16,205 Matching Annotations
  1. May 2022
    1. DICER1 syndrome encompasses a variety of benign and malignant manifestations including multinodular goitre

      Gene: DICER1 PMCID: PMC8451242 PMID: 34552563 Pathogenic Inheritance Pattern: Autosomal Dominant MultipleDiseaseEntities Disease Entity: DICER1 syndrome, multinodular goitre, cystic nephroma, anaplastic renal sarcoma, Wilms tumour, differentiated thyroid carcinoma, gynandroblastoma, ciliary body medulloepithelioma, embryonal rhabdomyosarcoma, pineoblastoma, pituitary blastoma, kidney cyst, pulmonary cyst, Sertoli-Leydig Cell Tumor. Mutation: Germline MultipleGeneVariants Variant & Clinvar IDs: c.3452_3453del (485534), c.316del (no ClinVar ID), c.171_172insAC (no ClinVar ID), c.3434del (no ClinVar ID), c.988C>T (933007), c.5388dup (no ClinVar ID) Zygosity: None provided. Case: At time of operation, the goitre patients living in Denmark were ages 21, 12, 21, 8, 14, and 16. Four underwent total thyroidectomies, and two underwent partial thyroidectomies. The patient originally aged 21 previously had a kidney cyst at age 14 and a pulmonary cyst at an unknown age. The patient aged 14 at time of partial thyroidectomy later manifested a Sertoli-Leydig Cell Tumor at age 15. All six patients were female. CasePresentingHPO: None provided. CasePreviousTesting: thyroidectomy gnomAD: ENSG00000100697.10, https://gnomad.broadinstitute.org/gene/ENSG00000100697 Mutation Type: Frameshift, Nonsense

    1. DICER1 syndrome is a rare genetic disorder that predisposes individuals to multiple cancer types.

      GeneName: DICER1 PMID: 29762508 HGNCID: N/A Inheritance Pattern: Autosomal dominant Disease Entity: Cancer Mutation: Germline Zygosity: Heterozygosity Variant: Unregistered Family Information: 12% of children with pleuropulmonary blastomas have cystic nephromas Case: 11 year old patient with Hodgkin lymphoma with DICER1 mutation in 2016.

    2. GeneName: DICER1 syndrome (pleuropulmonary blastoma familial tumor susceptibility syndrome), PMID (PubMed ID): 29762508, HGNCID: 17098, Inheritance pattern: autosomal-dominant disease, Disease entity: Plueropulomary Blastoma, Mutation: Somatic, Zygosity: heterozygous, Variant: multiple variants, Family information: NA, Case: young children, CasePresentingHPO: N/A, CasePreviousTesting: N/A, Gnomade #: N/A , Mutation type: deletion

    1. DICER1 syndrome is an autosomal-dominant, pleiotropic, tumor-predisposition disorder arising from pathogenic germline variants in DICER1, which encodes an endoribonuclease integral to processing microRNAs (1).

      Gene Name: DICER1 PMCID: PMC5443331 PMID: 28323992 HGNCID: not found Inheritance Pattern: autosomal dominant Disease Entity: thyroid cancer and familial multinodular doiter Mutation: germline loss-of-function mutation Zygosity: not provided Variant: c.1870C>T; p.Arg624a, c.1870C>T; p.Arg624a, c.1870C>T; p.Arg624a, c.1870C>T; p.Arg624a, c.3726C>A; p.Tyr1242a, c.3675C>G; p.Tyr1225a, c.3675C>G; p.Tyr1225a Family Information: 145 individuals with a DICER1 germline mutation and 135 controls from 48 families Case: family members used; both males and females used and no significant differences seen among sex; ages range from 20-40 with carriers being significantly younger than controls; no significant differences seen among ethnicity but participants located from the US, UK, and Great Britain CasePresentingHPOs: thyroid cancer or MNG diagnosis common to those with a DICER! mutation but with no chemotherapy or radiation treatment yet CasePreviousTesting: tested levels of thyroid-stimulating hormone, thyroxine, thyroxine-binding globulin, and serum albumin; thyroid palpation; thyroid ultrasound; Sanger or next-generation sequencing assays gnomAD: n/a Mutation Type: missense

    1. DICER1 gene is located on chromosome 14q32.13 and plays a crucial role in the control of protein translation; its product, dicer protein, is a ribonuclease (RNase) III endoribonuclease which is essential for the production of microRNAs (miRNA) which are formed by the cleavage of pre-miRNA or double-stranded RNA1–4. RNase III contains two domains, IIIa and IIIb which cleave 3p miRNA and 5p miRNA from the 3′ and 5′ pre-miRNA, respectively. These cleavages require magnesium ions at the interface between the IIIa and IIIb domains and the miRNA; this magnesium dependent catalytic processing occurs at specific residues, E1320, E1564, E1813 and D17092–4. miRNA has a pivotal role in regulating the expression of over 30% of protein-coding genes by its interaction with mRNA5. Given the impact of DICER1 in post-translational events, it is not entirely surprising that functional DICER1 is essential for vertebrate development as evidenced by developmental arrest and death of the embryo when both alleles are lost6,7. Conceptually, DICER1 can be regarded as either a tumor suppressor gene due to loss-of-function mutations or an oncogene due to gain-of-function mutations; it is thought to function as a haploinsufficient tumor suppressor gene with the loss of one allele leading to tumor progression but loss of both alleles having an inhibitory effect for tumor development implying that one intact allele is needed for cell survival8.A study led by one of the authors (DAH) identified germline loss-of-function DICER1 mutations affecting the RNase IIIb domain in affected families with pleuropulmonary blastoma (PPB)9, a rare dysembryonic lung malignancy of childhood which was not the only manifestation of this familial tumor predisposition syndrome; germline and somatic DICER1 mutations were subsequently identified in several other familial associated tumors in several extrapulmonary sites (Table 1). Individuals with germline DICER1 mutations also had non-neoplastic conditions including macrocephaly, renal structural anomalies, retinal abnormalities, dental perturbations, and the GLOW syndrome (global developmental delay, lung cysts, overgrowth and Wilms tumor). These associations encircle the DICER1 tumor predisposition syndrome (Online Mendelian Inheritance in Man numbers 606241, 601200 and 138800), with the estimation that 90% of those affected by this syndrome inherited a germline mutation from one of their parents, with a pattern of autosomal dominant inheritance10.
      • Gene Name: DICER1 Syndrome (OMIM 606241, 601200)
      • PMID: 34599283
      • Hugo Gene Nomenclature Committee (HGNCID): N/A
      • Inheritance Pattern: Autosomal Dominant
      • Disease Entity: pleuropulmonary blastoma (PPB), Sertoli-Leydig cell tumor, gynandroblastoma, embryonal rhabdomyosarcomas of the cervix and other sites, multinodular goiter, differentiated and poorly differentiated thyroid carcinoma, cervical-thyroid teratoma, cystic nephroma-anaplastic sarcoma of kidney, nasal chondromesenchymal hamartoma, intestinal juvenile-like hamartomatous polyp, ciliary body medulloepithelioma, pituitary blastoma, pineoblastoma, primary central nervous system sarcoma, embryonal tumor with multilayered rosettes-like cerebellar tumor, PPB-like peritoneal sarcoma, DICER1-associated presacral malignant teratoid neoplasm and other non-neoplastic associations.
      • Mutation: Germline
      • Zygosity: Heterozygous
      • Variant: has multiple variants associated with it
      • Family Information: Germ cell tumors have been reported in family members
      • Case: identified affected families w/ pleuropulmonary blastoma (PPB): germline and somatic DICER1 mutations also identified in other familial associated tumors
      • CasePreviousTesting: numerous studies confirmed relationship b/t DICER1 variants in carriers and development of range neoplasms and non-neoplastic conditions
    1. DICER1 syndrome is an autosomal-dominant, familial pleiotropic tumor-predisposition disorder1 caused by pathogenic germline variants in DICER1, an essential component of the microRNA processing pathway.

      GeneName: DICER1 PMID: 30715996 HGNCID: N/A Inherritence pattern: autosomal dominant Disease Entity: multiple gene variants mutation: germline Zygosity: N/A Variant: Not found Family Info: N/A

    1. Pathogenic germline variants in DICER1 underlie an autosomal dominant, pleiotropic tumor-predisposition disorder.

      gene name: DICER 1 PMID (PubMed ID): 33570641 HGNCID: n/a Inheritance Pattern: autosomal dominant Disease Entity: benign and malignant tumor mutation Mutation: somatic Zygosity: heterozygous Variant: n/a Family Information: n/a Case: people of all sexes, ages, ethnicities and races participated CasePresentingHPOs: individuals with DICER1-associated tumors or pathogenic germline DICER1 variants were recruited to participate CasePreviousTesting: n/a gnomAD: n/a

    1. DICER1 syndrome is a rare genetic condition predisposing to hereditary cancer and caused by variants in the DICER1 gene.

      GeneName: DICER1 PMID: 33552988 HGNCID: Unavailable Inheritance Pattern: Autosomal Dominant with reduced penetrance Disease Entity: Cystic nephroma, familial pleuropulmonary blastoma (PPB), ovarian Sertoli-Leydig cell tumor (SLCT), cervix embryonal rhabdomyosarcoma, multinodular goiter, Wilms' Tumor, Ciliary body medulloepithelioma, nasal chondromesenchymal hamartoma, differentiated thyroid carcinoma, pituitary blastoma, pineoblastoma, sarcomas of different sites. Mutation: germline mutation Zygosity: heterozygous Variant: ClinVar ID not listed Family Information: No family cases listed Case: No specific case mentioned gnomAD: N/A Mutation Type: Frameshift, Nonsense mutation

    1. Germline pathogenic variation in DICER1 underlies a tumor-predisposition disorder with increased risk for cervical embryonal rhabdomyosarcoma and ovarian sex-cord stromal tumors, particularly Sertoli-Leydig cell tumors.
      • GeneName: DICER1
      • PMID (PubMed ID): 31952842
      • HGNCID: N/A
      • Inheritance Pattern: Autosomal dominant
      • Disease Entity: Ovarian Sertoli-Leydig cell tumors or gynandroblastoma; cervical embryonal rhabdomyosarcoma; pleuropulmonary blastomas; ovarian tumors generally presented with virilization and amenorrhea during adolescence; metachronous ovarian tumors; thyroid disease; increases risk of thyroid cancer during pregnancy
      • Mutation: Germline
      • Zygosity: Heterozygous
      • Variant: No variant
      • Family Information: The pleuropulmonary blastoma locus was mapped to chromosome 14q using linkage analysis in families with multiple cases of pleuropulmonary blastoma, resulting in the identification of heterozygous germline variants in DICER. Sixty-four DICER1-carrier female patients from 32 predominantly non-Hispanic white families were confirmed to have a heterozygous germline DICER1 pathogenic variant and ranged in age from 2–72 years (median: 31 years).
    1. DICER1 variants cause a hereditary cancer predisposition

      -Gene: DICER1 -PMID: 29343557 -Inheritance Pattern: DICER1 is inherited as an autosomal dominant condition with decreased penetrance -Disease Entity: earlier onset disease, multisite disease, 0-2 site disease, cystic lung disease, familial disease, bilateral disease, stage IA/IB, bilateral disease -mutation: germline loss-of-function mutation, missense mutation, Intronic mutations, hotspot mutation, second somatic mutation, truncating mutations, biallelic mutation -zygosity: heterozygosity -Family History: -testing should be considered for those with a family history of DICER1-associated conditions so that appropriate surveillance can be undertaken. -Individuals at 50% risk of a germline pathogenic variant based on family history who do not pursue genetic testing should follow surveillance guidelines as -if they have a DICER1 mutation unless/until genetic testing confirms that they did not inherit the familial mutation When a pulmonary cyst is identified in a young child with a pathogenic germline -DICER1 variant or family history of a DICER1-associated condition, it should be assumed to be Type I PPB until proven otherwise

      Other Information: -Case: Risk for most DICER1-associated neoplasms is highest in early childhood and decreases in adulthood -affected phenotype may simply result from probabilities of generating the characteristic “loss-of-function plus hotspot” two hit typical of a DICER1 syndrome neoplasm. -Caseprevioustesting: presymptomatic testing of a minor child, should be discussed and factored into the decision process, as some individuals may choose, and have the right to choose, not to know their/their child’s genetic status. -gnomAD: n/a

  2. Apr 2022
    1. We previously identified germline loss-of-function DICER1 mutations in a human syndrome defined by the childhood lung neoplasm, pleuropulmonary blastoma (PPB), which arises during lung development.

      GeneName: DICER1 PMCID: PMC4398601, PMID: 25500911 HGNCID: not avaliable InheritancePattern: autosomal dominant DiseaseEthnicity: Pleuropulmonary blastoma Mutation: germline Zygosity: heterozygotes most common Variant: ClinVarID unavaliable FamilyInformation: family disease not mentioned gnomAD: not avaliable MutationType: missense

    1. DICER1 syndrome is an autosomal-dominant, pleiotropic tumor-predisposition disorder

      Gene Name:DICER1 PMID: 30715996 HGNCID: Not on document Inheritance Pattern: Autosomal Dominant Disease Entity: Pleiotropic Tumor-Predisposition Disorder Mutation: Pathogenic Germline Variants Zygosity: Not in document Variant: Not in document Family Information: An individual was found who had family members who were also affected by this mutation. Because of this, those family members were also chosen to participate in this study. Mutation Type: Missense Case: The study was done on more than one individual. Roughly more than half of the individuals were female

    1. The DICER1 syndrome

      Gene: DICER1 PMID: 30672147 HGNCID: n/a Inheritance Pattern: autosomal dominant Disease Entity: Pleuropulmonary Blastoma, Cystic Nephroma, Sertoli-Leydig tumors, Multinodular goiter, thyroid cancer, rhabdomysarcoma, pineoblastoma Mutation: Germline Zygosity: n/a MultipleGeneVariants Variant: p.Gly1824Val, p.Ser1160Tyr, p.Ala1578Thr, p.Leu1469Pro, p.Ser1160Tyr, p.Ile528Thr, p.Pro1836Leu, p.Glu904*, p.Tyr1835Ser, p.Ile528Thr, p.Arg1342His, p.Phe1650Cys, p.Trp1481Arg, p.Arg201His, p.Asp1390His, p.Trp1397Arg, p.Ala1578Thr <br /> Family Info: n/a gnomAD: n/a

    2. The DICER1 syndrome is an autosomal dominant tumor‐predisposition disorder

      Gene: DICER1 PMID: 30672147 HGNCID: Not found Inheritance Pattern: autosomal dominant Disease Entity: Pleuropulmonary Blastoma, Cystic Nephroma, Sertoli-Leydig tumors, Multinodular goiter, thyroid cancer, rhabdomysarcoma, pineoblastoma Mutation: germline Zygosity: not stated Variant: p.Asp1810Asn, c.4206+1G>C [splice],p.Gly1824Val, p.Ser1160Tyr, p.Ala1578Thr, p.Leu1469Pro, p.Ser1160Tyr, p.Ile528Thr, p.Pro1836Leu, p.Glu904, p.Tyr1835Ser, p.Ile528Thr, p.Arg1342His, p.Phe1650Cys, p.Trp1481Arg, p.Arg201His, p.Asp1390His, p.Asp1810Asn c.4206+1G>C, p.Trp1397Arg, p.Ala1578Thr Family Info: none provided

    1. DICER1 syndrome is a rare genetic condition predisposing to hereditary cancer and caused by variants in the DICER1 gene.

