NPJ Systems Biology and Applications最新文献

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular variability by capturing gene expression profiles of individual cells. The importance of cell-to-cell variability in determining and shaping cell function has been widely appreciated. Nevertheless, differential expression (DE) analysis remains a cornerstone method in analytical practice. Current computational analyses overlook the rich information encoded by variability within the single-cell gene expression data by focusing exclusively on mean expression. To offer a deeper understanding of cellular systems, there is a need for approaches to assess data variability rather than just the mean. Here we present spline-DV, a statistical framework for differential variability (DV) analysis using scRNA-seq data. The spline-DV method identifies genes exhibiting significantly increased or decreased expression variability among cells derived from two experimental conditions. Case studies show that DV genes identified using spline-DV are representative and functionally relevant to tested cellular conditions, including obesity, fibrosis, and cancer.

The ability to acclimate to changing environmental conditions is essential for the fitness and survival of plants. Not only are seasonal differences challenging for plants growing in different habitats but, facing climate change, the likelihood of encountering extreme weather events increases. Previous studies of acclimation processes of Arabidopsis thaliana to changes in temperature and light conditions have revealed a multigenic trait comprising and affecting multiple layers of molecular organization. Here, a combination of experimental and computational methods was applied to study the effects of changing light intensities during cold acclimation on the central carbohydrate metabolism of Arabidopsis thaliana leaf tissue. Mathematical modeling, simulation and sensitivity analysis suggested an important role of hexose phosphate balance for stabilization of photosynthetic CO₂ fixation. Experimental validation revealed a profound effect of temperature on the sensitivity of carbohydrate metabolism.

A molecular systems architecture is presented for the neuromuscular junction (NMJ) in order to provide a framework for organizing complexity of biomolecular interactions in amyotrophic lateral sclerosis (ALS) using a systematic literature review process. ALS is a fatal motor neuron disease characterized by progressive degeneration of the upper and lower motor neurons that supply voluntary muscles. The neuromuscular junction contains cells such as upper and lower motor neurons, skeletal muscle cells, astrocytes, microglia, Schwann cells, and endothelial cells, which are implicated in pathogenesis of ALS. This molecular systems architecture provides a multi-layered understanding of the intra- and inter-cellular interactions in the ALS neuromuscular junction microenvironment, and may be utilized for target identification, discovery of single and combination therapeutics, and clinical strategies to treat ALS.

Cancer metabolism is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding this heterogeneity and its regulatory mechanisms at single-cell resolution is crucial for developing personalized therapeutic strategies. In this study, we employed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging single-cell RNA sequencing (scRNA-seq) data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. These findings provide insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, paving the way for potential targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution.

Accurate tracking of live cells using video microscopy recordings remains a challenging task for popular state-of-the-art image processing-based object tracking methods. In recent years, many applications have attempted to integrate deep-learning frameworks for this task, but most still heavily rely on consecutive frame-based tracking or other premises that hinder generalized learning. To address this issue, we aimed to develop a novel deep-learning-based tracking method that assumes cells can be tracked by their spatio-temporal neighborhood, without a restriction to consecutive frames. The proposed method has the additional benefit that the motion patterns of the cells can be learned by the predictor without any prior assumptions, and it has the potential to handle a large number of video frames with heavy artifacts. The efficacy of the proposed method is demonstrated through biologically motivated validation strategies and compared against multiple state-of-the-art cell tracking methods on budding yeast recordings and simulated samples.

Phenotypic heterogeneity along the epithelial-mesenchymal (E-M) axis contributes to cancer metastasis and drug resistance. Recent experimental efforts have collated detailed time-course data on the emergence and dynamics of E-M heterogeneity in a cell population. However, it remains unclear how different intra- and inter-cellular processes shape the dynamics of E-M heterogeneity. Here, using Cell Population Balance model, we capture the dynamics of cell density along E-M phenotypic axis resulting from interplay between-(a) intracellular regulatory interaction among biomolecules, (b) cell division and death and (c) stochastic cell-state transition. We find that while the existence of E-M heterogeneity depends on intracellular regulation, heterogeneity gets enhanced with stochastic cell-state transitions and diminished by growth rate differences. Further, resource competition among E-M cells can lead to both bi-phasic growth of the total population and/or bi-stability in the phenotypic composition. Overall, our model highlights complex interplay between cellular processes shaping dynamic patterns of E-M heterogeneity.

With rapid advances in biological technology and computational approaches, inferring specific gene regulatory networks from data alone during cell fate decisions, including determining direct regulations and their intensities between biomolecules, remains one of the most significant challenges. In this study, we propose a general computational approach based on systematic perturbation, statistical, and differential analyses to infer network topologies and identify network differences during cell fate decisions. For each cell fate state, we first theoretically show how to calculate local response matrices based on perturbation data under systematic perturbation analysis, and we also derive the wild-type (WT) local response matrix for specific ordinary differential equations. To make the inferred network more accurate and eliminate the impact of perturbation degrees, the confidence interval (CI) of local response matrices under multiple perturbations is applied, and the redefined local response matrix is proposed in statistical analysis to determine network topologies across all cell fates. Then in differential analysis, we introduce the concept of relative local response matrix, which enables us to identify critical regulations governing each cell state and dominant cell states associated with specific regulations. The epithelial to mesenchymal transition (EMT) network is chosen as an illustrative example to verify the feasibility of the approach. Largely consistent with experimental observations, the differences of inferred networks at the three cell states can be quantitatively identified. The approach presented here can be also applied to infer other regulatory networks related to cell fate decisions.

Stochastic differential equations (SDEs) are one of the most commonly studied probabilistic dynamical systems, and widely used to model complex biological processes. Building upon the previously introduced idea of performing inference of dynamical systems by parametrising their coefficient functions via neural networks, we propose a novel formulation for an optimisation objective that combines simulation-based penalties with pseudo-likelihoods. This greatly improves prediction performance compared to the state-of-the-art, and makes it possible to learn a wide variety of dynamics without any prior assumptions on analytical structure.

More than 20% of the population across the world is affected by non-communicable inflammatory skin diseases including psoriasis, atopic dermatitis, hidradenitis suppurativa, rosacea, etc. Many of these chronic diseases are painful and debilitating with limited effective therapeutic interventions. This study aims to identify common regulatory pathways and master regulators that regulate the molecular pathogenesis of inflammatory skin diseases. We designed an integrative systems biology framework to identify the significant regulators across several diseases. Network analytics unraveled 55 high-value proteins as significant regulators in molecular pathogenesis which can serve as putative drug targets for more effective treatments. We identified IKZF1 as a shared master regulator in hidradenitis suppurativa, atopic dermatitis, and rosacea with known disease-derived molecules for developing efficacious combinatorial treatments for these diseases. The proposed framework is very modular and indicates a significant path of molecular mechanism-based drug development from complex transcriptomics data and other multi-omics data.

In the study of brain tumors, patient-derived three-dimensional sphere cultures provide an important tool for studying emerging treatments. The growth of such spheroids depends on the combined effects of proliferation and migration of cells, but it is challenging to make accurate distinctions between increase in cell number versus the radial movement of cells. To address this, we formulate a novel model in the form of a system of two partial differential equations (PDEs) incorporating both migration and growth terms, and show that it more accurately fits our data compared to simpler PDE models. We show that traveling-wave speeds are strongly associated with population heterogeneity. Having fitted the model to our dataset we show that a subset of the cell lines are best described by a "Go-or-Grow"-type model, which constitutes a special case of our model. Finally, we investigate whether our fitted model parameters are correlated with patient age and survival.