1. Stem Cells and Regenerative Medicine
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Insulin mutations impair beta-cell development in a patient-derived iPSC model of neonatal diabetes

  1. Diego Balboa  Is a corresponding author
  2. Jonna Saarimäki-Vire
  3. Daniel Borshagovski
  4. Mantas Survila
  5. Päivi Lindholm
  6. Emilia Galli
  7. Solja Eurola
  8. Jarkko Ustinov
  9. Heli Grym
  10. Hanna Huopio
  11. Juha Partanen
  12. Kirmo Wartiovaara
  13. Timo Otonkoski  Is a corresponding author
  1. University of Helsinki, Finland
  2. University of Eastern Finland and Kuopio University Hospital, Finland
  3. Helsinki University Central Hospital, Finland
  4. Children’s Hospital, University of Helsinki and Helsinki University Hospital, Finland
Research Article
Cite this article as: eLife 2018;7:e38519 doi: 10.7554/eLife.38519
7 figures, 1 table, 2 data sets and 3 additional files

Figures

Figure 1 with 3 supplements
Generation of a disease model of neonatal diabetes caused by insulin mutations.

(A) 24 hr glucose sensor curves of a newborn carrying the INS C96R mutation showing deterioration of glycemic control during the first 3 months of life. (B) Proinsulin model depicting the mutated disulphide bridge-forming cysteines. (C) Mutation correction strategy mediated by CRISPR/Cas9 stimulated homology directed repair, resulting in four INS C96R mutation corrected iPSC clones. (D) Flow cytometry for definitive endoderm marker CXCR4 on day 3 of differentiation of INS mutant and corrected cells lines (n = 1–7 independent differentiation experiments per cell line). (E) Flow cytometry for PDX1, NKX6.1 and INS on Stage 7 of differentiation (n = 3–16 independent differentiation experiments). (F) Whole-mount immunostaining for the pancreatic transcription factors PDX1 and NKX6.1 and islet hormones glucagon (GCG) and INS of Stage 7 differentiated islet-like clusters. Scale bars = 100 μm. Data represent mean ± SEM. **p < 0.01, Student’s t test.

https://doi.org/10.7554/eLife.38519.003
Figure 1—figure supplement 1
Clinical history of the people carrying insulin gene mutations.

(A) Clinical characteristics and family pedigree of the people carrying insulin gene mutations C96R and C109Y. NeoDM = Neonatal Diabetes Mellitus. (B) Glucose sensor readings and averages of the newborn carrying the C109Y mutation.

https://doi.org/10.7554/eLife.38519.004
Figure 1—figure supplement 2
iPSC lines characterization and correction of INS C96R mutation.

(A) Table of cell lines generated from people carrying insulin gene mutations. (B) Sequencing of C109Y G to A heterozygous mutation in HEL107.2 cell line. (C) Normal 46XY and 46XX karyotypes of the derived iPSC lines. (D) Immunocytochemistry for pluripotency markers. Scale bars = 500 μm. (E) Immunocytochemistry for markers of different germ layers in embryoid-body differentiated iPSC lines. AFP, alphafetoprotein (endoderm); SMA, smooth muscle actin (mesoderm); TUBB3, tubulin beta-3 (ectoderm). Scale bars = 200 μm. (F) Schematic representation of gRNA targeting the INSC96R mutation and the 70 b donor template used for the CRISPR-SpCas9-stimulated homology directed repair (HDR) correction. (G) Cutting efficiency of gRNA Ins8 and recombination efficiency of ssODNs repair templates complementary and non complementary to gRNA Ins8 in HEK293 cells. (H) Screening of corrected clones. BsrGI restriction of PCR amplicons coming from different HEL71.4, CRISPR-treated, single cell-sorted clones. Recombinant clones A2, F2, F10 and G6 were verified as corrected by Sanger sequencing (Figure 1c). (I) Normal 46XY karyotypes of HEL71.4 corrected clones.

https://doi.org/10.7554/eLife.38519.005
Figure 1—figure supplement 3
Differentiation efficiency of HEL71.4 mutant and corrected cell lines.

