A multi-modality approach to decode epithelial heterogeneity and function in metaplasia

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1) Background, key gaps in our understanding, and important challenges to be addressed. In response to injury, differentiated adult secretory cells in the gastrointestinal tract undergo metaplasia, or the conversion of one cell type to another. In the pancreas, this is called `acinar to ductal metaplasia' (ADM) and it is thought to function in tissue regeneration. The goals of our laboratory are to generate tools to understand the process of ADM, to identify the consortium of ADM cell types that emerge, and to determine their physiological role in tissue injury. 2) Description of recent progress by the PI. By the end of her post-doctoral work, the PI showed that differentiated, secretory cell types, such as tuft cells, form in the pancreas in response to ADM (DelGiorno et al. Frontiers in Physiology, 2020). Tuft cells are solitary chemosensory cells with myriad roles in mediating inflammation. Combining RNA sequencing, electron microscopy (EM), and mouse models of disease, she identified a functional role for tuft cells, the first description in any model of tumorigenesis (DelGiorno et al. Gastroenterology, 2020). Using single cell RNA sequencing (scRNA-seq) and EM, she has identified the formation of several additional secretory cell populations in ADM of unknown function, which are now being studied in the DelGiorno laboratory. 3) Overview of future research program. We propose to continue our work on ADM by creating a workflow combining Single cell RNA-seq and EM Analysis (SEMA) to study epithelial heterogeneity in pancreatic injury with and without the genetic manipulation of identified cell types. Volumetric electron microscopy (3DEM) will be used as a substrate on which to map the information provided by scRNA-seq to reveal the spatial relationships between cells and infer the function of individual cell types through the study of their organellar content. Our approach leverages scanning electron microscopy (SEM) to conduct a variety of experiments on the same tissue including: [1] imaging of dehydrated whole-mount samples, [2] wide-field “chip mapping” of large areas of ultrathin sections, and [3] large-scale serial section approaches for 3DEM acquisition. Mapping is enhanced using correlative light and electron microscopy to assign molecular markers identified in scRNA-seq by immunofluorescence to cells with defining characteristics in SEM (e.g., nuclear shape, distribution of mitochondria, etc). Our 3DEM datasets are amenable to high-throughput image processing (e.g., segmentation, geometry processing) using machine learning and deep learning tool kits. Together, these tools provide a novel discovery workflow, which will be applied to projects throughout our laboratory aimed at evaluating cellular heterogeneity in ADM. Our goals for the next five years are to use this SEMA approach to build a multi-dimensional atlas of the cellular content and spatial relationships of the various emergent cell types in ADM. Mouse models lacking individual ADM cell subtypes will be used to assess phenotypic effects on cellular makeup, relative positioning, and disease progression. Together, these studies will provide invaluable insight into the role of ADM in tissue healing and regeneration in the pancreas.