      Gene Name: DICER1 PMID:33552988 HGNCID: Unavailable Inheritance Pattern:Autosomal Dominant Disease Entity: familial pleuropulmonary blastoma (PPB),cystic nephroma, ovarian Sertoli-Leydig cell tumor (SLCT), multinodular goiter, cervix embryonal rhabdomyosarcoma, Wilms’ tumor, nasal chondromesenchymal hamartoma, ciliary body medulloepithelioma, differentiated thyroid carcinoma, pituitary blastoma, pineoblastoma, and sarcomas of different sites. Mutation: Nonsense, Frameshift<br /> Zygosity: Heterosygosity Variant:No ClinVar ID present Family Information:no diseases mentioned in family Case: no specified case in this article gnomAD: n/a Mutation type: Nonsense. frameshift

    2. DICER1 syndrome is a cancer-predisposing disorder caused by pathogenic variants in the DICER1 gene

      Gene: DICER1 PMCID: PMC7859642 PMID: 33552988 HGNCID: Unavailable Inheritance Pattern: Autosomal Dominant Disease Entity: familial pleuropulmonary blastoma (PPB),cystic nephroma, ovarian Sertoli-Leydig cell tumor (SLCT), multinodular goiter, cervix embryonal rhabdomyosarcoma, Wilms’ tumor, nasal chondromesenchymal hamartoma, ciliary body medulloepithelioma, differentiated thyroid carcinoma, pituitary blastoma, pineoblastoma, and sarcomas of different sites. Mutation: Germline Zygosity: Heterozygosity most common Variant: ClinVarID not available Family Information: No mention of disease within family Case: No case specified GnomAD: N/A Mutation Type: Nonsense or Frameshift

    1. DICER1 syndrome is a rare genetic disorder that predisposes individuals to multiple cancer

      GeneName: DICER1 PMID: 29762508 HGNCID: Can't find Inheritance: Autosomal Dominant Disease Entities: Endocrine and Reproductive Tumors Mutation: Somatic and germline Zygosity: Heterozygous Mutant: Can't find Family: Can't find

    2. DICER1 syndrome is a rare genetic disorder that predisposes individuals to multiple cancer types

      GeneName: DICER1 PMID (PubMed ID): 29762508 HGNCID: Unavailable Inheritance Pattern: Autosomal Dominant Disease Entity: cancer, rare genetic disorder, pleuroplumonary blastomas, cystic nephroma, rhabdomyosarcoma, multinodular goiter, thyroid cancer, overian Sertoli-Leydig cell tumors, and other meoplasias Mutations: Germline mutations or Somatic mutations Zygosity: Heterozygosity Variant: unregistered Family Information: Cystic nephromas has been reported in approximately 12% of children with pleuripulmonary blastomas or those with a family member with cystic nephroma. Patient with two DICER1 mutations and several of his family members shared these mutations. All members developed a least one type of tumor with differing origins. The patient was an 11-year old boy with a rare Hodgkin lymphoma with DICER1 in 2016. (c.5299delC and c.4616C>T).

    3. DICER1 syndrome is a rare genetic disorder that predisposes individuals to multiple cancer types.

      GeneName = DICER1 PMID = 29762508 HGNCID = Can't find Inheritance pattern = Autosomal dominant Disease entity = cancer, multinodular goiter, pleuropulmonary blastoma, cystic nephroma, ovarian Sertoli-Leydig cell tumor Mutation = germline OR somatic Zygosity = causes loss of heterozygosity Variant = unregistered Family = those with the mutation almost always passed it on

    1. DICER1 Syndrome

      GeneName: DICER1 PMID: 28323992 PMCID: PMC5443331 *No HGNCID found Inheritance pattern: autosomal-dominant Disease Entity: multinodular goiter and thyroid cancer Mutation: Germline Zygosity: not listed Variant: c.3726C>A; p.Tyr1242a, c.3675C>G; p.Tyr1225a Family Information: 145 individuals with DICER1 germline mutations from 48 family controls (135 individuals) that lacked the DICER1 mutation Case: male and female carriers as well as family members were studied. Ages: 20, 30, and 40 for both populations (DICER1 carriers were significantly younger than controls}. Population from Great Britain, UK, and USA (no significant difference between race, ethnicity, or sex found). CasePresentingHPOs: no previous therapeutic radiation or chemotherapy. Thyroid cancer or MNG diagnoses were likely reported with the DICER1 mutation CasePreviousTesting: Sequencing performed with Sanger or next-generation sequencing assays. DICER1 carriers underwent testing to obtain thyroid-stimulating hormone, thyroxine, thyroxine-binding globulin, and serum albumin levels as well as medical history and physical examinations (+thyroid palpation). Participants were also given thyroid US examinations. gnomAD: n/a Mutation Type: missense

    1. DICER1 syndrome (OMIM 606241, 601200)

      Gene Name: OMIN PMID: 34599283 Autosomal Dominant Gynandroblastoma cERMS Pediatric Paratesticular Sarcomas nephrolithiasis or nephrocalcinonsis Cystic Nephroma Anaplastic Sarcoma of Kidney Wilms tumor Cystic Hepatic Neoplasm Hamartomatous polyps

      Germline mutation heterozygosity Multiple Gene Variants There is usually a family history or a carrier for the mutation it rarely occurs out of nowhere.

    1. The DICER1 syndrome is an autosomal dominant tumor‐predisposi-tion disorder associated with pleuropulmonary blastoma, a rare pediatric lung cancer

      GeneName:DICER1 PMID (PubMed ID): PMCID: PMC6418698 PMID: 30672147 HGNCID: NOT LISTED<br /> Inheritance Pattern: Autosomal Dominant Disease Entity: Cancer; benign and malignant tumors including pleuropulmonary blastoma, cystic nephroma, Sertoli-Leydig cell tumors, multinodular goiter, Thryoid cancer, rhabdomyosarcoma, and pineoblastoma. Mutation: Somatic missense variation Mutation type: missense Zygosity: None stated Variant: unregistered…. Family Information: Characterize germline variants in familial early-onset clorectal cancer patients; The observation of germline DICER1 variation with uterine corpus endometrial carcinoma merits additional investigation. CasePresentingHPOs: uterine and rectal cancers in germline mutation

  3. Feb 2022
    1. https://www.zotero.org/save?type=

      URL for adding URL, ISBN, DOI, PMID, or arXiv IDs to one's Zotero account.

      I've created a mobile shortcut using the URL Forwarder app to accomplish this with a share functionality after highlighting an ISBN.

      Might also try using https://play.google.com/store/apps/details?id=com.srowen.bs.android&hl=en with the added custom search query custom search URL https://www.zotero.org/save?q=%s to see if that might work as well. This should allow using a scanner to get ISBN barcodes into the system as well. Useful for browsing at the bookstore.

      I should also create a javascript bookmarklet for this pattern as well.

      See also: - https://forums.zotero.org/discussion/77178/barcode-scanner - https://forums.zotero.org/discussion/76471/scanning-isbn-barcode-to-input-books-to-zotero-library

      Alternate URL paths for this: - https://www.zotero.org/save?type=isbn - https://www.zotero.org/save?q=

  4. Jun 2021
  5. Mar 2021
  6. Feb 2021
  7. Jan 2021
  8. Sep 2020
  9. May 2020
    1. RNA analysesFrozen tissue (optimal cutting temperature [OCT]) sections were obtained from the nPOD [18] and DiViD tissue collections. Tissue slides were fixed and laser-capture of islets conducted as previously described [19]. All islets in two to five sections of tissue from each donor were captured and pooled and RNA extracted using the Arcturus PicoPure RNA Isolation Kit (Applied Biosystems, Grand Island, NY, USA). Quality and quantity of RNA was determined on a Bioanalyzer 2100 instrument (Agilent Technologies, Santa Clara, CA, USA). Samples with sufficient quantity and quality of RNA were then subjected to gene expression analysis using Affymetrix expression arrays (GeneChip Human Gene 2.0 ST) and scaled normalised gene expression values produced as previously described [20]. The normalised expression data for 70 genes of interest were then subjected to analysis as described below.

      RNA Analysis

      (GeneChip HUman Gene 2.0 ST)

    1. MicroRNA expression profiles and target-gene predictionTwo-hundred Treg and Tconv cells were purified by fluorescence-activated cell sorting (Supplementary Figure 1) and microRNAs were analyzed with the human Megaplex RT-stem-loop microRNA Pool A v2.1 (Life Technologies, CA-USA). Expression of hsa-miR-125a-5p (here named miR-125a-5p) was also tested by TaqMan microRNA single assay qPCR on preamplified products using the following assays: hsa-miR-125a-5p (ID002198), snRNAU6 (ID001973), snRNAU44 (ID001094), and snRNAU48 (ID1006) (Life Technologies). Expression levels of each microRNA are reported as Cycles to Threshold (Ct) of PCR and Ct are normalized (dCt) using small RNAs endogenous controls (RNU6A, RNU48, RNU44). MicroRNAs were considered differentially expressed when the cutoff fold change was <0.5 or >2.0 and when the cutoff p-value was <0.05 using the 2-tailed “Student t-test” on normally distributed dCt values. Undetermined values were set to a maximum Ct of 40. Each amplification plot for every microRNA was manually checked to avoid false positive Ct and only microRNAs with a Ct <35 and with adequate efficiency amplification plots in all sample replicates were taken into consideration for 2−dCT calculations and subsequent statistical analyses. Targetscan6.2 and PICTAR algorithms were used to identify potential microRNA target genes.

      MicroRNA expression profiling

    1.  Human Sample QuantificationsFigures 3A and 3B for quantifications of immunostaining on human donor pancreas sections, 7-10 islets were scored for each donor for each marker: non-diabetic (n = 6), autoantibody-positive (n = 6) and autoantibody-positive T1D (n = 6) and data are represented as mean ± SD of the donors (Figures 3A and 3B) or total islets scored in each group (Figure S3D). Each dot represents a donor for the plots in Figure 3. For donor information see Figure S3A.Figure S3C for quantifications of SA-βgal+ beta cells 15-20 islets were scored from two pairs of age-matched non-diabetic and T1D donors. Comparisons were made between the groups using unpaired, two-tailed T-test and considered significant at p < 0.05.Figure S3D the same data from Figure 3 is shown, except broken down showing each islet stained in the donor groups for each marker. Each dot represents an islet.General comments on IHC: Stainings for different markers were performed from sections cut from at least two different regions of pancreas (e.g. pancreas head, body, tail).

      Human sample quantification

    2. Immunofluorescence Staining of Pancreas SectionsImmunohistochemistry of FFPE mouse and human pancreas sections was performed as described (Dhawan et al., 2015Dhawan S. Tschen S.I. Zeng C. Guo T. Hebrok M. Matveyenko A. Bhushan A. DNA methylation directs functional maturation of pancreatic β cells.J. Clin. Invest. 2015; 125: 2851-2860Crossref PubMed Scopus (58) Google Scholar). Briefly, five micron tissue sections were rehydrated with xylene and graded ethanol washes, immersed for 10 minutes in 1% peroxide and subjected to heat-mediated antigen retrieval with sodium citrate pH 6.0 for 7.5 minutes at 100% and 14 minutes at 50% power in a 1250W microwave. Sections were cooled to room temperature with tap water and then permeabilized with TBS containing 0.1% Triton-X-100 for 5 minutes. Sections were blocked with 2% normal donkey serum in protein block buffer (Dako) for 15 minutes and then incubated overnight at 4°C with primary antibodies. Primary antibodies used for immunohistochemistry are listed in the Key Resources Table. FITC or Cy3-conjugated secondary antibodies (Jackson Immunoresearch) were used to detect primary antibodies and sections were counterstained with DAPI (Vectashield). Images were taken on a Zeiss Axioscope2 wide-field fluorescence microscope with AxioVision software, image processing for figure preparation was performed on ImageJ.

      IF Staining of pancreas sections

    1. Islet MorphometryIslet morphometry was analyzed with Volocity version 6.1.1 (PerkinElmer) (12,19). Axio Imager (Zeiss) with X-Y stage and Orca-ER digital camera (Hamamatsu) was used to acquire thousands of islet images, with tens of thousands of nuclei analyzed per sample (Supplementary Table 4). All visible islets within one pancreatic section per individual were imaged for insulin with long exposure imaging (≥20× shutter time of standard exposure). Acinar and ductal tissue images were captured as negative controls for insulin staining.

      Islet morphometry

    2. Blinded Study 1: Classifying Insulinlow IsletsVirtually all islets in a pancreatic section were identified by DAPI and imaged. Negative control images (nonislet containing) were inserted into image stacks for subsequent blinded classification. For classification of insulinlow cells, T1D islets were separated into those exhibiting strong, moderate, or no insulinlow (described in detail below). Two examiners blinded to disease duration or phenotype independently classified and quantified insulinlow phenotypes in T1D islets. Islets were defined as containing five or more islet endocrine cells.

      Classifying Insulin-low islets

    3. Blinded Study 2: Calculating Insulinlow Islet AbundanceMost, if not all, T1D islets were imaged for synaptophysin, insulin, and Nkx6.1 for 10 control and 15 T1D pancreatic sections. Control islets were imaged with standard exposure; T1D islets were imaged with long exposure for insulin only. Blinded investigators quantified insulinlow islets as percentage of total synaptophysin-positive islets.

