Summary Modern single-cell technologies allow multiplexed sampling of cellular states within a tissue. However, computational tools that can infer developmental cell-state transitions reproducibly from such single-cell data are lacking. Here, we introduce p-Creode, an unsupervised algorithm that produces multi-branching graphs from single-cell data, compares graphs with differing topologies, and infers a statistically robust hierarchy of cell-state transitions that define developmental trajectories. We have applied p-Creode to mass cytometry, multiplex immunofluorescence, and single-cell RNA-seq data. As a test case, we validate cell state-transition trajectories predicted by p-Creode for intestinal tuft cells, a rare, chemosensory cell type. We clarify that tuft cells are specified outside of the Atoh1-dependent secretory lineage in the small intestine. However, p-Creode also predicts, and we confirm, that tuft cells arise from an alternative, Atoh1-driven developmental program in the colon. These studies introduce p-Creode as a reliable method for analyzing large datasets that depict branching transition trajectories. p-Creode is publicly available for download here: https://github.com/KenLauLab/pCreode.

BASIC AND TRANSLATIONAL AT cell expansion reduced chronic intestinal inflammation in mice. Strategies to support tuft cells might be developed for treatment of CD.

No abstract

Summary In the developing pancreas, transient Neurog3-expressing progenitors give rise to four major islet-cell types, α, β, δ, and γ; when and how the Neurog3+ cells choose cell-fate is unknown. Using single-cell RNAseq, trajectory analysis, and combinatorial lineage tracing, we showed here that the Neurog3+ cells co-expressing Myt1 (i.e., Myt1+Neurog3+) were biased towards β-cell fate; while those not simultaneously expressing Myt1 (Myt1-Neurog3+) favored α-fate. Myt1 manipulation only marginally affected α- vs. β-cell specification, suggesting Myt1 as a marker but not determinant for islet-cell type specification. The Myt1+Neurog3+ cells displayed higher Dnmt1 expression and enhancer methylation at Arx, an α-fate-promoting gene. Inhibiting Dnmts in pancreatic progenitors promoted α-cell specification, while Dnmt1 over-expression or Arx enhancer hyper-methylation favored β-cell production. Moreover, the pancreatic progenitors contained distinct Arx enhancer methylation states without transcriptionally definable sub-populations, a phenotype independent of Neurog3 activity. These data suggest that Neurog3-independent methylation on fate-determining gene-enhancers specifies distinct endocrine-cell programs.

Understanding heterogeneous cellular behaviors in a complex tissue requires the evaluation of signaling networks at single-cell resolution. However, probing signaling in epithelial tissues using cytometry-based single-cell analysis has been confounded by the necessity of single-cell dissociation, where disrupting cell-to-cell connections inherently perturbs native cell signaling states. Here, we demonstrate a novel strategy (Disaggregation for Intracellular Signaling in Single Epithelial Cells from Tissue—DISSECT) that preserves native signaling for Cytometry Time-of-Flight (CyTOF) and fluorescent flow cytometry applications. A 21-plex CyTOF analysis encompassing core signaling and cell-identity markers was performed on the small intestinal epithelium after systemic tumor necrosis factor-alpha (TNF-α) stimulation. Unsupervised and supervised analyses robustly selected signaling features that identify a unique subset of epithelial cells that are sensitized to TNF-α-induced apoptosis in the seemingly homogeneous enterocyte population. Specifically, p-ERK and apoptosis are divergently regulated in neighboring enterocytes within the epithelium, suggesting a mechanism of contact-dependent survival. Our novel single-cell approach can broadly be applied, using both CyTOF and multi-parameter flow cytometry, for investigating normal and diseased cell states in a wide range of epithelial tissues.

Function at the organ level manifests itself from a heterogeneous collection of cell types. Cellular heterogeneity emerges from developmental processes by which multipotent progenitor cells make fate decisions and transition to specific cell types through intermediate cell states. Although genetic experimental strategies such as lineage tracing have provided insights into cell lineages, recent developments in single-cell technologies have greatly increased our ability to interrogate distinct cell types, as well as transitional cell states in tissue systems. From single-cell data that describe these intermediate cell states, computational tools have been developed to reconstruct cell-state transition trajectories that model cell developmental processes. These algorithms, although powerful, are still in their infancy, and attention must be paid to their strengths and weaknesses when they are used. Here, we review some of these tools, also referred to as pseudotemporal ordering algorithms, and their associated assumptions and caveats. We hope to provide a rational and generalizable workflow for single-cell trajectory analysis that is intuitive for experimental biologists.

Cellular heterogeneity poses a significant challenge to understanding tissue level phenotypes and confounds conventional bulk analyses. To facilitate the analysis of signaling at the single-cell level in human tissues, we applied mass cytometry using CyTOF (Cytometry Time-of-Flight) to formalin-fixed paraffin-embedded (FFPE) normal and diseased intestinal specimens. We developed and validated a technique called FFPE-DISSECT (Disaggregation for Intracellular Signaling in Single Epithelial Cells from Tissue), a single-cell approach for characterizing native signaling states from embedded solid tissue samples. We applied FFPE-DISSECT coupled to mass cytometry and found differential signaling by tumor necrosis factor α (TNF-α) in intestinal enterocytes, goblet cells and enteroendocrine cells, implicating the role of the downstream RAS-RAF-MEK-ERK signaling pathway in dictating goblet cell identity. In addition, application of FFPE-DISSECT, mass cytometry, and data-driven computational analyses to human colon specimens confirmed reduced differentiation in colorectal cancer (CRC) compared to normal colon, and revealed quantitative increases in inter- and intra-tissue heterogeneity in CRC with regards to the modular regulation of signaling pathways. Specifically, modular co-regulation of the kinases P38 and ERK, the translation regulator 4EBP1, and the transcription factor CREB in the proliferative compartment of the normal colon was loss in CRC, as evidenced by their impaired coordination over samplings of single cells in tissue. Our data suggest that this single-cell approach, applied in conjunction with genomic annotation, such as microsatellite instability and mutations in KRAS and BRAF, allows rapid and detailed characterization of cellular heterogeneity from clinical repositories of embedded human tissues. FFPE-DISSECT coupled of mass cytometry can be used for deriving cellular landscapes from archived patient samples, beyond CRC, and as a high resolution tool for disease characterization and subtyping.

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such expansion or shrinkage, or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes, identification and definition of altered cell populations, and benchmarking batch correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions, and has broad utility for gaining insight on how cell populations respond to perturbations.