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Innocenti, S.

Publications and source records attributed to Innocenti, S..

4 recordsLinked to original sources

Comprehensive multi-site profiling of the malignant pleural mesothelioma micro-environment identifies candidate molecular determinants of histopathologic type

Pleural mesothelioma (PM) comprises sarcomatoid, epithelioid and biphasic histologic subtypes. Bulk PM RNA-sequencing identifies a histology-associated molecular gradient with features of epithelial-mesenchymal (EM) transition but cannot parse malignant, stromal, and immune tumor components. The mechanisms driving PM malignant cell phenotype and associated histology is not well-characterized. Here, we use single-cell RNA-sequencing (scRNA-seq) paired with exome, bulk RNA-sequencing, and histologic analysis of adjacent samples to characterize malignant cell EM state, parse the tumor microenvironment (TME), and identify candidate drivers of PM cell fate. We observe EM variation in malignant cells analogous to bulk samples. We characterize epithelioid and sarcomatoid malignant cell programs and identify a new uncommitted malignant cell EM phenotype enriched in biphasic histology samples. Using inferred CNVs we observe that single individual PM clones consist of cells exhibiting all three EM cell states. We find that distinct non-malignant microenvironments associated with tumors consisting of mostly cells in each state, and identify WNT inhibition, GAS6-AXL, and HBEGF-EGFR signaling as pathways associated with distinct EM cell states. These findings provide deeper insight into the molecular drivers of PM malignant cells and identify non-malignant cell signals as potential EMT and growth drivers in PM.

cancer biology↗

Streamlining the alignment of UAV and ULS forest point clouds: an approach based on ground and tree stems

Accurate co-registration of terrestrial and aerial point clouds may provide a high-resolution description of tree components for large forest areas. However, an automated approach for co-registering point clouds is still needed, given the challenges in geospatial data processing, particularly in complex topographical conditions. The main objective of this study is to present the application of a novel procedure for the co-registration of point clouds obtained from terrestrial and UAV surveys in Mediter-ranean forests.

bioinformatics↗

TreeArchTraits: an R package to analyse the architectural traits of trees using TLS data

1O_LIThe architecture of trees is significantly influenced by their interactions, directly affecting their functioning and structural development. Different tree architectural indicators (TAT) have evolved, including these interactions measurements. C_LIO_LIRecent advances in Terrestrial Laser Scanning data collection make measuring the three-dimensional characteristics of trees more more efficient. C_LIO_LIThe R package TreeArchTraits facilitates the processing of three-dimensional tree characteristics obtained through Terrestrial Laser Scanning, enabling the computation of various indices. C_LIO_LIA set of trees belonging to different forest conditions was used to demonstrate the TreeArchTraits potential for characterizing tree architectures. C_LI O_FIG O_LINKSMALLFIG WIDTH=173 HEIGHT=200 SRC="FIGDIR/small/560266v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@9a6dc5org.highwire.dtl.DTLVardef@9cae28org.highwire.dtl.DTLVardef@6dcc9dorg.highwire.dtl.DTLVardef@946706_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

Cell type-specific assessment of cholesterol distribution in models of neurodevelopmental disorders

Most nervous system disorders manifest through alterations in neuronal signaling based on abnormalities in neuronal excitability, synaptic transmission, and cell survival. However, such neuronal phenotypes are frequently accompanied - or even caused - by metabolic dysfunctions in neuronal or non-neuronal cells. The tight packing and highly heterogenous properties of neural, glial and vascular cell types pose significant challenges to dissecting metabolic aspects of brain disorders. Perturbed cholesterol homeostasis has recently emerged as key parameter associated with sub-sets of neurodevelopmental disorders. However, approaches for tracking and visualizing endogenous cholesterol distribution in the brain have limited capability of resolving cell type-specific differences. We here develop tools for genetically-encoded sensors that report on cholesterol distribution in the mouse brain with cellular resolution. We apply these probes to examine sub-cellular cholesterol accumulation in two genetic mouse models of neurodevelopmental disorders, Npc1 and Ptchd1 knock-out mice. While both genes encode proteins with sterol-sensing domains that have been implicated in cholesterol transport, we uncover highly selective and cell type-specific phenotypes in cholesterol homeostasis. The tools established in this work should facilitate probing sub-cellular cholesterol distribution in complex tissues like the mammalian brain and enable capturing cell type-specific alterations in cholesterol flow between cells in models of brain disorders.

neuroscience↗