Pseudotemporal Ordering of Single Cells Reveals Metabolic Control of Postnatal β Cell Proliferation

Abstract

Pancreatic β cell mass for appropriate blood glucose control is established during early postnatal life. β cell proliferative capacity declines postnatally, but the extrinsic cues and intracellular signals that cause this decline remain unknown. To obtain a high-resolution map of β cell transcriptome dynamics after birth, we generated single-cell RNA-seq data of β cells from multiple postnatal time points and ordered cells based on transcriptional similarity using a new analytical tool. This analysis captured signatures of immature, proliferative β cells and established high expression of amino acid metabolic, mitochondrial, and Srf/Jun/Fos transcription factor genes as their hallmark feature. Experimental validation revealed high metabolic activity in immature β cells and a role for reactive oxygen species and Srf/Jun/Fos transcription factors in driving postnatal β cell proliferation and mass expansion. Our work provides the first high-resolution molecular characterization of state changes in postnatal β cells and paves the way for the identification of novel therapeutic targets to stimulate β cell regeneration.

Keywords: Srf; amino acid metabolism; beta cell; catalase; mitochondrial; oxidative phosphorylation; proliferation; reactive oxygen species; single-cell RNA-seq; transcription factor.

Null distribution of Roughness obtained with random ordering

Red and green arrows depict the Roughness values obtained by the 1D Pseudotime Scale and DeLorean, respectively.

Cell association to different branches detected by Wishbone

tSNE map: cells belonging to the main trajectory are depicted in blue, cells deviating into two different branches are shown in green and red.

Expression values of Ins2, Atf3 and Srf scaled in a (0-1) range and displayed across the cell trajectory identified by Wishbone

Following a bifurcation point, two cell trajectories belonging to different branches are depicted with dotted and dashed lines.

Projection of external data on the Pseudotime Scale

Cells from 3-month-old mice (P90) and their corresponding median projection are depicted in pink.

Figure 1. Single-cell RNA-sequencing of beta-cells during postnatal development

A. Experimental overview. B. Correlation between average transcript levels of all genes detected in single cells and bulk cells. For single cells, gene expression was averaged across individual cells collected at different time points. Points are colored by postnatal (P) day collected (P1, red; P7, green; P14, blue; P21, orange; and P28, black). The same color-coding for groups was used across all figures. Pearson correlation coefficients r are given. C. Expression analysis of select genes showing variability in gene expression among single cells of the same age. Each point represents the gene expression level of an individual cell. Black bars represent the average of pooled single cells. D. Pairwise correlation of single-cell gene expression showing biological variation among single cells of the same age. The distribution of the pairwise Pearson’s correlation coefficients between single cells is plotted. E. Multidimensional scaling of single cell transcriptomes. The distance between any two cells reflects the similarity of their expression profiles. See also Figure S1 and Table S1.

Figure 2. 1D-PCA orders beta-cells by maturation progression

A. Workflow to generate a 1D-PCA-based pseudotemporal trajectory. Raw gene expression data was normalized by SVA-seq and processed using Median Absolute Deviation (MAD) to obtain the most variant genes. Cells were placed along a 1D trajectory that represents each cell’s likely placement along a continuum of maturation. B. Pseudotime ordering of single cells using 1D-PCA. Each data point represents a single cell colored by age collected. Median placement of cells from each age is marked on the pseudotime axis (black arrow). C. Samples were assigned to five groups based on time collected (left) or pseudotime ordering (right, pseudo-binned time). Each pseudo-binned time-ordered group contained an equal number of cells to that of the corresponding time-ordered point. D. Expression profiles of selected genes ordered by time collected (top panels), pseudo-binned time (middle panels), or pseudotime (bottom panels). See also Figure S2; S3 and Table S2, S3.

Figure 3. Postnatal beta-cell development is associated with expression changes of genes regulating amino acid uptake and metabolism, mitochondrial respiration, and ROS production

A. Schematic of workflow. To identify genes regulated during pseudotime, continuous GSEA was performed on annotated gene sets and de novo gene sets obtained by hierarchical clustering. B. Molecular pathways regulated in pseudotime from annotated gene sets. C. Heatmap showing average transcript expression of all genes within de novo gene sets showing significant correlation with pseudotime ordering (C1-C9). Number of genes in each set is shown on the right. D. Heatmaps for each de novo gene set showing expression of selected genes involved in cell proliferation (red), insulin secretion (green), regulation of ROS (black), mitochondrial function (orange), immediate early genes (purple), and amino acid metabolism (blue) with pseudotime. See also Figure S4 and Table S4.

