HHS Public Access Author manuscript Author Manuscript
Cell Metab. Author manuscript; available in PMC 2017 October 11. Published in final edited form as: Cell Metab. 2016 October 11; 24(4): 616–626. doi:10.1016/j.cmet.2016.09.007.
Single-cell mass cytometry analysis of the human endocrine pancreas Yue J. Wang1, Maria L. Golson1, Jonathan Schug1, Daniel Traum2, Chengyang Liu3, Kumar Vivek4, Craig Dorrell5, Ali Naji3, Alvin C. Powers6, Kyong-Mi Chang2, Markus Grompe5, and Klaus H. Kaestner1,* 1Department
Author Manuscript
of Genetics and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
2Medical
Research, Corporal Michael J. Crescenz Veterans Affairs Medical Center and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA 3Department
of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
4Albert
Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10467, USA
5Oregon
Stem Cell Center, Papé Family Pediatric Research Institute, Oregon Health and Science University, Portland, Oregon 97239, USA
6Departments
Author Manuscript
of Molecular Physiology and Biophysics and Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, 19147, USA
Summary The human endocrine pancreas consists of multiple cell types and plays a critical role in glucose homeostasis. Here, we apply mass cytometry technology to measure all major islet hormones, proliferative markers, and readouts of signaling pathways involved in proliferation at single-cell resolution. Using this innovative technology, we simultaneously examined baseline proliferation levels of all endocrine cell types from birth through adulthood, as well as in response to the mitogen harmine. High-dimensional analysis of our marker protein expression revealed three major clusters of beta-cells within individuals. Proliferating beta-cells are confined to two of the clusters.
Author Manuscript
Graphical Abstract *
Lead Contact. Correspondence to: [email protected]. Author contributions Conceptualization: K.H.K; Methodology: Y.J.W.; Formal Analysis: Y.J.W., M.L.G., and J.S. Investigation: Y.J.W. and M.L.G. Resources: D.T., A.C.P., A.N., C.L., K-M.C., M.G., and C.D; Writing: Original Draft: Y.J.W. and M.L.G.; Writing: Review and Editing: Y.J.W., M.L.G., D.T., and K.H.K.; Visualization: Y.J.W., M.L.G., and J.S.; Supervision: M.L.G. and K.H.K.; Funding Acquisition: K.H.K.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Wang et al.
Page 2
Author Manuscript Introduction Author Manuscript
The endocrine pancreas is organized into the islets of Langerhans and consists of at least five different endocrine cell types, each characterized by the production of a major hormone (Andralojc et al., 2009; Stefan et al., 1982). Cellular heterogeneity exists within each endocrine cell type. For instance, the insulin-producing beta-cells differ considerably in their metabolic responsiveness to glucose stimulation (Kiekens et al., 1992; Schuit et al., 1988; Van Schravendijk et al., 1992). Traditional methods to evaluate human pancreatic endocrine cell composition and function are either laborious, lack resolution, or both. While fluorescence-activated cell sorting captures a set of cellular parameters (Davey and Kell, 1996; Perfetto et al., 2004), spectral overlap limits multiplexing capability (Perfetto et al., 2004).
Author Manuscript
The recently developed mass cytometry technology greatly facilitates high-dimensional, quantitative analysis of biological samples at the single-cell level in a high throughput fashion (Bandura et al., 2009; Bendall et al., 2011; Ornatsky et al., 2010). In mass cytometry, antibodies are conjugated with lanthanide heavy metals instead of fluorophores, and their abundances are measured as discrete isotope masses (Bandura et al., 2009). As a result, mass cytometry is free of fluorescent bleeding and limited only by the number of unique elemental tags available within the detection range of the instrument (Bandura et al., 2009). Furthermore, the use of rare earth metals reduces background signal, and thus mitigates the issue of “autofluorescence” (Bendall et al., 2011). Since its introduction in 2011, mass cytometry has been employed in the field of immunology to great benefit (Bendall et al., 2011; Horowitz et al., 2013; Newell et al., 2012). Here, we adapt mass cytometry to examine cellular heterogeneity within the human endocrine pancreas at the molecular level.