      Classifying Insulin-low abundance

    1. Islet and Blood Vessel Segmentation Islet and blood vessel segmentation was performed in a similar manner as cell segmentation with the following modifications: i) for ilastik pixel classification, images were divided into islet, blood vessels, and “other” (that is non-islet, non-vessel) compartments based on substacks containing informative markers (i.e., SYP, CD99, CD31, CD45, AMY2A, KRT19, and Iridium); ii) in CellProfiler, islets and blood vessels were directly defined as primary objects. Distance to islet rim was measured by combining islet and cell masks, using a custom CellProfiler plugin (https://github.com/BodenmillerGroup/ImcPluginsCP)

      Islet and Blood Vessel Segmentation

    2. Cell Segmentation CellProfiler (Kamentsky et al., 2011) was subsequently used to define cell masks and quantify marker expression. To define cell borders, nuclei were first identified as primary objects based on ilastik probability maps and expanded through the cytoplasm compartment until either a neighboring cell or the background compartment was reached. Cell masks were generated for identification of single cells and used to extract single-cell information (marker abundance, spatial and neighborhood data) from the original images. The results were exported as csv tables for further analysis.

      Cell segmentation of images

    1. in-situ ZnT8186–194 MMr staining on frozen pancreatic sections

      In situ ZnT8 186-194 MMr staining

      from supplement: In-situ staining was performed as described (2). Briefly, unfixed, frozen sections were dried for 2 h, loaded with 1 µg of MMrs overnight at 4°C, washed gently with phosphate-buffered saline and fixed in 2% paraformaldehyde for 10 min. After a further wash, endogenous peroxidase activity was blocked with 0.3% H2O2. Sections were then incubated serially with rabbit anti-phycoerythrin, horseradish peroxidaseconjugated swine anti-rabbit and 3,3'-diaminobenzidine tetrahydrochloride substrate (Thermo Scientific). After a final wash, sections were counterstained with hematoxylin, dehydrated via sequential passages in 95-100% ethanol and xylene, mounted and analyzed using a Nikon Eclipse Ni microscope with NIS-Elements Analysis D software v4.40.)

    1. Multiplex ImmunofluorescenceMultiplex immunofluorescence was conducted using 4 primary antibodies to detect all neuroendocrine cells: secretogranin 3 (SCG3), beta-cells (insulin), vascular endothelium (CD34, CD31), and SMA following methods previously described27 (Table 2). Control stains showed that islet vessels were uniformly labeled by both CD34 and CD31 (Supplemental Fig. 1). Paraffin sections were dewaxed and rehydrated with TBS and with 0.05% TBST. Nonspecific binding was blocked by incubation in 10% goat serum in TBST for 1 hr at room temperature. Sections were incubated in primary antibodies to SCG3 for 1 hr at room temperature followed by tyramine signal amplification (TSA) with Opal 520 (PerkinElmer; Waltham, MA) conjugate followed by anti-CD34 for 1 hr at room temperature and TSA with Opal 670 conjugate. Sections were reblocked before incubation with anti-insulin antibody overnight at 4C, followed by TBST washes and incubation with goat antiguinea pig Alexa 405 (ThermoFisher Scientific; Waltham, MA) for 1 hr at room temperature. Tissues were reblocked and mouse anti-SMA-Cy3 antibody applied overnight at 4C. Sections were mounted with ProLong Gold Antifade (Life Technologies; Grand Island, NY). Additional sections were stained in 4 control donors and 8 donors with type 1 diabetes for SCG3, CD3, glucagon, and insulin and insulitis determined as previously described.27

      Multiplex immunofluorescence

    2. Morphometric AnalysesEndocrine and exocrine vessel morphometric analyses were performed using custom Python scripts created for FIJI software (v2.0; https://fiji.sc).28 Total islet area was identified by thresholding the SCG3+ area and applying morphological filters to acquire a smoothed islet boundary to include all SCG3+, CD34+, and SMA+ areas (Fig. 1). Islets were categorized for insulin immunopositivity (INS+/–) using a background-corrected insulin intensity and visual confirmation for every islet. Islet CD34+ area (%), insulin area (%), and SMA+ area (%) were determined via thresholding of the immunostained area divided by total islet area. Particle analysis was used to determine CD34+ islet vessel density (vessels/μm2 islet area) based on previously described methods for individual vessel counts and area (%).29 The plug-in Geodesic Diameters was used to determine vessel diameters via maximum inscribed circles for each vessel, and average vessel diameter was determined for each islet.30 Similar calculations were performed in the same image field for the peri-islet exocrine microvasculature excluding islets and major vessels and ducts.

      Morphometric analysis

    1. Proliferation AnalysisKi67+ islet endocrine (synaptophysin), α-cell (glucagon), PP, SS, ghrelin, and cytoplasmic Sox9 (Sox9Cyt) proliferation were calculated as % total cells. A subset of high proliferators was quantified as % intra-islet Ki67+ cells for insulin and all other markers. High proliferation was defined as having an islet endocrine cell replication rate >0.71%, corresponding to z score of 0.5.

      Proliferation analysis

    2. ImmunohistochemistryParaffin sections were incubated with primary antisera (Supplementary Table 3), followed by the appropriate secondary antisera conjugated to aminomethylcoumarin (AMCA), Cy2, Cy3, or Cy5 (Jackson ImmunoResearch) and DAPI (Molecular Probes, Eugene, OR) as previously described (1). Primary antisera were as follows: 1:100, ARX (AF7068; R&D Systems), β3 tubulin (NB100-1612; Novus Biologicals), CD3 (PA1-37282; Thermo Fisher Scientific), CD31 (ab28364; Abcam), chromagranin A (ab8204; Abcam), ghrelin (H-031-77; Phoenix Pharmaceuticals), GLUT1 (07-1401; Millipore), ISL1&2 (39.4D5; DSHB), INSM1 (sc-271408; Santa Cruz Biotechnology), NeuN (MAB377; Millipore), Nkx2.2 (ab191077; Abcam), Nkx6.1 (F55A12; DSHB), pancreatic polypeptide (PP) (18-0043; Invitrogen), PCNA (2586S; Cell Signaling Technology), PC1/3 (AB10553; Millipore), Pdx1 (NBP2-38865; Novus Biologicals), phospho-histone H3 (9701S; Cell Signaling Technology), proinsulin (GN-ID4; DSHB), SNAP25 (MAB331; Millipore), somatostatin (SS) (18-0078; Invitrogen), synaptotagmin 1A (ab133856; Abcam), Sox9 (AB5535; Millipore), Sox9 (pS181) (ab59252; Abcam), and synaptophysin (18-0130, Thermo Fisher Scientific, and AB6245, Abcam); 1:250, glucagon (ab8055 and ab10988; Abcam) and Ki67 (550609; BD Biosciences); and 1:1,500, insulin (A0564; Dako).

      IHC

    3. Islet MorphometryIslet endocrine and α-cell morphometry were assessed with Volocity 6.1.1 (PerkinElmer) as previously described (9). Zeiss AxioImager M1 (Carl Zeiss Microscopy) with automated X-Y stage and Orca ER camera (Hamamatsu) acquired images of tens of thousands of individual nuclei/sample (Supplementary Table 4).

      Islet morphometry

    4. TUNELApoptosis analysis was performed in a subset of available control and T1D samples as previously described (1). Total terminal deoxynucleotide TUNEL-positive islet endocrine cells were assessed in >85,000 islet cells/condition. Total TUNEL+ Sox9Cyt cells were assessed in 993 islet cells/condition. In every sample, TUNEL+ pancreatic ducts were imaged to ensure adequate TUNEL staining.

      TUNEL

    1. The residual β-cell mass was found to be 763 mg (methods as previously described by Campbell-Thompson et al. [11])

      Beta cell mass determination

    2. pancreatic islets examined (methods as previously described by Campbell-Thompson et al. [11]) from nine blocks encompassing the pancreas head, body, and tail regions demonstrated CD3+ infiltration (insulitis)

      Insulitis determination

    1. 2.2. In situ hybridizationTo detect IL-1β mRNA expression level, RNAscope® 2.0 High Definition BROWN Assay (ACD, Hayward, CA) was used according to the manufacturer’s instructions (see Supplementary material for more details).

      ISH of IL-1b mRNA

    2. 2.3. Indirect immunofluorescencePancreas sections were subjected to a standard triple indirect immunofluorescence (IF) staining protocol to determine the expression of IL-1β at the protein level and its localization in α and β cells (see Supplementary Materials for more details).

      IF of IL-1b protein

    3. Immunofluorescence (IF) Image analysis was performed with Image Pro Premier software on randomly selected islets. For each islet, the difference of the mean intensity in the positive area (MIP), and the mean intensity in the negative area (MIN) (background) was measured (MIP-MIN). The differences between the groups and within the regions of each section were presented as the mean of (MIP-MIN) ± SD. All techniques, image acquisition and analysis have been validated by histology and microscopy specialists, at LJI which enabled us to rightfully quantify the IL-1β expression level.

      Quantification of IL-1b IF

    4. 2.4. AnalysisIn situ hybridization (ISH) Islets were quantified by Image-pro Premier software (Media Cybernetics, Rockville, MD). For each islet, the percentage of the positive area (or positive staining, defined by Image-pro Premier software) within the islet boundary was assessed and the differences between the groups and within the regions of each section were presented as the mean of analyzed islets (mean±SD).

      Quantification of IL-1b ISH

    1. For CD8 quantification, pancreas sections were stained, and an average of 10–15 images (surface area of 1.215 mm2) from each tissue section were acquired using a Nikon digital DXM1200C camera and Nikon ACT-1C Camera Controller Software unless otherwise indicated. To determine the number of CD8 T cells infiltrating the pancreas, image analysis was performed by using a custom macro developed in MATLAB (The Mathworks, Inc., Natick, MA) and ImageJ (National Institutes of Health). Briefly, islet regions were identified as contiguous areas of insulin or glucagon staining at or above a threshold intensity value. The periphery of the islet was defined using a dilation tool and expanding its perimeter by 100 × 100 pixels (15–20 µm). CD8+ cells were identified as areas of CD8 staining using optimized and identical threshold values for intensity and size for all the images. A comparison between manual counts and software-assisted counts was performed in 15 images in order to validate the macro used to quantify CD8+ cells. In addition, all software-processed images were manually checked to identify any possible errors. For CD4 and CD11c counts, five images from each donor were analyzed manually. Average infiltration rates (cells/mm2) were calculated for each donor and used as individual and independent samples in the subsequent statistical analyses.β- and α-cell areas were determined as the percentage of the total area of the image that was positive for insulin or glucagon staining using a custom macro developed for ImageJ (National Institutes of Health).

      CD8, CD4, CD11c, beta and alpha cell imaging and quantification

    1. Stromal cell analysis and sorting We identified the four LNSC populations among CD45− cells using the aforementioned PDPN and CD31 markers, as well as HLA-DRhigh CD45low cells among CD45+ cells. The latter were also of interest as: (1) they had been included in previous LNSC studies because of their lower expression of CD45 and radioresistance; and (2) they are enriched in autoimmune regulator (AIRE)+ dendritic cells [16, 17]. In addition, human cells were stained for HLA-DR (MHC-II), HLA-A,B,C (MHC-I) and PD-L1, and mouse cells for I-Ag7 (MHC-II; detected with the cross-reacting anti-I-Ak antibody), H2-Kd (MHC-I) and PD-L1 (ESM Table 2). After staining, cells were acquired using a Fortessa cell analyser (BD, Franklin Lakes, NJ, USA) and FCS Express 6 (DeNovo Software, Glendale, CA, USA) was used for data analysis. Mean fluorescence intensity (MFI) values were normalised in each sample against CD45+ MHC-II− cells as control and represent a fold change. If the number of cells was sufficient, cell populations were sorted into TRI Reagent LS (Sigma, St Louis, MO, USA) using the BD Influx cell sorter and stored at −80°C.

      Stromal cell analysis and sorting

    2. Targeted analysis of gene expression in LNSCs from human PLNs using Biomark Total RNA was isolated from cells sorted in TRI Reagent LS, using a modified protocol of chloroform extraction followed by purification using an RNeasy Micro Kit (Qiagen, Hilden, Germany). cDNA was synthesised from 200 ng of total RNA for each sample using iScript Reverse Transcription Supermix (Bio-Rad, Hercules, CA, USA). All Delta Gene primers (Fluidigm, South San Francisco, CA, USA) were purchased pre-validated for assay performance (ESM Table 3). Samples were pre-amplified using a pool of all primers (minus endogenous controls; 50 nmol/l final) and 18 cycles, as per the Biomark protocol. Samples (16 in triplicates) were loaded onto Biomark 48 × 48 IFC chips (Fluidigm) and assayed against the 48 Eva Green-based assays (primers at 5 μmol/l final) listed in ESM Table 4. Target gene expression was calculated using the comparative method for relative quantification after normalisation to expression of the housekeeping HPRT1 gene, the expression of which was the most homogeneous across multiple samples compared with other housekeeping genes. An insufficient number of sorted cells, poor RNA quality (assessed using BioAnalyzer PicoChip; Agilent Technology, Waldbronn, Germany) or failed amplification were criteria for sample exclusion in the gene expression analysis. For comparison of relative gene expression between human and mouse LNSC subsets (our data vs Immunological Genome Project [ImmGen] RNA-Seq data [www.immgen.org]), we normalised gene expression to 100% in subsets in which it was most highly expressed in each set of data independently. Biomark data were deposited at: https://data.mendeley.com/datasets/d9rdzdmvyf/1 [18].

      Targeted analysis of gene expression in LNSCs from human PLNs using Biomark

    1. Antibody labeling and image acquisition Four to eight μm FFPE sections were stained with an antibody cocktail (Table S1) containing all antibodies. Briefly, tissue sections were de-paraffinized with xylene and carried through sequential rehydration from 100% Ethanol to 70% Ethanol before being transferred to PBS. Heat-induced antigen retrieval was performed in a decloaking chamber (Biocare Medical) at 95°C for 30 min in Tris/EDTA buffer (10mM Tris, 1mM EDTA, pH9.2). Slides were cooled to room temperature (RT) and were subsequently blocked with PBS+3%BSA for 1h at RT. Meanwhile, the antibody cocktail was prepared in PBS+1%BSA buffer, with the appropriate dilution for each of the antibodies (Table S1). Each slide was incubated with 100 μl of the antibody cocktail overnight at 4°C. The next day, slides were washed 3 times with PBS and labeled with 1:400 dilution of Intercalator-Ir (Fluidigm 201192B) in PBS for 30 min at RT. Slides were briefly washed with H2O three times and air dried for at least 30 minutes before IMC acquisition. The IMC was purchased from Fluidigm (Fluidigm, Hyperion Imaging System™). All IMC operation was performed following Fluidigm’s operation procedure. Briefly, following daily tuning of IMC, image acquisition was carried out following manufacturer’s instruction at a laser frequency of 200 Hz. 1000 μm x 1000 μm regions around islets were selected based on bright field images.

