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Cittaro, D.

Publications and source records attributed to Cittaro, D..

11 recordsLinked to original sources
bioRxiv2024

Development of a high-throughput 3D culture microfluidic platform for multi-parameter phenotypic and omics profiling of patient-derived organoids

Patient-derived organoids (PDOs) are poised to become central tools both in clinical practice, to preemptively identify patient optimal treatments, and in drug discovery, overcoming the limitations of cancer cell lines. However, the use of PDOs in these settings has been hampered by several bottlenecks, including sample requirements, assay time, and handling in the context of high-throughput assays. We developed a Microfluidic Platform for Organoids culture (MPO) that miniaturises and simplifies PDOs cultures in a 384-plate format. Both retrospective and prospective clinical studies demonstrate MPO predictive value and the straightforward implementation in the clinical setting. MPO allows subcellular phenotypic screenings, as imaging-based applications like Cell Painting, target engagement analyses, alongside the comprehensive definition of PDOs genomic, transcriptomic, proteomic, lipidomic, and metabolomic landscapes. Harnessing the pleiotropic capabilities of MPO, we uncovered the role of EZH2 inhibitors to prevent the long-term emergence of resistance to RAS inhibitors in metastatic colon cancer. In all, we demonstrate the potential of MPO to impact clinical practice, alongside exploration of the mechanisms underlying compound response and resistance.

cancer biology↗
bioRxiv2023

Evolutionary fingerprints of EMT in pancreatic cancers

Mesenchymal plasticity has been extensively described in advanced and metastatic epithelial cancers; however, its functional role in malignant progression, metastatic dissemination and therapy response is controversial. More importantly, the role of epithelial mesenchymal transition (EMT) and cell plasticity in tumor heterogeneity, clonal selection and clonal evolution is poorly understood. Functionally, our work clarifies the contribution of EMT to malignant progression and metastasis in pancreatic cancer. We leveraged ad hoc somatic mosaic genome engineering, lineage tracing and ablation technologies and dynamic genetic reporters to trace and ablate tumor-specific lineages along the phenotypic spectrum of epithelial to mesenchymal plasticity. The experimental evidences clarify the essential contribution of mesenchymal lineages to pancreatic cancer evolution and metastatic dissemination. Spatial genomic analysis combined with single cell transcriptomic and epigenomic profiling of epithelial and mesenchymal lineages reveals that EMT promotes with the emergence of chromosomal instability (CIN). Specifically tumor lineages with mesenchymal features display highly conserved patterns of genomic evolution including complex structural genomic rearrangements and chromotriptic events. Genetic ablation of mesenchymal lineages robustly abolished these mutational processes and evolutionary patterns, as confirmed by cross species analysis of pancreatic and other human epithelial cancers. Mechanistically, we discovered that malignant cells with mesenchymal features display increased chromatin accessibility, particularly in the pericentromeric and centromeric regions, which in turn results in delayed mitosis and catastrophic cell division. Therefore, EMT favors the emergence of high-fitness tumor cells, strongly supporting the concept of a cell-state, lineage-restricted patterns of evolution, where cancer cell sub-clonal speciation is propagated to progenies only through restricted functional compartments. Restraining those evolutionary routes through genetic ablation of clones capable of mesenchymal plasticity and extinction of the derived lineages completely abrogates the malignant potential of one of the most aggressive form of human cancer.

cancer biology↗
bioRxiv2023

Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks

Single cell profiling has become a common practice to investigate the complexity of tissues, organs and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or from the very same cells. Despite development of computational methods for data integration is an active research field, most of the available strategies have been devised for the joint analysis of two modalities and cannot accommodate a high number of them. To solve this problem, we here propose a multiomic data integration framework based on Wasserstein Generative Adversarial Networks (MOWGAN) suitable for the analysis of paired or unpaired data with high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated. Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.

bioinformatics↗
bioRxiv2022

In vitro selection of Engineered Transcriptional Repressors for targeted epigenetic silencing and initial evaluation of their specificity profile

