Resource

Single-Cell Mass Cytomet

ry Analysis of the HumanEndocrine Pancreas

Graphical Abstract

Highlights

d Mass cytometry promotes high-throughput phenotyping of

islets at a single-cell level

d Alpha cells maintain higher basal proliferation and are more

responsive to mitogens

d Beta cells exist in distinct states

Wang et al., 2016, Cell Metabolism 24, 616–626October 11, 2016 ª 2016 Elsevier Inc.http://dx.doi.org/10.1016/j.cmet.2016.09.007

Authors

Yue J. Wang, Maria L. Golson,

Jonathan Schug, ..., Kyong-Mi Chang,

Markus Grompe, Klaus H. Kaestner

Correspondencekaestner@mail.med.upenn.edu

In Brief

Wang et al. use innovative mass

cytometry to examine proliferation and

markers of signaling pathways at single-

cell resolution in human islets. The basal

proliferation rate of endocrine cells

declines with age, with alpha cells

maintaining higher replication potential.

Beta cells cluster into three distinct

groups, two of which include proliferating

beta cells.

Cell Metabolism

Resource

Single-Cell Mass Cytometry Analysisof the Human Endocrine PancreasYue J. Wang,1 Maria L. Golson,1 Jonathan Schug,1 Daniel Traum,2 Chengyang Liu,3 Kumar Vivek,4 Craig Dorrell,5 Ali Naji,3

Alvin C. Powers,6,7 Kyong-Mi Chang,2 Markus Grompe,5 and Klaus H. Kaestner1,8,*1Department of Genetics and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania,

Philadelphia, PA 19104, USA2Medical Research, Corporal Michael J. Crescenz Veterans Affairs Medical Center and Perelman School of Medicine, University of

Pennsylvania, Philadelphia, PA 19104, USA3Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA4Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10467, USA5Oregon Stem Cell Center, Pape Family Pediatric Research Institute, Oregon Health and Science University, Portland, OR 97239, USA6Departments of Molecular Physiology and Biophysics and Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine,

Vanderbilt University School of Medicine, Nashville, TN 19147, USA7Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN 37212, USA8Lead Contact

*Correspondence: kaestner@mail.med.upenn.edu

http://dx.doi.org/10.1016/j.cmet.2016.09.007

SUMMARY

The human endocrine pancreas consists of multiplecell types and plays a critical role in glucose homeo-stasis. Here, we apply mass cytometry technologyto measure all major islet hormones, proliferativemarkers, and readouts of signaling pathways in-volved in proliferation at single-cell resolution. Usingthis innovative technology, we simultaneously exam-ined baseline proliferation levels of all endocrine celltypes from birth through adulthood, as well as inresponse to the mitogen harmine. High-dimensionalanalysis of our marker protein expression revealedthree major clusters of beta cells within individuals.Proliferating beta cells are confined to two of theclusters.

INTRODUCTION

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-produc-

ing beta cells differ considerably in their metabolic responsive-

ness to glucose stimulation (Kiekens et al., 1992; Schuit et al.,

1988; Van Schravendijk et al., 1992). Traditional methods to eval-

uate human pancreatic endocrine cell composition and function

are either laborious, lack resolution, or both. While fluores-

cence-activated cell sorting captures a set of cellular parameters

(Davey andKell, 1996; Perfetto et al., 2004), spectral overlap limits

multiplexing capability (Perfetto et al., 2004).

The recently developed mass cytometry technology greatly fa-

cilitates high-dimensional, quantitative analysis of biological sam-

ples at the single-cell level in a high-throughput fashion (Bandura

et al., 2009; Bendall et al., 2011; Ornatsky et al., 2010). In mass

616 Cell Metabolism 24, 616–626, October 11, 2016 ª 2016 Elsevier

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). Further-

more, the use of rare earthmetals 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.

RESULTS

Overview of Mass Cytometry Technology Applied toHuman IsletsHuman pancreatic islet cells and cells isolated along with the is-

lets 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 subpopu-

lations, such as C-PEPTIDE (beta cells), GLUCAGON (alpha

cells), SOMATOSTATIN (delta cells), POLYPEPTIDE (PP cells),

GASTRIN (GASTRIN cells), GHRELIN (epsilon cells), PDX1 (beta

and delta cells), HNF1B (ductal cells), and CD49F (Integrin a6,

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), and pSTAT5 (Jackerott et al., 2006; Nielsen et al., 2001);

(4) signaling pathway reporters, such as AXIN2 forWNT signaling,

which functions during pancreas development, beta cell prolifer-

ation, 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

Inc.

Figure 1. Overview of Experimental

Procedure

(A) Workflow for sample processing and data

analysis. Whole islets were dispersed and labeled

with metal conjugated antibodies before loading

onto a CyTOF2 instrument. Following nebulization,

atomization, and ionization, the abundance of

different metal-conjugated antibodies within each

cell was determined.

(B) 2D biaxial plots, hierarchical clustering, and

t-SNE dimension reduction algorithm were em-

ployed in downstream data analysis.

(C) All events were gated first on singlets, ac-

cording to DNA content and event length (left).

Subsequently, live cells were gated based on

cisplatin exclusion (middle). After gating, individual

channels were visualized in biaxial plots. An

example of C-PEPTIDE versus EpCAM is shown

(right).

See Table S1 for antibodies used in the current

study and Table S2 for antibodies that failed quality

control. See Figure S1 for antibody validation and

Figure S2A for biaxial plots of individual antibody

channel.

for the retinoic acid pathway, which plays an important role in

beta cell maturation (Loudig et al., 2005; Micallef et al., 2005; Os-

trom et al., 2008), and GATA2 for variability in chromatin accessi-

bility (Buenrostro et al., 2015); and (5) markers of beta cell hetero-

geneity, such as CD9 and ST8SIA1 (Dorrell et al., 2016) (Figure S2

and Tables S1 and S2). In addition, an iridium-containing DNA in-

terchelator 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 2D 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 2, twowere 17, and 14 donors

were 20 years of age or older. Of donors 17 and older, 13 dis-

played normal blood glucose homeostasis and 3 had been diag-

nosed with type 2 diabetes (T2D) (Table S3).

Islet Cell Types Form Distinct ClustersWe performed analysis with viSNE, a visualization tool based on

the t-SNE (t-distributed stochastic neighbor embedding) algo-

rithm that maps multi-parameter relationships of cellular data

into two dimensions (Amir et al., 2013; Maaten and Hinton,

2008), on cells fromall donors. Cells weremappedby viSNEusing

all available antibody channels (a total of 24 antibodies; Figure S2

Cell Meta

and Table S1). Technical variation be-

tween batches and biological variation be-

tween donors prevented clustering of

multiple samples on the same viSNE plot.

Even when samples were barcoded and

processed together, donor variation domi-

nated the t-SNE dimension (Figure S3).

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 pro-

duced by each islet cell subtype (Figures 2 and S2B). Both endo-

crine and exocrine cells exhibited high levels of EpCAM expres-

sion, 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 endo-

crine cells (Sugiyama et al., 2007; Wang et al., 2014). Addition-

ally, 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, S2B, and S4) (Yu et al., 2012).