      IMC antibody staining and image acquisition

    1. Pancreas immunohistochemical and immunofluorescence analysisFFPE pancreatic sections (5 μm thickness) were analyzed as briefly follows. After deparaffinization and rehydration, sections were incubated with Tris-buffered saline (TBS, Sigma-Aldrich) supplemented with 3% H2O2 to block endogenous peroxidases (only for immunohistochemical experiments) and with TBS supplemented with 3% bovine serum albumin (BSA, Sigma-Aldrich) to reduce nonspecific reactions. Antigen retrieval was performed with 10 mM citrate buffer, pH 6.0.For immunohistochemical analysis, sections were incubated with rabbit polyclonal anti–human MPO (Abcam, ab45977) and swine polyclonal anti-rabbit IgG-HRP (DAKO, P0217) as secondary antibody. MPO signal was detected with 3,3′-diaminobenzidine (DAB Quanto, ThermoFisher Scientific, TA-060-HDX). Sections were then incubated with Mayer’s hematoxylin solution (Sigma-Aldrich) to counterstain nuclei, dehydrated, and mounted with Eukitt (Bio-Optica). Sections from the Siena cohort were also incubated with mouse monoclonal anti–human glucagon (R&D Systems, clone 181402, MAB1249) and goat anti-mouse IgG–alkaline phosphatase (ThermoFisher Scientific, 31320) as secondary antibody. Glucagon signal was detected with Liquid Fast Red (ThermoFisher Scientific, TA-060-AL) supplemented with levamisole endogenous alkaline phosphatase inhibitor (DAKO, Agilent Technologies, X3021).For immunofluorescence analysis, sections were incubated with rabbit polyclonal anti–human MPO (Abcam) and mouse anti–citrullinated histone H3 (anti-CitH3; citrulline R2 + R8 + R17, clone 7C10, LifeSpan Biosciences, LS-C144555) antibodies and with goat anti-rabbit IgG Alexa Fluor 488 (Molecular Probes, ThermoFisher Scientific, A11034) and goat anti-mouse IgG Alexa Fluor 594 (Molecular Probes, ThermoFisher Scientific, A11032) as secondary antibodies. nPOD sections were also stained with polyclonal guinea pig anti-glucagon antibody (LifeSpan Biosciences, LS-{"type":"entrez-nucleotide","attrs":{"text":"C20275","term_id":"1632546","term_text":"C20275"}}C20275) and goat anti-guinea pig IgG Alexa Fluor 647 (Molecular Probes, ThermoFisher Scientific, A21450) as secondary antibody. DNA was counterstained with Hoechst 33342 (ThermoFisher Scientific, 62249). Finally, sections were mounted using Vectashield (Vector Laboratories, H1000) mounting medium.OCT-embedded tissues (5 μm thickness) were methanol/acetone (1:1, –20°C) fixed, then blocked (PBS supplemented with 1% denatured BSA) and incubated with primary antibodies: mouse monoclonal anti-MPO (Bio-Rad, clone 4A4, 0400-0002) and polyclonal rabbit anti-CitH3 (citrulline R2 + R8 + R17, Abcam, ab5103). Sections were washed and incubated with the proper secondary antibodies: goat anti-mouse IgG (H+L) Alexa Fluor 488 (Jackson ImmunoResearch, 115-545-003) and goat anti-rabbit IgG (H+L) Alexa Fluor 546 (ThermoFisher Scientific, A11035). DNA was counterstained with Hoechst 33342. Sections were mounted on slides with homemade Mowiol mounting medium (glycerol, G5516; Mowiol 4-88, 81381; and Dabco 33-LV, 290734, Sigma-Aldrich).

      IHC and IF analysis of MPO+ cells in pancreas

    1. Formalin-fixed, paraffin-embedded pancreas sections were available from the Network for Pancreatic Organ Donors with Diabetes (nPOD) collection of organ donor pancreases. Analyses were performed with five healthy control (HC), four nondiabetic autoantibody-positive (AutoAb+) subjects, and eight individuals with T1D for whom formalin-fixed, paraffin-embedded specimens were available. All tissue processing procedures were conducted by the nPOD Organ Processing and Pathology Core in accordance with federal guidelines for organ donation and the University of Florida institutional review board. The case identification number, disease condition, patient clinical parameters, tissue histopathological scoring, and serum immunological testing data provided by the nPOD are listed in Supplemental Table 1. The institutional review board of IRCCS San Raffaele Scientific Institute (Milan Italy) approved all work reported. In situ hybridization was performed as previously described (36) to visualize viral RNA and CXCL10 RNA using the Quantigene ViewRNA technique based on branched DNA signal amplification technology, according to the manufacturer’s instructions. A probe set containing multiple oligonucleotides was used, designed to hybridize to human CXCL10 (Quantigene probes, CXCL10 gene; NCBI reference sequence, NM_001565) Tissue sections from the pancreas head, body, tail, and duodenum were analyzed, depending on availability. Quantification of cells positive for each probe was performed within eight randomly chosen fields for section (magnification ×20). The percentage of positive cells examined was scored as 0 (negative), 1 (≤20 cells per field), 2 (20 to 40 cells per field), and 3 (>40 cells per field). All the analyses were performed in blinded fashion.

      Quantigene assay of viral and CXCL10 RNA in human pancreas

    1. ImmunostainingSections from formalin-fixed paraffin-embedded donor-derived pancreata were obtained from the Network for Pancreatic Organ Donors With Diabetes (nPOD) repository, the Diabetes Virus Detection (DiViD) study, and the tissue bank of A.C.P. at Vanderbilt University (including age-matched control subjects without diabetes). Full details on all donors are listed in Supplementary Table 1. Paraffin sections were rehydrated, and antigen retrieval was performed in a decloaking chamber (Biocare Medical) in 50 mmol/L citrate buffer (pH 6). The following primary antibodies were used: guinea pig anti-insulin (1:400; DAKO), mouse antiglucagon (1:200; Abcam), rabbit anti-53BP1 (1:200; Bethyl), mouse anti-γ−H2AX Ser139 (1:3,000; Millipore), mouse anti-CD45 (1:100; DAKO), rabbit antiphosphorylated (phospho)-Kap1 (1:100; Bethyl), rabbit anti-p53 (1:400; Novocastra), rat anti-CD3 (1:300; Millipore), and rabbit anti–human growth hormone (hGH) (1:200; Abcam). Fluorophore-conjugated secondary antibodies used were donkey anti–guinea pig Alexa Fluor 488, donkey anti-rabbit Cy3/Cy5, and donkey anti-mouse Alexa Fluor 488/Cy3 (The Jackson Laboratory). DAPI (Invitrogen) was used as a nuclei marker. Horseradish peroxidase­–conjugated secondary antibody was donkey anti-rabbit (Histofine; Nichirei Biosciences). Diaminobenzidine (Lab Vision) was used as chromogen. Fluorescent images were taken with a Nikon C1 confocal microscope at 400× magnification. Bright-field images were taken with an Olympus BX53 at 400× magnification. Image quantification was performed using the ImageJ software.

      Immunostaining

    1. In-Situ HLA-A2 MMr StainingIn-situ immunohistochemistry staining was performed as described (Culina et al., 2018Culina S. Lalanne A.I. Afonso G. Cerosaletti K. Pinto S. Sebastiani G. Kuranda K. Nigi L. Eugster A. Osterbye T. et al.Islet-reactive CD8+ T cell frequencies in the pancreas, but not in blood, distinguish type 1 diabetic patients from healthy donors.Sci. Immunol. 2018; 3: eaao4013Crossref PubMed Scopus (31) Google Scholar). Unfixed, frozen sections were dried for 2 h, loaded with 1 μg of PE-labeled MMrs overnight at 4°C, washed gently with PBS and fixed in 2% paraformaldehyde for 10 min. After a further wash, endogenous peroxidase activity was blocked with 0.3% H2O2. Sections were then incubated serially with a rabbit anti-PE Ab (Abcam), horseradish peroxidase-conjugated swine anti-rabbit Ig (Dako) and 3,3′-diaminobenzidine tetrahydrochloride substrate (ThermoFisher). After a final wash, sections were counterstained with hematoxylin, dehydrated via sequential passages in 95%–100% ethanol and xylene, mounted, and analyzed using a Nikon Eclipse Ni microscope with NIS-Elements D software v4.40.In-situ immunofluorescence staining was performed similarly, but non-specific reactions were blocked with 5% goat serum for 2 h at room temperature before serial incubations with rabbit anti-PE Ab (1:250, 1.5 h at room temperature) and Alexa Fluor 594-conjugated goat anti-rabbit IgG (ThermoFisher; 1:500, 1 h at room temperature). After a further wash, sections were incubated for 1 h at room temperature with rat anti-CD8 mAb (Abcam; 1:100) together with mouse anti-CD45RO mAb (BioLegend; 1:200) followed, after one wash, by one final incubation for 1 h at room temperature with Alexa Fluor 488-conjugated goat anti-rat IgG together with Alexa Fluor 647-conjugated goat anti-mouse IgG (1:500/each; both from ThermoFisher). After DNA counterstaining with DAPI, sections were mounted and analyzed using a Leica TCS SP5 confocal laser scanning microscope with LAS software v2.6.0.7266.

      In situ HLA-A2 MMr staining

    2. HLA-A2 MMr AssaysAll peptides were synthesized at >90% purity (Synpeptides). HLA-A2 MMrs were produced and used as described (Culina et al., 2018Culina S. Lalanne A.I. Afonso G. Cerosaletti K. Pinto S. Sebastiani G. Kuranda K. Nigi L. Eugster A. Osterbye T. et al.Islet-reactive CD8+ T cell frequencies in the pancreas, but not in blood, distinguish type 1 diabetic patients from healthy donors.Sci. Immunol. 2018; 3: eaao4013Crossref PubMed Scopus (31) Google Scholar). Each pHLA-A2 complex was used at a final concentration of 8-27 nM and conjugated with fluorochrome-labeled streptavidin at a 1:4 ratio. The combinatorial MMr panel was first set up by staining HLA-A2+ PBMCs with the same set of fluorescent streptavidin-labeled MMrs, all loaded with the Flu MP58-66 epitope. Compensations were set using fluorescence-minus-one samples (i.e. omitting one streptavidin at a time). The concentration of each fluorescent MMr was corrected for the variable staining index of each streptavidin, in order to obtain a distinct double-MMr+ population for each fluorochrome pair. The identification of the same MMr+ population by each pair validated the panel. PBMCs were isolated by density gradient centrifugation using 50 ml Leucosep tubes (Greiner/Dominique Dutscher), washed twice in RPMI medium supplemented with AB human serum (Sigma), counted on a ThermoFisher Countess II automated counter and frozen in pre-chilled 10% DMSO solution in AIM-V medium (ThermoFisher) using CoolCell containers (Biocision) stored overnight at −80°C prior to transfer into liquid nitrogen. At thawing, PBMCs were immediately diluted in pre-warmed AIM-V medium. Following centrifugation and one additional wash in AIM-V, PBMCs were counted and rested in the presence of 50 nM dasatinib for 30 min at 37°C before magnetic depletion of CD8– cells (StemCell Technologies). Staining was performed for 20 min at 20°C in 20 μl PBS-dasatinib for 107 cells with the combinatorial double-coded MMr panels (Culina et al., 2018Culina S. Lalanne A.I. Afonso G. Cerosaletti K. Pinto S. Sebastiani G. Kuranda K. Nigi L. Eugster A. Osterbye T. et al.Islet-reactive CD8+ T cell frequencies in the pancreas, but not in blood, distinguish type 1 diabetic patients from healthy donors.Sci. Immunol. 2018; 3: eaao4013Crossref PubMed Scopus (31) Google Scholar) detailed in Figure 3, followed, without washing, by mAb and Live/Dead Aqua staining at 4°C for 20 min. After one wash, cells were acquired using a FACSAria III cytometer configured as detailed in Table S6. Candidate epitopes binding to HLA-A2 (Figure S2) that did not yield any appreciable MMr staining provided negative controls for each panel. Data were analyzed with FlowJo software as described in Figure 3. Cells were sequentially gated on small lymphocytes, singlets, live cells (Live/Dead Aqua–), CD3+CD8+ T cells and total PE+, PE-CF594+, APC+, BV650+, BV711+ and BV786+ MMr+ T cells. Using Boolean operators, these latter gates allowed to selectively visualize each double-MMr+ population by including only those events positive for the corresponding fluorochrome pair. For example, UCN31-9 MMr+ cells (PE+PE-CF594+) were visualized by gating on events that were PE+PE-CF594+APC−BV650−BV711−BV786−. Events negative for all MMr fluorochromes (PE−PE-CF594−APC−BV650−BV711−BV786−) were represented in the same PE/PE-CF-594 dot plot to set the double-MMr+ gate, as shown in Figures 4A–4F. CD45RA and CCR7 staining was subsequently visualized by gating on these MMr+ cells. Each dot plot of Figures 3B and 3C displays a color-coded overlay of each double-MMr+ fraction and of the MMr− population to visualize the separation of each epitope-reactive CD8+ T-cell fraction relative to the others.

      HLA-A2 MMr Assays

    1. Transmission Electron Microscopy (TEM)A JEOL 2010F transmission electron microscope (TEM) was used for imaging the crystals. The crystals were about evenly distributed across the used 314-square, 3 mm diameter, 200 mesh copper grids with type-B carbon supports (Ted Pella Redding CA, catalog number 1810). Typically, crystals of 3 squares were TEM imaged, that is, 1% of the applied crystals were counted and examined. Crystallinity was confirmed by the crystals’ electron diffraction, and then elemental compositions were determined by energy-dispersive X-ray spectroscopy. In the elemental analyses the peaks of all elements excepting carbon were integrated. The reported values are percentages of the integrals.

