Infiltration of tumor spheroids by activated immune cells.

Recent years have seen a tremendous growth of interest in understanding the role that the adaptive immune system could play in interdicting tumor progression. In this context, it has been shown that the density of adaptive immune cells inside a solid tumor serves as a favorable prognostic marker across different types of cancer. The exact mechanisms underlying the degree of immune cell infiltration is largely unknown. Here, we quantify the temporal dynamics of the density profile of activated immune cells around a solid tumor spheroid. We propose a computational model incorporating immune cells with active, persistent movement and a proliferation rate that depends on the presence of cancer cells, and show that the model able to reproduce semi-quantitatively the experimentally measured infiltration profile. Studying the density distribution of immune cells inside a solid tumor can help us better understand immune trafficking in the tumor micro-environment, hopefully leading towards novel immunotherapeutic strategies.

The Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy and the Epithelial-Mesenchymal Transition.

It would be highly desirable to find prognostic and predictive markers for triple-negative breast cancer (TNBC), a strongly heterogeneous and invasive breast cancer subtype often characterized by a high recurrence rate and a poor outcome. Here, we investigated the prognostic and predictive capabilities of ARIADNE, a recently developed transcriptomic test focusing on the epithelial-mesenchymal transition. We first compared the stratification of TNBC patients obtained by ARIADNE with that based on other common pathological indicators, such as grade, stage and nodal status, and found that ARIADNE was more effective than the other methods in dividing patients into groups with different disease-free survival statistics. Next, we considered the response to neoadjuvant chemotherapy and found that the classification provided by ARIADNE led to statistically significant differences in the rates of pathological complete response within the groups.

Nuclear biophysical changes during human melanoma plasticity.

Tumor plasticity is an emerging property of tumor cells which allows them to change their phenotype in dependence on the environment. The epithelial-mesenchymal transition plays a crucial role in helping cells to acquire a more aggressive phenotype when they are in the mesenchymal state. Herein we investigated the biophysical changes occurring during phenotypic switching in human melanoma cells considering the blebbines of the nuclei, their stiffness and the involvement of polycombs with lamins. We show that the formation of cellular heterogeneity involves many crucial nuclear changes including the interaction between different types of polycombs with lamins and chromosome accessibility. All together our results shed new light on the molecular mechanisms involved in the formation of an heterogeneous cell population during phenotypic switching. In particular, our results show that phenotypic switching in melanoma involves chromatin remodeling changing the transcriptional activity of cells and consequently their phenotype.

Metamaterial architecture from a self-shaping carnivorous plant.

As meticulously observed and recorded by Darwin, the leaves of the carnivorous plant Drosera capensis L. slowly fold around insects trapped on their sticky surface in order to ensure their digestion. While the biochemical signaling driving leaf closure has been associated with plant growth hormones, how mechanical forces actuate the process is still unknown. Here, we combine experimental tests of leaf mechanics with quantitative measurements of the leaf microstructure and biochemistry to demonstrate that the closure mechanism is programmed into the cellular architecture of D. capensis leaves, which converts a homogeneous biochemical signal into an asymmetric response. Inspired by the leaf closure mechanism, we devise and test a mechanical metamaterial, which curls under homogeneous mechanical stimuli. This kind of metamaterial could find possible applications as a component in soft robotics and provides an example of bio-inspired design.

Topography of epithelial-mesenchymal plasticity.

The transition between epithelial and mesenchymal states has fundamental importance for embryonic development, stem cell reprogramming, and cancer progression. Here, we construct a topographic map underlying epithelial-mesenchymal transitions using a combination of numerical simulations of a Boolean network model and the analysis of bulk and single-cell gene expression data. The map reveals a multitude of metastable hybrid phenotypic states, separating stable epithelial and mesenchymal states, and is reminiscent of the free energy measured in glassy materials and disordered solids. Our work not only elucidates the nature of hybrid mesenchymal/epithelial states but also provides a general strategy to construct a topographic representation of phenotypic plasticity from gene expression data using statistical physics methods.

TRPM2 Oxidation Activates Two Distinct Potassium Channels in Melanoma Cells through Intracellular Calcium Increase.

