Search for: All records

Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell–cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.

 

Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.

 

ABSTRACT Non-canonical/β-catenin-independent Wnt signaling plays crucial roles in tissue/cell polarity in epithelia, but its functions have been less well studied in mesenchymal tissues, such as the skeleton. Mutations in non-canonical Wnt signaling pathway genes cause human skeletal diseases such as Robinow syndrome and Brachydactyly Type B1, which disrupt bone growth throughout the endochondral skeleton. Ror2 is one of several non-canonical Wnt receptor/co-receptors. Here, we show that ror2−/− mutant zebrafish have craniofacial skeletal defects, including disruptions of chondrocyte polarity. ror1−/− mutants appear to be phenotypically wild type, but loss of both ror1 and ror2 leads to more severe cartilage defects, indicating partial redundancy. Skeletal defects in ror1/2 double mutants resemble those of wnt5b−/− mutants, suggesting that Wnt5b is the primary Ror ligand in zebrafish. Surprisingly, the proline-rich domain of Ror2, but not its kinase domain, is required to rescue its function in mosaic transgenic experiments in ror2−/− mutants. These results suggest that endochondral bone defects in ROR-related human syndromes reflect defects in cartilage polarity and morphogenesis. 

Research on the genetic mechanisms underlying human skeletal development and disease have largely relied on studies in mice. However, recently the zebrafish has emerged as a popular model for skeletal research. Despite anatomical differences such as a lack of long bones in their limbs and no hematopoietic bone marrow, both the cell types in cartilage and bone as well as the genetic pathways that regulate their development are remarkably conserved between teleost fish and humans. Here we review recent studies that highlight this conservation, focusing specifically on the cartilaginous growth zones (GZs) of endochondral bones. GZs can be unidirectional such as the growth plates (GPs) of long bones in tetrapod limbs or bidirectional, such as in the synchondroses of the mammalian skull base. In addition to endochondral growth, GZs play key roles in cartilage maturation and replacement by bone. Recent studies in zebrafish suggest key roles for cartilage polarity in GZ function, surprisingly early establishment of signaling systems that regulate cartilage during embryonic development, and important roles for cartilage proliferation rather than hypertrophy in bone size. Despite anatomical differences, there are now many zebrafish models for human skeletal disorders including mutations in genes that cause defects in cartilage associated with endochondral GZs. These point to conserved developmental mechanisms, some of which operate both in cranial GZs and limb GPs, as well as others that act earlier or in parallel to known GP regulators. Experimental advantages of zebrafish for genetic screens, high resolution live imaging and drug screens, set the stage for many novel insights into causes and potential therapies for human endochondral bone diseases. 

Abstract Single-cell RNA sequencing trades read-depth for dimensionality, often leading to loss of critical signaling gene information that is typically present in bulk data sets. We introduce DURIAN (Deconvolution and mUltitask-Regression-based ImputAtioN), an integrative method for recovery of gene expression in single-cell data. Through systematic benchmarking, we demonstrate the accuracy, robustness and empirical convergence of DURIAN using both synthetic and published data sets. We show that use of DURIAN improves single-cell clustering, low-dimensional embedding, and recovery of intercellular signaling networks. Our study resolves several inconsistent results of cell–cell communication analysis using single-cell or bulk data independently. The method has broad application in biomarker discovery and cell signaling analysis using single-cell transcriptomics data sets. 

The vertebrate hindbrain is segmented into rhombomeres (r) initially defined by distinct domains of gene expression. Previous studies have shown that noise-induced gene regulation and cell sorting are critical for the sharpening of rhombomere boundaries, which start out rough in the forming neural plate (NP) and sharpen over time. However, the mechanisms controlling simultaneous formation of multiple rhombomeres and accuracy in their sizes are unclear. We have developed a stochastic multiscale cell-based model that explicitly incorporates dynamic morphogenetic changes (i.e. convergent-extension of the NP), multiple morphogens, and gene regulatory networks to investigate the formation of rhombomeres and their corresponding boundaries in the zebrafish hindbrain. During pattern initiation, the short-range signal, fibroblast growth factor (FGF), works together with the longer-range morphogen, retinoic acid (RA), to specify all of these boundaries and maintain accurately sized segments with sharp boundaries. At later stages of patterning, we show a nonlinear change in the shape of rhombomeres with rapid left-right narrowing of the NP followed by slower dynamics. Rapid initial convergence improves boundary sharpness and segment size by regulating cell sorting and cell fate both independently and coordinately. Overall, multiple morphogens and tissue dynamics synergize to regulate the sizes and boundaries of multiple segments during development. 

Single-cell RNA sequencing (scRNA-seq) data provides unprecedented information on cell fate decisions; however, the spatial arrangement of cells is often lost. Several recent computational methods have been developed to impute spatial information onto a scRNA-seq dataset through analyzing known spatial expression patterns of a small subset of genes known as a reference atlas. However, there is a lack of comprehensive analysis of the accuracy, precision, and robustness of the mappings, along with the generalizability of these methods, which are often designed for specific systems. We present a system-adaptive deep learning-based method (DEEPsc) to impute spatial information onto a scRNA-seq dataset from a given spatial reference atlas. By introducing a comprehensive set of metrics that evaluate the spatial mapping methods, we compare DEEPsc with four existing methods on four biological systems. We find that while DEEPsc has comparable accuracy to other methods, an improved balance between precision and robustness is achieved. DEEPsc provides a data-adaptive tool to connect scRNA-seq datasets and spatial imaging datasets to analyze cell fate decisions. Our implementation with a uniform API can serve as a portal with access to all the methods investigated in this work for spatial exploration of cell fate decisions in scRNA-seq data. All methods evaluated in this work are implemented as an open-source software with a uniform interface.