      TEM imaging of crystals in pancreas

    1. Immunofluorescence Stainings and Image Acquisition Paraffinized sections (mouse and human) were prepared for immunofluorescence staining by heating the slides for 15 min at 55°C in an oven, deparaffinized (2 x 100% xylene 5 min each, 2 x 100% ethanol 5 min each, 2 x 95% ethanol 5 min each, 70% ethanol for 5 min) and rinsed in dH2O for 5 min. Antigen retrieval was performed by heating the slides at 95°C for 20 min in HistoVT pH 7.0 (Nacalai USA) for all antibodies used. Specimens were blocked in 5% goat serum PBS-T for 15 min at RT before incubating with primary antibody diluted in 1% goat serum PBS-T overnight at 4°C. For primary antibodies produced in goat, donkey serum was used as the blocking agent. Each slide was rinsed three times in PBS, for 5 min each. Specimens were incubated in fluorochrome-conjugated secondary antibody diluted in 1% goat serum PBS-T for 1 hr at RT in the dark. After rinsing as above, VectorShield with DAPI and coverslip were mounted and slides were allowed to cure overnight at 4°C in the dark before image acquisition. For apoptosis assessment, we used DeadEnd Fluorometric TUNEL (Promega) and performed the staining as recommended by manufacturer. A list of antibodies used can be found in Key Resources Table.Images were acquired using either ApoTome.2 (Zeiss) without structured illumination or LSM780 (Zeiss) and analyzed using ImageJ software unless otherwise stated.

      IF and image acquisition

    2. For human samples, analysis of H3K27me3, H3, Insulin and DAPI intensities were automated using customized Cell Profiler pipeline (available upon request).

      Image quantification of H3K27me, H3, Insulin, DAPI

      (Cell Profiler pipeline)

    1. Immunostained sections were examined with a Nikon Eclipse E600 epifluorescence-bright field microscope and digital images recorded. Group assignment was blinded to the investigator, except groups 1 and 4 where many islets were insulin-deficient. From each pancreatic section, islets with approximately ≥20 endocrine cells were imaged for the presence of IL-1β, insulin, glucagon, CD68 cells (macrophage marker) and CD3 cells (T cell marker). Each of the specific image sets from multiple acquisitions were merged with Adobe Photoshop CS6 (Adobe Systems, San Jose, CA, USA) following conversion of IL-1β immunostained cells to a greyscale fluorescence mode. Fifteen exocrine fields that were devoid of islet and ductal areas were selected randomly by microscopically traversing different regions of each pancreatic section and imaged at ×20 objective. The number of IL-1β-positive cells in each field was enumerated and the mean number per field per section determined. All data were included in the study. For each section, the number of IL-1β-positive cells in the peri- and intra-islet regions was enumerated and the mean number of cells per insulin-positive and -negative islet determined. Islets that showed immunostaining for IL-1β in alpha cells were scored microscopically as negative, weak or moderate intensity.

      Imaging and Quantitation of IL-1b, insulin, glucagon, CD68, CD3 cells

    2. The immunohistochemical specificity of anti-IL-1β (catalogue no. 12242; Cell Signaling Technology) has been validated by the manufacturer. Thus, by immunohistochemistry, it successfully detects IL-1β immunoreactive cells in human colonic sections from individuals with chronic colitis. Further, by western blotting, the same antibody detects mature recombinant human and mouse IL-1β and the human precursor (molecular mass of 34 kDa) in extracts of Raw 264.7 and human monocyte-derived THP-1 cells, following exposure to Brefeldin and lipopolysaccharide (LPS), respectively. Anti-IL-1β was replaced with diluent to act as negative controls. As further controls, paraffin sections of THP-1 cells, harvested after incubation with LPS (100 ng/ml for 3 h) and without (from Cell Signaling Technology), were evaluated in this laboratory for IL-1β immunohistochemical specificity. The immunostaining protocol for IL-1β was tested with formalin-fixed tonsil sections, supplied by Department of Surgery, University of Auckland. Sections from selected pancreas and tonsil were immunostained for IL-1β in two separate sections. They were then exposed to rabbit anti-CD68 or rabbit anti-CD3, followed by donkey anti-rabbit IgG-biotin and then streptavidin–Alexa 568. Pancreatic sections were immunostained subsequently for insulin as above, followed by donkey anti-guinea pig IgG–Alexa 488.

      IHC for IL-1b

    1. Histology and immunohistochemistryFor quantitative analyses of HS, HSPGs, insulin and glucagon localization in human islets, paraffin sections (4 μm thickness) of nPOD human pancreases and isolated human islets fixed in 10% neutral-buffered formalin were stained with hematoxylin and eosin (H&E) or by immunohistochemistry. Antigen retrieval for HS and Col18 was performed using 0.05% pronase (Calbiochem, Japan) [27, 28], whereas heat/citrate buffer (pH 6) was used for Sdc1 and heparanase [27, 28]. HS and HSPG core proteins were detected immunohistochemically using 10E4 anti-HS (1/5-1/10; https://dx.doi.org/10.17504/protocols.io.kvzcw76), anti-Col18 (1/100; https://dx.doi.org/10.17504/protocols.io.kvzcw76) and rat anti-mouse Sdc1 (CD138, 1/10; BD Biosciences) (https://dx.doi.org/10.17504/protocols.io.kv3cw8n) mAbs, with horseradish peroxidase-conjugated rabbit anti-mouse or anti-rat Ig (Dako, Carpinteria, USA). Heparanase was localized using the HP130 mouse anti-human heparanase mAb (1/5; Insight Biopharmaceuticals, Rehovot, Israel), biotinylated anti-mouse IgG (1/250) and avidin-biotin-complex (ABC reagent; PK-2200, Vector Laboratories, Burlingame, CA) (https://dx.doi.org/10.17504/protocols.io.kv4cw8w). Background staining was checked using the corresponding isotype control Ig and human pancreatic lymph node (PLN) was used as a positive control. Insulin and glucagon were detected using mouse anti-insulin (ascites; 1/250) or mouse anti-glucagon (ascites; 1/500) mAbs (Sigma-Aldrich) and biotinylated anti-mouse IgG/ABC reagent (https://dx.doi.org/10.17504/protocols.io.kv6cw9e). 3-amino-9-ethylcarbazole (AEC; Sigma-Aldrich) was used as the chromogen. Specimens were de-identified prior to morphometric analysis. Image J software with color deconvolution plugin was used for the quantitative analysis of the % of islet area stained [27, 28] in 7–10 islets/donor pancreas.

      Staining and quantitative analysis of HS, HSPGs, Insulin and Glucagon

    1. Development of an automated morphometric analysis processα-cell area was quantified on glucagon-stained slides (4 slides by patient, 2 from the head and 2 from the tail), β-cell area on insulin-stained slides (2 slides by patient, 1 from the head and from the tail), and non-exocrine-non-endocrine (non-acinar, non-insular) pancreatic tissue area on both insulin- and glucagon-stained slides (i.e. 6 slides by patient, 3 from the head and 3 from the tail).On each slide, three different areas were evaluated: the total pancreatic tissue area, the non-exocrine-non-endocrine pancreatic tissue area and the Fast Red-stained area, the latter corresponding either to alpha-cells or to beta-cells area, depending on the immunostaining.In R software, a picture is converted into 3 matrices, corresponding respectively to the blue, green and red color levels, each matrix element accounting for one pixel. The principles used for the selection of the pixels, belonging to the 3 predefined areas were the following.For total pancreatic tissue area delineation, the first step consisted in resizing the original picture to one hundredth of its full-resolution initial size. In other words, the original picture, whose size was between 1x108 and 6x108 pixels, was divided into squares of 10-pixel sides, each of these squares being integrated, in the resized picture, into one single central pixel, whose blue, green and red color levels were respectively the mean of blue, green and red levels from the 10x10 corresponding pixels in the original picture. This step allowed the smoothing of the existing discontinuities in the original picture to define a continuous pancreatic tissue surface (S2 Fig). The next step consisted then in the selection of any colored pixel, i.e. any pixel whose blue, green and red color levels were superior to the respective color levels of background pixels in the resized picture (Fig 1). The main difficulty in automating this process was to be able to take into account the differences in contrast and staining intensity between slides. The choice was therefore made to build an R script aimed at generating several propositions for total pancreatic tissue selection, by varying the color level threshold used by the different filters applied in the script (S1 File). An additional step of visual selection of the most accurate proposition by one investigator (FBS), in comparison to the original picture, was further carried out, allowing a critical step of visual control in the automated process (Fig 1).Open in a separate windowFig 1Example on the slide 14262.The first R script, used for the selection of the pixels belonging to total pancreatic tissue, generated 22 different propositions. The choice of the investigator is framed in red and the subsequent result indicated under the picture. An example of background pixel has been also pointed out on the picture miniature.For exocrine+endocrine pancreatic tissue area delineation, the first step also consisted in resizing the original picture, like in the total area delineation, but to a bigger size than for total pancreatic tissue, i.e. to one twentieth of its initial size. The next step consisted in the selection of either hematoxylin-stained blue pixels, i.e. colored pixels, whose blue level was superior to green and red levels, or to diaminobenzidine, brown, pixels or Fast Red, red, pixels, defined as colored pixels, whose red level was superior to green level. The R script intentionally generated automatically several propositions for exocrine+endocrine pancreatic tissue selection (S2 File). The choice of the most adequate selection by the investigator added a second step of visual control in the automated process (Fig 2).Open in a separate windowFig 2Example on the slide 14262.The second R script, used for the selection of the pixels belonging to functional pancreatic tissue, generated 16 different propositions. The choice of the investigator is framed in red and the subsequent results indicated under the picture.Given the small size of Fast Red-stained regions, the use of the full resolution picture was necessary for appropriate selection of the corresponding area. However, given the large size of the original picture, this required its split into several smaller pictures to allow easy manipulations within R software. The final Fast Red-stained selected area was then calculated by summing the results obtained on each small picture. The selection principle of Fast Red-stained pixels relied on the selection of colored pixels whose red level was higher than blue and green levels. However, the main difficulty encountered in this process was the presence of light pink artifact pixels on some slides, most often organized into large very slightly colored sheets, but sometimes with a color intensity similar to that of some weakly Fast Red-stained alpha- or beta islet cells. Thus, to gain detection sensitivity without losing specificity, a multi-step selection R-script was built (Fig 3) (S3 File). Briefly, assumption was made that every truly positive Fast Red-stained pixel should either display a high red level itself or a high red level pixel in its vicinity; artifact pixels being low in intensity and located in expanded regions only containing low red level pixels. Hence, the first step of the script selected high red level pixels (Fig 3B); the second step drew circles with a 50-pixel radius around each selected pixel (Fig 3C), thus pinpointing the regions where true Fast Red-stained pixels were located; and the final step identified the red pixels with a lower threshold within these circles as Fast Red-stained pixels (Fig 3D). The main drawback of this approach should be the inappropriate selection of islet neighboring tissue submitted to bleeding of the overstaining existing on some slides. However, the validation of the present methodology on some well-established parameters, as beta-cell mass, suggested that this effect was negligible (Fig 4). The whole process of picture analysis has been summarized on a flowchart (S3 Fig) (S4 File).

      Automated morphometric analysis

    1. IHC-IF.Six sections of formalin-fixed, paraffin-embedded pancreas specimens (4 μm thick and 30–200 μm apart) were labeled for each marker. All murine and human pancreas sections were stained with primary and secondary antibodies for detection of insulin, amyloid, CC3, TUNEL, RAGE, AGEs, S100B, and IAPP. Images were taken using a Leica fluorescence microscope. Quantitative analysis using MetaMorph LASF imaging software was performed by an investigator blinded to the experimental condition. Detailed methods for histological studies can be found in Supplemental Table 1 and the Supplemental Methods.

      IHC-IF

      (insulin, amyloid, CC3, TUNEL, RAGE, AGEs, S100B, IAPP)

    1. Immunohistochemistry and image digitalisationEach pancreas received was divided into a head, body and tail region, each of which was subjected to serial transverse sectioning. Within each region, tissue pieces were consecutively and alternately used for preparation of both formalin-fixed paraffin-embedded and frozen tissue blocks. Three consecutive paraffin sections were cut at 4 µm from one representative formalin-fixed paraffin-embedded tissue block within each region. All sections were deparaffinised and rehydrated with serial passage through changes of xylene and graded ethanol. All slides were subjected to heat-induced antigen retrieval in Target Retrieval Solution (Dako, Carpinteria, CA, USA). The tissue sections were double stained for insulin (polyclonal guinea pig anti-insulin,1:2000 dilution; catalogue no. A0564, RRID:AB_10013624; Dako) and one of the following markers: CD68 for macrophages (monoclonal mouse anti-CD68, 1:2000 dilution; catalogue no. M0814, RRID:AB_2314148; Dako); CD45 for leucocytes (monoclonal mouse anti-CD45, 1:200 dilution; catalogue no. M0701, RRID:AB_2314143; Dako) or Ki67 for DNA replication (monoclonal mouse anti-Ki67, 1:160 dilution; catalogue no. M7240, RRID:AB_2142367; Dako) as previously described [8]. Antigen–antibody binding was visualised using the EnVision G/2 Doublestain (peroxidase-DAB and alkaline phosphatase-Permanent Red; catalogue no. K5355; Dako) polymer system. Subsequently, the slides were counterstained with Mayer’s Hematoxylin (catalogue no. S3309; Dako), dehydrated in ethanol and mounted with Cytoseal XYL media (Richard-Allan Scientific, Kalamazoo, MI, USA). Stained slides were then digitalised and processed in preparation for statistical analysis (see ESM Methods for image acquisition and processing details).