Tumor microenvironments are often characterized by an increase in oxidative stress levels. We studied the response to oxidative stimulation in human primary (IGR39) or metastatic (IGR37) cell lines obtained from the same patient, performing patch-clamp recordings, intracellular calcium ([Ca2+]i) imaging, and RT-qPCR gene expression analysis. In IGR39 cells, chloramine-T (Chl-T) activated large K+ currents (KROS) that were partially sensitive to tetraethylammonium (TEA). A large fraction of KROS was inhibited by paxilline-a specific inhibitor of large-conductance Ca2+-activated BK channels. The TEA-insensitive component was inhibited by senicapoc-a specific inhibitor of the Ca2+-activated KCa3.1 channel. Both BK and KCa3.1 activation were mediated by an increase in [Ca2+]i induced by Chl-T. Both KROS and [Ca2+]i increase were inhibited by ACA and clotrimazole-two different inhibitors of the calcium-permeable TRPM2 channel. Surprisingly, IGR37 cells did not exhibit current increase upon the application of Chl-T. Expression analysis confirmed that the genes encoding BK, KCa3.1, and TRPM2 are much more expressed in IGR39 than in IGR37. The potassium currents and [Ca2+]i increase observed in response to the oxidizing agent strongly suggest that these three molecular entities play a major role in the progression of melanoma. Pharmacological targeting of either of these ion channels could be a new strategy to reduce the metastatic potential of melanoma cells, and could complement classical radio- or chemotherapeutic treatments.

Chromatin and Cytoskeletal Tethering Determine Nuclear Morphology in Progerin-Expressing Cells.

The nuclear morphology of eukaryotic cells is determined by the interplay between the lamina forming the nuclear skeleton, the chromatin inside the nucleus, and the coupling with the cytoskeleton. Nuclear alterations are often associated with pathological conditions as in Hutchinson-Gilford progeria syndrome, in which a mutation in the lamin A gene yields an altered form of the protein, named progerin, and an aberrant nuclear shape. Here, we introduce an inducible cellular model of Hutchinson-Gilford progeria syndrome in HeLa cells in which increased progerin expression leads to alterations in the coupling of the lamin shell with cytoskeletal or chromatin tethers as well as with polycomb group proteins. Furthermore, our experiments show that progerin expression leads to enhanced nuclear shape fluctuations in response to cytoskeletal activity. To interpret the experimental results, we introduce a computational model of the cell nucleus that explicitly includes chromatin fibers, the nuclear shell, and coupling with the cytoskeleton. The model allows us to investigate how the geometrical organization of the chromatin-lamin tether affects nuclear morphology and shape fluctuations. In sum, our findings highlight the crucial role played by lamin-chromatin and lamin-cytoskeletal alterations in determining nuclear shape morphology and in affecting cellular functions and gene regulation.

Unjamming of active rotators.

Active particle assemblies can exhibit a wide range of interesting dynamical phases depending on internal parameters such as density, adhesion strength or self-propulsion. Active self-rotations are rarely studied in this context, although they can be relevant for active matter systems, as we illustrate by analyzing the motion of Chlamydomonas reinhardtii algae under different experimental conditions. Inspired by this example, we simulate the dynamics of a system of interacting active disks endowed with active torques and self-propulsive forces. At low packing fractions, adhesion causes the formation of small rotating clusters, resembling those observed when algae are stressed. At higher densities, the model shows a jamming to unjamming transition promoted by active torques and hindered by adhesion. We also study the interplay between self-propulsion and self-rotation and derive a phase diagram. Our results yield a comprehensive picture of the dynamics of active rotators, providing useful guidance to interpret experimental results in cellular systems where rotations might play a role.

Explaining the dynamics of tumor aggressiveness: At the crossroads between biology, artificial intelligence and complex systems.

Facing metastasis is the most pressing challenge of cancer research. In this review, we discuss recent advances in understanding phenotypic plasticity of cancer cells, highlighting the kinetics of cancer stem cell and the role of the epithelial mesenchymal transition for metastasis. It appears that the tumor micro-environment plays a crucial role in triggering phenotypic transitions, as we illustrate discussing the challenges posed by macrophages and cancer associated fibroblasts. To disentangle the complexity of environmentally induced phenotypic transitions, there is a growing need for novel advanced algorithms as those proposed in our recent work combining single cell data analysis and numerical simulations of gene regulatory networks. We conclude discussing recent developments in artificial intelligence and its applications to personalized cancer treatment.

Bursts of activity in collective cell migration.