Mass Cytometry Facilitates Accurate and RapidQuantification of Human Islet CompositionBased 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 per-

centages 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 appre-

ciable (greater than 2%) in donors less than 2 years of age (Table

S4). The variability in cellular composition was partially age

dependent. Within control donors, delta and epsilon cell fre-

quencies displayed a significant negative correlation with age

(p = 0.042 and 0.00061, respectively, linear regression t test).

bolism 24, 616–626, October 11, 2016 617

Figure 2. viSNE Maps Show Distinct Clusters Representing Different Cell Types

(A and B) t-SNE analysis was performed with all antibody markers used in our experiments. Each dot in the viSNE map represents an individual cell. In all panels,

the same viSNEmap is shown, colored sequentially by the labeling intensity of C-PEPTIDE (C-PEP, beta cells), GLUCAGON (GCG, alpha cells), SOMATOSTATIN

(legend continued on next page)

618 Cell Metabolism 24, 616–626, October 11, 2016

Figure 3. Mass Cytometry Facilitates Quan-

titative Determination of Human Islet Cell-

Type Composition

(A) Percentage of different endocrine cell types in

20 donors. Donor ages range from 18 days to 65

years of age. Donor gender is indicated by F (fe-

male) or M (male). The three diabetic donors are

designated as ‘‘T2D.’’ Endocrine cell percentage is

listed as total of endocrine cells.

(B–G) Representative images from immunolabel-

ing of SOMATOSTATIN (SST, red) and INSULIN

(INS, green) (B, D, and F) or GLUCAGON (GCG,

red) and C-PEPTIDE (C-PEP, green) (C, E, and B)

in the 18-day (B and C), 19 month (D and E), and

47-year old (F and G) donors.

See Table S4 for percentage of endocrine pop-

ulations in each donor.

In our samples, the percentage of beta cells plateaued at

19 months old and declined thereafter. The average beta cell

percentage for children was 65.42% ± 16.08%, for non-diabetic

adult donors was 42.49% ± 13.81%, and for T2D donors was

38.69% ± 14.46%. The age-related rapid decrease of delta

(SST, delta cells), POLYPEPTIDE (PPY, PP cells), CD49F, and EpCAMantibodies. Representative data from tw

ACGZ275 (B).

See also Figure S2B for viSNE maps colored by each of the 24 antibodies, Figure S3 for barcoding experime

Figure S4 for biaxial plots of EpCAM and CD49F from all donors. See Table S3 for donor information.

Cell Meta

and epsilon cell percentages and the

early plateau of beta cell abundance are

consistent with previous reports (Andra-

lojc et al., 2009; Gregg et al., 2012; Rahier

et al., 1981; Stefan et al., 1983). The frac-

tions of cell populations observed by

mass cytometry analysis correlated with

those seen by immunolabeling from tis-

sue sections of the same donors (Figures

3B–3G).

Mass Cytometry AllowsSimultaneous Quantification ofProliferation in All Endocrine CellTypesIn 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-labeling cells with both Ki67 and IdU.

We detected a high level of correlation be-

tween these two proliferation markers

(Figure S5). We next examined how prolif-

eration changes with age in each endo-

crine population. Confirming previous re-

ports, 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;

o normal adult donors are shown: ACD1098 (A) and

nts demonstrating sample-to-sample variation, and

bolism 24, 616–626, October 11, 2016 619

Figure 4. Mass Cytometry Permits the Pre-

cise and Simultaneous Assessment of the

Proliferation Index for Different Endocrine

Cell Types

(A–C) The percentage of Ki67-positive endocrine

cells declines with age in all major endocrine lin-

eages. Beta cells (A), alpha cells (B), and delta

cells (C). Each dot represents an individual donor.

Regression lines and confidence intervals are also

shown. Regression is performed with the linear

regression command in R, with the model of

(Percentage of Ki67+ cells) �log (Age). R2 is

shown in each panel.

(D) Overlay of regression curves of Ki67 indexes

for alpha (red), beta (blue), and delta (black) cells.

See Figure S5 for correlation between Ki67 and

IdU signals in CyTOF. See Table S5 for quantifi-

cation of Ki67+ cells in each cell type for each

donor.

p = 3.853 10�5 between alpha and delta cells; paired t test); this

phenomenon was observed throughout the human lifespan (Fig-

ure 4D and Table S5).

Harmine Stimulates Proliferation of Multiple EndocrineCell PopulationsThe multiparametric measurement capability of mass cytome-

try provides a unique opportunity to monitor a variety of sig-

naling pathways in multiple cell types in response to various

stimuli, such as drug treatment. In a proof-of-principle experi-

ment, 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 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 was not as accurate

(Figure 5B and Table S5).

Mass Cytometry Reveals Multiple Beta Cell StatesMass 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 hor-

mone markers. This process segregated beta cells into several

620 Cell Metabolism 24, 616–626, October 11, 2016

groups within each donor, indicating the existence of beta-cell

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 bymeans of flow cytometry that multiple subtypes

exist within beta cells (Dorrell et al., 2016). Intriguingly, beta cells

with high Ki67+ cells often segregated to only one of these sub-

types (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.

We next asked whether features from these subgroups were

consistent among all the donors. To this end, we performed hier-

archical clustering on each of the subgroups acquired from

viSNE mapping, plus additional 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 self-organized

into three main clusters, labeled 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 ar-

rows, Figure 7A). The three clusters generated by the heatmap

mostly corresponded with the original beta cell group annota-

tions 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.

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 the 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 < 0.05, beta regression).

Figure 5. Alpha Cells Exhibit a Higher Baseline Replication and Are More Responsive to the Proliferation Stimulus Harmine Than Other

Endocrine Cells throughout Life

(A) Harmine treatment does not alter the cell-type percentage.

(B) Proliferation in individual donors’ endocrine cells with and without harmine treatment. For each donor, the proliferation rates of baseline (DMSO, blue) and

harmine-treated cells (Harmine, orange) are connected by dumbbell.

(C) Summary of the effect of harmine. Each dot represents the mean percentage of proliferating cells from all donors. Different endocrine populations are color-

coded as in (A). Alpha cells show a significantly higher response to harmine than beta, delta, and PP cells (two-way ANOVA with Tukey’s correction).

See Figure S6 for representative dot plots demonstrating the response of individual endocrine populations to harmine treatment. See Table S6 for quantification

of Ki67+ cells for each donor.

Cell Metabolism 24, 616–626, October 11, 2016 621

Figure 6. Beta Cells Contain Multiple Subtypes, with Proliferating Cells Concentrated within One Cluster

(A) viSNE map displaying subgroups within beta cells. All antibody channels except non-beta hormone markers were used for t-SNE computing. Colors indicate

density of cells. Subgroups were manually gated based on natural occurring groups revealed by different density centers. A graph from one representative donor

(ADGB379A) is shown.

(B) The same viSNE map as in (A) is shown, but individual cells are depicted and colored by Ki67 expression level. Red arrows indicate the cluster of Ki67+ cells.

See also Figure S7.

Subsequently, we compared protein expression between the

two Ki67+ groups and the quiescent group of beta cells that

did not contain any proliferating beta cells (C1) (Figure 7C).

Both Ki67+ clusters expressed significantly higher levels of the

cell-surface markers CD44, CD49F, and CD9, as well as

CYP26A1 and, reassuringly, Ki67. The Ki67+ cells in C2 ex-

pressed higher levels of PDGFRA, pERK1/2, pSTAT3, and

pSTAT5 compared to either the Ki67+ cells in C3 or the quiescent

C1 cells. Furthermore, the Ki67+ cells in C2 had even higher

levels of CD44 and CD49F compared to those in C3.

DISCUSSION

In this manuscript, we employed mass cytometry to simulta-

neously examine human endocrine cell composition, prolifera-

tion, and protein levels in a single assay. We were able to

corroborate previously published information showing (1)

high variation in the composition of the human islet compart-

ment (Blodgett et al., 2015; Brissova et al., 2005; Cabrera

et al., 2006); (2) the decline of human pancreatic endocrine

cell replication with age (Cnop et al., 2010; Gregg et al.,

2012; Meier et al., 2008; Perl et al., 2010; Reers et al.,

2009); and (3) the mitogenic effect of harmine on pancreatic

endocrine cells (Wang et al., 2015a). Since we assessed repli-

cation in all islet cell types at the same time, we were able to

overcome the traditional limitation of fluorescent co-immuno-

labeling on paraffin sections, for which generally a maximum

of four channels can be utilized (Wang et al., 2015b). Because

mass cytometry assays each cell individually, this experi-

mental design also bypasses manual counting and sectioning

bias, which are both time-consuming processes and may

lead to inaccurate measurements (Tang et al., 2012). In addi-

tion, the usage of rare earth metals in the conjugation of

antibodies for mass cytometry ensures a low background

622 Cell Metabolism 24, 616–626, October 11, 2016

from biological sources, and thus there is no need to correct

for autofluorescence (Bendall et al., 2011).

