Diabetes Volume 67, September 2018 1783 Pseudotime Ordering of Single Human b-Cells Reveals States of Insulin Production and Unfolded Protein Response Yurong Xin, Giselle Dominguez Gutierrez, Haruka Okamoto, Jinrang Kim, Ann-Hwee Lee, Christina Adler, Min Ni, George D. Yancopoulos, Andrew J. Murphy, and Jesper Gromada Diabetes 2018;67:1783–1794 | https://doi.org/10.2337/db18-0365 Proinsulin is a misfolding-prone protein, making its bio- misfolded protein load. This puts pressure on the endo- synthesis in the endoplasmic reticulum (ER) a stressful plasmic reticulum (ER) and results in accumulation of event. Pancreatic b-cells overcome ER stress by acti- misfolded proinsulin and cellular stress. The large demand vating the unfolded protein response (UPR) and reducing for proinsulin biosynthesis and folding makes b-cells insulin production. This suggests that b-cells transition highly susceptible to ER stress (7–9). between periods of high insulin biosynthesis and UPR- b-Cells are metabolically active, relying on oxidative mediated recovery from cellular stress. We now report phosphorylation for ATP generation (10). This generates ISLET STUDIES b the pseudotime ordering of single -cells from humans reactive oxygen species (ROS) and can result in oxidative without diabetes detected by large-scale RNA sequenc- stress. b-Cells have low antioxidant defense, further in- ing. We identified major states with 1) low UPR and low creasing their susceptibility to stress (11,12). ER stress and insulin gene expression, 2) low UPR and high insulin gene oxidative stress can enhance each other, because protein expression, or 3) high UPR and low insulin gene expres- misfolding results in the production of ROS, and these sion. The latter state was enriched for proliferating cells. Stressed human b-cells do not dedifferentiate and species can perturb the ER redox state and cause damage show little propensity for apoptosis. These data suggest to nascent proteins (13). b that human b-cells transition between states with high To counteract stress conditions, the -cells activate rates of biosynthesis to fulfill the body’s insulin require- a network of signaling pathways termed the unfolded ments to maintain normal blood glucose levels and UPR- protein response (UPR). This is composed of three parallel mediated recovery from ER stress due to high insulin pathways that are initiated by the ER transmembrane production. proteins IRE1, PERK, and ATF6 (14). These regulators trigger a signaling cascade that enhances protein folding activity, reduces ER workload, and promotes clearance of b-Cells of the endocrine pancreas produce large amounts misfolded proteins. Once cellular stress is cleared and of insulin, which is secreted in a finely regulated manner to homeostasis restored, this stress sensor program is deac- maintain blood glucose levels within a narrow range. In- tivated to prevent harmful UPR hyperactivation that could sulin biosynthesis accounts for .10% of total protein otherwise lead to apoptosis (14). production under basal conditions and increases up to b-Cell heterogeneity is well established at the functional 50% in the stimulated state (1,2). Proinsulin is a misfolding- level. Proteome and transcriptome studies confirmed the prone protein because up to 20% of initially synthe- heterogeneous nature of mouse and human b-cells and sized proinsulin fails to reach its mature conformation revealed genes and pathways characterizing these subpop- (3–6). Misfolded proinsulin is refolded or degraded. Under ulations (15). b-Cell heterogeneity likely represents dy- conditions of high insulin demand, proinsulin misfolding namic states rather than stable and distinct subpopulations can exceed the capacity of the b-cells to handle the because functional studies have shown that b-cells transition Regeneron Pharmaceuticals, Tarrytown, NY Y.X. and G.D.G. are co–first authors. Corresponding author: Jesper Gromada, [email protected]. © 2018 by the American Diabetes Association. Readers may use this article as fi Received 29 March 2018 and accepted 9 June 2018. long as the work is properly cited, the use is educational and not for pro t, and the work is not altered. More information is available at http://www.diabetesjournals This article contains Supplementary Data online at http://diabetes .org/content/license. .diabetesjournals.org/lookup/suppl/doi:10.2337/db18-0365/-/DC1. 