Dense monolayers of living cells display intriguing relaxation dynamics, reminiscent of soft and glassy materials close to the jamming transition, and migrate collectively when space is available, as in wound healing or in cancer invasion. Here we show that collective cell migration occurs in bursts that are similar to those recorded in the propagation of cracks, fluid fronts in porous media, and ferromagnetic domain walls. In analogy with these systems, the distribution of activity bursts displays scaling laws that are universal in different cell types and for cells moving on different substrates. The main features of the invasion dynamics are quantitatively captured by a model of interacting active particles moving in a disordered landscape. Our results illustrate that collective motion of living cells is analogous to the corresponding dynamics in driven, but inanimate, systems.

Complexity in cancer stem cells and tumor evolution: Toward precision medicine.

In this review, we discuss recent advances on the plasticity of cancer stem cells and highlight their relevance to understand the metastatic process and to guide therapeutic interventions. Recent results suggest that the strict hierarchical structure of cancer cell populations advocated by the cancer stem cell model must be reconsidered since the depletion of cancer stem cells leads the other tumor cells to switch back into the cancer stem cell phenotype. This plasticity has important implications for metastasis since migrating cells do not need to be cancer stem cells in order to seed a metastasis. We also discuss the important role of the immune system and the microenvironment in modulating phenotypic switching and suggest possible avenues to exploit our understanding of this process to develop an effective strategy for precision medicine.

Impact of the cross-talk between circular and messenger RNAs on cell regulation.

Circular RNAs (circRNA) are non-coding RNAs characterized by a closed-loop structure providing increased stability and enhancing their ability to compete with messenger RNAs (mRNA) for microRNA (miRNA) binding. An important open question is if circRNA plays a physiological role in the cells regulating critical cellular functions. We address this question with a combination of theoretical modeling and experimental analysis. Theoretically, we investigated two possible scenarios, one in which circRNAs act as sponges for miRNAs but there is no other relation between the two RNAs and the other one where circRNAs are co-generated with their corresponding mRNAs. We thus compared the results with data reporting the level of circRNAs (ZEB1, CANCX, ABCC1) modulating specific miRNAs in 27 cell lines. Due to the high stability of circRNAs, we show that a miRNA mediated cross-talk between circRNA and mRNA appears for a broad range of physiological parameters. This confirms the relevance of circRNAs in cell regulation, suggesting that they could be used as biomarkers.

From jamming to collective cell migration through a boundary induced transition.

Cell monolayers provide an interesting example of active matter, exhibiting a phase transition from flowing to jammed states as they age. Here we report experiments and numerical simulations illustrating how a jammed cellular layer rapidly reverts to a flowing state after a wound. Quantitative comparison between experiments and simulations shows that cells change their self-propulsion and alignment strength so that the system crosses a phase transition line, which we characterize by finite-size scaling in an active particle model. This wound-induced unjamming transition is found to occur generically in epithelial, endothelial and cancer cells.

Modeling mechanical control of spindle orientation of intestinal crypt stem cells.

Tissue development requires a control over the sequence of symmetric and asymmetric stem cell divisions to obtain the specific numbers of differentiated cells populating the tissue and stem cells residing in the niche. A good experimental model to study this process is the mouse intestinal crypt development, where it has been shown that stem cells follow an optimal strategy in which asymmetric division occurs only after all symmetric divisions have taken place to reach a fixed number of cells in the niche in the shortest time. Here we introduce a model of stem cell division that is able to explain the experimentally observed stem cell population dynamics by the effect of mechanical forces acting on the spindle. We also observe that the mechanically induced strategy for development is sub-optimal and crucially depends on the stiffness of the spindle. These findings highlight the crucial importance of mechanical forces for the development and maintenance of the intestinal crypt.

Volume Changes During Active Shape Fluctuations in Cells.

Cells modify their volume in response to changes in osmotic pressure but it is usually assumed that other active shape variations do not involve significant volume fluctuations. Here we report experiments demonstrating that water transport in and out of the cell is needed for the formation of blebs, commonly observed protrusions in the plasma membrane driven by cortex contraction. We develop and simulate a model of fluid-mediated membrane-cortex deformations and show that a permeable membrane is necessary for bleb formation which is otherwise impaired. Taken together, our experimental and theoretical results emphasize the subtle balance between hydrodynamics and elasticity in actively driven cell morphological changes.

Conformational mechanism for the stability of microtubule-kinetochore attachments.