Gene inactivation is instrumental to study gene function and represents a promising strategy for the treatment of a broad range of diseases. Among traditional technologies, RNA interference suffers from partial target abrogation and requirement for life-long treatments. On the other hand, artificial nucleases can impose stable gene inactivation through induction of DNA Double Strand Breaks, but recent studies are questioning the safety of this approach. Targeted epigenetic editing via Engineered Transcriptional Repressors (ETRs) may represent a solution, as a single administration of specific ETRs combinations can lead to durable silencing without DNA breaks induction. ETRs are proteins containing a programmable DNA Binding Domain (DBD) and effectors from naturally occurring transcriptional repressors. Specifically, a combination of three ETRs equipped with the KRAB domain of human ZNF10, the catalytic domain of human DNMT3A and human DNMT3Lwas shown to induce heritable repressive epigenetic states on the ETR-target gene. The hit-and-run nature of this platform, the lack of impact on the DNA sequence of the target, and the possibility to revert on-demand the repressive state by DNA demethylation, make epigenetic silencing a game-changing tool. A critical step is the identification of the proper position on the target gene where to tether the ETRs in order to maximize on target and minimizing off-target silencing. Performing this step in the final ex vivo or in vivo preclinical setting can be cumbersome. Taking CRISPR-Cas9 system as a paradigmatic DBD for ETRs, here we describe a protocol consisting in the in vitro screen of gRNAs coupled to the triple ETRs combination for efficient on-target silencing, followed by evaluation of the genome-wide specificity profile of top hits. This allows to reduce the initial repertoire of candidate gRNAs to a short list of promising ones, whose complexity is suitable for their final evaluation in the therapeutically relevant setting of interest.

synthetic biology↗
bioRxiv2022

Contrast Subgraphs Allow Comparing Homogeneous and Heterogeneous Networks Derived from Omics Data

Biological networks are often used to describe the relationships between relevant entities, in particular genes and proteins, and are a powerful tool for functional genomics. Many important biological problems can be investigated by comparing biological networks between different conditions, or networks obtained with different techniques. We show that contrast subgraphs, a recently introduced technique to identify the most important structural differences between two networks, provide a versatile tool for comparing gene and protein networks of diverse origin. We show in three concrete examples how contrast subgraphs can provide new insight in functional genomics by extracting the gene/protein modules whose connectivity is most altered between two conditions or experimental techniques.

bioinformatics↗
bioRxiv2022

Dimensionality reduction and statistical modeling of scGET-seq data

Single cell multiomics approaches are innovative techniques with the ability to profile orthogonal features in the same single cell, giving the opportunity to dig more deeply into the stochastic nature of individual cells. We recently developed scGET-seq, a technique that exploits a Hybrid Transposase (tnH) along with the canonical enzyme (tn5), which is able to profile altogether closed and open chromatin in a single experiment. This technique adds an important feature to the classic scATAC-seq assays. In fact, the lack of a closed chromatin signal in scATAC: (i) restricts sampling of DNA sequence to a very small portion of the chromosomal landscapes, substantially reducing the ability to investigate copy number alteration and sequence variations, and (ii) hampers the opportunity to identify regions of closed chromatin, that cannot be distinguished between non-sampled open regions and truly closed. scGET-seq overcomes these issues in the context of single cells. In this work, we describe the latest advances in the statistical analysis and modeling of scGET-seq data, touching several aspects of the computational framework: from dimensionality reduction, to statistical modeling, and trajectory analysis.

bioinformatics↗
bioRxiv2020

Neural precursor cells contribute to decision-making by tuning striatal connectivity via secretion of IGFBPL-1

The adult brain retains endogenous neural stem/precursors cells (eNPCs) in two major neurogenic niches, the subgranular zone (SGZ) in the hippocampus and the subventricular zone (SVZ). While in humans and rodents the SGZ contributes to memory functions by generating new neurons that integrate into hippocampal circuitry, the role of eNPCs of the SVZ is less clear. SVZ-eNPCs contribute to the generation of new neurons fated to the olfactory bulb (OB) in rodents but not in humans where they are thought to contribute to striatal neurogenesis as a result of tissue damage. Here, we show in mice and humans a novel non-neurogenic physiological role of adult SVZ-eNPCs in supporting cognitive functions by regulating striatal neuronal activity. We first provide evidence that GABAergic transmission between parvalbumin-expressing fast-spiking interneurons (FSIs) and medium spiny neurons (MSNs) is tuned by SVZ-eNPCs via secretion of Insulin-Like Growth Factor Binding Protein Like 1 (IGFBPL-1) that, in turn, regulates the Insulin-Like Growth Factor (IGF-1) signalling cascade. Consistently, selective ablation of SVZ-eNPCs and in vivo disruption of the IGF-signalling determine the impairment of intrastriatal coherence. A finding associated with a higher failure rate of GABAergic transmission mediated by FSIs and with striatum-related behavioural dysfunctions impairing decision making. Human validation studies revealed IGFBPL-1 expression in the SVZ and in foetal and induced-pluripotent stem cell-derived NPCs as well as a strong correlation in neurological patients between SVZ pathological damage, reduction of striatum volume and impairment of information processing speed. All in all, our results highlight a novel non-neurogenic homeostatic role exerted by SVZ-eNPCs on striatal GABAergic neurons that might contribute to cognitive processes involving decisions-making tasks.