      IHC and image digitalisation

      (Ins, CD68, CD45, Ki67)

    1. CD3, CD68, and glucagon stainingThe coimmunostaining for CD3, CD68, and glucagon was performed on the automated platform Discovery XT-VENTANA. After deparaffinization and antigen retrieval using CC1 solution, slides were incubated 1 hour at 37°C with a mix of anti-CD3 (A0452, Dako) and anti-CD68 (M0876, Dako) antibodies, before incubating anti-mouse (FP-SC 4110, Interchim) and anti-rabbit (FP-SB 5110, Interchim) secondary antibodies for 45 minutes at 37°C. Anti–glucagon-FITC antibody (BS-3796-A488, Interchim) was subsequently incubated for 45 minutes at 37°C. Slides were digitalized with the slide scanner NANOZOOMER 2.0RS/C10730-12 (Hamamatsu; objective ×20, resolution 0.46 μm/pixel). Image analysis was performed on the whole pancreas slide by using an algorithm from HALO platform combined with a tissue classifier (Indica Labs). Segmentation between exocrine/endocrine pancreas and other tissue types (connective tissues) were processed using Tissue Classifier based on color, texture, and contextual features. Islet regions were identified as a contiguous area of glucagon. CD3 and CD68 cells were detected according to thresholds of intensity within the endocrine and exocrine pancreas. The algorithm calculates the total number of cells within each pancreas part, the number of CD3+ cells, the number of CD68+ cells, and the areas of endocrine and exocrine parts of the pancreas. Quantification was performed under blinded conditions, with anonymized slides from control and T1D donors.

      CD3, CD68, and glucagon staining

    2. The HERV-W-Env IHC was performed on the automated platform Benchmark (Ventana, Roche) with the detection kit UltraView DAB (brown chromogen), without pretreatment. GN_mAb_Env03 monoclonal antibody was developed by GeNeuro and has already been validated in several publications (24, 25, 30). GN_mAb_Env03 monoclonal antibody was used at a concentration of 5 μg/ml for the 77 slides. Eight additional slides (4 T1D, 4 non-T1D) were used as controls, with mouse IgG2a isotype. Counterstaining was applied with hematoxylin II and bluing reagent. Slides were digitized with the slide scanner (Hamamatsu), objective ×20, and quantification was made using Indica Labs HALO platform. An algorithm was designed based on pattern recognition that discriminates pancreas tissue (analyzed areas) from fatty inclusions, vasculo-nervous structures, and surrounding connective tissue (excluded areas). Image analysis based on red, green, and blue (RGB) spectra was used to detect brown staining (DAB) within the positive areas (pancreas). The algorithm was designed to allow the detection of specific brown staining according to a threshold of intensity, and nonspecific edge staining of sections was not taken into account. It calculates pancreas area (mm²), staining area (mm²), and percentage (% stained area/pancreas area). Quantification was performed under blinded conditions, with anonymized slides from control and T1D donors.

      HERV-W-Env staining using GN_mAb_Env03 antibody

    1. Immunohistochemistry Sections (4μm) from formalin fixed paraffin embedded pancreas tissues were deparaffinized and rehydrated with serial passage through changes of xylene and graded ethanol. All slides were subjected to heat induced antigen retrieval in Target Retrieval Solution (Dako). The tissue sections were stained for insulin as a part of routine collection protocol for nPOD tissues (previously described (Campbell-Thompson et al., 2016; Campbell-Thompson et al., 2012b)) or double stained for insulin (polyclonal guinea pig anti-insulin,1:1000 dilution, Dako (Santa Clara, CA)) and glucagon (monoclonal mouse anti-glucagon, 1:5000 dilution, Abcam, Cambridge, MA) by immunohistochemistry (IHC), scanned using an Aperio CS Scanscope (Leica/Aperio, Vista, CA), and stored in the nPOD online digital pathology database (eSLIDE version 12, Leica/Aperio). For donors indicated in Table S1, scanned images of insulin-stained slides available from the block(s) nearest to the tissues used for total protein extraction (described above) were evaluated for fractional insulin area using Indica Labs, Inc image analysis software (Corrales, NM). For insulin and glucagon double stained slides, antigen-antibody binding was visualized using the EnVision G/2 Doublestain System (Dako). For control (n=5) and T1D subjects (n=11), glucagon + insulin positive islets, glucagon positive islets, insulin positive single cells, and clusters of two to five insulin positive cells were annotated by hand using the Aperio viewing platform and analyzed using Image Scope, Leica Biosystems analysis software (Version 12.1.0.5029, Buffalo Grove, IL).

      IHC and quantification of glucagon + insulin cells

    2. Proinsulin, Insulin, C-peptide, Islet Amyloid Polypeptide and Glucagon by ELISA and Luminex Commercially available kits from ALPCO (Salem, NH) were utilized to measure total proinsulin, insulin and C-peptide from pancreas protein extracts as indicated in Table S1. The proinsulin, insulin and C-peptide assays are specific and do not cross react with each other. Glucagon was measured with a commercial ELISA assay provided by Mercodia (Winston Salem, NC). Islet amyloid polypeptide was measured by an assay from Millipore (Billerica, MA) using magnetic bead technology (Luminex). A total protein Bradford assay from Thermo-Fisher (Waltham, MA) was performed on each supernatant and used to normalize mass and extraction efficiency.

      Proinsulin, Insulin, C-peptide, Islet Amyloid Polypeptide and Glucagon by ELISA and Luminex

    1. In situ hybridizationSections were dried for 1 hour at 60°C, pretreated and hybridized with an hs-ITPR2 probe (Homo sapiens inositol 145-trisphosphate receptor type 2 (ITPR2) mRNA) for 2 hours at 40°C. Probes were custom-designed and labeled for use with RNAscope 2.5HD (Advanced Cell Diagnostics, Newark, CA, USA). Some sections were stained with Hs-PPIB or dapB probes as positive and negative controls, respectively. Amplification steps were performed prior to the detection of signals with 3,3′-diaminobenzidine. Sections were counterstained and mounted with Permount (Fisher scientific, Waltham MA, USA).

      ISH with hs-ITPR2

    2. ImmunofluorescencePancreatic slides were deparaffinized with Slidebrite (BioCare Medical, Concord CA) and dehydrated using graded ethanol concentrations. Slides were boiled in antigen retrieval citrate buffer pH6, blocked for 1hr with 2% normal goat serum, 2% bovine serum albumin, 0.5% Tween 20 in phosphate buffer saline followed by incubation with primary antibodies overnight at 4°C using different combinations. The staining series included antibodies to IP3R2 (Abcam, Cambridge, MA, USA 1:50), Translocase of outer mitochondrial membrane 20 (TOM20) (Santa Cruz biotechnologies, Dallas Texas, USA 1:50), VDAC-1 (Abcam, 1:100), mitofusin-2 (Abcam, 1:100), insulin (Dako, Carpinteria, CA, USA 1:150), glucagon (Abcam, 1:200). Secondary antibodies coupled to a fluorochrome (AF488, AF555, AF647, Life Technologies, Grand Island, NY, USA) were added for 30 min at RT according to the species of primary antibodies. Nuclei were stained with Hoechst 33342 (Sigma-Aldrich) and preparations were mounted in Prolong Gold anti-fade reagent (Life Technologies). Slides were analyzed with a slide scanner AxioScan.Z1 (Carl Zeiss SAS, Marly le Roi, France) at x40 magnification.

      IF for IP3R2, TOM20, VDAC-1, mitofusin-2, insulin and glucagon

    3. In situ proximity ligation assayDuolink II in situ proximity ligation assay (PLA) (Olink Bioscience, Uppsala Sweden) enables detection, visualization, and quantification of protein interactions (<40 nm) as an individual dot by microscopy. Primary antibodies to assess ER-mitochondria interactions were against IP3R2 (1:100) and VDAC-1 (1:200) as previously described [7]. Digitized slides were analyzed at x20 magnification. When necessary, beta cells and alpha cells were identified using anti-insulin and anti-glucagon antibody staining on the same slide. Dots were quantified in each islet using the Zen program and Fiji-ImageJ software and expressed as percentage of dots per nucleus. Experiments were performed at least twice using 2 to 5 non-consecutive slides for each donor. For Min6-B1 cultures, ER-mitochondria-interactions were assessed using a fluorescent PLA assay, employing antibodies directed against VDAC-1 (Abcam, 1:100) and IP3R1 (Santa Cruz laboratories, Dallas TX, USA 1:500) as described previously [10]. Experiments with Min6-B1 cells were performed at least three times, with a minimum of five fields taken per condition.

      In situ proximity ligation assay (PLA) for assessing ER-mitochondria interactions

    1. Immunostaining and morphometric evaluation Paraffin sections were incubated overnight with primary antibody (Key Resources Table). For each antibody, sections were stained and imaged in parallel such that the staining intensity reflects the protein expression. For quantification, images were captured systematically covering the whole section in confocal mode on a Zeiss LSM 710 microscope. Every cluster of insulin-stained cells (3–7 cells) or islet (8 or more cells)/section was evaluated; sections were coded and read blindly. For each age, 3–4 animals were evaluated for each staining. For human samples, sections from one block from the body of the pancreas from donors as listed in Key Resources Table.

      Immunostaining and morphometric evaluation

      (includes p53BP1 and IGF1r)

    1. Total terminal deoxynucleotide transferase–mediated dUTP nick end labelingApoptosis analysis was performed in a random sampling of T1D cases, as previously described (30), with modification; sections were predigested in 0.004% trypsin and identified with Cy5-labeled reagents. Total terminal deoxynucleotide transferase–mediated dUTP nick end labeling (TUNEL)–positive β cells were assessed in >95,000 islet cells per condition. In every sample, TUNEL-positive pancreatic ducts were imaged to ensure adequate TUNEL staining.

      TUNEL

    2. Proliferation analysisKi-67+ cells were measured within insulin-stained slides or ×20 magnification colorimetric images from optimal cutting temperature compound (OCT)–embedded pancreas sections prepared by the nPOD core laboratory, available through the nPOD Datashare. Colorimetric insulin and DAPI were identified using a sample-specific red-green-blue color range, established through sampling of multiple data points. Ki-67+ β-cell and Ki-67+ acinar proliferation was calculated as percent total cell population. Acinar cells were identified as nonislet cells containing DAPI.

      Proliferation analysis for beta and acinar cells

    3. Slides were imaged to quantify β-cell morphometry using Volocity 6.1.1 (PerkinElmer, Waltham, MA), as previously described (29). Images were acquired with Zeiss Axio Imager M1 (Carl Zeiss Microscopy, Thornwood, NY) with an automated X-Y stage and captured with Orca ER camera (Hamamatsu, Bridgewater, NJ), resulting in images of tens of thousands of individual nuclei per sample (summarized in Supplemental Table 3).

      Beta cell morphometry

    1. Whole sections of pancreas stained for insulin, Ki67, and alcian blue with hematoxylin counterstain were digitally scanned using Aperio ScanScope (Aperio Technologies, Vista, CA). Analysis was performed using Aperio ImageScope version 11.0.2.725. Three sections per case, 1 each from head, body, and tail, were analyzed, except in 1 T1D case [6031] and 7 control cases (6010, 6012, 6013, 6015, 6017, 6021, 6022) in which only head and tail sections were available. All sections were examined and quantified in a blinded manner.Interlobular ducts were defined as ductal structures embedded in mesenchyme and possessing a PDG compartment. PDGs were identified as coiled invaginations composed of columnar epithelium arising from interlobular ducts and lying within the mesenchyme surrounding those ducts. The number of interlobular duct epithelial cells and PDG cells was counted, as was the number of those cells with nuclei staining for Ki67 and cytoplasmic staining for insulin. Small ducts, defined as intralobular ducts not embedded in mesenchyme, were comparably analyzed.

      Ki67, Ins staining and analysis in PDG

    1. Immunohistochemistry was performed using a standard immunoperoxidase approach, as previously described [21]. To examine multiple antigens within the same FFPE section, samples were probed in a sequential manner with up to three different antibodies (ESM Tables 3, 4). The mean fluorescence intensity (MFI) of stained antigens was measured using ImageJ Version 1.50b Java 1.8.0_77; https://imagej.nih.gov/ij/download.html. Some slides were processed with isotype control antisera to confirm the specificity of labelling (ESM Fig. 1). Frozen sections were stained using a standard immunofluorescence approach [22].

      Immunohistochemistry

      (HLA, Ins, Glu, NLRC5, STAT1, B2M abs determined from supplementary)

    2. Using the Affymetrix Human Gene 2.0 ST array, CEL files were generated from both control and type 1 diabetic donors, as previously described [24]. Raw signal-intensity values from Affymetrix spike-in controls demonstrated that array hybridisation had been successful (i.e. bioB<bioC<bioD<Cre). Data quality was verified by measuring the positive vs negative area under the curve. Raw signal-intensity values from all arrays were robust multichip average background corrected, quantile normalised, median polish summarised and log2 transformed [25–27]. NetAffx-determined probe-set annotations for HLA genes (Affymetrix) were re-mapped according to RefSeq, release 73 (15 November 2015; see ftp://ftp.ncbi.nlm.nih.gov/refseq/release/release-catalog/archive/). For each HLA gene, where multiple mappings were possible (i.e. HLA-A, -B, -C and -F), probe sets were annotated according to eight major haplotypes incorporated into the human genome assembly, as previously described [28]. Because probe sets shared mappings, it was not possible to identify HLA subtypes uniquely using this gene chip; rather, transcript clusters were used to examine changes in global gene expression. Processing was carried out using the Partek Genomics Suite, version 6.5 (Partek, St Louis, MO, USA). The resulting normalised expression data for specific genes of interest were then subjected to analysis as described below.

      HLA gene expression profiling

    1. Human pancreas sections were deparaffinized, followed by acidic-pH heat–mediated antigen retrieval. Cultures of single pancreatic islet cells were fixed with 4% paraformaldehyde. Samples were blocked and permeabilized in PBS with 0.3% Triton X-100 and 10% goat or donkey serum. Primary antibodies CHOP (1:100; sc-575, Santa Cruz Biotechnology), GAD6 mouse monoclonal antibody (mAb) against C-terminus of GAD65 (31) (1:1,000); N-GAD65 mouse mAb against the N-terminus of GAD65 (32) (1:300), giantin (1:1,000; ab24586, Abcam); insulin (1:10,000; 4011-01, Linco); insulin (1:2,000; ab14042, Abcam), and CD3 (1:30; M7254, Dako) were incubated overnight at 4°C in PBS with 0.3% Triton X-100 and 1% goat or donkey serum. Alexa Fluor conjugated secondary antibodies (Molecular Probes) were incubated at 1:200 dilution in PBS with 0.3% Triton X-100 for 30 min at room temperature.Image Capture, Analysis, and QuantificationSamples were imaged on a Zeiss LSM700 confocal microscope with 63×/1.40 NA Plan-Apochromat oil-immersion objective for single islet cells and 40×/1.30 NA Plan-Apochromat oil-immersion objective for pancreatic tissue sections. All images for quantification within a single experiment were captured with the same laser power and detector gain. The ratio of GAD65 mean fluorescence intensity (MFI) in the Golgi compartment and post-Golgi vesicles compared with the rest of the cytosol was calculated with a custom ImageJ macro. Individual β-cells in a given field of view were identified and outlined by hand. For each cell, the macro automatically defined a region of interest (ROI) outlining the Golgi compartment, identified by giantin costain or by characteristic morphology and brightness thresholding of GAD65 stain, and GAD65+ vesicles, identified by brightness thresholding of GAD65+ bright puncta. A second ROI defined the remainder of the cell, excluding the Golgi, GAD65+ vesicles, and nucleus. GAD65 Golgi accumulation was reported as the ratio of MFI for the two ROIs.