Author Manuscript
Results Overview of mass cytometry technology applied to human islets Human pancreatic islet cells and cells isolated along with the islets were labeled with a total of 24 antibodies that passed quality-control (Figures 1A and S1). The targets of these antibodies include the following groups: (1) markers of pancreatic subpopulations, such as C-PEPTIDE (beta cells), GLUCAGON (alpha cells), SOMATOSTATIN (delta cells), POLYPEPTIDE (PP cells), GASTRIN (GASTRIN cells), GHRELIN (epsilon cells), PDX1
Cell Metab. Author manuscript; available in PMC 2017 October 11.
Wang et al.
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Author Manuscript Author Manuscript
(beta and delta cells), HNF1B (ductal cells) and CD49F (Integrin α6, acinar, ductal and subgroups of endocrine cells) (Sugiyama et al., 2007; Wang et al., 2014); (2) a replication marker, Ki67; (3) markers associated with beta-cell proliferation and metabolic activities, such as PDGFRA (Chen et al., 2011), pCREB (Hussain et al., 2006; Jhala et al., 2003), pERK1/2 (Bernal-Mizrachi et al., 2014), pS6 (Balcazar et al., 2009), pSTAT3 (Saxena et al., 2007), pSTAT5 (Jackerott et al., 2006; Nielsen et al., 2001); (4) signaling pathway reporters, such as AXIN2 for WNT signaling, which functions during pancreas development, beta-cell proliferation, and pathophysiology of diabetes (Dabernat et al., 2009; Jho et al., 2002; Rulifson et al., 2007; Sladek et al., 2007), Cleaved-CASPASE3 (Cl-CASPASE3) for apoptosis, CPY26A1 for the retinoic acid pathway, which plays an important role in betacell maturation (Loudig et al., 2005; Micallef et al., 2005; Ostrom et al., 2008), and GATA2 for variability in chromatin accessibility (Buenrostro et al., 2015); and (5) markers of betacell heterogeneity, such as CD9 and ST8SIA1 (Dorrell et al., 2016) (Figure S2; Tables S1 and S2). In addition, an iridium-containing DNA interchelator was used as a cell indicator and cisplatin as a viability marker (Table S1) (Fienberg et al., 2012; Ornatsky et al., 2008). Data were analyzed using both traditional two-dimensional maps and multi-parametric analysis algorithms (Figure 1B). Biaxial maps were used for initial gating and assessment of antibody labeling efficiency and specificity (Figures 1C and S2A). Event length, the DNA intercalator iridium, and cisplatin exclusion were used to gate live single cells for downstream analysis (Figure 1C). Islet cells from 20 different donors, covering ages from 18 days to 65 years, were examined (Table S3). Four donors were at or below the age of two, two were 17, and 14 donors were 20 years of age or older. Of donors 17 and older, 13 displayed normal blood glucose homeostasis and 3 had been diagnosed with type 2 diabetes (T2D) (Table S3).
Author Manuscript
Islet cell types form distinct clusters We performed analysis with viSNE, a visualization tool based on the t-SNE (t-distributed stochastic neighbor embedding) algorithm that maps multi-parameter relationships of cellular data into two dimensions (Amir el et al., 2013; Maaten, 2008), on cells from all donors. Cells were mapped by viSNE using all available antibody channels (a total of 24 antibodies, Figure S2 and Table S1). Technical variation between batches and and biological variation between donors prevented clustering of multiple samples on the same viSNE plot. Even when samples were barcoded and processed together, donor variation dominated the tSNE dimension (Figure S3).
Author Manuscript
Accordingly, we used viSNE to visualize cells on a per-donor basis. As expected, major cell clusters were formed largely based on expression of the major endocrine hormones produced by each islet cell subtype (Figures 2 and S2B). Both endocrine and exocrine cells exhibited high levels of EpCAM expression, indicating epithelial origin (Figures 2A, 2B and S2B) (Trzpis et al., 2007). In accordance with the literature, CD49F was enriched in acinar and ductal cells as well as in a subset of endocrine cells (Sugiyama et al., 2007; Wang et al., 2014). Additionally, CD49F was expressed in a subset of cells that was negative for EpCAM, suggesting the existence of a previously unidentified mesenchymal subtype within islet preparations (red arrows in Figures 2 and S2B, and Figure S4) (Yu et al., 2012).
