Cells are extremely dynamical and they adapt to the environmanet, deciphering chemical, physical and biological cues. We are particularly interested in the process of mechanosensing and how it influences physiology and pathology of living cells.


We aim at developing and exploiting advanced tools for the quantification of mechanical and morphological parameters of single cells, to extend the knowledge we have of the cellular phenotype in terms of physical properties.


Because science is often a random walk in a forest of questions, we are keen to mix advanced tools and technical skills to address intriguing problems in biology and engineering.

Understanding the molecular basis of mechanosensing
The piezo1 and piezo2 mechanosensitive ion channels

Piezo1 and piezo2 were identified in 2010 as essential components of mechanically activated channels in mammalian cells (Coste2010). Recently, the high resolution structure of the piezo1 functional trimer was disclosed (Saotome2018, Zhao2018, Guo2017), providing intringuing insights in the potential mechanisms of action, conferring to the channel the peculiar mechanosensitivity. We are tremendously fascinated by these molecules, and we aim at deepening the current knowledge about their biophysics, with particular interest in the role of and interaction with the plasma membrane.

Evaluating the role of mechanosensing in regulating cell homoeostasis
Influence of piezo1 expression on the cellular phenotype

After the identification of piezo1 and the recognition of its mechanosensitive role, this channel has been widely studied and its involvement in key physiological and pathological processes has been highlighted. A number of genetic disorders has been linked to piezo1 mutations (Alper2017) and high levels of expression of piezo1 have been indicated as an unfavourable prognostic factor in many different cancers (Protein Atlas). Nevertheless, the role of piezo1 in establishing the cellular phenotype is still strongly unclear, and we are interested in investigating how the altered expression/functionality of piezo1 influences the cellular organization.

Investigating the mechanobiology of aging and neurodegenerative disorders
Biomechanics of Alzheimer's disease

In 1906 Alois Alzheimer first identified amyloid plaques and neurofibrillary tangles in the brain of a previous patient affected by presenile dementia. Since then, the scientific community has tried to clarify the etiology of the now called Alzheimer's disease (AD), but still several obscure points remain in the cause of this pathology. One of the few consolidated aspect in the molecular basis of AD is the presence of aggregation-prone mis-processed fragments of APP, the so called Aβ, that should play a role in the loss of functionality of adult neurons. The precise mechanism of action of Aβ has not been identified, but recent studies highlighted two interesting aspects: the potential involvement of the cellular prion protein (hPrP) as a strong interactor of Aβ, and the possibility that Aβ acts by altering the mechanical properties of the plasma membrane. We are interested in addressing these intuitions and verify the combined role of Aβ and hPrP on the biomechanics of single cells, in view of a more general picture involving the mechanobiology of the aging brain (Phillip2015).

Vitale2018 Tsushima2015 Galante2012 Thellung2013
Measuring mechanical properties of single cells
Nanoindentation-based cell mechanics

Mechanical properties of single cells have been studied with several approaches, to provide insights in their structural organization (mainly the cytoskeleton), and to look for direct connections with the physiology and pathology of the cell population. In particular, nanoindentation approaches have demonstrated their effectiveness in the evaluation of single cell mechanics, while several technical issues in data analysis and protocol design still need to be addressed to provide full reliability and repeatability of the results. Moreover, to move the focus of biomechanics from the specificity of the single cell to the variability of an entire population, an effort is required to provide higher throughput in the analysis.

Gavazzo2017 Mescola2012
Performing cell phenotyping based on morphometric parameters
Quantitative Phase Imaging

Standard imaging (microscopy) typically measures the absorption of a sample to white light, providing a 2D intensity map that can be recorded with a digital camera. This approach is not effective with quasi-transparent samples, such as cells, in which the intensity of the electromagnetic field is almost not altered by the presence of the sample, but only its phase is significantly affected. Phase microscopy indicates a class of methods addressing this issue, aiming at acquiring a phase map of the sample, either to enhance the contrast or to provide a quantitative reconstruction of the shape. We experimented different quantitative phase imaging/microscopy (QPM) methods to obtain quantitative map of cells, based on digital holography or the transport of intensity equation. In combination with automated imaging cytometry, QPM provides a way to measure morphometric parameters of single cells with high throughput, eventually allowing to correlate them to the physiological state of the cell population (phenotyping).

Identify a robust and high throughput approach to measure cell mechanosensitivity
MEMS-based mechanosensitivity assay

Mechanosensitivity, the ability of cells to respond to mechanical stimuli, is mediated by several molecular structures. The most direct mechanism to transduce forces is provided by the action of mechanosensitive ion channels, opening upon membrane strecth and leading to a corresponding ionic flow through the plasma membrane. The de-facto standard approch to study this mechanism is patch clamp electrophysiology, in which the mechanical stimulus is provided by the pressure in the patch pipette and the activity of the channel is directly measured in terms of current. Nevertheless, this approach is technically tricky and time consuming, hindering the possibility to adopt it to achieve the high throughput required to characterize cellular populations. We are intersted in developing a fast and reliable quantitative assay for mechanosensitivity and we are now exploring the use of different miniaturized M.E.M.S. force sensors in combination with fluorescence microscopy.

Measuring temperature with nanometer resolution in air an liquid environment
Scanning thermal microscopy

Temperature is a key physical quantity in several processes. In particular, the dynamics of small nanoscale structures is highly influenced by the temperature, providing for example the "fuel" for the diffusion of molecules or the scale of energies in kinetic processes. Despite its importance in nanotechnology, very few systems exist to probe the temperature to scales under few µm, and we started exploiting a commercial Scanning Thermal Microscope add-on to address this fascinating topic either in technology or biology.

High throughput and high resolution in flow imaging of biological samples
In-chip acoustofluidic imaging

In the last years microfluidics has emerged as a promising approach to create lab-on-chip devices and design cheap high throughput biophysical assays. We are interested in exploring the limits and opportunities of the integration of microfluidics and imaging, in particular quantitative imaging. In order to provide a fine control on the position of the samples flowing through the chip, we are evaluating the possibility to incorporate an ultrasonic actuator in the measurement cell, to provide acoustic trapping of single cells and particles.

Cacace2018 Cacace2017
Measuring and counting algae abundance in sea and fresh water

Monitoring water to look for phyto- and zoo-plankton is a massive activity performed by experts around the world mainly either for water quality assessment or for ecological mapping. Recent developments in artificial intelligence, mostly driven by the application of deep learning algorithms, provides an exciting opportunity for this field, to automate the complex and time consuming task of species identification. Nevertheless, to fully exploit this innovation, it is required to provide to the AI layer a good and reproducible set of images or spectroscopy data. We have been working in this field, exploiting our optical and automation background to identify a robust, cheap and reliable approach to "digitize the water" for further analysis.

Vassalli2018 Sbrana2017