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Valle, F.

Publications and source records attributed to Valle, F..

8 recordsLinked to original sources
bioRxiv2021

Emergent Statistical Laws in Single-Cell Transcriptomic Data

Large scale data on single-cell gene expression have the potential to unravel the specific transcriptional programs of different cell types. The structure of these expression datasets suggests a similarity with several other complex systems that can be analogously described through the statistics of their basic building blocks. Transcriptomes of single cells are collections of messenger RNA abundances transcribed from a common set of genes just as books are different collections of words from a shared vocabulary, genomes of different species are specific compositions of genes belonging to evolutionary families, and ecological niches can be described by their species abundances. Following this analogy, we identify several emergent statistical laws in single-cell transcriptomic data closely similar to regularities found in linguistics, ecology or genomics. A simple mathematical framework can be used to analyze the relations between different laws and the possible mechanisms behind their ubiquity. Importantly, treatable statistical models can be useful tools in transcriptomics to disentangle the actual biological variability from general statistical effects present in most component systems and from the consequences of the sampling process inherent to the experimental technique. Author summaryGene expression profiles represent how different cells use their genetic information. Similarly, books are specific collections of words chosen from a shared vocabulary, and many complex systems can be ultimately described by the statistics of their basic components. Leveraging on this analogy, we identified several emergent statistical laws in single-cell transcriptomic data that are universally found in complex component systems. A simple mathematical description sets these laws in a treatable quantitative framework and represents a useful tool for dissecting the different sources of gene expression variability.

systems biology↗
bioRxiv2021

Stiffness of fluid and gel phase lipid nanovesicles: weighting the contributions of membrane bending modulus and luminal pressurization

The mechanical properties of biogenic membranous compartments are thought to be relevant in numerous biological processes; however, their quantitative measurement remains challenging for most of the already available Force Spectroscopy (FS)-based techniques. In particular, the debate on the mechanics of lipid nanovesicles and on the interpretation of their mechanical response to an applied force is still open. This is mostly due to the current lack of a unified model being able to describe the mechanical response of gel and fluid phase lipid vesicles and to disentangle the contributions of membrane rigidity and luminal pressure. In this framework, we herein propose a simple model in which the contributions of membrane rigidity and luminal pressure to the overall vesicle stiffness are described as a series of springs; this approach allows estimating the two contributions for both gel and fluid phase liposomes. Atomic Force Microscopy-based FS (AFM-FS), performed on both vesicles and Supported Lipid Bilayers (SLBs), is exploited for obtaining all the parameters involved in the model. Moreover, the use of coarse-grained full-scale molecular dynamics simulations allowed for better understanding the differences in the mechanical responses of gel and fluid phase bilayers and supported the experimental findings. Results suggest that the pressure contribution is similar among all the probed vesicle types; however, it plays a dominant role in the mechanical response of lipid nanovesicles presenting a fluid phase membrane, while its contribution becomes comparable to the one of membrane rigidity in nanovesicles with a gel phase lipid membrane. The herein presented results offer a simple way to quantify two of the most important parameters in vesicular nanomechanics, and as such represent a first step towards a currently unavailable, unified model for the mechanical response of gel and fluid phase lipid nanovesicles.

biophysics↗
bioRxiv2021

The anatomical logic of smell

The sense of smell helps us navigate the environment, but its molecular architecture and underlying logic remain unknown. The spatial location of odorant receptor genes (Olfrs) in the nose is widely thought to be independent of the structural diversity of the odorants they detect. Using spatial transcriptomics, we created a genome-wide 3D atlas of the mouse olfactory mucosa (OM). Topographic maps of genes differentially expressed in space reveal that both Olfrs and non-Olfrs are distributed in a continuous and overlapping fashion over five broad zones in the OM. The spatial locations of Olfrs correlate with the mucus solubility of the odorants they recognize, providing direct evidence for the chromatographic theory of olfaction. This resource resolved the molecular architecture of the mouse OM, and will inform future studies on mechanisms underlying Olfr gene choice, axonal pathfinding, patterning of the nervous system, and basic logic for the peripheral representation of smell.

neuroscience↗
bioRxiv2020

EVs analysis in the COVID-19 era: insights on serum inactivation protocols towards downstream isolation and analysis