Cell Metab. Author manuscript; available in PMC 2017 October 11.
Wang et al.
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Mass cytometry facilitates accurate and rapid quantification of human islet composition
Author Manuscript Author Manuscript
Based on the clusters assembled by the viSNE graph, we gated endocrine subtypes to evaluate the endocrine cell composition for all donors (Figures 2 and 3A). The proportion of endocrine cells varied dramatically from donor to donor, with beta-cell percentages ranging from 25% to 80%, alpha-cell from 2% to 67%, and delta-cell from 5% to 25%. PP and epsilon cells constitute a small proportion of endocrine cells, with epsilon cells only appreciable (more than 2%) in donors less than two years of age (Table S4). The variability in cellular composition was partially age-dependent. Within control donors, delta- and epsilon-cell frequencies displayed a significant negative correlation with age (p=0.042 and 0.00061, respectively, linear regression t-test). In our samples, the percentage of beta-cells plateaued at 19 month old and declined thereafter. The average beta-cell percentage for children was 65.42 ± 16.08 %, for non-diabetic adult donors 42.49 ± 13.81%, and for T2D donors 38.69 ± 14.46%. The age-related rapid decrease of delta- and epsilon-cell percentages and the early plateau of beta-cell abundance are consistent with previous reports (Andralojc et al., 2009; Gregg et al., 2012; Rahier et al., 1981; Stefan et al., 1983). The fractions of cell populations observed by mass cytometry analysis correlated with those seen by immunolabeling from tissue sections of the same donors (Figure 3B–G). Mass cytometry allows simultaneous quantification of proliferation in all endocrine cell types
Author Manuscript
In addition to evaluating the cellular composition of human islets in a high-throughput manner, we also incorporated the proliferation marker Ki67 in our mass cytometry assay (Figure 4). We confirmed that Ki67 was marking cells that had entered the S-phase of the cell cycle by co-labelling cells with both Ki67 and IdU. We detected a high level of correlation between these two proliferation markers (Figure S5). We next examined how proliferation changes with age in each endocrine population. Confirming previous reports, we demonstrated that beta-cell proliferation was highest in the neonatal sample, followed by an exponential decline after childhood (Figure 4A) (Butler et al., 2003; Gregg et al., 2012). Like beta cells, alpha and delta cells displayed diminished proliferation with age (Figures 4B and 4C). Of the three major endocrine cell types, alpha cells had the highest basal replication (p=0.001 between alpha and beta cells; p=3.85×10−5 between alpha- and delta-cells; paired t-test); this phenomenon was observed throughout the human life-span (Figure 4D and Table S5). Harmine stimulates proliferation of multiple endocrine cell populations
Author Manuscript
The multiparametric measurement capability of mass cytometry provides a unique opportunity to monitor a variety of signaling pathways in multiple cell types in response to various stimuli, such as drug treatment. In a proof-of-principle experiment, we treated islets with the DYRK1A inhibitor harmine, which increases human beta-cell replication (Wang et al., 2015a). Employing mass cytometry, we found no significant change in endocrine cell composition upon harmine treatment (paired t-test), likely because the mitogenic effect of harmine is not restricted to one population (Figures 5A, 5B and S6 and Table S6). Intriguingly, alpha cells from control as well as T2D adults maintained a significantly more robust response to mitogenic signals than beta, delta and PP cells (p≤0.001, two-way
Cell Metab. Author manuscript; available in PMC 2017 October 11.
Wang et al.