(A) Outline of the iPSC to beta cell differentiation protocol. (B) Immunocytochemistry for pancreatic progenitor markers PDX1, NKX6.1, SOX9 and NGN3 and endocrine markers CHGA and INS at the end of Stage 4. (C) Cytometry for NKX6.1 and INS of INS mutant (HEL71.4 and HEL107.2) and INS corrected iPSC lines differentiated to Stage 7. (D) Quantification by cytometry of INS staining intensity in S7 INS+ cells for INS C96R and INS corrected cells. Data represent mean ± SEM, n = 7–9. (E) Quantification of INS staining intensity from S7 immunostainings for INS C96R and INS corrected cells. Data represent mean ± SEM, n = 7–9.

https://doi.org/10.7554/eLife.38519.006
Figure 2 with 4 supplements
Single cell RNA sequencing revealed increased ER-stress and reduced proliferation in INS mutant beta-like cells.

(A) Single cell RNAseq clustering analysis of Stage 7 islet-like aggregates cells derived from both INS C96R and INS corrected iPSC. A total of 1991 post-QC cells mapped to the Baron et al. (2016) dataset were used for clustering. Four distinct clusters were identified: beta-like cells (308 cells), endocrine progenitor cells (236 cells), alpha-like cells (1252 cells) and proliferating alpha-like cells (45 cells). (B) Violin plots showing log-normalized expression of selected marker genes for each cluster. See also Supplementary file 1 – Table 2. (C) Expression of INS in the different cell populations clusters. (D) Volcano plots illustrate the differentially expressed genes between INS C96R and INS corrected cells in beta-like and progenitor clusters. Violin plots show the relative expression of INS, unfolded protein response (UPR) gene HSPA5 (BIP), proliferation and oxidative phosphorylation related genes that are differentially expressed (fold change ≥ 1.3, adjusted p value < 0.05) between INS C96R and INS corrected cells in the beta-like and progenitor clusters. See also Supplementary file 1 – Table 3. (E) Differentiation trajectory inferred from pseudotime analysis of the beta-like and progenitor clusters. (F) Heatmaps show the normalized, smoothed expression of INS, proliferation (red) and ER-stress (yellow) genes that are differentially regulated across pseudotime between INS C96R and INS corrected cells.

https://doi.org/10.7554/eLife.38519.007
Figure 2—source data 1

Single-cell RNA-seq gene count matrices and alignment statistics.

https://doi.org/10.7554/eLife.38519.012
Figure 2—source data 2

HALLMARK_APOPTOSIS and curated C2 gene sets from Broad Institute’s Molecular Signatures Database (MSigDB).

https://doi.org/10.7554/eLife.38519.014
Figure 2—figure supplement 1
Single cell RNA sequencing data analysis strategy.

(A) Steps in the analysis of the scRNAseq data. (B) Clustering refinement by mapping the Stage 7 scRNAseq cell clusters to the published InDrop dataset of human adult islets by Baron et al. (2016). (C) Selected summary of gene sets that are up- and down-regulated between INS C96R and INS corrected cells in beta-like and progenitor clusters. See also Supplementary file 1 – Tables 3-6.

https://doi.org/10.7554/eLife.38519.008
Figure 2—figure supplement 2
Pseudotime analysis of INS C96R vs INS corrected cells.

(A) Pseudotime trajectory inferred from beta-like and progenitor clusters (non-reversed). (B) Heatmap clustering of selected marker genes differentially expressed genes between the two progenitor branches. (C) Violin plots representing the expression of selected markers genes in the two progenitor populations. Dot plots represent the percentage of cells in a given progenitor population expressing a particular marker, and the scaled mean relative expression of the gene. (D) Relative expression of the genes differentially regulated along pseudotime between INS C96R and INS corrected cells, presented as a heatmap in Figure 2F. Graphs represent individual single cell expression levels and the expression trend fitted over pseudotime.

https://doi.org/10.7554/eLife.38519.009
Figure 2—figure supplement 3
Single-cell RNA-seq quality control with mitochondrial and apoptosis count filters.