In the current study,wedemonstrate large donor-to-donor vari-

ation in protein expression and cellular composition, resulting in

inconsistent mapping positions on viSNE, even for demultiplexed

samples after barcoding (Figures 2, 3, and S3). This variation

could result frombona fide differences between donors, technical

variation in islet isolations, technical variations in CyTOF sample

preparations, or day-to-day differences in CyTOF2 sensitivity.

However, since barcoding experiment controls for technical vari-

ation between sample preparations, a major source of variation is

likely endogenous differences between samples. Unfortunately,

for the analysis of human islets, we are dependent on organ

availability and tissue processing at multiple academic medical

centers, which contributes to some of the observed variability

between samples and limits the ability to multiplex donors.

Despite donor-to-donor differences, we demonstrated that a

population of CD49F+; EpCAM� cells exists in most donors’ islet

preparations (Figures 2, S2, and S4). CD49F has been associ-

ated with stem cells; the CD49F+; EpCAM� population may

thus represent a novel mesenchymal stem cell population iso-

lated along with pancreatic islets (Yu et al., 2012). Furthermore,

when we analyzed our single-cell RNA-seq performed on the hu-

man pancreas, this population was also observed (Wang et al.,

2016).

Our mass cytometry procedure gives higher values for Ki67+

beta cells compared with immune-labeling methods in pancre-

atic sections, often obtained from autopsy specimens (Atkinson

et al., 2015; Butler et al., 2003; Gregg et al., 2012; Kassem et al.,

2000; Wang et al., 2015b). This discrepancy could stem from

differences in tissue treatments: our cells were maintained in

culture up to the point of analysis, in contrast to most reports

of human beta cell proliferation, in which cadaveric tissues

are processed and embedded in paraffin, a process that has

Figure 7. Three Main Clusters of Beta Cells

Are Observed from Multiple Donors, and

Cells with Relatively High Levels of Ki67

Separate into Two of the Clusters

(A) Hierarchical clustering of groups of beta cells

from all donors reveals three major clusters,

labeled C1, C2, and C3. Ki67+ cells separate into

two groups, co-segregating with either C2 or C3

clusters (magenta and blue arrows within black

box). Each column of the heatmap represents one

subgroup from viSNE map (an example of which is

shown in Figure 6A), with the addition of pop-

ulations of Ki67+ beta cells gated directly from in-

dividual donor. Each row of the heatmap repre-

sents the relative expression level of one protein.

16 antibody channels are utilized in samples from

all donors.

(B) Census of the percentage of cells in each

cluster from individual donors, ordered by age.

T2D samples are shown on the right.

(C) Expression levels of the 16 proteins from cells in

C1, Ki67+ cells in C2, and Ki67+ cells in C3. Each

data point representsmean expression +SE of one

of the three populations, normalized to C1. a, b,

and c indicate significance as calculated by a two-

way ANOVA test with Tukey’s correction. a, com-

parison between C1 and Ki67+ in C2; b, compari-

son between C2 and Ki67+ in C3; c, comparison

between Ki67+ in C2 and Ki67+ in C3.

See also Figure S7 for each cluster mapped back

onto a viSNE plot.

recently been shown to have the potential to cause degradation

of tissue morphology and protein integrity, leading to an under-

estimation of the replication rate (Sullivan et al., 2015). In addi-

tion, in our study, we assessed on average tens of thousands

of beta cells in each donor, which facilitates the capture of rare

proliferating cells. Alternatively, the higher rate reported here

could be a result of the more sensitive quantitative measurement

obtained from mass cytometry.

We also demonstrated that of all pancreatic endocrine cell

types, alpha cells have the highest basal replication rate and are

most responsive to the mitogen harmine across all donors (Fig-

ures 4 and 5). At present, it is unknown whether the lifespan of

a human alpha cell is shorter than that of beta and delta cells.

However, it is tempting to speculate that the higher rate of alpha

cell replication, together with the notion of islet cell plasticity,

would allow conversion of supernumerary alpha cells into beta

cells as part of islet homeostasis or in response to metabolic

stress. Combined with recent discoveries that alpha cells main-

tain both activating and repressing histone-mark bivalency at

thousands of loci and can be converted to beta cells upon genetic

Cell Meta

or epigenetic manipulations, this finding

raises the possibility that the expanding

alpha cell population may serve as a po-

tential therapeutic target for type 1 and

type 2 diabetes (Bramswig et al., 2013;

Collombat et al., 2009; Thorel et al.,

2010; Yang et al., 2011; Ye et al., 2015).

While the Stewart group has already re-

ported an increase in beta cell replication,

along with some proliferation in other endocrine cell types upon

treatment with harmine, they did not examine the mitogenic

response of endocrine cells from patients with T2D (Wang

et al., 2015a). We show that endocrine cells from two donors

with T2D increase proliferation in response to harmine in a

fashion similar to that of those from non-diabetic organ donors,

with a greater response in alpha cell compared to beta cell pro-

liferation (Figure 5).

Using high-dimensional, multi-parameter analysis, we demon-

strate that beta cells form three major groups that may reflect

different cellular states. Proliferating beta cells occupy two of

these three clusters (Figure 7A). The fraction of cells in C1 is posi-

tively correlated with age, while the fraction of cells in C3 is

inversely correlated with age (Figure 7B). There are two potential

explanations: (1) with age, some of the beta cells switch from a

more proliferative state (C3) to a more quiescent state (C1);

and (2) the net turnover of the C3 population is more negative

compared with the other populations. Another interesting dis-

covery is that T2D donors have significantly lower cell percent-

ages in C2, suggesting either that the lack of this population

bolism 24, 616–626, October 11, 2016 623

may contribute to the development of T2D or that it may be

vulnerable to the metabolic toxicity accompanying diabetes.

Further study is required to determine the biological significance

of these different beta cell groups.

We identify heterogeneity within the proliferating beta cells as

well. Among the markers that are more highly expressed in both

of these two groups of Ki67+ cells, CD44 and CD49F have been

reported to mark highly proliferative pancreatic progenitor cells

and pancreatic cancer initiating cells (Figure 7C) (Li et al.,

2007; Sugiyama et al., 2007). In addition, proteins that are upre-

gulated significantly in the C2 Ki67+ cells, i.e., PDGFRA, pERK1/

2, pSTAT3, and pSTAT5, have previously reported roles in beta

cell proliferation (Figure 7C) (Bernal-Mizrachi et al., 2014; Chen

et al., 2011; Jackerott et al., 2006; Nielsen et al., 2001; Saxena

et al., 2007).

In conclusion, this study reports the first use of mass cytome-

try in a solid tissue. The high-throughput, multi-parametric na-

ture of mass cytometry, combined with its single-cell resolution

and direct determination of relative protein levels, will greatly

facilitate the understanding of biology in several fields. Using

this technology, we found that of all the major endocrine cell

types, alpha cells have both the highest baseline level of replica-

tion as well as the highest mitotic response to harmine. We

further document that human beta cells exist in distinct cellular

states or subtypes, confirming recent findings (Dorrell et al.,

2016). In summary, mass cytometry represents a powerful tool

for diabetes researchers.

