Identification and characterization of in vitro expanded hematopoietic stem cells

Hematopoietic stem cells (HSCs) cultured outside the body are the fundamental component of a wide range of cellular and gene therapies. Recent efforts have achieved > 200‐fold expansion of functional HSCs, but their molecular characterization has not been possible since the majority of cells are non‐HSCs and single cell‐initiated cultures have substantial clone‐to‐clone variability. Using the Fgd5 reporter mouse in combination with the EPCR surface marker, we report exclusive identification of HSCs from non‐HSCs in expansion cultures. By directly linking single‐clone functional transplantation data with single‐clone gene expression profiling, we show that the molecular profile of expanded HSCs is similar to proliferating fetal HSCs and reveals a gene expression signature, including Esam, Prdm16, Fstl1, and Palld, that can identify functional HSCs from multiple cellular states. This “repopulation signature” (RepopSig) also enriches for HSCs in human datasets. Together, these findings demonstrate the power of integrating functional and molecular datasets to better derive meaningful gene signatures and opens the opportunity for a wide range of functional screening and molecular experiments previously not possible due to limited HSC numbers.     

U‐CIE [/juː ‘siː/]: Color encoding of high‐dimensional data

Data visualization is essential to discover patterns and anomalies in large high‐dimensional datasets. New dimensionality reduction techniques have thus been developed for visualizing omics data, in particular from single‐cell studies. However, jointly showing several types of data, for example, single‐cell expression and gene networks, remains a challenge. Here, we present ‘U‐CIE, a visualization method that encodes arbitrary high‐dimensional data as colors using a combination of dimensionality reduction and the CIELAB color space to retain the original structure to the extent possible. U‐CIE first uses UMAP to reduce high‐dimensional data to three dimensions, partially preserving distances between entities. Next, it embeds the resulting three‐dimensional representation within the CIELAB color space. This color model was designed to be perceptually uniform, meaning that the Euclidean distance between any two points should correspond to their relative perceptual difference. Therefore, the combination of UMAP and CIELAB thus results in a color encoding that captures much of the structure of the original high‐dimensional data. We illustrate its broad applicability by visualizing single‐cell data on a protein network and metagenomic data on a world map and on scatter plots.     

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.     

CellRegMap: a statistical framework for mapping context‐specific regulatory variants using scRNA‐seq

Single‐cell RNA sequencing (scRNA‐seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population‐scale scRNA‐seq studies in hundreds of individuals, allowing to assay genetic effects with single‐cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA‐seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype–context interactions of known eQTL variants using scRNA‐seq data. This model‐based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine‐grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.     

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.     

Gruffi: an algorithm for computational removal of stressed cells from brain organoid transcriptomic datasets

Organoids enable in vitro modeling of complex developmental processes and disease pathologies. Like most 3D cultures, organoids lack sufficient oxygen supply and therefore experience cellular stress. These negative effects are particularly prominent in complex models, such as brain organoids, and can affect lineage commitment. Here, we analyze brain organoid and fetal single‐cell RNA sequencing (scRNAseq) data from published and new datasets, totaling about 190,000 cells. We identify a unique stress signature in the data from all organoid samples, but not in fetal samples. We demonstrate that cell stress is limited to a defined subpopulation of cells that is unique to organoids and does not affect neuronal specification or maturation. We have developed a computational algorithm, Gruffi, which uses granular functional filtering to identify and remove stressed cells from any organoid scRNAseq dataset in an unbiased manner. We validated our method using six additional datasets from different organoid protocols and early brains, and show its usefulness to other organoid systems including retinal organoids. Our data show that the adverse effects of cell stress can be corrected by bioinformatic analysis for improved delineation of developmental trajectories and resemblance to in vivo data.     

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.