(A) Distributions of mitochondrial counts per cell in mutant and corrected samples. (B) Distributions of apoptosis gene counts per cell in mutant and corrected samples. Cells with values above the threshold (red vertical line) were filtered out.

https://doi.org/10.7554/eLife.38519.010
Figure 2—figure supplement 4
Cluster composition and robustness.

(A) A tSNE plot showing the original clusters (before merging and refinement) identified by Seurat. (B) The same tSNE plot with cells colored by mutation status. (C) Bar graph showing the proportions of samples within the original clusters. (D) Robustness of clusters. The boxplots show the maximum proportions of co-clustered cells from a given original cluster in 1000 subsampling runs. The proportions are shown also for the null distribution of shuffled cell-cluster pairs.

https://doi.org/10.7554/eLife.38519.011
Figure 3 with 1 supplement
In vitro differentiated INS mutant cells presented increased ER-stress associated with reduced proliferation and insulin content.

(A) Immunohistochemistry for ER-stress markers (BIP, GRP170, MANF) and proliferation marker KI67 along with INS in Stage 7 cells. Scale bars = 100 μm. (B) Quantification of (A). Percentage of Stage 7 insulin positive cells that express BIP, GRP170, MANF and KI67 (n = 3–7 independent differentiation experiments per genotype). (C) qRT-PCR of beta-cell and ER-stress markers (n = 5–6 independent differentiation experiments per genotype). (D) Sensitivity to ER-stress-induced apoptosis of Stage 7 cells. Percentage of insulin positive cells that are labeled by TUNEL assay in control conditions and after treatment with ER-stress inducers brefeldin A (BFA), thapsigargin (TGA) and tunicamycin (TM). (E) Static sequentially stimulated insulin secretion of Stage 7 cells, presented as fractional secretion of total INS content. Low G = 3.3 mM glucose; Hi G = 20 mM glucose; TOL = tolbutamide 100 μM; KCl = 30 mM KCl; FSK = 1 μM forskolin. (n = 8–12 independent stimulations per genotype). (F) Human insulin content in Stage 7 cells. Cell mass normalized by DNA content (n = 8–12 independent stimulations per cell genotype). Data represent mean ± SEM. Student’s t test, *p < 0.05, **p < 0.01.

https://doi.org/10.7554/eLife.38519.015
Figure 3—figure supplement 1
Characterization of in vitro Stage 7 cells and 3 months old grafts.

Related to Figures 3, 4 and 5. (A) Ratio of human proinsulin to insulin content of Stage 7 islet-like aggregates. (B) Human proinsulin content of Stage 7 islet-like aggregates. (C) Ratio of human proinsulin to human insulin secretion in response to maximal stimulation with Low glucose + KCl of Stage 7 islet-like aggregates. (D) Overnight secretion of human MANF by Stage 7 islet-like aggregates. ***p < 0.001, Student’s t test for (B), Student’s t test with Welch’s correction for (C). Data represent mean ± SEM, n = 8–12 independent stimulations per cell genotype. (E) Immunohistochemistry for apoptosis markers TUNEL and CASP3 in INS+ cells. (F) Immunohistochemistry for ER-stress markers BIP, MANF, GRP170 and proinsulin (PROINS) in INS C109Y 3 month old grafts. Scale bars = 100 µm.

https://doi.org/10.7554/eLife.38519.016
Reduced insulin secretion and increased proinsulin accumulation in transplanted INS mutant beta-cells.