neuroscience↗
bioRxiv2020

Nested Stochastic Block Models Applied to the Analysis of Single Cell Data

Single cell profiling has been proven to be a powerful tool in molecular biology to understand the complex behaviours of heterogeneous system. The definition of the properties of single cells is the primary endpoint of such analysis, cells are typically clustered to underpin the common determinants that can be used to describe functional properties of the cell mixture under investigation. Several approaches have been proposed to identify cell clusters; while this is matter of active research, one popular approach is based on community detection in neighbourhood graphs by optimisation of modularity. In this paper we propose an alternative and principled solution to this problem, based on Stochastic Block Models. We show that such approach not only is suitable for identification of cell groups, it also provides a solid framework to perform other relevant tasks in single cell analysis, such as label transfer. To encourage the use of Stochastic Block Models, we developed a python library, schist, that is compatible with the popular scanpy framework.

bioinformatics↗
bioRxiv2018

Somatic mutability in cancer predicts the phenotypic relevance of germline mutations

Genomic sequence mutations in both the germline and somatic cells can be pathogenic. Several authors have observed that often the same genes are involved in cancer when mutated in somatic cells and in genetic diseases when mutated in the germline. Recent advances in high-throughput sequencing techniques have provided us with large databases of both types of mutations, allowing us to investigate this issue in a systematic way. Here we show that high-throughput data about the frequency of somatic mutations in the most common cancers can be used to predict the genes involved in abnormal phenotypes and diseases. The predictive power of somatic mutation patterns is largely independent of that of methods based on germline mutation frequency, so that they can be fruitfully integrated into algorithms for the prioritization of causal variants. Our results confirm the deep relationship between pathogenic mutations in somatic and germline cells, provide new insight into the common origin of cancer and genetic diseases and can be used to improve the identification of new disease genes.

genomics↗
bioRxiv2018

A novel truncating variant of GLI2 associated with Culler-Jones syndrome impairs Hedgehog signalling

BackgroundGLI2 encodes for a transcription factor that controls the expression of several genes in the Hedgehog pathway. Mutations in GLI2 have been described as causative of a spectrum of clinical phenotypes, notably holoprosencephaly, hypopituitarism and postaxial polydactyl. Methods: In order to identify causative genetic variant, we performed exome sequencing of a trio from an Italian family with multiple affected individuals presenting clinical phenotypes in the Culler-Jones syndrome spectrum. We performed a series of assays, both in vitro and in ovo (Chicken model) to test the functional properties of GLI2 mutation.\n\nResultsHere we report a novel deletion c.3493delC (p.P1167LfsX52) in the C-terminal activation domain of GLI2, and cell-based functional assays confirmed the pathogenicity of the identified variant and revealed a dominant-negative effect of mutant GLI2 on Hedgehog signalling.\n\nConclusionOur results highlight the variable clinical manifestation of GLI2 mutations and emphasize the value of functional characterisation of novel gene variants to assist genetic counselling and diagnosis.

genetics↗
bioRxiv2017

Patterns of cancer somatic mutations predict genes involved in phenotypic abnormalities and genetic diseases

Genomic sequence mutations in both the germline and somatic cells can be pathogenic. Several authors have observed that often the same genes are involved in cancer when mutated in somatic cells and in genetic diseases when mutated in the germline. Recent advances in high-throughput sequencing techniques have provided us with large databases of both types of mutations, allowing us to investigate this issue in a systematic way. Here we show that high-throughput data about the frequency of somatic mutations in the most common cancers can be used to predict the genes involved in abnormal phenotypes and diseases. The predictive power of somatic mutation patterns is largely independent of that of methods based on germline mutation frequency, so that they can be fruitfully integrated into algorithms for the prioritization of causal variants. Our results confirm the deep relationship between pathogenic mutations in somatic and germline cells, provide new insight into the common origin of cancer and genetic diseases and can be used to improve the identification of new disease genes.

genomics↗