Regulating the stability of microtubule (MT)-kinetochore attachments is fundamental to avoiding mitotic errors and ensuring proper chromosome segregation during cell division. Although biochemical factors involved in this process have been identified, their mechanics still need to be better understood. Here we introduce and simulate a mechanical model of MT-kinetochore interactions in which the stability of the attachment is ruled by the geometrical conformations of curling MT-protofilaments entangled in kinetochore fibrils. The model allows us to reproduce, with good accuracy, in vitro experimental measurements of the detachment times of yeast kinetochores from MTs under external pulling forces. Numerical simulations suggest that geometrical features of MT-protofilaments may play an important role in the switch between stable and unstable attachments.

Artificial Intelligence for Precision Oncology of Triple-Negative Breast Cancer: Learning from Melanoma.

Thanks to new technologies using artificial intelligence (AI) and machine learning, it is possible to use large amounts of data to try to extract information that can be used for personalized medicine. The great challenge of the future is, on the one hand, to acquire masses of biological data that nowadays are still limited and, on the other hand, to develop innovative strategies to extract information that can then be used for the development of predictive models. From this perspective, we discuss these aspects in the context of triple-negative breast cancer, a tumor where a specific treatment is still lacking and new therapies, such as immunotherapy, are under investigation. Since immunotherapy is already in use for other tumors such as melanoma, we discuss the strengths and weaknesses identified in the use of immunotherapy with melanoma to try to find more successful strategies. It is precisely in this context that AI and predictive tools can be extremely valuable. Therefore, the discoveries and advancements in immunotherapy for melanoma provide a foundation for developing effective immunotherapies for triple-negative breast cancer. Shared principles, such as immune system activation, checkpoint inhibitors, and personalized treatment, can be applied to TNBC to improve patient outcomes and offer new hope for those with aggressive, hard-to-treat breast cancer.

Senescent cells in growing tumors: population dynamics and cancer stem cells.

Tumors are defined by their intense proliferation, but sometimes cancer cells turn senescent and stop replicating. In the stochastic cancer model in which all cells are tumorigenic, senescence is seen as the result of random mutations, suggesting that it could represent a barrier to tumor growth. In the hierarchical cancer model a subset of the cells, the cancer stem cells, divide indefinitely while other cells eventually turn senescent. Here we formulate cancer growth in mathematical terms and obtain predictions for the evolution of senescence. We perform experiments in human melanoma cells which are compatible with the hierarchical model and show that senescence is a reversible process controlled by survivin. We conclude that enhancing senescence is unlikely to provide a useful therapeutic strategy to fight cancer, unless the cancer stem cells are specifically targeted.

Harnessing deep learning to forecast local microclimate using global climate data.

Microclimate is a complex non-linear phenomenon influenced by both global and local processes. Its understanding holds a pivotal role in the management of natural resources and the optimization of agricultural procedures. This phenomenon can be effectively monitored in local areas by employing models that integrate physical laws and data-driven algorithms relying on climate data and terrain conformation. Climate data can be acquired from nearby meteorological stations when available, but in their absence, global climate datasets describing 10 km-scale areas are often utilized. The present research introduces an innovative microclimate model that combines physical laws and deep learning to reproduce temperature and relative humidity variations at the meter-scale within a study area located in the Lombardian foothills. The model is exploited to perform a comparative study investigating whether employing the global climate dataset ERA5 as input reduces model's accuracy in reproducing the microclimate variations compared to using data collected by the Lombardy Regional Environment Protection Agency (ARPA) from a nearby meteorological station. The comparative analysis shows that using local meteorological data as inputs provides more accurate results for microclimate modeling. However, in situations where local data is not available, the use of global climate data remains a viable and reliable approach.

Quantitative analysis of disease-related metabolic dysregulation of human microbiota.

The metabolic activity of all the micro-organism composing the human microbiome interacts with the host metabolism contributing to human health and disease in a way that is not fully understood. Here, we introduce STELLA, a computational method to derive the spectrum of metabolites associated with the microbiome of an individual. STELLA integrates known information on metabolic pathways associated with each bacterial species and extracts from these the list of metabolic products of each singular reaction by means of automatic text analysis. By comparing the result obtained on a single subject with the metabolic profile data of a control set of healthy subjects, we are able to identify individual metabolic alterations. To illustrate the method, we present applications to autism spectrum disorder and multiple sclerosis.