(A) Hematoxylin-eosin staining of Stage 7 islet-like cell clusters transplanted under the kidney capsule of NSG mice and retrieved after 3 months. (B) Monthly tracking of INS C96R, INS C109Y and INS corrected grafts functionality by measuring circulating human C-peptide in randomly fed transplanted mice (n = 3–11 independent transplanted animals per cell genotype and time point; Kruskal-Wallis test for 1 and 3 months, Mann-Whitney test for 2, 5 and 6 months). (C–D) Intraperitoneal glucose tolerance test (IPGTT) in mice transplanted for 3 and 6 months. C-peptide levels measured on fasted animals 0 min and 40 min after glucose injection. (n = 5–8 independent transplanted animals per cell genotype and time points; Kruskal-Wallis test for (C), Mann-Whitney test for (D)). (E) Ratio of human proinsulin to human C-peptide in fasted mice (F) and 40 min after glucose injection (G) at 3 and 6 months after transplantation (n = 3–8 independent transplanted animals per cell genotype and time point). (F) Immunohistochemistry for insulin (INS) and proinsulin (PROINS) in 3-months old grafts. Scale bars = 50 μm. (G) Higher magnification of immunohistochemistry for insulin (INS) and proinsulin (PROINS) in 3-months old grafts. Scale bars = 20 μm. (H) Percentage of INS+ cells stained for PROINS in 3 month old grafted beta-like cells (n = 3–5; Kruskal-Wallis test). Data represent mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001.

https://doi.org/10.7554/eLife.38519.017
Figure 5 with 2 supplements
Transplanted INS mutant beta-cells presented increased expression of ER-stress markers.

(A–C) Immunohistochemistry for ER-stress markers BIP, GRP170 and MANF together with INS in 3 months old grafts. Scale bars = 100 μm. (D) Closer magnification of immunohistochemistry for MANF. Scale bars 20 μm. (E) Quantification of (A–C). Percentage of insulin positive cells expressing BIP, GRP170 or MANF in 3 months old grafts (n = 3–5 independent transplanted animals per genotype; Kruskal-Wallis test). (F) Dynamic changes in the percentage of insulin positive cells expressing BIP between Stage 7 and 6 months old grafts (n = 3–6; Student’s t test). Data represent mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001.

https://doi.org/10.7554/eLife.38519.018
Figure 5—figure supplement 1
Increased ER-stress and reduced proliferation in 1 and 6–month grafts.

(A) Immunohistochemistry for endocrine hormones GCG, INS and CHGA, ER-stress marker BIP and proliferation marker KI67 in 1 month old grafts, and its quantification (n = 4–5; n.s. = not significant using Mann-Whitney test; **p < 0.01 using Student’s t test with Welch’s correction). (B) Immunohistochemistry for endocrine hormones GCG, INS and CHGA, ER-stress marker BIP and proliferation marker KI67 in 6 months old grafts, and its quantification. Data represent mean ± SEM, n = 3–4 independent grafts per cell genotype. Scale bars = 100 µm. (C–D) Quantification of BIP and PROINS staining mean fluorescence intensity per individual INS+ cell across genotypes and timepoints. ***p < 0.001, Student’s t test. Data represent mean ± SEM, n = 44–268, a.u. = arbitrary units.

https://doi.org/10.7554/eLife.38519.019
Figure 5—figure supplement 2
Correlation of proliferation markers with ER-stress marker levels in INS+ cells.

(A–C) Quantification of BIP, INS and PROINS staining mean fluorescence intensity per individual proliferating (PCNA+ or KI67+) and non-proliferating INS+ cell across genotypes and timepoints. *p < 0.05, **p < 0.01, ***p < 0.001, Student’s t test. Data represent mean ± SEM, n = 7–268, a.u. = fluorescence intensity arbitrary units.

https://doi.org/10.7554/eLife.38519.020
Altered endocrine cell proportions and reduced PDX1 expression in INS mutant grafts.