EXPERIMENTAL PROCEDURES

Antibody Conjugation

Antibodies were conjugated according to the manufacturer’s protocol (Fluid-

igm Multi-Metal Maxpar Kit). Briefly, the supplied polymer was linked with a

lanthanide metal of choice in buffer L at 37�C for 30 to 40 min. The antibodies

were partially reduced in buffer R plus TCEP and then purified by buffer ex-

change using a 50 kDa Amicon filter (Millipore, UFC505096). Themetal-loaded

polymers were concentrated on a 3 kDa Amicon filter (Millipore, UFC500396),

added to the reduced antibody, and conjugated at 37�C for 1.5–2 hr. Conju-

gated antibodies were thenwashed in bufferW and diluted 1:1 in Antibody Sta-

bilizer (Candor Biosciences, 131050).

Islet Acquisition and Culture

Human islets were acquired from the Diabetes Research Center of the Univer-

sity of Pennsylvania (NIH DK 19525), Prodo Labs, and the Integrated Islet Dis-

tribution Program (IIDP) (https://iidp.coh.org/). Islets were cultured in Prodo

Islet Media (PIMS Standard) with 5% human albumin serum and a glucose

concentration of 5.8 mM. For stimulation of proliferation, islets were incubated

with 10 mM harmine or the vehicle DMSO (Sigma, 286044) for 72–96 hr.

The studies performed here were deemed IRB exempt by the Institutional

Review Board of the University of Pennsylvania.

Isolation and Labeling of Dissociated Cells

Approximately 20,000 human islet equivalents (IEQs) were dissociated using

0.05% trypsin; trypsin was neutralized by the addition of an equal volume of

100% FBS. Cells were passed through a BD FACS tube with a 40 mm strainer

top (BD Biosciences, 352235). Dissociated cells were washed once with PBS

containing 10% FBS and once with PBS. Cells were resuspended at a density

of 10 3 106/ml in PBS with cisplatin diluted to 1:4,000 and incubated at room

temperature (RT) for 5 min. Cisplatin labeling was stopped by the addition of a

53 volume of PBS with 10% FBS. The cells were then washed twice with PBS

with 10% FBS. Dissociated cells were fixed with FoxP3 Fixation/Permeabiliza-

tion buffer (eBioscience, 00-5123 and 00-5223) at RT for 2 hr followed by twice

washing with FoxP3 permeabilization buffer (eBioscience, 00-8333). Antibody

624 Cell Metabolism 24, 616–626, October 11, 2016

labeling was performed in FoxP3 permeabilization buffer for 8 hr at 4�C at a

concentration of up to 2 million cells per 300 ml of antibody cocktail, followed

by twice washing with FoxP3 permeabilization buffer. Cells were then incu-

bated with the DNA intercalator Iridium (Fluidigm, 201192A) at a dilution of

1:4,000 in Fluidigm Fixation and Permeabilization buffer (Fluidigm, 201067)

at RT for 1 hr.

Mass Cytometry Data Acquisition

Mass cytometry data were obtained onCyTOF2 (Fluidigm). Immediately before

applying the samples to the CyTOF2 instrument, the cells were washed twice

with PBS and twice with MilliQ H2O. Cells were resuspended in MilliQ H2O at a

density of 53 105/ml and mixed together with 4-element normalization beads

(Fluidigm, 201078). Cell events were acquired at a rate of 300–500 events per

second. Bead normalization was performed with the Nolan laboratory bead

normalizer (Finck et al., 2013). Only loop 1 of the CyTOF instrument was

used throughout the entire study.

Antibody Selection and Titration

Antibodies were validated by the following three methods. (1) Immunofluores-

cent labeling (Figures S1A–S1E): the labeling efficiency of each antibody was

tested by direct labeling of human islet cells in suspension. Briefly, human

pancreatic cells were dissociated and labeled with the test antibodies

following the same procedure as when performing mass cytometry. Subse-

quently, Cy3-conjugated secondary antibodies were applied. Cells were cyto-

spun onto microscope slides and imaged under a fluorescent scope. Only

those antibodies displaying the expected staining patterns were used in down-

stream experiments. (2) Flow cytometry (Figure S1F): cells were dissociated

and labeled as inmass cytometry experiment, followed by secondary antibody

staining in the Cy3 channel. Cellular events were subsequently acquired by a

BD LSRII following a standard flow cytometry protocol. (3) Stimulation fol-

lowed by CyTOF2 sample acquisition (Figure S1G): human islets were (a) incu-

bated in retinoic acid at 50 nM final concentration for 72 hr; (b) serum starved

overnight, followed by stimulation with 1.25 ng/ml Leptin for 4 hr; or (c) serum

starved for 48 hr, followed by stimulation with Prolactin at 200 ng/ml for 30min.

After stimulation, cells were dissociated and processed following a normal

mass cytometry sample preparation protocol. Antibodies that passed initial

quality control were titrated in CyTOF with 1:100, 1:200, 1:500, 1:1,000, and

1:10,000 dilutions. Unlabeled cells were used as a negative control.

Barcoding

Barcoding was performed for select donors following the manufacturer’s pro-

tocol (Fluidigm, 101-0804 B1) (Table S3). Briefly, human pancreatic islet cells

were dissociated, labeled with cisplatin, and fixed as described above.

Following fixation, cells were washed twice in permeabilization buffer and

incubated with an assigned barcode at RT for 30 min. Barcoded samples

were combined and then processed as above.

Data Analysis

viSNE analyses were performed using the Cytobank implement (https://www.

cytobank.org/). For hierarchical clustering, expression levels of each protein

were normalized by the maximum value of the channel within each donor.

Rows and columns were clustered by hclust complete linkage with Euclidean

distance. To estimate factors involved in partition of cells into the C1, C2, and

C3 clusters, betareg package for r was employed, with age, T2D status,

gender, ethnicity, and BMI as covariates and a logit link function. Default set-

tings were used for other parameters (Cribari-Neto and Zeileis, 2010). Other

statistical analysis methods are described within results and figure legends.

SUPPLEMENTAL INFORMATION

Supplemental Information includes seven figures and six tables and can be

found with this article online at http://dx.doi.org/10.1016/j.cmet.2016.09.007.

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 & Editing, Y.J.W., M.L.G., D.T., and K.H.K.; Visualiza-

tion, Y.J.W., M.L.G., and J.S.; Supervision, M.L.G. and K.H.K.; Funding Acqui-

sition, K.H.K.

ACKNOWLEDGMENTS

The authors would like to thank Dr. Takuya Ohtani for his excellent technical

support in operating the CyTOF and Drs. Wade Rogers, Bertram Bengsch,

Shilpa Rao, Adam Zahm, as well as Amber Wang for their expertise and insight

into data processing. We also want to thank Dr. Andrew Raim for consulting on

statistical analysis. This study was supported by UC4DK104119 to K.H.K.,

DK104211 and DK106755 to A.C.P., and the Vanderbilt Diabetes Research

and Training Center (DK20593). The CyTOF facility was supported by the

VISN4 High Cost High Tech Equipment Fund, the Medical Research Program

at the Philadelphia Corporal Michael J. Crescenz VA Medical Center, and the

University of Pennsylvania Institute for Immunology.

Received: April 6, 2016

Revised: July 26, 2016

Accepted: September 21, 2016

Published: October 11, 2016

REFERENCES

Amir, A.D., Davis, K.L., Tadmor, M.D., Simonds, E.F., Levine, J.H., Bendall,

S.C., Shenfeld, D.K., Krishnaswamy, S., Nolan, G.P., and Pe’er, D. (2013).

viSNE enables visualization of high dimensional single-cell data and reveals

phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552.

Andralojc, K.M., Mercalli, A., Nowak, K.W., Albarello, L., Calcagno, R., Luzi, L.,

Bonifacio, E., Doglioni, C., and Piemonti, L. (2009). Ghrelin-producing epsilon

cells in the developing and adult human pancreas. Diabetologia 52, 486–493.

