy of force field reconstructions. Additionally, we conducted experiments using cell lines and gels of distinct stiffness to demonstrate that the Demons algorithm is capable of real-time analysis and is effective at clustering cells according to their mechanotype. 1.2 Statement of significanceLarge cellular ensembles encompass a wide variety of traits that require detailed but high-throughput technologies for characterisation. Granular descriptions of cell populations enable to describe their heterogeneity are essential to understand biological phenomena in the search of effective treatments. Most advances in the field of Traction Force Microscopy (TFM) are oriented towards high resolution force measurements at sub-micron adhesion scale that reveal the intricacies of molecular interactions. Here, we propose a strategy to exploit TFM for characterizing the mechanotypes of large cell ensembles. We benchmark and show the capacities of using the Demons algorithm to measure bead displacement fields, the improvement in force reconstructions obtained, and experimentally demonstrate that single cell analysis of cell dynamics can be used to cluster and describe different cell types.

biophysics↗

bioRxiv2024

Chromatin Remodeling by the Histone Methyltransferase SETD2 Drives Lipotoxic Injury in Cardiometabolic Heart Failure with Preserved Ejection Fraction

BackgroundCardiometabolic heart failure with preserved ejection fraction (cHFpEF) is highly prevalent and associates with a poor outcome. Pathological gene expression in heart failure is accompanied by changes in active histone marks without major alterations in DNA methylation. Histone 3 trimethylation at lysine 36 (H3k36me3) - a chromatin signature induced by the histone methyltransferase SETD2 - strongly correlates with changes in gene expression in human failing hearts; however, its role is poorly understood. Here we investigate the role of SETD2 in cHFpEF. MethodsMice with cardiomyocyte-specific deletion of SETD2 (c-SETD2-/-) were generated and subjected to high fat diet feeding and L-NAME treatment for 15 weeks to induce cHFpEF. Cardiac function and exercise tolerance were assessed by echocardiography and Treadmill exhaustion test. Chromatin immunoprecipitation assays (ChIP) were performed to investigate SETD2/H3k36me3 enrichment on gene promoters. SETD2 gain- and loss-of-function experiments were performed in cultured cardiomyocytes (CMs) exposed to palmitic acid (PA). SETD2 expression was also investigated in left ventricular (LV) myocardial specimens from patients with cHFpEF and control donors. ResultsSETD2 was upregulated in cHFpEF mouse hearts and its chromatin mark H3k36me3 was enriched on the promoter of sterol regulatory element-binding transcription factor 1 (SREBP1) gene. SETD2 activation in cHFpEF led to SREBP1 upregulation, triglyceride accumulation and lipotoxic damage. Of note, cardiomyocyte-specific deletion of SETD2 in mice prevented HFpEF-related hypertrophy, diastolic dysfunction and lung congestion while improving exercise tolerance. SETD2 deletion blunted H3K36me3 enrichment on SREBP1 promoter thus leading to a marked rewiring of the cardiac lipidome and restoration of autophagic flux. SETD2 depletion in PA-treated CMs prevented SREBP1 upregulation, whereas SETD2 overexpression recapitulated lipotoxic damage. Finally, SETD2 was upregulated in LV specimens from cHFpEF patients and its pharmacological inhibition by EZM0414 attenuated CM stiffness. ConclusionsTherapeutic modulation of SETD2/H3k36me3 axis might prevent lipotoxic injury and cardiac dysfunction in cHFpEF.

physiology↗

bioRxiv2023

Predicting T cell activation based on intracellular calcium fluctuations

Adoptive T cell therapies rely on the transduction of T cells with a predetermined antigen receptor which redirects their specificity towards tumor-specific antigens. Despite the development of multiple platforms for tumor-specific T cell receptor (TCR) discovery, this process remains time consuming and skewed toward high-affinity TCRs. Specifically, the methods for identifying therapeutically-relevant TCR sequences, predominantly achieved through the enrichment of antigen-specific T cells, represents a major bottleneck for the broader application of TCR-engineered cell therapies. Fluctuation of intracellular calcium levels in T cells is a well described, proximal readout of TCR signaling. Hence, it is an attractive candidate marker for identifying antigen-specific T cells that does not require in vitro antigen-specific T cell expansion. However, calcium fluctuations downstream of TCR engagement with antigen are highly variable; we propose that appropriately-trained machine learning algorithms may allow for T cell classification from complex datasets such as those related to polyclonal T cell signaling events. Using deep learning tools, we demonstrate efficient and accurate prediction of antigen-specificity based on intracellular Ca2+ fluctuations of in vitro-stimulated CD8+ T cells. Using a simple co-culture assay to activate monoclonal TCR transgenic T cells of known specificity, we trained a convolutional neural network to predict T cell reactivity, and we test the algorithm against T cells bearing a distinct TCR transgene as well as a polyclonal T cell response. This approach provides the foundation for a new pipeline to fast-track antigen specific TCR sequence identification for use in adoptive T cell therapy. Significance StatementWhile T cells engineered to express a cancer-specific T cell receptor (TCR) are emerging as a viable approach for personalized therapies, the platforms for identifying clinically-relevant TCR sequences are often limited in the breadth of antigen receptors they identify or are cumbersome to implement on a personalized basis. Here, we show that imaging of intracellular calcium fluctuations downstream of TCR engagement with antigen can be used, in combination with artificial intelligence approaches, to accurately and efficiently predict T cell specificity. The development of cancer-specific T cell isolation methods based on early calcium fluctuations may avoid the biases of current methodologies for the isolation of patient-specific TCR sequences in the context of adoptive T cell therapy.

immunology↗