(A) Immunohistochemistry for endocrine hormones glucagon (GCG), insulin (INS), chromogranin A (CHGA), C-peptide (CPEP), pancreatic polypeptide (PP), ghrelin (GHRL), somatostatin (SST), transcription factors PDX1 and NKX6.1, and proliferation marker MK67 (KI67) on 3 months old grafts. Scale bars = 100 μm. (B) Quantification of immunohistochemistry presented in (A) for proportions of monohormonal cells in 3 months old grafts. (n = 4–5, Student’s t test for all except for GHRL where Mann-Whitney test was used) (C) Proportions of polyhormonal cells in 3 months old grafts. (D) Quantification of immunohistochemistry presented in (A). Percentage of c-peptide/insulin positive cells expressing PDX1, NKX6.1 or KI67 in 3 months old grafts (n = 5–6 independent transplanted animals per genotype, Student’s t test with Welch’s correction). (E) Dynamic changes in the percentage of insulin positive cells expressing KI67 between Stage 7 and 6 months old grafts (n = 4–6; Student’s t test). (F) Dynamic changes in the percentage of insulin positive cells expressing PCNA between Stage 7 and 6 months old grafts (n = 4–6; Student’s t test). Data represents individual values and mean ± SEM. See also Figure 5—figure supplement 1.

https://doi.org/10.7554/eLife.38519.021
Transplanted INS mutant beta-like cells presented reduced mTORC1 signaling, reduced size and decreased mitochondrial respiratory chain subunits expression.

(A) Immunohistochemistry for the mTORC1 activity marker pS6 and INS in 3 months old grafts. (B) Immunohistochemistry for E-Cadherin (CDH1) and INS to quantify beta-cell size. Scale bars in (A) and (B) = 50 μm. (C) Quantification of (A) and (B) (n = 4–5 independent transplanted animals per genotype; Student’s t test). (D) Immunohistochemistry for mitochondrial proteins cytochrome oxidase subunit 1 (MT-CO1) and 2 (MT-CO2), transporter of the outer membrane 20 (TOM20) and INS in 3 months old grafts. Quantification of the mean fluorescence intensity of each immunostaining per individual INS+ cell (n > 100 individual INS+ cells per genotype, from 4 to 5 independent transplanted animals per genotype; Student’s t test). Scale bars = 20 μm. **p < 0.01, ***p < 0.001.

