A quantitative analysis of the interplay of environment,neighborhood, and cell state in 3D spheroids

Cells react to their microenvironment by integrating external stimuli into phenotypic decisions via an intracellular signaling network. To analyze the interplay of environment, local neighborhood, and internal cell state effects on phenotypic variability, we developed an experimental approach that enables multiplexed mass cytometric imaging analysis of up to 240 pooled spheroid microtissues. We quantified the contributions of environment, neighborhood, and intracellular state to marker variability in single cells of the spheroids. A linear model explained on average more than half of the variability of 34 markers across four cell lines and six growth conditions. The contributions of cell‐intrinsic and environmental factors to marker variability are hierarchically interdependent, a finding that we propose has general implications for systems‐level studies of single‐cell phenotypic variability. By the overexpression of 51 signaling protein constructs in subsets of cells, we also identified proteins that have cell‐intrinsic and cell‐extrinsic effects. Our study deconvolves factors influencing cellular phenotype in a 3D tissue and provides a scalable experimental system, analytical principles, and rich multiplexed imaging datasets for future studies.     

Understanding of mouse and human bladder at single‐cell resolution: integrated analysis of trajectory and cell‐cell interactive networks based on multiple scRNA‐seq datasets

ObjectivesTo elaborately decipher the mouse and human bladders at single‐cell levels.Materials and MethodsWe collected more than 50,000 cells from multiple datasets and created, up to date, the largest integrated bladder datasets. Pseudotime trajectory of urothelium and interstitial cells, as well as dynamic cell‐cell interactions, was investigated. Biological activity scores and different roles of signaling pathways between certain cell clusters were also identified.ResultsThe glucose score was significantly high in most urothelial cells, while the score of H3 acetylation was roughly equally distributed across all cell types. Several genes via a pseudotime pattern in mouse (Car3, Dkk2, Tnc, etc.) and human (FBLN1, S100A10, etc.) were discovered. S100A6, TMSB4X, and typical uroplakin genes seemed as shared pseudotime genes for urothelial cells in both human and mouse datasets. In combinational mouse (n = 16,688) and human (n = 22,080) bladders, we verified 1,330 and 1,449 interactive ligand‐receptor pairs, respectively. The distinct incoming and outgoing signaling was significantly associated with specific cell types. Collagen was the strongest signal from fibroblasts to urothelial basal cells in mouse, while laminin pathway for urothelial basal cells to smooth muscle cells (SMCs) in human. Fibronectin 1 pathway was intensely sent by myofibroblasts, received by urothelial cells, and almost exclusively mediated by SMCs in mouse bladder. Interestingly, the cell cluster of SMCs 2 was the dominant sender and mediator for Notch signaling in the human bladder, while SMCs 1 was not. The expression of integrin superfamily (the most common communicative pairs) was depicted, and their co‐expression patterns were located in certain cell types (eg, Itgb1 and Itgb4 in mouse and human basal cells).ConclusionsThis study provides a complete interpretation of the normal bladder at single‐cell levels, offering an in‐depth resource and foundation for future research.     

TATA and paused promoters active in differentiated tissues have distinct expression characteristics

Core promoter types differ in the extent to which RNA polymerase II (Pol II) pauses after initiation, but how this affects their tissue‐specific gene expression characteristics is not well understood. While promoters with Pol II pausing elements are active throughout development, TATA promoters are highly active in differentiated tissues. We therefore used a genomics approach on late‐stage Drosophila embryos to analyze the properties of promoter types. Using tissue‐specific Pol II ChIP‐seq, we found that paused promoters have high levels of paused Pol II throughout the embryo, even in tissues where the gene is not expressed, while TATA promoters only show Pol II occupancy when the gene is active. The promoter types are associated with different chromatin accessibility in ATAC‐seq data and have different expression characteristics in single‐cell RNA‐seq data. The two promoter types may therefore be optimized for different properties: paused promoters show more consistent expression when active, while TATA promoters have lower background expression when inactive. We propose that tissue‐specific genes have evolved to use two different strategies for their differential expression across tissues.     

Optogenetic actuator – ERK biosensor circuits identify MAPK network nodes that shape ERK dynamics

Combining single‐cell measurements of ERK activity dynamics with perturbations provides insights into the MAPK network topology. We built circuits consisting of an optogenetic actuator to activate MAPK signaling and an ERK biosensor to measure single‐cell ERK dynamics. This allowed us to conduct RNAi screens to investigate the role of 50 MAPK proteins in ERK dynamics. We found that the MAPK network is robust against most node perturbations. We observed that the ERK‐RAF and the ERK‐RSK2‐SOS negative feedback operate simultaneously to regulate ERK dynamics. Bypassing the RSK2‐mediated feedback, either by direct optogenetic activation of RAS, or by RSK2 perturbation, sensitized ERK dynamics to further perturbations. Similarly, targeting this feedback in a human ErbB2‐dependent oncogenic signaling model increased the efficiency of a MEK inhibitor. The RSK2‐mediated feedback is thus important for the ability of the MAPK network to produce consistent ERK outputs, and its perturbation can enhance the efficiency of MAPK inhibitors.     

CODEX,a neural network approach to explore signaling dynamics landscapes

Current studies of cell signaling dynamics that use live cell fluorescent biosensors routinely yield thousands of single‐cell, heterogeneous, multi‐dimensional trajectories. Typically, the extraction of relevant information from time series data relies on predefined, human‐interpretable features. Without a priori knowledge of the system, the predefined features may fail to cover the entire spectrum of dynamics. Here we present CODEX, a data‐driven approach based on convolutional neural networks (CNNs) that identifies patterns in time series. It does not require a priori information about the biological system and the insights into the data are built through explanations of the CNNs'' predictions. CODEX provides several views of the data: visualization of all the single‐cell trajectories in a low‐dimensional space, identification of prototypic trajectories, and extraction of distinctive motifs. We demonstrate how CODEX can provide new insights into ERK and Akt signaling in response to various growth factors, and we recapitulate findings in p53 and TGFβ‐SMAD2 signaling.     

Growth‐dependent heterogeneity in the DNA damage response in Escherichia coli

In natural environments, bacteria are frequently exposed to sub‐lethal levels of DNA damage, which leads to the induction of a stress response (the SOS response in Escherichia coli). Natural environments also vary in nutrient availability, resulting in distinct physiological changes in bacteria, which may have direct implications on their capacity to repair their chromosomes. Here, we evaluated the impact of varying the nutrient availability on the expression of the SOS response induced by chronic sub‐lethal DNA damage in E. coli. We found heterogeneous expression of the SOS regulon at the single‐cell level in all growth conditions. Surprisingly, we observed a larger fraction of high SOS‐induced cells in slow growth as compared with fast growth, despite a higher rate of SOS induction in fast growth. The result can be explained by the dynamic balance between the rate of SOS induction and the division rates of cells exposed to DNA damage. Taken together, our data illustrate how cell division and physiology come together to produce growth‐dependent heterogeneity in the DNA damage response.