Biology subjects

Vallardi, G.

Publications and source records attributed to Vallardi, G..

6 recordsLinked to original sources

bioRxiv2024

Spatial and temporal signatures of cell competition revealed by K-function analysis

It is generally hypothesised that in MDCK wild-type versus ScrKD co-cultures mechanical competition leads to the physical elimination of ScrKD cells, while in MDCK wild-type versus RasV12 co-cultures biochemical competition results in the non-apoptotic extrusion of RasV12 cells. However, the exact spatiotemporal signatures of competitive interactions in these systems remains largely unknown. Utilising advanced single-cell image analysis, this research quantifies the spatiotemporal dynamics of these competitive interactions. Time-lapse microscopy, combined with cellular segmentation, tracking, cell-state classification, and K-function clustering analysis provide detailed insights into how wild-type cell proliferation relates to the elimination of mutant cells. We find striking differences in the underlying processes of each competition type. In the ScrKD competition, elimination seems driven by diffuse proliferative forces unrelated to the immediate elimination site. In contrast, RasV12 cell elimination is closely linked to local increases in wild-type cell mitoses prior to the RasV12 extrusion. Both competitions trigger an increase in local wild-type cell proliferation post-elimination, although the timing of these responses vary. This study not only sheds light on the diverse mechanisms of cell competition but also underscores the complexity of cellular interactions in tissue dynamics, providing new perspectives on cellular quality control and a new quantitative approach to characterise these interactions.

biophysics↗

bioRxiv2021

Learning the rules of cell competition without prior scientific knowledge

AO_SCPLOWBSTRACTC_SCPLOWDeep learning is now a powerful tool in microscopy data analysis, and is routinely used for image processing applications such as segmentation and denoising. However, it has rarely been used to directly learn mechanistic models of a biological system, owing to the complexity of the internal representations. Here, we develop an end-to-end machine learning model capable of learning the rules of a complex biological phenomenon, cell competition, directly from a large corpus of time-lapse microscopy data. Cell competition is a quality control mechanism that eliminates unfit cells from a tissue and during which cell fate is thought to be determined by the local cellular neighborhood over time. To investigate this, we developed a new approach ({tau}-VAE) by coupling a probabilistic encoder to a temporal convolution network to predict the fate of each cell in an epithelium. Using the{tau} -VAEs latent representation of the local tissue organization and the flow of information in the network, we decode the physical parameters responsible for correct prediction of fate in cell competition. Remarkably, the model autonomously learns that cell density is the single most important factor in predicting cell fate - a conclusion that is in agreement with our current understanding from over a decade of scientific research. Finally, to test the learned internal representation, we challenge the network with experiments performed in the presence of drugs that block signalling pathways involved in competition. We present a novel discriminator network that, using the predictions of the{tau} -VAE, can identify conditions which deviate from the normal behaviour, paving the way for automated, mechanism-aware drug screening.

cell biology↗

bioRxiv2020

Automated deep lineage tree analysis using a Bayesian single cell tracking approach

Single-cell methods are beginning to reveal the intrinsic heterogeneity in cell populations, which arises from the interplay or deterministic and stochastic processes. For example, the molecular mechanisms of cell cycle control are well characterised, yet the observed distribution of cell cycle durations in a population of cells is heterogenous. This variability may be governed either by stochastic processes, inherited in a deterministic fashion, or some combination of both. Previous studies have shown poor correlations within lineages when observing direct ancestral relationships but remain correlated with immediate relatives. However, assessing longer-range dependencies amid noisy data requires significantly more observations, and demands the development of automated procedures for lineage tree reconstruction. Here, we developed an open-source Python library, btrack, to facilitate retrieval of deep lineage information from live-cell imaging data. We acquired 3,500 hours of time-lapse microscopy data of epithelial cells in culture and used our software to extract 22,519 fully annotated single-cell trajectories. Benchmarking tests, including lineage tree reconstruction assessments, demonstrate that our approach yields high-fidelity results and achieves state-of-the-art performance without the requirement for manual curation of the tracker output data. To demonstrate the robustness of our supervision-free cell tracking pipeline, we retrieve cell cycle durations and their extended inter- and intra-generational family relationships, for up to eight generations, and up to fourth cousin relationships. The extracted lineage tree dataset represents approximately two orders of magnitude more data, and longer-range dependencies, than in previous studies of cell cycle heritability. Our results extend the range of observed correlations and suggest that strong heritable cell cycling is present. We envisage that our approach could be extended with additional live-cell reporters to provide a detailed quantitative characterisation of biochemical and mechanical origins to cycling heterogeneity in cell populations.

biophysics↗

bioRxiv2019

A spatio-temporal mathematical model for the dynamics of paclitaxel in cell monolayers.

The authors have withdrawn their manuscript due to the discovery of fundamental issues with the implementation of the ODE/PDE model, whose output was shown in Figure 5 of the original version of this paper, rendering the results and conclusion unreliable. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

cancer biology↗

bioRxiv2019

PP1 and PP2A use opposite phospho-dependencies to control distinct processes at the kinetochore

PP1 and PP2A-B56 are major serine/threonine phosphatase families that achieve specificity by colocalising with substrates. At the kinetochore, however, both phosphatases localise to an almost identical molecular space and yet they still manage to regulate unique pathways and processes. By switching or modulating the positions of PP1/PP2A-B56 at kinetochores, we show that their unique downstream effects are not due to either the identity of the phosphatase or its precise location. Instead, these phosphatases signal differently because their kinetochore recruitment can be either inhibited (PP1) or enhanced (PP2A) by phosphorylation inputs. Mathematical modelling explains how these inverse phospho-dependencies elicit unique forms of cross-regulation and feedback, which allows otherwise indistinguishable phosphatases to produce distinct network behaviours and control different mitotic processes. Therefore, the kinetochore uses PP1 and PP2A-B56 because their binding motifs respond to kinase inputs in opposite ways. Genome-wide motif analysis suggests that many other pathways also select for these same key features, implying that these similar catalytic enzymes may have diverged during evolution to allow opposite modes of phospho-regulation.

cell biology↗

bioRxiv2018

A molecular explanation for PP2A-B56 isoform specificity during mitosis

PP2A-B56 is a serine/threonine phosphatase complex that regulates several major mitotic processes, including sister chromatid cohesion, kinetochore-microtubule attachment and the spindle assembly checkpoint. We show here that these key functions are shared between B56 isoforms which localise differentially to either the centromere or kinetochore. The centromeric B56 isoforms rely on a specific interaction with Sgo2, whereas the kinetochore isoforms bind preferentially to BubR1 and other proteins containing an LxxIxE motif. The molecular basis for this differential localisation can be mapped to a small C-terminal region in B56 that regulates these key interactions and defines whether PP2A-B56 complexes bind to either the centromere or kinetochore. Together, this study describes how different PP2A-B56 complexes utilise isoform-specific interactions to control distinct processes during mitosis.

cell biology↗