Single-cell messenger RNA sequencing reveals rare intestinal cell types

Abstract

Understanding the development and function of an organ requires the characterization of all of its cell types. Traditional methods for visualizing and isolating subpopulations of cells are based on messenger RNA or protein expression of only a few known marker genes. The unequivocal identification of a specific marker gene, however, poses a major challenge, particularly if this cell type is rare. Identifying rare cell types, such as stem cells, short-lived progenitors, cancer stem cells, or circulating tumour cells, is crucial to acquire a better understanding of normal or diseased tissue biology. To address this challenge we first sequenced the transcriptome of hundreds of randomly selected cells from mouse intestinal organoids, cultured self-organizing epithelial structures that contain all cell lineages of the mammalian intestine. Organoid buds, like intestinal crypts, harbour stem cells that continuously differentiate into a variety of cell types, occurring at widely different abundances. Since available computational methods can only resolve more abundant cell types, we developed RaceID, an algorithm for rare cell type identification in complex populations of single cells. We demonstrate that this algorithm can resolve cell types represented by only a single cell in a population of randomly sampled organoid cells. We use this algorithm to identify Reg4 as a novel marker for enteroendocrine cells, a rare population of hormone-producing intestinal cells. Next, we use Reg4 expression to enrich for these rare cells and investigate the heterogeneity within this population. RaceID confirmed the existence of known enteroendocrine lineages, and moreover discovered novel subtypes, which we subsequently validated in vivo. Having validated RaceID we then applied the algorithm to ex vivo-isolated Lgr5-positive stem cells and their direct progeny. We find that Lgr5-positive cells represent a homogenous abundant population of stem cells mixed with a rare population of Lgr5-positive secretory cells. We envision broad applicability of our method for discovering rare cell types and the corresponding marker genes in healthy and diseased organs.

Human cerebral organoids recapitulate gene expression programs of fetal neocortex development

“…Single-cell RNA-seq (scRNA-seq) is a powerful method to unbiasedly identify molecularly distinct cell types in heterogeneous tissues (13)(14)(15)(16). ScRNA-seq can also identify cells as intermediate between types, and cells can then be computationally aligned to delineate differentiation paths (15,17).…”

End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data

“…inDrop allows the profiling of thousands of cells, and hence, rare cell populations of even <1% are likely to be well represented. Detection of rare cells is a powerful use of single-cell RNA sequencing (Grün et al 2015), and its application to T1D disease progression will likely transform our current understanding of this disease. For example, detection of activated macrophages is critical as they may be indicators of autoimmune disease and T1D progression in particular.…”

Development of 3D Tissue Reconstruction Method from Single-cell RNA-seq Data

“…Then, we generated sphere models by applying the whitening/sphering transformation employed in original 3D PCA-based reconstruction method [3]. Here, each cell is mapped only to the surface of the sphere.…”

“…However, to generate artificial and functional tissues/organs in 3D manner is still challenging due to their structural complexity. Recently, the single-cell analysis technology has evolved to obtain cell profiles at much higher resolution than ever before, which enables us to investigate cellular heterogeneity in such complex tissues/organs in more details [1][2][3].…”