Page 5
Author Manuscript
ANOVA with Tukey’s correction) (Figure 5C). Harmine had a comparable effect in stimulating replication in T2D endocrine cells, indicating that a long-standing impaired metabolic state did not prevent a proliferative response of endocrine cells to this drug (Figure 5B). Due to the low cell numbers of epsilon and PP populations, the measurement of their proliferation in the adult samples were not as accurate (Figure 5B and Table S5). Mass cytometry reveals multiple beta-cell states
Author Manuscript
Mass cytometry provides the unique opportunity to interrogate a large number of cells with multiple parameters at the single-cell level in parallel, thus facilitating the discovery of novel biology, such as the identification of rare cell types and the revelation of subtype-specific behavior (Bendall et al., 2011; Bodenmiller et al., 2012). To take advantage of this opportunity, we gated exclusively for beta-cells and then performed viSNE analysis on each donor, using all antibody channels except non-beta hormone markers. This process segregated beta-cells into several groups within each donor, indicating the existence of betacell subtypes, or, at a minimum, beta-cell states (Figure 6A). Using contour maps as a guide, we hand-delineated beta-cell clusters (Figure S7A). The existence of multiple beta-cell states is consistent with a recent finding by means of flow cytometry that multiple subtypes exist within β-cells (Dorrell et al., 2016). Intriguingly, beta cells with high Ki67+ cells often segregated to only one of these subtypes (Figure 6B), even when clustering was performed with the exclusion of the Ki67 channel (data not shown). This result may indicate that a state of beta-cells exists that is poised to enter the cell cycle.
Author Manuscript
We next asked whether features from these subgroups were consistent among all the donors. To this end, we performed hierarchical clustering on each of the subgroups acquired from viSNE mapping, plus addition groups containing Ki67+ beta cells from every donor (Figure 7A). Normalized median protein expression levels of each beta-cell group were used as the input for this process. Subgroups from all the donors organized into three main clusters, labelled C1, C2 and C3, each with distinct protein expression patterns (Figure 7A). The groups of Ki67+ beta cells segregated to C2 and C3 (magenta and blue arrows, Figure 7A). The three clusters generated by the heatmap mostly corresponded with the original beta-cell group annotations based on cell density separation on viSNE, albeit with fewer clusters apparent in each donor than originally called (Figure S7). The C2 cluster mapped to the group of beta cells containing most Ki67+ cells.
Author Manuscript
Donor-to-donor variations in the percentage of cells contained within each cluster were noticeable (Figure 7B). To further explore factors involved in the partition of cells in each cluster, we built a regression model with age, T2D status, gender, ethnicity and BMI as predictors. The regression model indicated that fraction of cells in C1 positively correlated with age and negatively correlated with BMI, while the percentage of cells in the C2 cluster correlated negatively with T2D status and the fraction of cells in the C3 cluster correlated negatively with age (p
Cell Metab. Author manuscript; available in PMC 2017 October 11. Published in final edited form as: Cell Metab. 2016 October 11; 24(4): 616–626. doi:10.1016/j.cmet.2016.09.007.
Single-cell mass cytometry analysis of the human endocrine pancreas Yue J. Wang1, Maria L. Golson1, Jonathan Schug1, Daniel Traum2, Chengyang Liu3, Kumar Vivek4, Craig Dorrell5, Ali Naji3, Alvin C. Powers6, Kyong-Mi Chang2, Markus Grompe5, and Klaus H. Kaestner1,* 1Department
Author Manuscript
of Genetics and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
2Medical
Research, Corporal Michael J. Crescenz Veterans Affairs Medical Center and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA 3Department
of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
4Albert
Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10467, USA
5Oregon
Stem Cell Center, Papé Family Pediatric Research Institute, Oregon Health and Science University, Portland, Oregon 97239, USA
6Departments
Author Manuscript
of Molecular Physiology and Biophysics and Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, 19147, USA
Summary The human endocrine pancreas consists of multiple cell types and plays a critical role in glucose homeostasis. Here, we apply mass cytometry technology to measure all major islet hormones, proliferative markers, and readouts of signaling pathways involved in proliferation at single-cell resolution. Using this innovative technology, we simultaneously examined baseline proliferation levels of all endocrine cell types from birth through adulthood, as well as in response to the mitogen harmine. High-dimensional analysis of our marker protein expression revealed three major clusters of beta-cells within individuals. Proliferating beta-cells are confined to two of the clusters.
Author Manuscript
Graphical Abstract *
Lead Contact. Correspondence to: [email protected]. Author contributions Conceptualization: K.H.K; Methodology: Y.J.W.; Formal Analysis: Y.J.W., M.L.G., and J.S. Investigation: Y.J.W. and M.L.G. Resources: D.T., A.C.P., A.N., C.L., K-M.C., M.G., and C.D; Writing: Original Draft: Y.J.W. and M.L.G.; Writing: Review and Editing: Y.J.W., M.L.G., D.T., and K.H.K.; Visualization: Y.J.W., M.L.G., and J.S.; Supervision: M.L.G. and K.H.K.; Funding Acquisition: K.H.K.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Wang et al.