Figure 4. Amino acid supplementation increases beta-cell proliferation

A. Amino acid transporter and metabolism genes downregulated with pseudotime with Pearson correlation coefficients. B. Heatmap showing Pearson correlation of gene expression in all 387 beta-cells comparing proliferation genes with genes encoding amino acid transporters and metabolizing enzymes downregulated with pseudotime. Proliferation genes are depicted in rows and amino acid transporters and metabolizing enzymes in columns. C. Plasma concentration of individual amino acids in mice at P1 (blue) and P28 (red). Data shown as mean ± SEM (n= 4 mice per group). D, E. Percentage of EdU⁺ beta-cells (D) and non-beta-cells (E) in islets from mice at P28 supplemented with nucleotides or amino acids. F. Quantitative RT-PCR analysis of proliferation genes after supplementation with serine, tyrosine or nucleotides. mRNA levels in control islets were set as 1. G. Glutamine uptake in mice at P1 (blue) and P28 (red). H. Schematic summarizing the observed gene expression changes during beta-cell maturation and effects on proliferation. In E-G, data are shown as mean ± SEM of three independent experiments. TCA, tricarboxylic acid cycle; Gls, glutaminase; Slc, solute carrier. * P < 0.05, ** P < 0.01, *** P < 0.001.

Figure 5. Mitochondrial ROS production promotes beta-cell proliferation

A. FACS analysis of TMRM fluorescence intensity. Blue: postnatal day (P) 1 without FCCP; Green: P1 with FCCP; Red: P28 without FCCP; Purple: P28 with FCCP. Mitochondrial TMRE/MitoTracker Green uptake ratio is shown on the right. B. Schematic of pathways regulating ROS clearance by antioxidant enzymes. C. Downregulated genes with pseudotime involved in ROS regulation with their Pearson correlation coefficient. D. FACS analysis of MitosoxRed fluorescence intensity at P1 (blue) and P28 (red). Mitochondrial MitosoxRed/MitoTracker Green ratios are shown on the right. E. Schematic of alleles in RIP-Cre;mCAT;R26^YFP mice (mCAT mice). Red triangles indicate loxP sites. F, G. Representative immunofluorescence staining for insulin (green), BrdU (red), and DAPI (blue) (F) and quantification of the percentage of beta-cells expressing BrdU (G) in 6-week-old control (RIP-Cre) and mCAT mice. White arrows indicate Ins⁺Ki67⁺ cells in (F). H. Quantification of the beta-cell area relative to total pancreatic area in 6-week-old mCAT and control mice. Data shown as mean ± SEM of three independent experiments (A,D) or three mice per group (G,H). Scale bar= 20 μm. SOD, superoxide dismutase; GR, glutamate receptor; GSH, glutathione; GSSG, glutathione disulfide; GPX, glutathione peroxidase; CAT, catalase; TRX, thioredoxin; PRX, peroxidase. * P < 0.05, ** P < 0.01. See also Figure S5.

Figure 6. Transcription factors and target genes regulated during postnatal beta-cell development

A. Workflow to identify TFs driving gene expression changes during pseudotime. Cis-regulatory analysis was performed on the most significant genes positively and negatively correlating with pseudotime (P <0.01) followed by annotation of TF binding motifs at their enhancers. B, C. Top TF binding motifs for up-regulated genes (B) and down-regulated genes (C) during pseudotime. Enrichment P values are shown. D. The top 2 up-regulated (red) and top 30 down-regulated (blue) TFs regulated with pseudotime with their Pearson correlation coefficient. E. Network of interactomic connections of TFs regulated during pseudotime with other pseudotemporally regulated genes. Green nodes represent TFs while light blue nodes represent pseudotemporally regulated genes. The size of the nodes is adjusted proportionally to the correlation coefficient of the gene expression level with pseudotime coordinates. Genes in red are associated with cell proliferation. See also Figure S6 and Table S5; S6.

Figure 7. Srf regulates proliferation genes in beta-cells

A. Heatmap showing Pearson correlation of gene expression profiles in all 387 beta-cells comparing proliferation genes with top pseudotemporally-regulated oxidative phosphorylation genes and TFs. Proliferation genes are depicted in red, oxidative phosphorylation genes in orange and TFs in black. B. Overview of RNA-seq analysis of lentiviral Srf overexpression in islets at P28. C. GSEA plots showing enrichment of proliferation genes regulated during pseudotime (left) and genes down-regulated during pseudotime (right) as an effect of Srf overexpression. RNA-seq data are from three independent transduction experiments. D. RPKM values of TFs Fos, Junb and Egr1 (top panel) and proliferation genes Mki67, Pcna and Ccne1 (bottom panel) in RNA-seq data from control and Srf-overexpressing islets. Data shown as mean ± SEM. E. Summary of metabolic regulators and effector TFs driving early neonatal beta-cell proliferation as revealed by reconstructing a pseudotemporal time course of beta-cell maturation, experimental validation, and prior literature. ** P < 0.01, *** P < 0.001. See also Figure S7 and Table S7.