Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles
Background Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kang, Randy B. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2023 |
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| Übergeordnetes Werk: |
Enthalten in: Genome medicine - London : BioMed Central, 2009, 15(2023), 1 vom: 01. Mai |
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| Übergeordnetes Werk: |
volume:15 ; year:2023 ; number:1 ; day:01 ; month:05 |
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| DOI / URN: |
10.1186/s13073-023-01179-2 |
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| Katalog-ID: |
SPR050257110 |
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| 245 | 1 | 0 | |a Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles |
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| 520 | |a Background Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. Methods We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. Results First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. Conclusions In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo. | ||
| 650 | 4 | |a Diabetes |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Human islet |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Human pancreatic beta cell |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Single-cell RNA sequencing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Single-nucleus RNA sequencing |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Lu, Geming |4 aut | |
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10.1186/s13073-023-01179-2 doi (DE-627)SPR050257110 (SPR)s13073-023-01179-2-e DE-627 ger DE-627 rakwb eng Kang, Randy B. verfasserin aut Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. Methods We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. Results First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. Conclusions In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo. Diabetes (dpeaa)DE-He213 Human islet (dpeaa)DE-He213 Human pancreatic beta cell (dpeaa)DE-He213 Single-cell RNA sequencing (dpeaa)DE-He213 Single-nucleus RNA sequencing (dpeaa)DE-He213 Human islet graft (dpeaa)DE-He213 Li, Yansui aut Rosselot, Carolina aut Zhang, Tuo aut Siddiq, Mustafa aut Rajbhandari, Prashant aut Stewart, Andrew F. aut Scott, Donald K. aut Garcia-Ocana, Adolfo (orcid)0000-0002-6883-6176 aut Lu, Geming aut Enthalten in Genome medicine London : BioMed Central, 2009 15(2023), 1 vom: 01. Mai (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:15 year:2023 number:1 day:01 month:05 https://dx.doi.org/10.1186/s13073-023-01179-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 01 05 |
| spelling |
10.1186/s13073-023-01179-2 doi (DE-627)SPR050257110 (SPR)s13073-023-01179-2-e DE-627 ger DE-627 rakwb eng Kang, Randy B. verfasserin aut Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. Methods We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. Results First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. Conclusions In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo. Diabetes (dpeaa)DE-He213 Human islet (dpeaa)DE-He213 Human pancreatic beta cell (dpeaa)DE-He213 Single-cell RNA sequencing (dpeaa)DE-He213 Single-nucleus RNA sequencing (dpeaa)DE-He213 Human islet graft (dpeaa)DE-He213 Li, Yansui aut Rosselot, Carolina aut Zhang, Tuo aut Siddiq, Mustafa aut Rajbhandari, Prashant aut Stewart, Andrew F. aut Scott, Donald K. aut Garcia-Ocana, Adolfo (orcid)0000-0002-6883-6176 aut Lu, Geming aut Enthalten in Genome medicine London : BioMed Central, 2009 15(2023), 1 vom: 01. Mai (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:15 year:2023 number:1 day:01 month:05 https://dx.doi.org/10.1186/s13073-023-01179-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 01 05 |
| allfields_unstemmed |
10.1186/s13073-023-01179-2 doi (DE-627)SPR050257110 (SPR)s13073-023-01179-2-e DE-627 ger DE-627 rakwb eng Kang, Randy B. verfasserin aut Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. Methods We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. Results First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. Conclusions In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo. Diabetes (dpeaa)DE-He213 Human islet (dpeaa)DE-He213 Human pancreatic beta cell (dpeaa)DE-He213 Single-cell RNA sequencing (dpeaa)DE-He213 Single-nucleus RNA sequencing (dpeaa)DE-He213 Human islet graft (dpeaa)DE-He213 Li, Yansui aut Rosselot, Carolina aut Zhang, Tuo aut Siddiq, Mustafa aut Rajbhandari, Prashant aut Stewart, Andrew F. aut Scott, Donald K. aut Garcia-Ocana, Adolfo (orcid)0000-0002-6883-6176 aut Lu, Geming aut Enthalten in Genome medicine London : BioMed Central, 2009 15(2023), 1 vom: 01. Mai (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:15 year:2023 number:1 day:01 month:05 https://dx.doi.org/10.1186/s13073-023-01179-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 01 05 |