Since the outbreak of COVID-19 crisis, the handling of biological samples from confirmed or suspected SARS-CoV-2 positive individuals demanded the use of inactivation protocols to ensure laboratory operators safety. While not standardized, these practices can be roughly divided in two categories, namely heat inactivation and solvent-detergent treatments. As such, these routine procedures should also apply to samples intended for Extracellular Vesicles (EVs) analysis. Assessing the impact of virus inactivating pre-treatments is therefore of pivotal importance, given the well-known variability introduced by different pre-analytical steps on downstream EVs isolation and analysis. Arguably, shared guidelines on inactivation protocols tailored to best address EVs-specific requirements will be needed among the EVs community, yet deep investigations in this direction havent been reported so far. In the attempt of sparking interest on this highly relevant topic, we here provide preliminary insights on SARS-CoV-2 inactivation practices to be adopted prior serum EVs analysis by comparing solvent/detergent treatment vs. heat inactivation. Our analysis entailed the evaluation of EVs recovery and purity along with biochemical, biophysical and biomolecular profiling by means of Nanoparticle Tracking Analysis, Western Blotting, Atomic Force Microscopy, miRNA content (digital droplet PCR) and tetraspanin assessment by microarrays. Our data suggest an increase in ultracentrifugation (UC) recovery following heat-treatment, however accompanied by a marked enrichment in EVs-associated contaminants. On the contrary, solvent/detergent treatment is promising for small EVs (< 150 nm range), yet a depletion of larger vesicular entities was detected. This work represents a first step towards the identification of optimal serum inactivation protocols targeted to EVs analysis.

biochemistry↗
bioRxiv2020

A Topic model analysis of TCGA transcriptomic data

Topic modelling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modelling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The established cancer subtype organization is well reconstructed in the inferred latent topic structure. Moreover, we identify specific topics that are enriched in genes known to play a role in the corresponding disease and are strongly related to the survival probability of patients. Finally, we show that a simple neural network classifier operating in the low dimensional topic space is able to predict with high accuracy the cancer subtype of a test expression sample.

cancer biology↗
bioRxiv2020

Gold Nanoparticles interacting with Synthetic Lipid Rafts: an AFM investigation

Inorganic nanoparticles (NPs) represent promising examples of engineered nanomaterials, providing interesting biomedical solutions in several fields, like therapeutics and diagnostics. Despite the extensive number of investigations motivated by their remarkable potential for nanomedicinal applications, the interactions of NPs with biological interfaces are still poorly understood. The effect of NPs on living organisms is mediated by biological barriers, such as the cell plasma membrane, whose lateral heterogeneity is thought to play a prominent role in NPs adsorption and uptake pathways. In particular, biological membranes feature the presence of rafts, i.e. segregated lipid micro and/or nano-domains in the so-called liquid ordered phase (Lo), immiscible with the surrounding liquid disordered phase (Ld). Rafts are involved in various biological functions and act as sites for the selective adsorption of materials on the membrane. Indeed, the thickness mismatch present along their boundaries generates energetically favorable conditions for the adsorption of NPs. Despite its clear implications in NPs internalization processes and cytotoxicity, a direct proof of the selective adsorption of NPs along the rafts boundaries is still missing to date. Here we use multicomponent Supported Lipid Bilayers (SLBs) as reliable synthetic models, reproducing the nanometric lateral heterogeneity of cell membranes. After being characterized by Atomic Force Microscopy (AFM) and Neutron Reflectivity (NR), multi-domain SLBs are challenged by prototypical inorganic nanoparticles, i.e. citrated gold nanoparticles (AuNPs), under simplified and highly controlled conditions. By exploiting AFM, we demonstrate that AuNPs preferentially target lipid phase boundaries as adsorption sites. The herein reported study consolidates and extends the fundamental knowledge on NPs-membrane interactions, which constitute a key aspect to consider when designing NPs-related biomedical applications.

biophysics↗
bioRxiv2019

AFM-based High-Throughput Nanomechanical Screening of Single Extracellular Vesicles

We herein describe an Atomic Force Microscopy (AFM)-based experimental procedure which allows the simultaneous mechanical and morphological characterization of several hundred individual nanosized vesicles within the hour timescale. When deposited on a flat rigid surface from aqueous solution, vesicles are deformed by adhesion forces into oblate spheroids whose geometry is a direct consequence of their mechanical stiffness. AFM image analysis can be used to quantitatively measure the contact angle of individual vesicles, which is a size-independent descriptor of their deformation and, consequently, of their stiffness. The same geometrical measurements can be used to infer vesicle diameter in its original, spherical shape. We demonstrate the applicability of the proposed approach to natural vesicles obtained from different sources, recovering their size and stiffness distributions by simple AFM imaging in liquid. We show how the combined EV stiffness/size readout is able to discriminate between subpopulations of vesicular and non-vesicular objects in the same sample, and between populations of vesicles with similar sizes but different mechanical characteristics. We also discuss a force spectroscopy calibration procedure to quantitatively link the stiffness of EVs to their average contact angle. Finally, we discuss expected extensions and applications of the methodology.

biophysics↗