Cellular signalling networks

Methods that analyse heterogeneity and compare tissue microenvironments using spatial omics data are challenging to develop. Here, the authors present SOTIP, a method that can perform spatial heterogeneity, spatial domain, and differential microenvironment analyses across multiple spatial omics modalities.

Mutations in BRCA1/2 are associated with a homologous recombination deficiency phenotype in BRCA-associated cancers. Reversion mutations can restore BRCA1/2 function and result in treatment resistance in these cancer-types. Here, the authors show that, in select cases, reversion mutations in BRCA1/2 can indicate prior BRCA-mediated tumorigenesis in non-canonical histologies.

KRAS wildtype metastatic pancreatic ductal adenocarcinoma (mPDAC) could represent a distinct molecular entity from other PDACs. Here, the authors analyse KRAS wildtype mPDAC tumours using genomics and transcriptomics and find molecular similarities with cholangiocarcinomas.

After acute injury, kidneys either successfully repair/regenerate or become fibrotic. Here the authors use scRNA-seq to study adaptive/maladaptive kidney regeneration and identify proinflammatory/fibrotic proximal tubule cells with pharmacologically targetable pyroptosis/ferroptosis signatures.

Cellular contexts such as disease state, organismal life stage and tissue microenvironment, shape intercellular communication, and ultimately affect an organism’s phenotypes. Here, the authors present Tensor-cell2cell, an unsupervised method for deciphering context-driven intercellular communication.

Multiple methods to infer cell-cell communication (CCC) from single cell data are currently available. Here, the authors systematically compare 16 CCC inference resources and 7 methods, and develop the LIANA framework as an interface to use and compare all these approaches.

Many diseases are caused by disruptions to the network of biochemical reactions that allow cells to respond to external signals. Here Nilsson et al develop a method to simulate cellular signaling using artificial neural networks to predict cellular responses and activities of signaling molecules.

Chronic obstructive pulmonary disease is a leading cause of death worldwide, while our understanding of cell-specific mechanisms underlying its pathobiology remains incomplete. Here the authors perform scRNA-seq of human lung tissue to identify transcriptional changes in alveolar niche cells associated with the disease.

Ordinary differential equation (ODE) models are widely used to understand multiple processes. Here the authors show how the concept of mini-batch optimization can be transferred from the field of Deep Learning to ODE modelling.

Complex biomolecular networks are fundamental to the functioning of living systems, both at the cellular level and beyond. In this paper, the authors develop a systems framework to elucidate the interplay of networks and the spatial localisation of network components.

Our ability to interpret single-cell multivariate signaling responses is still limited. Here the authors introduce fractional response analysis (FRA), involving fractional cell counting, capable of deconvoluting heterogeneous multivariate responses of cellular populations.

Network motif models focus on small sub-networks in biological systems to quantitatively describe overall behavior but they often overlook time delays. Here, the authors systematically examine the most common network motifs via delay differential equations (DDE), often leading to more concise descriptions.

Oncogenic KRAS signalling is required for tumor initiation; however KRAS-dependency at advanced stages is less understood. Here, the authors show that, in established KRAS-driven pancreatic cancer, KRAS-ablation does not affect intrinsic tumorigenic capacity but elicits antitumor immune response, highlighting the importance of KRAS-driven immune suppression in tumor maintenance.

Understanding how cells discriminate between stimuli is an ongoing challenge. Here, the authors propose a mathematical framework for inferring the mutual information encoded in temporal signaling dynamics and use it to study how information is transmitted over time in response to different stimuli in NFκB, MAPK and p53 signaling pathways.

Kinases drive fundamental changes in cell state, but predicting kinase activity based on substrate-level changes can be challenging. Here the authors introduce a computational framework that utilizes similarities between substrates to robustly infer kinase activity.

Single-cell methods record molecule expressions of cells in a given tissue, but understanding interactions between cells remains challenging. Here the authors show by applying systems biology and machine learning approaches that they can infer and analyze cell-cell communication networks in an easily interpretable way.

Bulk and single-cell transcriptomic data can be a source of novel insights into how cells interact with each other. Here the authors develop ICELLNET, a global, biologically validated, and easy-to-use framework to dissect cell communication from individual or multiple cell-based transcriptomic profiles.

Single cell expression data allows for inferring cell-cell communication between cells expressing ligands and those expressing their cognate receptors. Here the authors present an updated and curated database of ligand-receptor pairs and a Python-based toolkit to construct and analyse communication networks from single cell and bulk expression data.

Single-cell technologies are increasingly prominent in clinical applications, but predictive modelling with such data in large cohorts has remained computationally challenging. We developed a new algorithm, ‘VoPo’, for predictive modelling and visualization of single cell data for translational applications.

Proteome activity has a major role in cancer progression and response to drugs. Here, the authors use comprehensive proteomic and phosphoproteomic data, in conjunction with drug-sensitivity screens, to generate a community resource consisting of landscapes of pathway and kinase activity across different cell lines

Predicting an individual's response to therapy is an important goal for precision medicine. Here, the authors use an algorithm that takes into account the interaction type and directionality of signalling pathways in protein–protein interactions and find that their pathway analysis can predict essential genes, which may be a target for therapy.

Segregation of an MSH1 RNAi transgene produces non-genetic memory that displays transgenerational inheritance in Arabidopsis. Here, the authors compare memory and non-memory full-sib progenies to show the involvement of DNA methylation reprogramming, involving the RdDM pathway, in transition to a heritable memory state.

Understanding deregulation of biological pathways in cancer can provide insight into disease etiology and potential therapies. Here, as part of the PanCancer Analysis of Whole Genomes (PCAWG) consortium, the authors present pathway and network analysis of 2583 whole cancer genomes from 27 tumour types.

