Alan J. Simmons

Cytometry-based single-cell analysis of intact epithelial signaling reveals MAPK activation divergent from TNF-α-induced apoptosis in vivo.

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.

Impaired coordination between signaling pathways is revealed in human colorectal cancer using single-cell mass cytometry of archival tissue blocks

A single-cell analytical approach in the clinic may lead to more effective treatment strategies for cancer patients. Detecting intratumoral heterogeneity in the clinic The clinical approach to treating cancer has become more patient-directed, because tumors of the same tissue type can vary considerably between patients. Analysis of formalin-fixed, paraffin-embedded (FFPE) tissue currently provides tissue-level data to inform the therapeutic strategy. However, these techniques miss the heterogeneity between cells within the same tumor; thus, an indicated strategy may kill some portions of the tumor (but not the entire tumor), and clinicians and patients must constantly fight new tumor growth. Simmons et al. developed a technique that provides single-cell–level data from clinical FFPE samples. Using this technique, called FFPE-DISSECT, the authors found distinct signaling profiles in different cells of the proliferative colon crypt and additional differences in the ways in which these profiles were disrupted in malignant crypt cells. This approach may enable more effective early treatment strategies and ultimately better outcomes for patients. Cellular heterogeneity poses a substantial challenge to understanding tissue-level phenotypes and confounds conventional bulk analyses. To analyze signaling at the single-cell level in human tissues, we applied mass cytometry using cytometry time of flight to formalin-fixed, paraffin-embedded (FFPE) normal and diseased intestinal specimens. This technique, called FFPE-DISSECT (disaggregation for intracellular signaling in single epithelial cells from tissue), is a single-cell approach to characterizing signaling states in embedded tissue samples. We applied FFPE-DISSECT coupled to mass cytometry and found differential signaling by tumor necrosis factor–α in intestinal enterocytes, goblet cells, and enteroendocrine cells, implicating the downstream RAS-RAF-MEK pathway in determining goblet cell identity. Application of this technique and computational analyses to human colon specimens confirmed the reduced differentiation in colorectal cancer (CRC) compared to normal colon and revealed increased intratissue and intertissue heterogeneity in CRC with quantitative changes in the regulation of signaling pathways. Specifically, coregulation of the kinases p38 and ERK, the translation regulator 4EBP1, and the transcription factor CREB in proliferating normal colon cells was lost in CRC. Our data suggest that this single-cell approach, applied in conjunction with genomic annotation, enables the rapid and detailed characterization of cellular heterogeneity from clinical repositories of embedded human tissues. This technique can be used to derive cellular landscapes from archived patient samples (beyond CRC) and as a high-resolution tool for disease characterization and subtyping.

Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut

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.

Deciphering tumor heterogeneity from FFPE tissues: Its promise and challenges

ABSTRACT An impediment to the understanding of cancer is the heterogeneous nature of cell populations within a tumor microenvironment. We reported a method to query protein signaling in single epithelial cells from formalin-fixed paraffin-embedded (FFPE) colorectal cancer tissues. Here, we discuss the feasibility and limitations of this approach for investigating signaling state heterogeneity.

The ErbB3 receptor tyrosine kinase negatively regulates Paneth cells by PI3K-dependent suppression of Atoh1

Paneth cells (PCs), a secretory population located at the base of the intestinal crypt, support the intestinal stem cells (ISC) with growth factors and participate in innate immunity by releasing antimicrobial peptides, including lysozyme and defensins. PC dysfunction is associated with disorders such as Crohn’s disease and necrotizing enterocolitis, but the specific pathways regulating PC development and function are not fully understood. Here we tested the role of the neuregulin receptor ErbB3 in control of PC differentiation and the ISC niche. Intestinal epithelial ErbB3 knockout caused precocious appearance of PCs as early as postnatal day 7, and substantially increased the number of mature PCs in adult mouse ileum. ErbB3 loss had no effect on other secretory lineages, but increased expression of the ISC marker Lgr5. ErbB3-null intestines had elevated levels of the Atoh1 transcription factor, which is required for secretory fate determination, while Atoh1+ cells had reduced ErbB3, suggesting reciprocal negative regulation. ErbB3-null intestinal progenitor cells showed reduced activation of the PI3K–Akt and ERK MAPK pathways. Inhibiting these pathways in HT29 cells increased levels of ATOH1 and the PC marker LYZ. Conversely, ErbB3 activation suppressed LYZ and ATOH1 in a PI3K-dependent manner. Expansion of the PC compartment in ErbB3-null intestines was accompanied with elevated ER stress and inflammation markers, raising the possibility that negative regulation of PCs by ErbB3 is necessary to maintain homeostasis. Taken together, our data suggest that ErbB3 restricts PC numbers through PI3K-mediated suppression of Atoh1 levels leading to inhibition of PC differentiation, with important implications for regulation of the ISC niche.

Quantitative assessment of cell population diversity in single-cell landscapes

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 as 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 and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, 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 into tissue-level perturbations at the single-cell resolution.