https://doi.org/10.7554/eLife.38519.022

Tables

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional
information
Cell line
(Homo sapiens)
Male
HEL71.4Biomedicum Stem
Cell
Center, University of
Helsinki
Cell line (Homo sapiens)
Male
HEL71.4 - corrected clones A2, F2, F10 and G6Biomedicum Stem
Cell
Center, University of
Helsinki
Cell line
(Homo sapiens)
Female
HEL107.2Biomedicum Stem Cell
Center, University of
Helsinki
Recombinant
DNA reagent
CAG-Cas9-T2A-EGFP-ires-puroDOI: 10.1016/j.celrep.2017.03.055Addgene plasmid # 78311
AntibodyRabbit anti-OCT4Santa Cruz Biotechnology Cat# sc-9081; RRID:AB_2167703ICC; (1:500)
AntibodyMouse anti-TRA1-60Thermo Fisher Scientific
Cat# MA1-023; RRID:AB_2536699
ICC; (1:50)
AntibodyRat anti-SSEA3Millipore Cat# MAB4303; RRID:AB_177628ICC; (1:70)
AntibodyRabbit anti-AFPDako Cat# A0008; RRID:AB_2650473ICC; (1:500)
AntibodyMouse anti-SMASigma-Aldrich Cat# A2547; RRID:AB_476701ICC; (1:400)
AntibodyMouse anti-TUJ1R and D Systems
Cat# MAB1195; RRID:AB_357520
ICC; (1:500)
AntibodyGoat anti-PDX1R and D Systems Cat# AF2419; RRID:AB_355257ICC, IHC; (1:200)
AntibodyMouse anti-NKX6.1DSHB Cat# F55A10; RRID:AB_532378ICC, IHC; (1:200)
AntibodyRabbit anti-SOX9Millipore Cat# AB5535; RRID:AB_2239761ICC; (1:500)
AntibodySheep anti-NEUROG3R and D Systems Cat# AF3444; RRID:AB_2149527ICC; (1:500)
AntibodyGuinea pig anti-INSDako Cat# A0564; RRID:AB_10013624ICC, IHC; (1:1000)
AntibodyRabbit anti-C-peptideCell Signaling Technology Cat# 4593S;
RRID:AB_10691857
IHC; (1:150)
AntibodyMouse anti-ProINSDSHB Cat# GS-9A8;
RRID:AB_532383
IHC; (1:200)
AntibodyMouse anti-GCGSigma-Aldrich
Cat# G2654;
RRID:AB_259852
IHC; (1:1000)
AntibodyRabbit anti-CHGADako Cat# A0564IHC; (1:500)
AntibodyRabbit anti-SSTDako Cat# A0566; RRID:AB_10013726IHC; (1:1000)
AntibodyGoat anti-PPYSigma-Aldrich Cat# SAB2500747; RRID:AB_10611538IHC; (1:1000)
AntibodyGoat anti-GHRLSanta Cruz Biotechnology Cat# sc-10368; RRID:AB_2232479IHC; (1:300)
AntibodyRabbit
anti-KI67
Leica Microsystems Cat# NCL-Ki67p; RRID:AB_442102IHC; (1:500)
AntibodyMouse anti-PCNAThermo Fisher Scientific Cat# MA5-11358; RRID:AB_10982348IHC; (1:200)
AntibodyRabbit anti-BIPCell Signaling Technology Cat# 3177S; RRID:AB_2119845IHC; (1:250)
AntibodyRabbit anti-GRP170Abcam Cat# ab124884; RRID:AB_10973544IHC; (1:200)
AntibodyGoat anti-MANFSanta Cruz Biotechnology Cat# sc-34560; RRID:AB_670934IHC; (1:300)
AntibodyMouse anti-CDH1
(E-Cadherin)
BD Biosciences Cat# 610181; RRID:AB_397580IHC; (1:500)
AntibodyRabbit anti-pS6Cell Signaling Technology Cat# 4858; RRID:AB_916156IHC; (1:400)
AntibodyMouse anti-MT-CO1Abcam Cat# ab14705; RRID:AB_2084810IHC; (1:200)
AntibodyRabbit anti-MT-CO2Abcam Cat# ab79393; RRID:AB_1603751IHC; (1:100)
AntibodyRabbit anti-TOM20Santa Cruz Biotechnology Cat# sc-11415; RRID:AB_2207533IHC; (1:250)
AntibodyRabbit anti-Caspase3Cell Signaling Technology Cat# 9664; RRID:AB_2070042IHC; (1:250)
AntibodyMouse Anti-CD184 (CXCR4) Monoclonal Antibody, Phycoerythrin Conjugated,
Clone 12G5
BD Biosciences Cat# 555974; RRID:AB_396267FC; (1:1)
AntibodyMouse IgG2a, kappa Isotype Control, Phycoerythrin
Conjugated, Clone G155-178 antibody
BD Biosciences Cat# 563023FC; (1:1)
AntibodyInsulin (C27C9) Rabbit Antibody (Alexa Fluor 647 Conjugate)Cell Signaling Technology Cat# 9008; RRID:AB_2687822FC; (1:80)
AntibodyRabbit IgG Isotype Control (Alexa Fluor 647 Conjugate)
antibody
Cell Signaling Technology Cat# 3452S; RRID:AB_10695811FC; (1:40)
AntibodyMouse Anti-NKX6.1
Phycoerythrin Conjugated
BD Biosciences Cat# 555574FC; (1:40)
AntibodyMouse IgG1, kappa Isotype Control, Phycoerythrin Conjugated, Clone MOPC-21 antibodyBD Biosciences Cat# 555749; RRID:AB_396091FC; (1:40)
AntibodyMouse Anti-NKX6-1 Alexa Fluor 647 ConjugatedBD Biosciences Cat# 563338FC; (1:40)
AntibodyMouse IgG1 kappa isotype control
Alexa 647 Conjugated