How reproducible human kidney organoids derived from different iPSC lines are, and how faithful they are to human kidney tissue remain unclear. Here, the authors use four human iPSC lines to derive kidney organoids and show how organoid composition is reproducible, comparable to human tissue and of improved quality after transplantation.

Multi-omic profiling is a powerful approach to dissecting molecular mechanisms in disease. Here the authors generate whole proteome, phosphoproteome and transcriptome profiles from two mouse models of high-grade glioma driven by different oncogenes, and validate identified master regulators with a CRISPR screen.

The fraction of protein-protein interactions (PPIs) that can be disrupted without fitness effect is unknown. Here, the authors model how disease-causing mutations and common mutations carried by healthy people perturb the interactome, and estimate that <20% of human PPIs are completely dispensable.

The directions of most human protein-protein interactions (PPIs) remain unknown. Here, the authors use cancer genomic and drug response data to infer the direction of signal flow in the human PPI network and show that the directed network improves drug target and cancer driver gene prioritization.

The genetic and pathogenetic basis of heart failure is incompletely understood. Here, the authors present a high-fidelity tissue collection from rapidly preserved failing and non-failing control hearts which are used for eQTL mapping and network analysis, resulting in the prioritization of PPP1R3A as a heart failure gene.

Genome-wide association studies (GWAS) have so far uncovered more than 200 loci for multiple sclerosis (MS). Here, the authors integrate data from various sources for a cell type-specific pathway analysis of MS GWAS results that specifically highlights the involvement of the immune system in disease pathogenesis.

Synergistic interactions may arise between regulators in complex molecular networks. Here, the authors develop OptiCon, a computational method for de novo identification of synergistic key regulators and investigate their potential roles as candidate targets for combination therapy.

Protein phosphorylation has various regulatory functions. Here, the authors map 241 phosphorylation hotspot regions across 40 eukaryotic species, showing that they are enriched at interfaces and near catalytic residues, and enable the discovery of functionally important phospho-sites.

With the increasing obtainability of multi-OMICs data comes the need for easy to use data analysis tools. Here, the authors introduce Metascape, a biologist-oriented portal that provides a gene list annotation, enrichment and interactome resource and enables integrated analysis of multi-OMICs datasets.

Whole-cell models hold great promise for fundamental and translational biology, but genome-scale modelling of signalling networks has been a challenge. Here, the authors present a genome-scale, mechanistic and executable model of the network controlling and executing the S. cerevisiae cell cycle.

Chemical perturbation of specific protein–protein interactions is notoriously difficult, yet necessary when complete inhibition of a signalling pathway is detrimental to the cell. Here, the authors use a systems approach and identify two first-in-class small molecules that specifically inhibit TNF-induced NF-κB activation.

Many organs develop through branching morphogenesis, but whether the underlying mechanisms are shared is unknown. Here, the authors show that a ligand-receptor based Turing mechanisms, similar to that observed in lung development, likely underlies branching morphogenesis of the kidney.

Repurposing approved drugs could accelerate treatment options for various diseases. Here, the authors use network proximity of disease gene products and drug targets in the human protein interactome to identify drug-disease associations for cardiovascular disease, and validate these using longitudinal healthcare data.

Innate immunity combines intra- and intercellular signalling to develop responses that limit pathogen spread. Here the authors analyse feedback and feedforward loops connecting IRF3, NF-κB and STAT pathways, and suggest they allow coordinating cell fate decisions in cellular populations in response to the virus-mimicking agent poly(I:C).

Genomic alterations underlie the variability of drug responses between cancers, but our mechanistic understanding is limited. Here the authors use the p53 network to study how rewiring of signalling networks by genomic alterations impact their dynamic response to pharmacological perturbation.

The NUDIX hydrolases are known to be involved in several cellular processes and diseases, such as cancer, but remain poorly characterized as a family. Here, the authors provide a comprehensive analysis of the structural, biochemical, and expression properties of 18 human NUDIX proteins, and begin to address their functional inter-relationships.

Deregulation of E2F family transcription factors is associated with cancer progression and metastasis. Here, the authors construct a map of the regulatory network around the E2F family, and using gene expression profiles, identify tumour type-specific regulatory cores and receptor expression signatures associated with epithelial-mesenchymal transition in bladder and breast cancer.

Robust quantification of the differentiation potential of single cells is a task of great importance. Here the authors integrate single-cell RNA-Seq profiles with a cellular interaction network to compute the signaling entropy, and show that it can identify normal and cancer stem-cell phenotypes.

mTORC1 is known to mediate the signalling activity of amino acids. Here, the authors combine modelling with experiments and find that amino acids acutely stimulate mTORC2, IRS/PI3K and AMPK, independently of mTORC1. AMPK activation through CaMKKβ sustains autophagy under non-starvation conditions.

Pathway analysis aids interpretation of large-scale gene expression data, but existing algorithms fall short of providing robust pathway identification. The method introduced here includes coexpression analysis and gene importance estimation to robustly identify relevant pathways and biomarkers for patient stratification.

Many drugs are small molecule inhibitors of cell signalling. Through single cell analysis and mathematical modelling here the authors show that cell-to-cell variability diversifies inhibition response into digital and analogue, and that the two translate into distinct long-term functional responses.

Cell-to-cell communication relies upon interactions between secreted ligands and cell surface receptors. Here, Ramilowski et al.present a draft cell-to-cell communication network based on expression of ligand-receptor pairs in 144 different human cell types.

During development, waves of gene expression are required for segmentation of the body axis. In this study, repeated splitting of a wave of hedgehog gene expression is shown during segmentation of the spiderAchaearanea tepidariorum.