Atkinson, M.A., von Herrath, M., Powers, A.C., and Clare-Salzler, M. (2015).

Current concepts on the pathogenesis of type 1 diabetes–considerations for

attempts to prevent and reverse the disease. Diabetes Care 38, 979–988.

Balcazar, N., Sathyamurthy, A., Elghazi, L., Gould, A., Weiss, A., Shiojima, I.,

Walsh, K., and Bernal-Mizrachi, E. (2009). mTORC1 activation regulates

beta-cell mass and proliferation by modulation of cyclin D2 synthesis and sta-

bility. J. Biol. Chem. 284, 7832–7842.

Bandura, D.R., Baranov, V.I., Ornatsky, O.I., Antonov, A., Kinach, R., Lou, X.,

Pavlov, S., Vorobiev, S., Dick, J.E., and Tanner, S.D. (2009). Mass cytometry:

technique for real time single cell multitarget immunoassay based on induc-

tively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81,

6813–6822.

Bendall, S.C., Simonds, E.F., Qiu, P., Amir, A.D., Krutzik, P.O., Finck, R.,

Bruggner, R.V., Melamed, R., Trejo, A., Ornatsky, O.I., et al. (2011). Single-

cell mass cytometry of differential immune and drug responses across a hu-

man hematopoietic continuum. Science 332, 687–696.

Bernal-Mizrachi, E., Kulkarni, R.N., Scott, D.K., Mauvais-Jarvis, F., Stewart,

A.F., and Garcia-Ocana, A. (2014). Human b-cell proliferation and intracellular

signaling part 2: still driving in the dark without a road map. Diabetes 63,

819–831.

Blodgett, D.M., Nowosielska, A., Afik, S., Pechhold, S., Cura, A.J., Kennedy,

N.J., Kim, S., Kucukural, A., Davis, R.J., Kent, S.C., et al. (2015). Novel

Observations From Next-Generation RNA Sequencing of Highly Purified

Human Adult and Fetal Islet Cell Subsets. Diabetes 64, 3172–3181.

Bodenmiller, B., Zunder, E.R., Finck, R., Chen, T.J., Savig, E.S., Bruggner,

R.V., Simonds, E.F., Bendall, S.C., Sachs, K., Krutzik, P.O., and Nolan, G.P.

(2012). Multiplexed mass cytometry profiling of cellular states perturbed by

small-molecule regulators. Nat. Biotechnol. 30, 858–867.

Bramswig, N.C., Everett, L.J., Schug, J., Dorrell, C., Liu, C., Luo, Y., Streeter,

P.R., Naji, A., Grompe, M., and Kaestner, K.H. (2013). Epigenomic plasticity

enables human pancreatic a to b cell reprogramming. J. Clin. Invest. 123,

1275–1284.

Brissova, M., Fowler, M.J., Nicholson, W.E., Chu, A., Hirshberg, B., Harlan,

D.M., and Powers, A.C. (2005). Assessment of human pancreatic islet architec-

ture and composition by laser scanning confocal microscopy. J. Histochem.

Cytochem. 53, 1087–1097.

Buenrostro, J.D., Wu, B., Litzenburger, U.M., Ruff, D., Gonzales, M.L., Snyder,

M.P., Chang, H.Y., and Greenleaf, W.J. (2015). Single-cell chromatin accessi-

bility reveals principles of regulatory variation. Nature 523, 486–490.

Butler, A.E., Janson, J., Bonner-Weir, S., Ritzel, R., Rizza, R.A., and Butler,

P.C. (2003). Beta-cell deficit and increased beta-cell apoptosis in humans

with type 2 diabetes. Diabetes 52, 102–110.

Cabrera, O., Berman, D.M., Kenyon, N.S., Ricordi, C., Berggren, P.O., and

Caicedo, A. (2006). The unique cytoarchitecture of human pancreatic islets

has implications for islet cell function. Proc. Natl. Acad. Sci. USA 103, 2334–

2339.

Chen, H., Gu, X., Liu, Y., Wang, J., Wirt, S.E., Bottino, R., Schorle, H., Sage, J.,

and Kim, S.K. (2011). PDGF signalling controls age-dependent proliferation in

pancreatic b-cells. Nature 478, 349–355.

Cnop, M., Hughes, S.J., Igoillo-Esteve, M., Hoppa, M.B., Sayyed, F., van de

Laar, L., Gunter, J.H., de Koning, E.J., Walls, G.V., Gray, D.W., et al. (2010).

The long lifespan and low turnover of human islet beta cells estimated bymath-

ematical modelling of lipofuscin accumulation. Diabetologia 53, 321–330.

Collombat, P., Xu, X., Ravassard, P., Sosa-Pineda, B., Dussaud, S., Billestrup,

N., Madsen, O.D., Serup, P., Heimberg, H., and Mansouri, A. (2009). The

ectopic expression of Pax4 in the mouse pancreas converts progenitor cells

into alpha and subsequently beta cells. Cell 138, 449–462.

Cribari-Neto, F., and Zeileis, A. (2010). Beta Regression in R. J. Stat. Softw. 34,

1–24.

Dabernat, S., Secrest, P., Peuchant, E., Moreau-Gaudry, F., Dubus, P., and

Sarvetnick, N. (2009). Lack of beta-catenin in early life induces abnormal

glucose homeostasis in mice. Diabetologia 52, 1608–1617.

Davey, H.M., and Kell, D.B. (1996). Flow cytometry and cell sorting of hetero-

geneous microbial populations: the importance of single-cell analyses.

Microbiol. Rev. 60, 641–696.

Dorrell, C., Schug, J., Canaday, P.S., Russ, H.A., Tarlow, B.D., Grompe, M.T.,

Horton, T., Hebrok, M., Streeter, P.R., Kaestner, K.H., and Grompe, M. (2016).

Human islets contain four distinct subtypes of b cells. Nat. Commun. 7, 11756.

Fienberg, H.G., Simonds, E.F., Fantl, W.J., Nolan, G.P., and Bodenmiller, B.

(2012). A platinum-based covalent viability reagent for single-cell mass cytom-

etry. Cytometry A 81, 467–475.

Finck, R., Simonds, E.F., Jager, A., Krishnaswamy, S., Sachs, K., Fantl, W.,

Pe’er, D., Nolan, G.P., and Bendall, S.C. (2013). Normalization of mass cytom-

etry data with bead standards. Cytometry A 83, 483–494.

Gregg, B.E., Moore, P.C., Demozay, D., Hall, B.A., Li, M., Husain, A., Wright,

A.J., Atkinson, M.A., and Rhodes, C.J. (2012). Formation of a human b-cell

population within pancreatic islets is set early in life. J. Clin. Endocrinol.

Metab. 97, 3197–3206.

Horowitz, A., Strauss-Albee, D.M., Leipold, M., Kubo, J., Nemat-Gorgani, N.,

Dogan, O.C., Dekker, C.L., Mackey, S., Maecker, H., Swan, G.E., et al.

(2013). Genetic and environmental determinants of human NK cell diversity re-

vealed by mass cytometry. Sci. Transl. Med. 5, 208ra145.

Hussain, M.A., Porras, D.L., Rowe, M.H., West, J.R., Song, W.J., Schreiber,

W.E., andWondisford, F.E. (2006). Increased pancreatic beta-cell proliferation

mediated by CREB binding protein gene activation. Mol. Cell. Biol. 26, 7747–

7759.

Jackerott, M., Møldrup, A., Thams, P., Galsgaard, E.D., Knudsen, J., Lee, Y.C.,

and Nielsen, J.H. (2006). STAT5 activity in pancreatic beta-cells influences the

severity of diabetes in animal models of type 1 and 2 diabetes. Diabetes 55,

2705–2712.

