Biology subjects

Navarro, N.

Publications and source records attributed to Navarro, N..

4 recordsLinked to original sources

bioRxiv2022

Analysis of cancer genomic amplifications identifies druggable collateral dependencies within the amplicon

The identification of novel therapeutic targets for specific cancer molecular subtypes is crucial for the development of precision oncology. In the last years, CRISPR/Cas9 screens have accelerated the discovery and validation of new targets associated with different tumor types, mutations or fusions. However, there are still many cancer vulnerabilities associated with specific molecular features that remain to be explored. Here we used data from CRISPR/Cas9 screens in 954 cancer cell lines to identify gene dependencies associated with 16 common cancer genomic amplifications. We found that high-copy number genomic amplifications generate multiple collateral dependencies within the amplified region in 94% of cases. Further, to prioritize candidate targets for each chromosomal region amplified, we integrated gene dependency parameters with both druggability data and subcellular location. Finally, analysis of the relationship between gene expression and gene dependency leads to the identification of genes, the expression of which may constitute predictive biomarkers of dependency.

cancer biology↗

bioRxiv2022

Near Infrared Nanosensors Enable Optical Imaging of Oxytocin with Selectivity over Vasopressin in Acute Mouse Brain Slices

Oxytocin plays a critical role in regulating social behaviors, yet our understanding of its role in both neurological health and disease remains incomplete. Real-time oxytocin imaging probes with the spatiotemporal resolution relevant to its endogenous signaling are required to fully elucidate oxytocin function in the brain. Herein we describe a near-infrared oxytocin nanosensor (nIROx), a synthetic probe capable of imaging oxytocin in the brain without interference from its structural analogue, vasopressin. nIROx leverages the inherent tissue-transparent fluorescence of single-walled carbon nanotubes (SWCNT) and the molecular recognition capacity of an oxytocin receptor peptide fragment (OXTp) to selectively and reversibly image oxytocin. We employ these nanosensors to monitor electrically stimulated oxytocin release in brain tissue, revealing oxytocin release sites with a median size of 3 m which putatively represents the spatial diffusion of oxytocin from its point of release. These data demonstrate that covalent SWCNT constructs such as nIROx are powerful optical tools that can be leveraged to measure neuropeptide release in brain tissue.

bioengineering↗

bioRxiv2022

Single-cell mapping of tumor heterogeneity in pediatric rhabdomyosarcoma reveals developmental signatures with therapeutic relevance

Rhabdomyosarcoma (RMS) is an aggressive human pediatric cancer. Despite robust expression of myogenic regulatory factors, RMS cells are blocked in a proliferative state and do not terminally differentiate. The extent to which the skeletal muscle lineage is represented in RMS tumors and the mechanisms leading to developmental arrest remain elusive. Here, we combined single-cell RNA sequencing (scRNAseq), mass cytometry (CyTOF) and high-content imaging to resolve RMS heterogeneity. ScRNAseq and CyTOF analysis of a total of 17 patient-derived primary cultures and three cell lines uncovered plastic myogenic subpopulations that delineate a branched trajectory. The less aggressive embryonal RMS (eRMS) harbor primarily muscle stem cell (MuSC)-like cells and exhibit sparse commitment to differentiation. The more aggressive alveolar RMS (aRMS) comprise primarily actively cycling committed progenitors with a paucity of differentiated cells. The oncogenic fusion protein PAX3:FOXO1 sustains aRMS cells in the cycling trajectory loop, which we show can re-wired towards differentiation upon its downregulation or by dual pharmacological RAF and MEK inhibition. Our findings provide insights into the developmental states and trajectories underlying RMS progression and identify the RAS pathway as a promising target of differentiation therapy for human aRMS. STATEMENT OF SIGNIFICANCEWe present the first comprehensive single-cell transcriptomic and proteomic atlas of pediatric rhabdomyosarcoma (RMS), in which we identify impaired myogenic trajectories with prognostic value. We demonstrate that RAS pathway inhibitors disrupt the oncogenic trajectory and induce terminal differentiation, revealing novel therapeutic targets for the aggressive alveolar RMS subtype.

cancer biology↗

bioRxiv2021

Machine learning enables discovery of DNA-carbon nanotube sensors for serotonin

DNA-wrapped single walled carbon nanotube (SWNT) conjugates have remarkable optical properties leading to their use in biosensing and imaging applications. A critical limitation in the development of DNA-SWNT sensors is the current inability to predict unique DNA sequences that confer a strong analyte-specific optical response to these sensors. Here, near-infrared (nIR) fluorescence response datasets for ~100 DNA-SWNT conjugates, narrowed down by a selective evolution protocol starting from a pool of ~1010 unique DNA-SWNT candidates, are used to train machine learning (ML) models to predict new unique DNA sequences with strong optical response to neurotransmitter serotonin. First, classifier models based on convolutional neural networks (CNN) are trained on sequence features to classify DNA ligands as either high response or low response to serotonin. Second, support vector machine (SVM) regression models are trained to predict relative optical response values for DNA sequences. Finally, we demonstrate with validation experiments that integrating the predictions of ensembles of the highest quality CNN classifiers and SVM regression models leads to the best predictions of both high and low response sequences. With our ML approaches, we discovered five new DNA-SWNT sensors with higher fluorescence intensity response to serotonin than obtained previously. Overall, the explored ML approaches introduce an important new tool to predict useful DNA sequences, which can be used for discovery of new DNA-based sensors and nanobiotechnologies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/457145v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@9e1a2corg.highwire.dtl.DTLVardef@1c84e68org.highwire.dtl.DTLVardef@1939d1eorg.highwire.dtl.DTLVardef@30424e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