BD Biosciences Cat# 557714; RRID:AB_396823FC; (1:40)
AntibodyMouse Anti-PDX1 Phycoerythrin ConjugatedBD Biosciences Cat# 562161; RRID:AB_10893589FC; (1:40)
AntibodyDonkey anti-Rabbit IgG (H + L) Highly Cross-Adsorbed
Secondary Antibody, Alexa Fluor 350
Thermo Fisher Scientific Cat# A10039; RRID:AB_2534015IHC; (1:500)
AntibodyDonkey anti-Mouse IgG (H + L) Highly
Cross-Adsorbed Secondary Antibody,
Alexa Fluor 350
Thermo Fisher Scientific Cat# A10035; RRID:AB_2534011IHC; (1:500)
AntibodyDonkey anti-Rabbit IgG (H + L) Highly Cross-Adsorbed Secondary Antibody,
Alexa Fluor 488
Thermo Fisher Scientific Cat# A-21206; RRID:AB_2535792ICC, IHC; (1:500)
AntibodyDonkey anti-Mouse IgG (H + L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 488Thermo Fisher Scientific Cat# A-21202; RRID:AB_141607ICC, IHC; (1:500)
AntibodyGoat anti-Guinea Pig IgG (H + L)
Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 594
Thermo Fisher Scientific Cat# A-11076; RRID:AB_2534120ICC, IHC; (1:500)
AntibodyDonkey anti-Sheep IgG (H + L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 594Thermo Fisher Scientific Cat# A-11016; RRID:AB_2534083ICC, IHC; (1:500)
AntibodyDonkey anti-Goat IgG (H + L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 594Thermo Fisher Scientific Cat# A-11058; RRID:AB_2534105ICC, IHC; (1:500)
AntibodyDonkey anti-Goat IgG (H + L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 488Thermo Fisher Scientific Cat# A-11055; RRID:AB_2534102ICC, IHC; (1:500)
AntibodyDonkey anti-Mouse IgG (H + L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 594Thermo Fisher Scientific Cat# A-21203; RRID:AB_2535789ICC, IHC; (1:500)
AntibodyDonkey anti-Rabbit IgG (H + L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 594Thermo Fisher Scientific Cat# A-21207; RRID:AB_141637ICC, IHC; (1:500)
AntibodyGoat anti-Rat IgM Heavy Chain Cross-Adsorbed Secondary Antibody, Alexa Fluor 488Thermo Fisher Scientific Cat# A-21212; RRID:AB_2535798ICC, IHC; (1:500)
AntibodyDonkey anti-Mouse IgG (H + L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 594Thermo Fisher Scientific Cat# A-21203; RRID:AB_2535789ICC, IHC; (1:500)
AntibodyDonkey anti-Goat IgG (H + L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 594Thermo Fisher Scientific Cat# A-11058; RRID:AB_2534105ICC, IHC; (1:500)
Sequence-based reagentCYCLOG qRT-PCR primer pairNM_004792Fw: TCTTGTCAATGGCCAACAGAG; Rv: GCCCATCTAAATGAGGAGTTG;84 bp
Sequence-based reagentPDX1 qRT-PCR primer pairNM_004792Fw: TCTTGTCAATGGCCAACAGAG; Rv: GCCCATCTAAATGAGGAGTTG; 84 bp
Sequence-based reagentNKX6.1
qRT-PCR primer pair
NM_000209.3Fw: AAGTCTACCAAAGCTCACGCG; Rv: CGTAGGCGCCGCCTGC; 52 bp
Sequence-based reagentCHGA
qRT-PCR primer pair
NM_001275.3Fw: AACCGCAGACCAGAGGACCA; Rv: GTCTCAGCCCCGCCGTAGT;102 bp
Sequence-based reagentINS qRT-PCR primer pairNM_020999Fw: GACGACGCGAAGCTCACCAA; Rv: TACAAGCTGTGGTCCGCTAT; 98 bp
Sequence-based reagentBIP (HSPA5) qRT-PCR primer pairNM_005347.4Fw: TGGCTGGAAAGCCACCAAGATGCT; Rv: GGGGGAGGGCCTGCACTTCCAT; 116 bp
Sequence-based reagentsXBP1 qRT-PCR primer pairNM_001079539.1Fw: CTGCTGAGTCCGCAGCAGGTGCA; Rv: GGTCCAAGTTGTCCAGAATGC; 129 bp
Sequence-based reagentCHOP (DDIT3) qRT-PCR primer pairNM_001195053.1Fw: GCACCTCCCAGAGCCCTCACTC; Rv: CCCGGGCTGGGGAATGACCA;120 bp
Sequence-based reagentATF3 qRT-PCR primer pairNM_001206488.2Fw: AGAAAGAGTCGGAGAAGC; Rv: TGAAGGTTGAGCATGTATATC; 103 bp
Sequence-based reagentATF4 qRT-PCR primer pairNM_001675.2Fw: AAGGCGGGCTCCTCCGAATGG; Rv: CAATCTGTCCCGGAGAAGGCATCC;89 bp
Sequence-based reagentATF6 qRT-PCR primer pairNM_001675.2Fw: ACCTGCTGTTACCAGCTACCACCCA; Rv: GCATCATCACTTCGTAGTCCTGCCC;120 bp
Sequence-based reagentMANF
qRT-PCR primer pair
NM_006010.4Fw: GGCGACTGCGAAGTTTGTAT; Rv: TTGCTTCCCGGCAGAACTTT; 121 bp
Sequence-based reagentGRP170 (HYOU1) qRT-PCR primer pairNM_001130991.2Fw: GTCCAAGGGCATCAAGGCTC; Rv: TTCTGCGCTGTCCTCTACCA;
103 bp