Jhala, U.S., Canettieri, G., Screaton, R.A., Kulkarni, R.N., Krajewski, S., Reed,

J., Walker, J., Lin, X., White, M., and Montminy, M. (2003). cAMP promotes

pancreatic beta-cell survival via CREB-mediated induction of IRS2. Genes

Dev. 17, 1575–1580.

Jho, E.H., Zhang, T., Domon, C., Joo, C.K., Freund, J.N., and Costantini, F.

(2002). Wnt/beta-catenin/Tcf signaling induces the transcription of Axin2, a

negative regulator of the signaling pathway. Mol. Cell. Biol. 22, 1172–1183.

Cell Metabolism 24, 616–626, October 11, 2016 625

Kassem, S.A., Ariel, I., Thornton, P.S., Scheimberg, I., and Glaser, B. (2000).

Beta-cell proliferation and apoptosis in the developing normal human

pancreas and in hyperinsulinism of infancy. Diabetes 49, 1325–1333.

Kiekens, R., In ’t Veld, P., Mahler, T., Schuit, F., Van De Winkel, M., and

Pipeleers, D. (1992). Differences in glucose recognition by individual rat

pancreatic B cells are associated with intercellular differences in glucose-

induced biosynthetic activity. J. Clin. Invest. 89, 117–125.

Li, C., Heidt, D.G., Dalerba, P., Burant, C.F., Zhang, L., Adsay, V., Wicha, M.,

Clarke, M.F., and Simeone, D.M. (2007). Identification of pancreatic cancer

stem cells. Cancer Res. 67, 1030–1037.

Loudig, O., Maclean, G.A., Dore, N.L., Luu, L., and Petkovich, M. (2005).

Transcriptional co-operativity between distant retinoic acid response ele-

ments in regulation of Cyp26A1 inducibility. Biochem. J. 392, 241–248.

Maaten, L., and Hinton, G. (2008). Visualizing Data using t-SNE. J. Mach.

Learn. Res. 9, 2579–2605.

Meier, J.J., Butler, A.E., Saisho, Y., Monchamp, T., Galasso, R., Bhushan, A.,

Rizza, R.A., and Butler, P.C. (2008). Beta-cell replication is the primary mech-

anism subserving the postnatal expansion of beta-cell mass in humans.

Diabetes 57, 1584–1594.

Micallef, S.J., Janes, M.E., Knezevic, K., Davis, R.P., Elefanty, A.G., and

Stanley, E.G. (2005). Retinoic acid induces Pdx1-positive endoderm in differ-

entiating mouse embryonic stem cells. Diabetes 54, 301–305.

Newell, E.W., Sigal, N., Bendall, S.C., Nolan, G.P., and Davis, M.M. (2012).

Cytometry by time-of-flight shows combinatorial cytokine expression and vi-

rus-specific cell niches within a continuum of CD8+ T cell phenotypes.

Immunity 36, 142–152.

Nielsen, J.H., Galsgaard, E.D., Møldrup, A., Friedrichsen, B.N., Billestrup, N.,

Hansen, J.A., Lee, Y.C., and Carlsson, C. (2001). Regulation of beta-cell

mass by hormones and growth factors. Diabetes 50 (Suppl 1 ), S25–S29.

Ornatsky, O.I., Lou, X., Nitz, M., Schafer, S., Sheldrick, W.S., Baranov, V.I.,

Bandura, D.R., and Tanner, S.D. (2008). Study of cell antigens and intracellular

DNA by identification of element-containing labels and metallointercalators

using inductively coupled plasma mass spectrometry. Anal. Chem. 80,

2539–2547.

Ornatsky, O., Bandura, D., Baranov, V., Nitz, M., Winnik, M.A., and Tanner, S.

(2010). Highly multiparametric analysis by mass cytometry. J. Immunol.

Methods 361, 1–20.

Ostrom,M., Loffler, K.A., Edfalk, S., Selander, L., Dahl, U., Ricordi, C., Jeon, J.,

Correa-Medina, M., Diez, J., and Edlund, H. (2008). Retinoic acid promotes the

generation of pancreatic endocrine progenitor cells and their further differen-

tiation into beta-cells. PLoS ONE 3, e2841.

Perfetto, S.P., Chattopadhyay, P.K., and Roederer, M. (2004). Seventeen-

colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol.

4, 648–655.

Perl, S., Kushner, J.A., Buchholz, B.A., Meeker, A.K., Stein, G.M., Hsieh, M.,

Kirby, M., Pechhold, S., Liu, E.H., Harlan, D.M., and Tisdale, J.F. (2010).

Significant human beta-cell turnover is limited to the first three decades of

life as determined by in vivo thymidine analog incorporation and radiocarbon

dating. J. Clin. Endocrinol. Metab. 95, E234–E239.

Rahier, J., Wallon, J., and Henquin, J.C. (1981). Cell populations in the endo-

crine pancreas of human neonates and infants. Diabetologia 20, 540–546.

Reers, C., Erbel, S., Esposito, I., Schmied, B., Buchler, M.W., Nawroth, P.P.,

and Ritzel, R.A. (2009). Impaired islet turnover in human donor pancreata

with aging. Eur. J. Endocrinol. 160, 185–191.

Rulifson, I.C., Karnik, S.K., Heiser, P.W., ten Berge, D., Chen, H., Gu, X.,

Taketo, M.M., Nusse, R., Hebrok, M., and Kim, S.K. (2007). Wnt signaling reg-

ulates pancreatic beta cell proliferation. Proc. Natl. Acad. Sci. USA 104, 6247–

6252.

Saxena, N.K., Vertino, P.M., Anania, F.A., and Sharma, D. (2007). leptin-

induced growth stimulation of breast cancer cells involves recruitment of his-

626 Cell Metabolism 24, 616–626, October 11, 2016

tone acetyltransferases and mediator complex to CYCLIN D1 promoter via

activation of Stat3. J. Biol. Chem. 282, 13316–13325.

Schuit, F.C., In’t Veld, P.A., and Pipeleers, D.G. (1988). Glucose stimulates

proinsulin biosynthesis by a dose-dependent recruitment of pancreatic beta

cells. Proc. Natl. Acad. Sci. USA 85, 3865–3869.

Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P.,

Vincent, D., Belisle, A., Hadjadj, S., et al. (2007). A genome-wide association

study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885.

Stefan, Y., Orci, L., Malaisse-Lagae, F., Perrelet, A., Patel, Y., and Unger, R.H.

(1982). Quantitation of endocrine cell content in the pancreas of nondiabetic

and diabetic humans. Diabetes 31, 694–700.

Stefan, Y., Grasso, S., Perrelet, A., and Orci, L. (1983). A quantitative immuno-

fluorescent study of the endocrine cell populations in the developing human

pancreas. Diabetes 32, 293–301.

Sugiyama, T., Rodriguez, R.T., McLean, G.W., and Kim, S.K. (2007).

Conserved markers of fetal pancreatic epithelium permit prospective isolation

of islet progenitor cells by FACS. Proc. Natl. Acad. Sci. USA 104, 175–180.

Sullivan, B.A., Hollister-Lock, J., Bonner-Weir, S., and Weir, G.C. (2015).

Reduced Ki67 Staining in the Postmortem State Calls Into Question Past

Conclusions About the Lack of Turnover of Adult Human b-Cells. Diabetes

64, 1698–1702.

Tang, L.H., Gonen, M., Hedvat, C., Modlin, I.M., and Klimstra, D.S. (2012).

Objective quantification of the Ki67 proliferative index in neuroendocrine tu-

mors of the gastroenteropancreatic system: a comparison of digital image

analysis with manual methods. Am. J. Surg. Pathol. 36, 1761–1770.

Thorel, F., Nepote, V., Avril, I., Kohno, K., Desgraz, R., Chera, S., and Herrera,

P.L. (2010). Conversion of adult pancreatic alpha-cells to beta-cells after

extreme beta-cell loss. Nature 464, 1149–1154.