      Staining and quantification of GAD65 Golgi accumulation

    1. Morphometric analysis was carried out with a semiautomated process, as described in Supplemental Methods and Supplemental Figure 1. This process allows analysis of the whole section. Lymph nodes (found in five slides from control Ab− subjects, 1%), and regions with edge artifacts or nonspecific staining were manually excluded. For each slide, we quantified the total tissue area and separated it into exocrine and nonexocrine tissue (adipose and mesenchymal tissues, large ducts, and vessels) vs the insulin-stained area. The percentage of the total tissue area was converted into mass by multiplying it by the weight of the pancreas when available. When the weight of the pancreas was not available, only the ratio of the endocrine area to the total area was calculated. We used Photoshop CS4 (Adobe Systems) to perform thresholding on whole sections, and measurements were performed using ImageJ (http://rsbweb.nih.gov/ij/). Given that only the cytoplasm was stained for β-cells using the insulin Ab, we developed another Photoshop script to quantify the surface of the entire islet.

      Morphometric analysis of section images

    1. Insulitis Leukocyte PhenotypingParaffin sections from blocks having the maximum insulitis frequency for each donor were stained, and positive leukocytes/insulitic islets (six or more CD3+ cells) were counted using multi-immunofluorescence. Serial sections (4 μm) were dewaxed and rehydrated with Tris buffer. Heat-induced antigen retrieval was performed using Trilogy (Cell Marque, Rocklin, CA) at 95° for 20 min followed by rinsing in water for 20 min. The staining series was designed to phenotype leukocytes (total leukocytes [CD45], T [CD3] and B [CD20] lymphocytes, T-lymphocyte subsets [CD4 and CD8], and monocytes [dendritic cells (CD11c) and macrophages (CD68)]) (21) in conjunction with subtyping islets for insulin immunopositivity. The staining series was as follows: 1) CD45+glucagon+insulin, 2) CD20+CD3+glucagon, 3) CD8+CD4+glucagon, and 4) CD11c+CD68+insulin. Chromogranin A staining was also used to delineate endocrine cells. Antigens are listed in order of primary antibody incubation, and the corresponding secondary antibody and conjugated fluorochrome (AF488-AF555-AF647) (Supplementary Table 2). After blocking, sections were sequentially incubated with the primary antibody followed by the appropriate secondary antibody. For anti-CD4, a Cy3 Tyramide Signal Amplification Kit (PerkinElmer, Waltham, MA) was used according to the manufacturer's instructions. All sections were mounted with ProLong Gold Antifade mounting media containing DAPI (Life Technologies, Grand Island, NY). Positive controls included human spleen, tonsil, and donor intrapancreatic lymph nodes, and negative controls included omission of the primary antibody.The numbers of leukocytes/insulitic islets were determined using multichannel image acquisition software on a Zeiss Axiophot microscope (AxioVision; Carl Zeiss Inc., Thornwood, NY). Fluorescent channels were viewed in combination with DAPI to count the number of positive leukocytes/islet.

      Insulitis Leukocyte Phenotyping

    2. β-Cell and α-Cell Area and MassInsulin- and glucagon-immunopositive areas were determined using the IHC sections to estimate β-cell and α-cell areas, respectively, in relation to total tissue area using a single Aperio colocalization algorithm (22). An average of six sections was used per donor (two sections/head, body, and tail regions). The β-cell and α-cell areas were expressed as a ratio (percent) to the total sectional area, including acinar and interstitial regions, to permit the use of pancreata weights. The average β-cell and α-cell area per pancreas was calculated from regional area averages. The β-cell or α-cell mass (in milligrams) was calculated by multiplication of the respective average area and pancreas weight (in grams).

      Beta Cell and Alpha Cell Area and Mass

    3. Insulitis Screening and Insulitic Islet Subtyping for Insulitis FrequenciesPancreata were processed to formalin-fixed paraffin blocks for each pancreas region (head, body, and tail) as previously described (20). For each donor, serial sections (average two blocks per region) were stained by hematoxylin-eosin and two double-immunohistochemistry (IHC) stains (Ki67 and insulin, CD3 and glucagon) (Supplementary Table 2) (21). When insulitic islets were found in a given donor, additional blocks were screened (as detailed below). Stained sections were scanned at ×20 magnification using an Aperio CS scanner (Leica/Aperio, Vista, CA), and all images were stored in an online pathology database (eSLIDE; Leica/Aperio).Screening for insulitic islets was performed on CD3+ glucagon–stained sections. An islet was defined as ≥10 α-cells. Insulitis was defined as an islet with six or more CD3+ cells immediately adjacent to or within the islet with three or more islets per pancreas section, according to recent criteria (4). Islets with insulitis were marked in an image layer using ImageScope software (Leica/Aperio). The two IHC serial images were aligned using the synchronization tool, and insulin+ islets were also marked on the image layer. All islets/sections from donors with insulitis were subsequently subtyped as follows: 1) insulin+ CD3−, 2) insulin+ CD3+, 3) insulin− CD3+, and 4) insulin− CD3− (see Table 1 for numbers of islets analyzed). Then, all islets were counted by subtype. The process was reversed for AAb+ donors (i.e., islet subtypes were counted using the Ki67-insulin image after markup for CD3+ insulitic islets and insulin− islets using the CD3-glucagon image). The number of pancreas sections subtyped for insulitis ranged from 2 to 16 sections/donor (8.1 ± 4.1 sections/donor, n = 162 sections) (Table 1). The lowest number of available sections was due to partial pancreas recovery (tail only in nPOD 6198).Insulitis frequency (percent) was calculated as the total number of insulitic islets (sum of insulin+ CD3+ and insulin− CD3+ islets) divided by the total number of islets (sum of four subtypes). The frequency of insulin+ insulitic islets in relation to the total number of insulin+ islets was determined by the ratio of (insulin+ CD3+ islets)/(sum of insulin+ CD3− and insulin+ CD3+ islets) with similar calculations for the frequency of insulin− insulitic islets (insulin− CD3+)/(sum of insulin− CD3+ and insulin− CD3−).

      Insulitis Screening and Insulitic Islet Subtyping for Insulitis Frequencies

      see also: https://www.ncbi.nlm.nih.gov/pubmed/24006089

    1. The pancreas was weighed intact and/or after dividing it into three regions (head, body and tail) by cutting the junction between head and body at the notch and dividing the remaining portion in half for body and tail regions.

      Pancreas weighing

    1. To monitor the cellular distribution of cyclin-D isoforms, pancreatic sections were stained with either an anti-cyclin-D3 antibody plus HRP–goat anti-mouse IgG and Alexa Fluor 488 tyramide (Life Technologies, Eugene, OR, USA), or an anti-cyclin-D1 antibody plus HRP–goat anti-rabbit IgG and Alexa Fluor 488 tyramide. The tyramide amplification steps were used to enhance the fluorescence signal and were performed according to the manufacturer’s instructions (Life Technologies). Pancreatic sections were co-stained with an anti-glucagon antibody raised in either mouse or rabbit (each from Abcam) and with guinea pig anti-insulin (Dako, Ely, UK) plus relevant secondary antibodies labelled with Alexa Fluor 568 and Alexa Fluor 647 (Life Technologies), respectively. Images were captured under fluorescence illumination using a Leica AF6000 microscope (Leica, Milton Keynes, UK).

      Staining for cyclin-D3 and cyclin-D1

    1. Insulitis was defined by a recent guideline which stipulates that the total number of leucocytes in close contact with the islet boundary (peri-insulitis) and within the intra-islet areas is equal to or greater than 15 [29].

      Quantification of insulitis

    2. Peri- and intra-islet CD45 cells were enumerated manually in all sections. For the diabetic group, observers were blinded to the case details, including autoantibody status. Islets with approximately ≥20 endocrine cells were analysed. Single glucagon and insulin cells scattered within the exocrine region were not enumerated, while sections from diabetic cases containing small islets (20 cells) but harbouring at least one insulin cell were recorded. The total numbers of insulin-positive and -negative islets in the pancreatic head, body and tail were also recorded in each section.

      Quantification of CD45 cells and islets

    3. Sections (5 μm) were de-paraffinised, rehydrated and subjected to antigen retrieval with citrate buffer containing 0.05% Tween-20 (Sigma-Aldrich, St Louis, MO, USA). During immunohistochemistry, PBS, pH 7.4, was employed as a wash step. Sections were equilibrated in PBS and blocked with 5% normal goat serum (Sigma-Aldrich) for 1 h at 37°C. A mixture of guinea pig anti-insulin serum (A0564, dilution 1:600; Dako, Glostrup, Denmark) and rabbit anti-glucagon serum (A0565, dilution 1:200; Dako) in 5% normal goat serum (Sigma-Aldrich) was applied and incubated for 1 h at 37°C. Highly cross-adsorbed species-specific goat anti-guinea pig IgG-Alexa 568 (A11075; Invitrogen, Eugene, OR, USA) and goat anti-rabbit IgG-Alexa 488 (A11034, dilution 1:600 in 5% normal goat serum; Invitrogen) were then applied as a mixture and incubated as in the previous step. Sections were incubated with mouse anti-human CD45 (M0701; Dako; clones 2B11 + PD7/26, dilution 1:100 in PBS + 0.1% Tween-20) for 16 h at 4°C, washed and reacted with 3% H2O2 for 15 min. After washing, sections were incubated sequentially with donkey anti-mouse IgG-biotin (715-065-150; Jackson ImmunoResearch, West Grove, PA, USA, dilution 1:200 in PBS + 0.1% Tween-20) and streptavidin-horseradish peroxidase (016-030-084; Jackson ImmunoResearch, dilution 1:200 in PBS/0.1% Tween-20). They were finally exposed to a diaminobenzidine chromogenic mixture (Sigma-Aldrich) to visualise CD45 cells. Non-immune serum or IgG from the immunising species and omission of primary antibodies acted as negative controls.Sections were examined with a Nikon Eclipse E600 microscope under epifluorescence and bright field microscopy and digital images were recorded. All islets in a section with ≥20 endocrine cells were imaged for the presence of insulin, glucagon and CD45 cells and each of the three image sets from multiple acquisitions merged with Adobe Photoshop CS4 following conversion of CD45-positive cells to a greyscale fluorescence mode.

      Staining and imaging of CD45, insulin, glucagon in pancreatic sections

    1. For morphometric comparison of human islets from 12-LO-positive and -negative cases, a composite image of 50 visual fields at ×200 magnification (area of 50 000 μm2) was captured using the mosaic function of AxioVision analysis software (v4.7; Zeiss). Five composite images of the pancreas were obtained per case, and at least 3 cases per group were studied. Areas positive for insulin or 12-LO were defined by setting a threshold signal for each antigen, and the size of the positive area was quantified automatically based on this threshold. Hematoxylin and eosin (HE)-stained images on the nPOD website from serial sections of the blocks we analyzed were used to determine the islet area for each donor. To quantitate the PP staining intensity, images of 30 islets from the uncinate and nonuncinate areas of pancreas sections immunostained for PP were analyzed for mean densitometric values of PP+ areas using AxioVision v4.7. To normalize staining, mean densitometric background staining with nonimmune serum was subtracted from values obtain for the PP sections. Observers blinded to the clinical information of donors performed all measurements

      12-LO staining and analysis

    1. Paraffin embedded pancreatic sections from female cadaveric donors including non-diabetic controls and type 2 diabetes patients were obtained from the Juvenile Diabetes Research Foundation (JDRF)-sponsored Network for Pancreatic Organ Donors with Diabetes (nPOD) program (http://www.jdrfnpod.org/for-investigators/online-pathology-information/). Insulin positive (Ins+) β-cells were used for staining and quantification.

      Staining and quantification of UPR markers

    1. Concomitant fluorescence in situ hybridization (FISH) and immunofluorescenceParaffin sections were deparaffinized in a xylene series followed by heat mediated antigen retrieval. Slides were then dehydrated in an ethanol series (70%, 80% and 100%) and air dried. Frozen sections were fixed in ice-cold pure ethanol for 10 min and air-dried prior to in situ hybridization. Purified human islets were cultured on rat tail type I collagen coated coverslips. They were fixed in 1% paraformaldehyde for 15 min, then simultaneously permeabilized and blocked in 0.1% (w/v) saponin (Sigma, S4521), 3% BSA solution for 45 min, and subsequently dehydrated in ethanol series. After pre-treatments, tissue sections or cell preparations were incubated with X/Y chromosome FISH probes (Vysis CEP X Spectrum Orange™/Y Spectrum Green™ Direct labelled Fluorescent DNA probe, Abbot Molecular Inc., Des Plaines, IL) as described previously [10]. To study aneuploidy/polyploidy, the CEP 18 probe (Abbot Molecular Inc.) was used in combination with the X/Y probe. After post-hybridization washes, sections or cells were incubated with primary antibodies (and controls as appropriate) specific for insulin (DAKO, A0564), glucagon (DAKO, A0565), somatostatin (Santa Cruz, SC-13099), GATA4 (Santa Cruz, SC-1237), Cytokeratin-19 (Abcam, Ab9221), Ki67 (Abcam, Ab833), CD68 (DAKO, m0876), CD45 (DAKO, M0701), nestin (Millipore MAB5326), or CD34 (Invitrogen, 073403) for 2 hrs at 37°C. After probing with the corresponding fluorescent secondary antibody for 1 hr at room temperature, sections were washed three times in 1× PBS solution, dehydrated, and counterstained with DAPI permanent mounting media (VECTASHIELD®, Vector laboratories, CA).Image analysisFluorescent imaging Images were captured using an Olympus BX41 fluorescent microscope fitted with an AxioCam MRm digital camera (Zeiss, Germany). The X and Y chromosomes were analyzed using the ISIS fluorescent imaging software (Zeiss, Germany) designed for FISH.Confocal microscopy To confirm MMc frequencies, pancreatic tissue sections were screened using a Leica SP5 confocal imaging system at the Wolfson Bioimaging Facility, School of Biochemistry, University of Bristol, UK. Z-stack images of each tissue section were analyzed for FISH and immunofluorescence to allow visualization of X and Y chromosome signals in all planes of the nucleus.Counting strategyFISH signals were checked in individual filter channels to ensure signal fidelity and that the visualization of two red dots representing two copies of the X chromosome was not caused by immunofluorescence cross talk. Only cells that showed clear XY (red and green dot) or XX (two red dots) signals within single nuclei were counted. Any cells with overlapping nuclei were excluded from the analysis. X chromosome “splits” that occasionally appeared as two juxtapositional dots due to DNA breakage were not considered as XX signals. A FISH success rate (the frequency of nuclei with two clear signals visible) of greater than 60% was required before further analysis.All T1D and matched control tissues were stained for FISH and insulin. Overall, as many nuclei as possible were counted (>1000 at least), including at least 20 islets per section. MMc were counted in insulin positive and insulin negative cell fractions. For T1D case 1, 2, and 3, MMc were also examined in CD45+ population. An additional independent, blinded scorer reviewed each candidate female cell.