Page 2
Author Manuscript Introduction Author Manuscript
The endocrine pancreas is organized into the islets of Langerhans and consists of at least five different endocrine cell types, each characterized by the production of a major hormone (Andralojc et al., 2009; Stefan et al., 1982). Cellular heterogeneity exists within each endocrine cell type. For instance, the insulin-producing beta-cells differ considerably in their metabolic responsiveness to glucose stimulation (Kiekens et al., 1992; Schuit et al., 1988; Van Schravendijk et al., 1992). Traditional methods to evaluate human pancreatic endocrine cell composition and function are either laborious, lack resolution, or both. While fluorescence-activated cell sorting captures a set of cellular parameters (Davey and Kell, 1996; Perfetto et al., 2004), spectral overlap limits multiplexing capability (Perfetto et al., 2004).
Author Manuscript
The recently developed mass cytometry technology greatly facilitates high-dimensional, quantitative analysis of biological samples at the single-cell level in a high throughput fashion (Bandura et al., 2009; Bendall et al., 2011; Ornatsky et al., 2010). In mass cytometry, antibodies are conjugated with lanthanide heavy metals instead of fluorophores, and their abundances are measured as discrete isotope masses (Bandura et al., 2009). As a result, mass cytometry is free of fluorescent bleeding and limited only by the number of unique elemental tags available within the detection range of the instrument (Bandura et al., 2009). Furthermore, the use of rare earth metals reduces background signal, and thus mitigates the issue of “autofluorescence” (Bendall et al., 2011). Since its introduction in 2011, mass cytometry has been employed in the field of immunology to great benefit (Bendall et al., 2011; Horowitz et al., 2013; Newell et al., 2012). Here, we adapt mass cytometry to examine cellular heterogeneity within the human endocrine pancreas at the molecular level.
Author Manuscript
Results Overview of mass cytometry technology applied to human islets Human pancreatic islet cells and cells isolated along with the islets were labeled with a total of 24 antibodies that passed quality-control (Figures 1A and S1). The targets of these antibodies include the following groups: (1) markers of pancreatic subpopulations, such as C-PEPTIDE (beta cells), GLUCAGON (alpha cells), SOMATOSTATIN (delta cells), POLYPEPTIDE (PP cells), GASTRIN (GASTRIN cells), GHRELIN (epsilon cells), PDX1
Cell Metab. Author manuscript; available in PMC 2017 October 11.
Wang et al.
Page 3
Author Manuscript Author Manuscript
(beta and delta cells), HNF1B (ductal cells) and CD49F (Integrin α6, acinar, ductal and subgroups of endocrine cells) (Sugiyama et al., 2007; Wang et al., 2014); (2) a replication marker, Ki67; (3) markers associated with beta-cell proliferation and metabolic activities, such as PDGFRA (Chen et al., 2011), pCREB (Hussain et al., 2006; Jhala et al., 2003), pERK1/2 (Bernal-Mizrachi et al., 2014), pS6 (Balcazar et al., 2009), pSTAT3 (Saxena et al., 2007), pSTAT5 (Jackerott et al., 2006; Nielsen et al., 2001); (4) signaling pathway reporters, such as AXIN2 for WNT signaling, which functions during pancreas development, beta-cell proliferation, and pathophysiology of diabetes (Dabernat et al., 2009; Jho et al., 2002; Rulifson et al., 2007; Sladek et al., 2007), Cleaved-CASPASE3 (Cl-CASPASE3) for apoptosis, CPY26A1 for the retinoic acid pathway, which plays an important role in betacell maturation (Loudig et al., 2005; Micallef et al., 2005; Ostrom et al., 2008), and GATA2 for variability in chromatin accessibility (Buenrostro et al., 2015); and (5) markers of betacell heterogeneity, such as CD9 and ST8SIA1 (Dorrell et al., 2016) (Figure S2; Tables S1 and S2). In addition, an iridium-containing DNA interchelator was used as a cell indicator and cisplatin as a viability marker (Table S1) (Fienberg et al., 2012; Ornatsky et al., 2008). Data were analyzed using both traditional two-dimensional maps and multi-parametric analysis algorithms (Figure 1B). Biaxial maps were used for initial gating and assessment of antibody labeling efficiency and specificity (Figures 1C and S2A). Event length, the DNA intercalator iridium, and cisplatin exclusion were used to gate live single cells for downstream analysis (Figure 1C). Islet cells from 20 different donors, covering ages from 18 days to 65 years, were examined (Table S3). Four donors were at or below the age of two, two were 17, and 14 donors were 20 years of age or older. Of donors 17 and older, 13 displayed normal blood glucose homeostasis and 3 had been diagnosed with type 2 diabetes (T2D) (Table S3).