Trzpis, M., McLaughlin, P.M., de Leij, L.M., and Harmsen, M.C. (2007).

Epithelial cell adhesionmolecule: more than a carcinomamarker and adhesion

molecule. Am. J. Pathol. 171, 386–395.

Van Schravendijk, C.F., Kiekens, R., and Pipeleers, D.G. (1992). Pancreatic

beta cell heterogeneity in glucose-induced insulin secretion. J. Biol. Chem.

267, 21344–21348.

Wang, Y.J., McAllister, F., Bailey, J.M., Scott, S.G., Hendley, A.M., Leach,

S.D., and Ghosh, B. (2014). Dicer is required for maintenance of adult pancre-

atic acinar cell identity and plays a role in Kras-driven pancreatic neoplasia.

PLoS ONE 9, e113127.

Wang, P., Alvarez-Perez, J.C., Felsenfeld, D.P., Liu, H., Sivendran, S., Bender,

A., Kumar, A., Sanchez, R., Scott, D.K., Garcia-Ocana, A., and Stewart, A.F.

(2015a). A high-throughput chemical screen reveals that harmine-mediated in-

hibition of DYRK1A increases human pancreatic beta cell replication. Nat.

Med. 21, 383–388.

Wang, P., Fiaschi-Taesch, N.M., Vasavada, R.C., Scott, D.K., Garcıa-Ocana,

A., and Stewart, A.F. (2015b). Diabetes mellitus–advances and challenges in

human b-cell proliferation. Nat. Rev. Endocrinol. 11, 201–212.

Wang, Y.J., Schug, J., Won, K.J., Liu, C., Naji, A., Avrahami, D., Golson, M.L.,

and Kaestner, K.H. (2016). Single cell transcriptomics of the human endocrine

pancreas. Diabetes, db160405.

Yang, Y.P., Thorel, F., Boyer, D.F., Herrera, P.L., and Wright, C.V. (2011).

Context-specific a- to-b-cell reprogramming by forced Pdx1 expression.

Genes Dev. 25, 1680–1685.

Ye, L., Robertson, M.A., Hesselson, D., Stainier, D.Y., and Anderson, R.M.

(2015). Glucagon is essential for alpha cell transdifferentiation and beta cell

neogenesis. Development 142, 1407–1417.

Yu, K.R., Yang, S.R., Jung, J.W., Kim, H., Ko, K., Han, D.W., Park, S.B., Choi,

S.W., Kang, S.K., Scholer, H., and Kang, K.S. (2012). CD49f enhances multi-

potency and maintains stemness through the direct regulation of OCT4 and

SOX2. Stem Cells 30, 876–887.

Cell Metabolism, Volume 24

Supplemental Information

Single-Cell Mass Cytometry Analysis

of the Human Endocrine Pancreas

Yue J. Wang, Maria L. Golson, Jonathan Schug, Daniel Traum, Chengyang Liu, KumarVivek, Craig Dorrell, Ali Naji, Alvin C. Powers, Kyong-Mi Chang, MarkusGrompe, and Klaus H. Kaestner

Figure S1. Related to Figure 1. Antibody validation. (A-E) Dispersed human islets cells were labelled as in mass cytom-etry experiments, followed by staining using Cy3-conjugated secondary antibodies. Cells were cytospun and co-stained with INS (green) (A-D) or DAPI (blue) (E). The metal-conjugated antibodies tested are (A) PDX1, (B) SOMATOSTATIN (SST), (C) POLYPEPTIDE (PPY), (D) GHRELIN (GHRL), and (E) GATA2. (F) Dispersed human islets cells were labeled with heavy-metal conjugated antibodies, followed by indirect immunolabeling with Cy3-conjugated secondary antibodies. Cells were acquired via an LSRII flow cytometer. Each panel shows the conjugated antibody versus SSC. The gate was se-lected using an unlabeled negative control (upper left). Antibodies shown include, in clockwise order, C-PEPTIDE, GLU-CAGON, ST8SIA1, CD9, and GASTRIN. (G) Human pancreatic cells were stimulated and labeled with heavy-metal con-jugated antibodies, after which they were acquired using a CyTOF2 mass cytometer. Each panel shows EpCAM versus cor-responding antibodies. The upper row displays unstimulated cells, while the lower row exhibits profiles upon stimulation. From left to right, the stimulation and corresponding antibody channels are as follows: left, Retinoic acid (RA), CYP26A1; middle, Leptin, pSTAT3; right, Prolactin, pSTAT5.

Figure S2. Related to Figures 1 and 2. Overview of the 24 antibody channels. (A) Biaxial dotplots present EpCAM versus all antibodies used in the current study. Signals from one representative donor (ADAH342T2D) is shown. Colors in-dicate cell density. (B) viSNE graphs from the same donor as in (A), colored individually by each of the 24 antibodies used. The viSNE analysis was performed with the entire antibody panel. The red arrow in the CD49F plot indicates the CD49F+; EpCAM- population for this donor.

Figure S3. Related to Figure 2. Donor-to-donor variation is still apparent after barcoding and processing samples simultaneously. (A) Overlay of histograms for three demultiplexed donors, showing signals from EpCAM, C-PEPTIDE, and PDGFRA. (B) viSNE maps reveal that cells from the three different donors cluster mainly by donor.

Figure S4. Related to Figure 2. Dotplots for EpCAM versus CD49F for all donors. Colors indicate cell density.

Figure S5. Related to Figure 4. Ki67-labeled cells have entered the cell cycle. A dotplot for Ki67 versus IdU127 demon-strates high correlation between the two markers.

Figure S6. Related to Figure 5. Harmine treatment promote proliferation in all endocrine cell types. Representa-tive dotplots are shown for Ki67 in each pancreatic endocrine hormone channel for one representative donor (AC-GZ275) with harmine or vehicle (DMSO) treatment. (A) Effect of harmine in the bulk. (B) Effect of harmine in individ-ual endocrine populations. Upper row, DMSO. Lower row, Harmine. From left to right, the endocrine markers are, C-PEPTIDE (beta cells), GLUCAGON (alpha cells), SOMATOSTATIN (delta cells), GHRELIN (epsilon cells), POLY-PEPTIDE (PP cells).

Category Elemental Isotope Targets Supplier and Cat. No.

Final Concentration in the cocktail*

Hormones

Nd144 C-PEPTIDE Millipore 05-1109 0.195 µg/ml

Sm147 GLUCAGON Santa Cruz Biotechonlogy sc-13091 0.5 µg/ml

Eu151 SOMATOSTATIN Santa Cruz Biotechnology sc-13099 0.16 µg/ml

Sm152 POLYPEPTIDE Abcam ab77192 0.2 µg/ml

Lu175 GASTRIN Santa Cruz Biotechnology sc-7783 0.32 µg/ml

Yb176 GHRELIN Santa Cruz Biotechnology sc-10368 0.198 µg/ml Pan Epithelial markers Pr141 EpCAM Fluidigm 3141006B 3.3 µl/ml

