Heterogeneity of human pancreatic β-cells

Abstract

Background: Human pancreatic β-cells are heterogeneous. This has been known for a long time and is based on various functional and morphological readouts. β-Cell heterogeneity could reflect fixed subpopulations with distinct functions. However, recent pseudotime analysis of large-scale RNA sequencing data suggest that human β-cell subpopulations may rather reflect dynamic interchangeable states characterized by low expression of genes involved in the unfolded protein response (UPR) and low insulin gene expression, low UPR and high insulin expression or high UPR and low insulin expression.

Scope of review: This review discusses findings obtained by single-cell RNA sequencing combined with pseudotime analysis that human β-cell heterogeneity represents dynamic interchangeable functional states. The physiological significance and potential implications of β-cell heterogeneity in the development and progression of diabetes is highlighted.

Major conclusions: The existence of dynamic functional states allow β-cells to transition between periods of high insulin production and UPR-mediated stress recovery. The recovery state is important since proinsulin is a misfolding-prone protein, making its biosynthesis in the endoplasmic reticulum a stressful event. The transition of β-cells between dynamic states is likely controlled at multiple levels and influenced by the microenvironment within the pancreatic islets. Disturbances in the ability of the β-cells to transition between periods of high insulin biosynthesis and UPR-mediated stress recovery may contribute to diabetes development. Diabetes medications that restore the ability of the β-cells to transition between the functional states should be considered.

Keywords: Diabetes; Heterogeneity; Human β-cell; Insulin; Pancreatic islet.

Figure 1

Schematic depicting the workflow of large-scale single cell RNA sequencing of human pancreatic islet cells. Pancreatic islets were obtained from cadaver organ donors. Islets were cultured and subsequently dissociated into single cells, which were then loaded into a single cell instrument for their individual capture and preparation of RNAseq libraries. Sequencing was performed to the appropriate specifications. Single cell sequencing data were processed (demultiplexing, alignment, filtering and UMI counting). Further analysis to assess cell heterogeneity can be performed using a variety of software packages that provide tools to identify cell clusters, cell-type subpopulations and cluster enriched genes among others.

Figure 2

Workflow of human β-cell heterogeneity analysis. Single cell RNA sequencing data from pancreatic islet cells was used to perform single-cell analysis. Here, four different β-cell subpopulations were found with 488 enriched genes in total (enriched genes in all four subpopulations). Each dot represents a cell, with each color depicting the four distinct subpopulations. Other islet cells are shown in gray for reference. Pseudotime analysis was performed encompassing 6,241 β-cells and using the 488 enriched genes originated in the single-cell analysis. Here, the color of each cell emphasizes the distribution of cells from the four subpopulations observed in the single-cell analysis along the pseudotime trajectory , . Adapted from Ref. Xin et al. .

Figure 3

Schematic illustrating the principles of pseudotime cell ordering. (A) Cells display asynchronous activity within distinct functional states. Purifying cells with similar level of activity at distinct functional stages is difficult or impossible. (B) As cells move between functional states they undergo transcriptional re-configuration (some genes being silenced, others activated). Through machine learning techniques, the sequence of gene expression changes is determined and the trajectory that best fits the data is constructed. Based on the molecular profile of each human β-cell pseudotime analysis infers the order of gene activation events and assigns each cell a value of progress within the trajectory.

Figure 4

Pseudotime analysis shows dynamic states of INS expression and stress recovery. (A) An UPR score was calculated using a gene set obtained from IPA Ingenuity. Briefly, the score is the average of scaled UMI of all genes in the gene set. The distribution of the score was calculated by random selection of the genes for the specific gene set with 1,000 iterations. The empirical P value was calculated against the distribution of the score. The score value of each cell was plotted into pseudotime ordering. (B) INS expression pattern is shown in pseudotime ordering. Each dot represents a cell and the color highlights the level of composite score or gene expression. (C) Pseudotime trajectory where each dot represents a cell and the grayscale color highlights the trajectory ranging from 0 to 8.9 . (D) Human β-cells undergoing active insulin biosynthesis and secretion (INS^hiUPR^lo) are likely to become stressed, transitioning to a period of recovery encompassing UPR activation and low INS expression (INS^loUPR^hi). Following recovery, β-cells transition to a state characterized by low INS expression and reduced UPR activation (INS^loUPR^lo), where they are nearly ready to become actively secreting again. Among these states, proliferating β-cells were primarily found in the state of low INS expression and high UPR activation. Adapted from Ref. Xin et al. .