Author Manuscript
Islet cell types form distinct clusters We performed analysis with viSNE, a visualization tool based on the t-SNE (t-distributed stochastic neighbor embedding) algorithm that maps multi-parameter relationships of cellular data into two dimensions (Amir el et al., 2013; Maaten, 2008), on cells from all donors. Cells were mapped by viSNE using all available antibody channels (a total of 24 antibodies, Figure S2 and Table S1). Technical variation between batches and and biological variation between donors prevented clustering of multiple samples on the same viSNE plot. Even when samples were barcoded and processed together, donor variation dominated the tSNE dimension (Figure S3).
Author Manuscript
Accordingly, we used viSNE to visualize cells on a per-donor basis. As expected, major cell clusters were formed largely based on expression of the major endocrine hormones produced by each islet cell subtype (Figures 2 and S2B). Both endocrine and exocrine cells exhibited high levels of EpCAM expression, indicating epithelial origin (Figures 2A, 2B and S2B) (Trzpis et al., 2007). In accordance with the literature, CD49F was enriched in acinar and ductal cells as well as in a subset of endocrine cells (Sugiyama et al., 2007; Wang et al., 2014). Additionally, CD49F was expressed in a subset of cells that was negative for EpCAM, suggesting the existence of a previously unidentified mesenchymal subtype within islet preparations (red arrows in Figures 2 and S2B, and Figure S4) (Yu et al., 2012).
Cell Metab. Author manuscript; available in PMC 2017 October 11.
Wang et al.
Page 4
Mass cytometry facilitates accurate and rapid quantification of human islet composition
Author Manuscript Author Manuscript
Based on the clusters assembled by the viSNE graph, we gated endocrine subtypes to evaluate the endocrine cell composition for all donors (Figures 2 and 3A). The proportion of endocrine cells varied dramatically from donor to donor, with beta-cell percentages ranging from 25% to 80%, alpha-cell from 2% to 67%, and delta-cell from 5% to 25%. PP and epsilon cells constitute a small proportion of endocrine cells, with epsilon cells only appreciable (more than 2%) in donors less than two years of age (Table S4). The variability in cellular composition was partially age-dependent. Within control donors, delta- and epsilon-cell frequencies displayed a significant negative correlation with age (p=0.042 and 0.00061, respectively, linear regression t-test). In our samples, the percentage of beta-cells plateaued at 19 month old and declined thereafter. The average beta-cell percentage for children was 65.42 ± 16.08 %, for non-diabetic adult donors 42.49 ± 13.81%, and for T2D donors 38.69 ± 14.46%. The age-related rapid decrease of delta- and epsilon-cell percentages and the early plateau of beta-cell abundance are consistent with previous reports (Andralojc et al., 2009; Gregg et al., 2012; Rahier et al., 1981; Stefan et al., 1983). The fractions of cell populations observed by mass cytometry analysis correlated with those seen by immunolabeling from tissue sections of the same donors (Figure 3B–G). Mass cytometry allows simultaneous quantification of proliferation in all endocrine cell types
Author Manuscript
In addition to evaluating the cellular composition of human islets in a high-throughput manner, we also incorporated the proliferation marker Ki67 in our mass cytometry assay (Figure 4). We confirmed that Ki67 was marking cells that had entered the S-phase of the cell cycle by co-labelling cells with both Ki67 and IdU. We detected a high level of correlation between these two proliferation markers (Figure S5). We next examined how proliferation changes with age in each endocrine population. Confirming previous reports, we demonstrated that beta-cell proliferation was highest in the neonatal sample, followed by an exponential decline after childhood (Figure 4A) (Butler et al., 2003; Gregg et al., 2012). Like beta cells, alpha and delta cells displayed diminished proliferation with age (Figures 4B and 4C). Of the three major endocrine cell types, alpha cells had the highest basal replication (p=0.001 between alpha and beta cells; p=3.85×10−5 between alpha- and delta-cells; paired t-test); this phenomenon was observed throughout the human life-span (Figure 4D and Table S5). Harmine stimulates proliferation of multiple endocrine cell populations