| allfieldsGer |
10.1186/s13073-023-01179-2 doi (DE-627)SPR050257110 (SPR)s13073-023-01179-2-e DE-627 ger DE-627 rakwb eng Kang, Randy B. verfasserin aut Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. Methods We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. Results First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. Conclusions In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo. Diabetes (dpeaa)DE-He213 Human islet (dpeaa)DE-He213 Human pancreatic beta cell (dpeaa)DE-He213 Single-cell RNA sequencing (dpeaa)DE-He213 Single-nucleus RNA sequencing (dpeaa)DE-He213 Human islet graft (dpeaa)DE-He213 Li, Yansui aut Rosselot, Carolina aut Zhang, Tuo aut Siddiq, Mustafa aut Rajbhandari, Prashant aut Stewart, Andrew F. aut Scott, Donald K. aut Garcia-Ocana, Adolfo (orcid)0000-0002-6883-6176 aut Lu, Geming aut Enthalten in Genome medicine London : BioMed Central, 2009 15(2023), 1 vom: 01. Mai (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:15 year:2023 number:1 day:01 month:05 https://dx.doi.org/10.1186/s13073-023-01179-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 01 05 |
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10.1186/s13073-023-01179-2 doi (DE-627)SPR050257110 (SPR)s13073-023-01179-2-e DE-627 ger DE-627 rakwb eng Kang, Randy B. verfasserin aut Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. Methods We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. Results First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. Conclusions In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo. Diabetes (dpeaa)DE-He213 Human islet (dpeaa)DE-He213 Human pancreatic beta cell (dpeaa)DE-He213 Single-cell RNA sequencing (dpeaa)DE-He213 Single-nucleus RNA sequencing (dpeaa)DE-He213 Human islet graft (dpeaa)DE-He213 Li, Yansui aut Rosselot, Carolina aut Zhang, Tuo aut Siddiq, Mustafa aut Rajbhandari, Prashant aut Stewart, Andrew F. aut Scott, Donald K. aut Garcia-Ocana, Adolfo (orcid)0000-0002-6883-6176 aut Lu, Geming aut Enthalten in Genome medicine London : BioMed Central, 2009 15(2023), 1 vom: 01. Mai (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:15 year:2023 number:1 day:01 month:05 https://dx.doi.org/10.1186/s13073-023-01179-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 1 01 05 |
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Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles Diabetes (dpeaa)DE-He213 Human islet (dpeaa)DE-He213 Human pancreatic beta cell (dpeaa)DE-He213 Single-cell RNA sequencing (dpeaa)DE-He213 Single-nucleus RNA sequencing (dpeaa)DE-He213 Human islet graft (dpeaa)DE-He213 |
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single-nucleus rna sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles |
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Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles |
| abstract |
Background Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. Methods We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. Results First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. Conclusions In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo. © The Author(s) 2023 |
| abstractGer |
Background Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. Methods We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. Results First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. Conclusions In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo. © The Author(s) 2023 |
| abstract_unstemmed |
Background Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. Methods We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. Results First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. Conclusions In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo. © The Author(s) 2023 |
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Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles |
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https://dx.doi.org/10.1186/s13073-023-01179-2 |
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Li, Yansui Rosselot, Carolina Zhang, Tuo Siddiq, Mustafa Rajbhandari, Prashant Stewart, Andrew F. Scott, Donald K. Garcia-Ocana, Adolfo Lu, Geming |
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Li, Yansui Rosselot, Carolina Zhang, Tuo Siddiq, Mustafa Rajbhandari, Prashant Stewart, Andrew F. Scott, Donald K. Garcia-Ocana, Adolfo Lu, Geming |
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