1784 RNAseq and Human b-Cell States Diabetes Volume 67, September 2018 between periods of activity and rest. The transitions are not Expression Omnibus database using accession number synchronized between b-cells within individual islets (15). GSE114297. In this study, we applied large-scale RNA sequencing Single-Cell Data Analysis (RNAseq) to single human pancreatic islet cells from Cells were removed if the number of detected genes was donors without diabetes. The b-cells were ordered accord- ,500, total number of UMI was ,3,000, or viability score ing to their pseudotime. This was obtained by projecting was .0.2 (16). Viability score was defined by the ratio each cell onto a trajectory, and the ordered sequence of the between the sum of MT-RNR2, MT-ND1, MT-CO1, MT- cells was used to study dynamic changes in gene expres- CO2, MT-ATP8, MT-ATP6, MT-CO3, and MT-CYB expres- sion. This provides a higher resolution view of the gene sion (UMI) and total UMI. A high score indicates low expression landscape due to their dynamic and heteroge- viability. Mclust (R package) was used to assess cell- neous nature and helps understand the complex biolog- cell contamination and identify islet endocrine cell types. ical processes governing b-cell function. We identified DensityMclust function estimated bimodal distribution of distinct states with low UPR and low or high insulin gene GCG, INS, SST, PPY, and GHRL expression, namely, high- expression as well as cells with high UPR activation and expression and low-expression modes. Cells with more low insulin expression. The gene signatures and enriched pathways for each state are described. than one hormone in the high-expression mode were excluded (e.g., GCGhigh and INShigh). Cells with a single- RESEARCH DESIGN AND METHODS hormone in the high-expression mode were identified as + + + + + Human Islets GCG (a), INS (b), SST (d), PPY (PP), and GHRL (e) Islets from 12 donors without diabetes were obtained cells. With the retained single-hormone endocrine and from Prodo Laboratories (Supplementary Table 1). Donor nonendocrine cells, expression data were normalized by information and diabetes status were obtained from the the total UMI and scaled by a factor of 10,000 at cell level. patient’s medical record and, if available, the hemoglobin Seurat package identified cell clusters, cell-type subpop- A1c. Islets were digested at 37°C (10 min) using TrypLE ulations, and cluster-enriched genes. A total of 1,166 Express (Life Technologies). Cells were filtered (30-mm), variable genes were used for the principal component centrifuged, and resuspended (13 PBS containing 0.04% analysis. Cell clusters were identified using FindClusters BSA). Trypan blue staining revealed 91.2 6 3.3% (n = 12) (19 principal components and 0.8 resolution). The first cell viability. two t-Distributed Stochastic Neighbor Embedding di- mensions were used to visualize cell clusters. Enriched RNA Fluorescence In Situ Hybridization genes for each cluster were identified by FindMarkers Dissociated cells were placed on slides using Cytospin and (.25% cell detection; P , 0.05 and log-scale fold change fixed (10% neutral buffered formalin) for 35 min. Islets .0.25). b-Cell subpopulation markers were defined as were fixed in the same way, embedded in paraffin, and enriched genes in one subpopulation over the rest of cut (6-mm sections). Cells and islet sections were permea- the subpopulations, as described above. Enriched endo- bilized and hybridized with mRNA probes (INS and DDIT3, crine genes were obtained by comparing endocrine cells FTL, HSPA5,orSQSTM1), according to the manufacturer’s (a-, b-, d-, PP-, and e-cells) with the nonendocrine cells, instructions (Advanced Cell Diagnostics). A fluorescent kit as described above. was used to amplify the mRNA signal, and fluorescein, Cy3, The alignment analysis of human islet cells between and Cy5 were detected using a microscope slide scanner this study and three previous studies (17–19) was performed (Axio-Scan.Z1; Zeiss). Fluorescence intensities were de- by Seurat using canonical correlation analysis. The union of termined using the HALO image software module for the top 2,000 variable genes of this study and one published RNA fluorescence in situ hybridization (FISH) analysis islet study were used to correlate and integrate data from (Indica Laboratories). the two studies. Common cell types and subpopulations Single-Cell RNAseq and Read Mapping were aligned between the two studies and visualized in two Cells were loaded on a Chromium Single Cell Instrument t-Distributed Stochastic Neighbor Embedding dimensions. (10x Genomics). RNAseq libraries were prepared using Chromium Single Cell 39 Library, Gel Beads & Mutiplex Pseudotime Trajectory Reconstruction Kit (10x Genomics). Sequencing was performed on the b-Cell subpopulation markers were used by monocle v2.4 Illumina NextSeq500 using Read-1 for transcript read (R package) to construct single-cell pseudotime ordering and Read-2 for three indices, I7 index for cell barcode, I5 using the default setting. The b-cell trajectory includes two index for sample index, and unique molecular identifier branch points,