Author Manuscript
The multiparametric measurement capability of mass cytometry provides a unique opportunity to monitor a variety of signaling pathways in multiple cell types in response to various stimuli, such as drug treatment. In a proof-of-principle experiment, we treated islets with the DYRK1A inhibitor harmine, which increases human beta-cell replication (Wang et al., 2015a). Employing mass cytometry, we found no significant change in endocrine cell composition upon harmine treatment (paired t-test), likely because the mitogenic effect of harmine is not restricted to one population (Figures 5A, 5B and S6 and Table S6). Intriguingly, alpha cells from control as well as T2D adults maintained a significantly more robust response to mitogenic signals than beta, delta and PP cells (p≤0.001, two-way
Cell Metab. Author manuscript; available in PMC 2017 October 11.
Wang et al.
Page 5
Author Manuscript
ANOVA with Tukey’s correction) (Figure 5C). Harmine had a comparable effect in stimulating replication in T2D endocrine cells, indicating that a long-standing impaired metabolic state did not prevent a proliferative response of endocrine cells to this drug (Figure 5B). Due to the low cell numbers of epsilon and PP populations, the measurement of their proliferation in the adult samples were not as accurate (Figure 5B and Table S5). Mass cytometry reveals multiple beta-cell states
Author Manuscript
Mass cytometry provides the unique opportunity to interrogate a large number of cells with multiple parameters at the single-cell level in parallel, thus facilitating the discovery of novel biology, such as the identification of rare cell types and the revelation of subtype-specific behavior (Bendall et al., 2011; Bodenmiller et al., 2012). To take advantage of this opportunity, we gated exclusively for beta-cells and then performed viSNE analysis on each donor, using all antibody channels except non-beta hormone markers. This process segregated beta-cells into several groups within each donor, indicating the existence of betacell subtypes, or, at a minimum, beta-cell states (Figure 6A). Using contour maps as a guide, we hand-delineated beta-cell clusters (Figure S7A). The existence of multiple beta-cell states is consistent with a recent finding by means of flow cytometry that multiple subtypes exist within β-cells (Dorrell et al., 2016). Intriguingly, beta cells with high Ki67+ cells often segregated to only one of these subtypes (Figure 6B), even when clustering was performed with the exclusion of the Ki67 channel (data not shown). This result may indicate that a state of beta-cells exists that is poised to enter the cell cycle.
Author Manuscript
We next asked whether features from these subgroups were consistent among all the donors. To this end, we performed hierarchical clustering on each of the subgroups acquired from viSNE mapping, plus addition groups containing Ki67+ beta cells from every donor (Figure 7A). Normalized median protein expression levels of each beta-cell group were used as the input for this process. Subgroups from all the donors organized into three main clusters, labelled C1, C2 and C3, each with distinct protein expression patterns (Figure 7A). The groups of Ki67+ beta cells segregated to C2 and C3 (magenta and blue arrows, Figure 7A). The three clusters generated by the heatmap mostly corresponded with the original beta-cell group annotations based on cell density separation on viSNE, albeit with fewer clusters apparent in each donor than originally called (Figure S7). The C2 cluster mapped to the group of beta cells containing most Ki67+ cells.
Author Manuscript
Donor-to-donor variations in the percentage of cells contained within each cluster were noticeable (Figure 7B). To further explore factors involved in the partition of cells in each cluster, we built a regression model with age, T2D status, gender, ethnicity and BMI as predictors. The regression model indicated that fraction of cells in C1 positively correlated with age and negatively correlated with BMI, while the percentage of cells in the C2 cluster correlated negatively with T2D status and the fraction of cells in the C3 cluster correlated negatively with age (p