      FISH and image quantification of MMc

    1. Human ADORA1-VAR. Human pancreas RNA samples with intact 18S and 28S rRNA were used for these studies. Primers were designed to examine if a human equivalent of the mouse Adora1-Var is expressed in the pancreas (Table 2, primer set 5). The forward primer spans exon 2 and 4 and only detects a splice variant lacking exon 3 of the ADORA1 gene ({"type":"entrez-nucleotide","attrs":{"text":"NM_000674.2","term_id":"115305570","term_text":"NM_000674.2"}}NM_000674.2). Primers targeting only exon 3 were designed to detect the full-length human ADORA1 gene (Table 2, primer set 4). For cloning into TOPO, primers spanning the start and stop codon of ADORA1-VAR were used (Table 2, primer set 3). Fusion proteins were synthesized by standard subcloning techniques using the PCR primers listed in Supplementary Table 1.

      Human ADORA1-VAR RT-PCR

    1. Paraffin-embedded pancreatic sections from diabetes-free controls and T1D patients were obtained from the Juvenile Diabetes Research Foundation (JDRF)-sponsored nPOD program (http://www.jdrfnpod.org/online-pathology.php). Sections were used for staining and quantification of expression levels of selected UPR markers.

      Staining and quantification of UPR markers

    1. Human Patient SamplesFrozen sections of human pancreata were obtained from the Network for Pancreatic Organ Donors with Diabetes (nPOD), a collaborative T1D research project sponsored by the Juvenile Diabetes Research Foundation International (JDRF). We used sample/CaseID as indicated on the nPOD website. A total of four T1D and five T2D samples were selected for this study based on their blood glucose, body mass index (BMI) values, and history of diabetes. Samples from healthy patients (n=5) were used as a control. All patient samples used in the study were excised from the tail of the pancreas.Immunohistochemistry of Pancreatic Sections and CellsHuman and mouse pancreatic sections were co-immunostained with IC2, anti-insulin and anti-glucagon or anti-somatostatin antibodies. Frozen sections (5–7-µm thick) were fixed with 4% formaldehyde for 5 min, washed with PBS and blocked with 2% BSA in PBS for 1 hr at room temperature. IC2 (1 µg/ml) and guinea pig anti-insulin (Abcam, Cambridge, MA) and rabbit anti-glucagon antibodies (Abcam), diluted to 1:50 and 1:100, respectively, were mixed in 2% BSA, added to the sections and incubated at room temperature for 2 hr. Then, sections were washed with PBS and incubated in the mixture of goat anti-rat IgM-AF594 (1: 1000 dilution, Invitrogen, Carlsbad, CA), goat anti-guinea pig IgG (H+L)-FITC (1:200 dilution, Abcam) and goat anti-rabbit IgG (H+L) AF-680 (1:500 dilution, Invitrogen) for 2 hr. Next, the sections were washed with PBS, mounted in ProLong® Gold anti-fade reagent with DAPI (Invitrogen), and observed under fluorescence microscope. For pancreatic sections from human patients, the fluorescence intensity was scored on a scale of 1 to 5 by two blinded investigators. In all of these experiments, an irrelevant purified rat monoclonal IgM was used as control for IC2. Staining without primary antibodies was also used as a control.

      Staining and scoring for IC2, insulin, glucagon and somatostatin

    1. Combined immunofluorescence For co-localisation studies, anti-insulin and anti-VP1 immunoreactivity were detected using an AlexaFluor 488-conjugated anti-guinea pig antibody and an AlexaFluor 568-conjugated anti-mouse antibody, respectively (Invitrogen, Paisley, UK). To determine whether the enteroviral VP1 protein co-localised with either PKR or Mcl-1 in beta cells, primary antibodies were incubated as described in ESM Table 3. The primary antibodies were detected with relevant goat secondary antibodies conjugated to AlexaFluor 488 or 568 (Invitrogen) or with goat anti-guinea pig DyLight 405 (Stratech, Newmarket, UK). Control sections were stained with relevant primary and secondary antisera to confirm that no cross-reactivity was detected. Sections were mounted in Vectashield hard-set mounting medium (Vector Laboratories, Peterborough, UK) under glass coverslips. Images were captured using a Nikon Eclipse 80i microscope (Nikon, Kingston upon Thames, UK) and overlaid using NIS-Elements BR 3.0 software (Nikon) to study the relative localisation of each antigen. Sections directly adjacent to those stained using the combined method were stained with an anti-glucagon (rabbit; Dako) or an anti-insulin (guinea pig; Dako) antibody using a standard immunoperoxidase technique to determine total islet numbers in the sections and to distinguish insulin-containing islets (ICIs) from insulin-deficient islets (IDIs).

      VP1 combined staining with insulin, PKR and Mcl-1

    1. Quantitative real-time RT–PCR in tissue, cells, and polysome fractionsTotal RNA was extracted using Trizol reagent and the Qiagen RNeasy mini kit or micro kit, as previously described (Yip et al., 2009). First-strand cDNA was generated using the iScript cDNA synthesis kit (Biorad). Quantitative PCR was performed to measure mouse Eif4g3, Eif4g1, Casp3, Deaf1, Ins2, Fgb, Ela, Ppy, Tyr, Ambp, Gapdh, and Actb mRNA levels, and human EIF4G3, EIF4G1, CASP3 and ACTB, and GAPDH mRNA levels. cDNA was preamplified using the Taqman PreAmp Mastermix (Applied Biosystems) prior to QPCR for Ins2 and for gene expression measured in polysome fractions and LNSC subsets. For all other experiments, cDNA was not pre-amplified. qPCR assays were performed using the 7900HT Fast Real Time PCR System (Applied Biosystems), Taqman Gene Expression Arrays (Applied Biosystems), and SsoFast Probes Supermix (Biorad). For human CASP3 measurements, Quantitect primers (Qiagen) and SsoFast EvaGreen Supermix (Biorad) were used. The comparative Ct method for relative quantification (ΔΔCt) was used, and expression was normalized with housekeeping gene expression.

      Quantitative RT-PCR of human (EIF4G3, EIF4G1, CASP3, ACTB, GAPDH) and mouse

    1. Initial screening for insulin, MHC class I, and CD8. For characterization of the sections before tetramer staining, staining for insulin, CD8, and HLA-ABC (all at room temperature) was performed using the antibodies listed in Table 3. Fixation was with acetone and sections were blocked with goat serum in a standard immunofluorescent staining protocol. An islet was determined as hyperexpressing MHC class I based on a threshold of three unequivocally positive cells, which was the maximum number found in all control islets examined.Table 3.Antibodies used for characterization of the sections prior to tetramer stainingAntigenPrimary antibodyDetection antibodyInsulinPolyclonal guinea pig anti-insulin (Dako; 1/140 dilution, 1-h incubation)Polyclonal goat anti–guinea pig IgG, highly cross-adsorbed, Alexa Fluor 488 (Invitrogen; 1/1000 dilution, 30-min incubation)HLA-ABCMouse monoclonal (clone W6/32) IgG2a against a monomorphic epitope on the 45 kD polypeptide products of the HLA-A, B and C loci (Dako; 1/100 dilution, 1 h incubation)Polyclonal goat anti–mouse IgG2a, isotype-specific, Alexa Fluor 594 (Invitrogen; 1/1000 dilution, 30-min incubation)CD8Mouse monoclonal (clone HIT8a) IgG1 against the CD8 alpha subunit (BD; 1/100 dilution, 1-h incubation)F(ab’)2 fragment of goat anti-mouse IgG, Alexa Fluor 594 (Invitrogen; 1/1000 dilution, 30-min incubation)

      Initial screening for insulin, MHC class I, and CD8

    1. nPOD central AAb core laboratoryThe nPOD central AAb core laboratory has a long history of excellence in the type 1 diabetes AAb field 10, 13, 15, 21, 22, participating routinely in the Diabetes AAb Standardization Program (DASP), now renamed IASP. The nPOD central AAb core began testing for ZnT8A in nPOD cases using RBA prior to the introduction of the ZnT8A ELISA. This core also tests for IAA as there is currently no reliable ELISA for this analyte. Every case with available serum that is referred to nPOD is tested via RBA for GADA, IA‐2A, ZnT8A and IAA for either confirmation of the ELISA screening results, or for determination of final AAb status. In cases of discrepancy, the RBA supersedes the ELISA as the result reported on the nPOD website (www.jdrfnpod.org).

      nPOD AAb core laboratory

    1. Morphometric analysis Beta cell area (defined as the area of cells stained by insulin) was quantified using computer-assisted morphometric analysis of slides digitally stored in the Aperio System. We obtained the ratio between the insulin stained areas and the total pancreatic section area, i.e. the relative volume of beta cells in patients with diabetes and in normal controls. For each diabetic pancreas we then determined the beta cell index defined as the relative volume divided by the mean relative volume of beta cells of controls multiplied by 100. The beta cell index thus expressed the beta cell area of each diabetic pancreas as a percentage of normal control pancreases. Double immunofluorescent staining for insulin and glucagon was used to determine the percentage of islets devoid of beta cells in pancreases with residual beta cells (i.e. the percentage of insulin-deficient islets). In each section we determined the percentage of insulin-deficient islets (i.e. islets composed of non-beta cell vs the total number of islets). Only islets completely devoid of beta cells were considered insulin-deficient islets.

      Morphometric analysis of beta cells and islets

    2. For each childhood-onset case, the pancreas was processed in three regions (head, body and tail) and fixed overnight in 10% (vol./vol.) neutral buffered formalin (ThermoFisher, Waltham, MA, USA) prior to processing to paraffin blocks. Tissue sections (5 μm) were obtained stained with haematoxylin and eosin, immunohistochemistry and immunofluorescence. Pancreatic tissue sections were stained: (1) with antibodies to insulin or glucagon, and a panleucocyte marker CD45 and/or a T cell marker (CD3; and in a subset of diabetic pancreases with CD4, CD8, CD20 and CD68 antibodies); and (2) with a cocktail of antibodies to endocrine non-beta cell hormones composed of glucagon, pancreatic polypeptide and somatostatin. In diabetic pancreases with insulitis (as defined below), the infiltrating T cells were further characterised with antibodies to CD4 and CD8 (as well antibodies to CD20 and CD68 to detect B lymphocytes and macrophages). Antibody binding was detected with appropriate secondary antibodies to guinea pig, rabbit and mouse immunoglobulins conjugated with alkaline phosphatase and peroxidase (MACH2 Polymer Systems; Biocare, Concord, CA, USA), followed by chromagen development with fast red (Vector, Burlingame, CA, USA) or diaminobenzidine (Vector). A scanner (CS ScanScope; Aperio, Vista, CA, USA) was used to produce whole slide images for both haematoxylin and eosin and immunohistochemistry-stained slides. In a subset of diabetic pancreases (pancreases containing residual beta cells), the pancreas was also double-stained with antibodies to insulin and class I HLA molecules. Immunofluorescence was performed by incubating the tissue sections with antibody to insulin, glucagon and survivin (Abcam, Cambridge, MA, USA), and with appropriate anti-rabbit and anti-guinea pig immunoglobulins conjugated with aminomethylcoumarin acetate, Cy3 or Cy5. The sections were photographed at ×20 magnification using a microscope B651 (Olympus America, Center Valley, PA, USA) connected with a digital imaging system (Image pro plus, version 6.2; Media Cybernetics, Bethesda, MD, USA) with a camera (Pro 150ES; Pixera, San Jose, CA, USA). Additional images were recorded on an epifluorescence microscope (Microphot FXA; Nikon Instruments, Melville, NY, USA) with a monochrome digital camera (Roper Micromax; PerkinElmer, Waltham, MA, USA) and appropriate software (Intelligent Imaging Innovations, Denver, CO, USA). The photos are displayed in pseudocolour. Immunoperoxidase staining of survivin was performed by incubating the sections with antibody to survivin and visualising with Cytomation Envision+System-HRP (DAB; Dako, Carpenteria, CA, USA). Class I HLA immunostaining was performed with mouse monoclonal anti-human HLA-ABC, Clone W6/32 (Dako), directed against a monomorphic epitope on the 45 kDa polypeptide products of the HLA-A, -B and -C loci. Insulin was detected with polyclonal guinea pig anti-swine insulin (Dako). All secondary goat antibodies (Life Technologies, Carlsbad, CA, USA) were highly cross-adsorbed against the host species of the other primary antibodies and conjugated with Alexa 488 (green) or 594 (red). Anti-fade reagent (Prolong Gold) with DAPI (Life Technologies) was applied upon staining and sections were imaged using an epifluorescence microscope (Eclipse 80i; Nikon) equipped with a mercury arc lamp (X-Cite, Mississauga, ON, Canada) and a digital camera (DXM1200C; Nikon). An air objective (×20; Nikon) was used with a 0.75 N.A. Positive and negative controls for HLA-ABC staining included human spleen and isotope-matched primaries, respectively. Cross-reactivity with the insulin primary was also experimentally excluded. In additional experiments, HLA immunofluorescence staining was performed with rabbit polyclonal antibody (Abcam). An irrelevant rabbit polyclonal antibody served as a negative control.

      Insulin, glucagon, survivin and HLA staining

  10. Jan 2020
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