Data availability

Single cell RNA sequencing raw data was deposited in GEO under GSE115257 Source data for single cell RNA sequencing as well as code scripts for analysis have been provided.

The following data sets were generated
  1. 1
    NCBI Gene Expression Omnibus
    1. D Balboa
    2. D Borshagovski
    3. M Survila
    (2018)
    ID GSE115257. The raw single-cell RNA sequencing data used in the study.
The following previously published data sets were used
  1. 1
    NCBI Gene Expression Omnibus
    1. A Veres
    2. M Baron
    (2016)
    ID GSE84133. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure.

Additional files

Supplementary file 1

Table 1: Predicted Ins8 gRNA off-target sites assessed for mutations by Sanger sequencing. Table 2: Single-cell RNA-seq analysis cluster marker genes. Table 3: Differentially expressed genes in beta-like cluster between INS C96R vs INS corrected cells. Table 4: Differentially expressed genes in progenitor cluster between INS C96R vs INS corrected cells.Table 5: Gene Set Enrichment Analysis of INS C96R vs INS corrected cells. Table 6: Gene Ontology Analysis of INS C96R vs INS corrected cells. Table 7: Differentially expressed genes between pseudotime analysis progenitor branches. Table 8: Differentially expressed genes along pseudotime between INS C96R vs INS corrected cells. Table 9: Single-cell RNA-seq reads and quality control statistics.

https://doi.org/10.7554/eLife.38519.023
Source code 1

Python and R scripts used in the analysis of the single-cell data in this manuscript.

https://doi.org/10.7554/eLife.38519.024
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https://doi.org/10.7554/eLife.38519.025

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