Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space

Discretised Flux Balance Analysis for Reaction-Diffusion Simulation of Single-Cell Metabolism

Metabolites have to diffuse within the sub-cellular compartments they occupy to specific locations where enzymes are, so reactions could occur. Conventional flux balance analysis (FBA), a method based on linear programming that is commonly used to model metabolism, implicitly assumes that all enzymatic reactions are not diffusion-limited though that may not always be the case. In this work, we have developed a spatial method that implements FBA on a grid-based system, to enable the exploration of diffusion effects on metabolism. Specifically, the method discretises a living cell into a two-dimensional grid, represents the metabolic reactions in each grid element as well as the diffusion of metabolites to and from neighbouring elements, and simulates the system as a single linear programming problem. We varied the number of rows and columns in the grid to simulate different cell shapes, and the method was able to capture diffusion effects at different shapes. We then used the method to simulate heterogeneous enzyme distribution, which suggested a theoretical effect on variability at the population level. We propose the use of this method, and its future extensions, to explore how spatiotemporal organisation of sub-cellular compartments and the molecules within could affect cell behaviour.

Bridging live-cell imaging and next-generation cancer treatment

By providing spatial, molecular and morphological data over time, live-cell imaging can provide a deeper understanding of the cellular and signalling events that determine cancer response to treatment. Understanding this dynamic response has the potential to enhance clinical outcome by identifying biomarkers or actionable targets to improve therapeutic efficacy. Here, we review recent applications of live-cell imaging for uncovering both tumour heterogeneity in treatment response and the mode of action of cancer-targeting drugs. Given the increasing uses of T cell therapies, we discuss the unique opportunity of time-lapse imaging for capturing the interactivity and motility of immunotherapies. Although traditionally limited in the number of molecular features captured, novel developments in multidimensional imaging and multi-omics data integration offer strategies to connect single-cell dynamics to molecular phenotypes. We review the effect of these recent technological advances on our understanding of the cellular dynamics of tumour targeting and discuss their implication for next-generation precision medicine.

Involvement of cell shape and lipid metabolism in glioblastoma resistance to temozolomide

Temozolomide (TMZ) has been used as standard-of-care for glioblastoma multiforme (GBM), but the resistance to TMZ develops quickly and frequently. Thus, more studies are needed to elucidate the resistance mechanisms. In the current study, we investigated the relationship among the three important phenotypes, namely TMZ-resistance, cell shape and lipid metabolism, in GBM cells. We first observed the distinct difference in cell shapes between TMZ-sensitive (U87) and resistant (U87R) GBM cells. We then conducted NMR-based lipid metabolomics, which revealed a significant increase in cholesterol and fatty acid synthesis as well as lower lipid unsaturation in U87R cells. Consistent with the lipid changes, U87R cells exhibited significantly lower membrane fluidity. The transcriptomic analysis demonstrated that lipid synthesis pathways through SREBP were upregulated in U87R cells, which was confirmed at the protein level. Fatostatin, an SREBP inhibitor, and other lipid pathway inhibitors (C75, TOFA) exhibited similar or more potent inhibition on U87R cells compared to sensitive U87 cells. The lower lipid unsaturation ratio, membrane fluidity and higher fatostatin sensitivity were all recapitulated in patient-derived TMZ-resistant primary cells. The observed ternary relationship among cell shape, lipid composition, and TMZ-resistance may be applicable to other drug-resistance cases. SREBP and fatostatin are suggested as a promising target-therapeutic agent pair for drug-resistant glioblastoma.

Adaptive resistance is not responsible for long-term drug resistance in a cellular model of triple negative breast cancer

Resistance to cancer therapeutics represents a leading cause of mortality and is particularly important in cancers, such as triple negative breast cancer, for which no targeted therapy is available, as these are only treated with traditional chemotherapeutics. Cancer, as well as bacterial, drug resistance can be intrinsic, acquired or adaptive. Adaptive cancer drug resistance is gaining attention as a mechanism for the generation of long-term drug resistance as is the case with bacterial antibiotic resistance. We have used a cellular model of triple negative breast cancer (CAL51) and its drug resistance derivative (CALDOX) to gain insight into genome-wide expression changes associated with long-term doxorubicin (a widely used anthracycline for cancer treatment) resistance and doxorubicin-induced stress. Previous work indicates that both naïve and resistance cells have a functional p53-p21 axis controlling cell cycle at G1, although this is not a driver for drug resistance, but down-regulation of TOP2A (topoisomerase IIα). As expected, CALDOX cells have a signature characterized, in addition to down-regulation of TOP2A, by genes and pathways associated with drug resistance, metastasis and stemness. Both CAL51 and CALDOX stress signatures share 12 common genes (TRIM22, FAS, SPATA18, SULF2, CDKN1A, GDF15, MYO6, CXCL5, CROT, EPPK1, ZMAT3 and CD44), with roles in the above-mentioned pathways, indicating that these cells have similar functional responses to doxorubicin relaying on the p53 control of apoptosis. Eight genes are shared by both drug stress signatures (in CAL51 and CALDOX cells) and CALDOX resistant cells (FAS, SULF2, CDKN1A, CXCL5, CD44, SPATA18, TRIM22 and CROT), many of them targets of p53. This corroborates experimental data indicating that CALDOX cells, even in the absence of drug, have activated, at least partially, the p53-p21 axis and DNA damage response. Although this eight-gene signature might be an indicator of adaptive resistance, as this transient phenomenon due to short-term stress may not revert to its original state upon withdrawal of the stressor, previous experimental data indicates that the p53-p21 axis is not responsible for doxorubicin resistance. Importantly, TOP2A is not responsive to doxorubicin treatment and thus absent in both drug stress signatures. This indicates that during the generation of doxorubicin resistance, cells acquire genetic changes likely to be random, leading to down regulation of TOP2A, but selected during the generation of cells due to the presence of drug in the culture medium. This poses a considerable constraint for the development of strategies aimed at avoiding the emergence of drug resistance in the clinic.

Prediction of Genetic Groups within Brettanomyces bruxellensis through Cell Morphology Using a Deep Learning Tool

Brettanomyces bruxellensis is described as a wine spoilage yeast with many mainly strain-dependent genetic characteristics, bestowing tolerance against environmental stresses and persistence during the winemaking process. Thus, it is essential to discriminate B. bruxellensis isolates at the strain level in order to predict their stress resistance capacities. Few predictive tools are available to reveal intraspecific diversity within B. bruxellensis species; also, they require expertise and can be expensive. In this study, a Random Amplified Polymorphic DNA (RAPD) adapted PCR method was used with three different primers to discriminate 74 different B. bruxellensis isolates. High correlation between the results of this method using the primer OPA-09 and those of a previous microsatellite analysis was obtained, allowing us to cluster the isolates among four genetic groups more quickly and cheaply than microsatellite analysis. To make analysis even faster, we further investigated the correlation suggested in a previous study between genetic groups and cell polymorphism using the analysis of optical microscopy images via deep learning. A Convolutional Neural Network (CNN) was trained to predict the genetic group of B. bruxellensis isolates with 96.6% accuracy. These methods make intraspecific discrimination among B. bruxellensis species faster, simpler and less costly. These results open up very promising new perspectives in oenology for the study of microbial ecosystems.