Signaling Pathways

Nd142 CASPASE3 (Cleaved) Fluidigm 3142004A 0.25 µl/ml

Nd145 GATA2 Abnova H00002624-M01 0.0875 µg/ml

Nd150 pSTAT5 Fluidigm 3150005A 0.5 µl/ml

Gd156 AXIN2 Abcam ab32197 0.15 µg/ml

Gd158 pSTAT3 Fluidigm 3158005A 0.5 µl/ml

Tb159 CYP26A1 Santa Cruz Biotechnology sc-53618 0.085 µg/ml

Gd160 PDGFRA Fluidigm 3160007A 0.5 µl/ml

Dy163 CD9 eBioscience 14-0098 0.19 µg/ml

Dy164 CD49F Fluidigm 3164006B 1 µl/ml

Ho165 pCREB Fluidigm 3165009A 0.1 µl/ml

Er166 CD44 Fluidigm 3166001B 1 µl/ml

Er167 pERK Fluidigm 3167005A 3.3 µl/ml

Er168 Ki67 Fluidigm 3168007B 5 µl/ml

Er170 HNF1B Santa Cruz Biotechnology sc-22840x 2 µg/ml

Yb171 PDX1 Santa Cruz Biotechnology sc-14664 0.62 µg/ml

Yb172 pS6 Fluidigm 3172008A 1 µl/ml

Yb174 ST8SIA1 In house, Grompe lab 0.19 µg/ml

CyTOF cell labeling

IdU127 IdU Fluidigm 201127 1:1000 in

culture medium

Ir191 Iridium Fluidigm 201192B 1:4000

Ir193 Iridium

Pt195 Cisplatin Fluidigm 201064 1:4000 *The concentrations of metal-conjugated antibodies acquired directly from Fluidigm are reported as µl/ml. Other antibody concentrations are reported as µg/ml. Table S1. Antibodies and other labeling products used for mass cytometry analysis. Related to Figure 1.

ALDH BD 611194 AMY1 Sigma A8273 CD24 Fluidigm 3169004B CD49f-APC Biolegend 313615 CHGA eBioscience 14-6549-93 CHGA BD Pharmingen 564562 FKHR (FOXO1) santa cruz sc-11350 GCK Sigma (protein atlas) HPA007093 GLUT2 Millipore 07-1402 HES1 MBL D134-3 HNF1α santa cruz sc-6547 HNF1α santa cruz sc-10791 HNF-3β santa cruz sc-6554X HNF4α santa cruz sc-8987 HNF4α santa cruz sc-6556x HNF4α R&D PP-H-1415-00 INS Invitrogen 180067 INS Dako A0564 Islet-1 & 2 homeobox DSHB 39.4D5 NEUROD santa cruz sc-1084 NKX2.2 BD Pharmingen 564731 NKX2.2 santa cruz sc-15013 NKX2.2 Sigma (protein atlas) HPA003468 NKX6.1 DSHB F55A2c NKX6.1 santa cruz sc-15030 NKX6.1 Sigma (protein atlas) HPA036774 p-AKT (S473) cell signaling 9271s p-AKT (T308) cell signaling 5106s PAX4 BCBC AB3234 PAX6 santa cruz sc-11357 PAX6 Biolegend prb-278p P-CREB (S133) cell signaling 9198L PC1/PC3 Millipore AB10553 PC2 Millipore AB15610 PDX1 abcam ab47383 PSA-NCAM eBioscience 14-9118-80 RFX3 Sigma (protein atlas) HPA035689 RFX6 Sigma (protein atlas) HPA037696 SOX-9 Millipore AB5535 SOX-9 santa cruz sc-17340 STAT3 (Y705) ROCKLAND 600-401-C64S Table S2. Antibodies that failed quality control. Related to Figure 1.

*barcoding test Table S3. Tissue donor characteristics and culture conditions. Related to Figure 2.

UNOS ID# Age Gender Ethnicity BMI HgbA1c % Culture time (days) Harmine ICRH85 18 d F Hispanic 16.2 NA 7 Yes

ACJW006 10 m M African American 19.5 NA 8 Yes ICRH80 19 m F White 18 NA 3 ICRH76 2 y M White 13.6 5.4 4

ADGB379 17 y M White 25.6 5.0 7 Yes

ADGC274 17 y F African American 34 NA 6 ACKB010 20 y M African American 30.9 5.6 12 Yes ACGZ275 33 y F Hispanic 23.5 4.9 15 Yes ACGP337 36 y M White 29 4.1 8 ICRH79 38 y M White 25.3 NA 7

ACJT115 47 y M Hispanic 25.8 NA 10 ICRH87 49 y F White 31.6 5.2 13 Yes ICRH89 55 y M White 23.2 5.2 6 Yes

ACK2055 57 y F White 20.6 4.9 8 Yes ACD1098 57 y M White 29.4 NA 6 ACFC227 59 y F White 23.1 5.1 5 ACDF368 59 y M African American 20.4 5.5 11

ADAH342T2D 42 y M White 43.7 6.6 7 Yes ADGC040T2D 59 y M White 31.6 NA 6 Yes ACEQ344T2D 65 y F Hispanic 20.1 8.5 7

HP16170* 19 y M African American 25.3 NA 5 ADFM425* 28 y M White 27.7 NA 8 ADFR455* 53 y M White 31.1 NA 3

Table S4. Percentages of endocrine cells in each donor. Related to Figure 3.

Beta cells Alpha cells Delta cells PP cells Epsilon cells

18d 56.26614 15.27017 25.77746 0.072322 6.128703 10m 80.45889 7.877629 4.665392 2.06501 4.933078

19m 77.4461 1.741294 11.77446 7.131012 1.907131

2y 47.49621 22.61002 15.02276 12.29135 2.579666 17y 36.4743 48.01623 12.5789 1.517884 1.412684

17y 39.83066 45.86784 7.877784 5.779496 0.644211 20y 42.61733 46.14105 10.56078 0.452159 0.228678

33y 27.8013 65.00141 4.769969 1.947502 0.479819 36y 25.69815 66.80333 7.23885 0.054667 0.205002

38y 36.84711 56.72836 5.568698 0.614621 0.24121

47y 40.54718 52.0417 7.229025 0.113807 0.068284 49y 58.96484 32.08984 7.255859 1.103516 0.585938

55y 64.94925 28.16525 4.938565 1.765893 0.181041 57y 50.9603 39.34092 7.511561 1.570637 0.61659

57y 39.59276 40.68285 11.22995 8.371041 0.123406

59y 69.78216 19.23011 10.65095 0.125757 0.211015 59y 31.36058 46.62347 5.095938 16.89509 0.024919

42yT2D 24.84936 66.98813 5.259865 2.82376 0.078891 59yT2D 53.69669 35.15014 6.989654 3.053242 1.11027

65yT2D 37.51224 43.01883 18.71803 0.707368 0.04353

*: for populations where no Ki67+ cells were observed, the upper limits of replication rate was estimated by 1 over total number of cells detected.

Table S5. Percentages of Ki67+ cells in beta, alpha, and delta cells per donor. Related to Figure 4.

Age

Percentage of Ki67+ cells alpha beta delta

18 d 6.22 3.76 2.65

10 m 6.8 1.05 1.64

19 m 3.53 3.1 1.22

2 y 0.82 0.85 <0.24*

17 y 1.76 1.91 0.4

17 y 3.43 1.42 0.73

20 y 3.48 0.39 0.49

33 y 2.4 0.55 <0.29*

36 y 1.37 0.27 0.13

38 y 6.68 0.56 1.08

47 y 2.2 0.21 0.19

49 y 0.06 <0.02* <0.13*

55 y 0.99 0.13 0.06

57 y 0.8 0.2 0.17

57 y 3.44 0.26 <0.18*

59 y 0.33 0.04 0.04

59 y 1.55 0.36 <0.24*

Donor Treatment alpha beta delta PP epsilon

18d DMSO 6.22 3.76 2.65 <14.3* 7.11

Harmine 18.11 2.08 11.44 <16.7* 31.34

10m DMSO 6.8 1.05 1.64 7.41 3.1

Harmine 18.3 2.41 4.18 10 10.11

17 y DMSO 3.43 1.42 0.73 0.32 3.03

Harmine 19.01 1.24 2.91 1.26 10.42

20y DMSO 3.48 0.39 0.49 3.45 2.27

Harmine 7.1 0.63 1.4 <2.94* <2.04*

33y DMSO 2.4 0.55 <0.29* <0.7* 2.86

Harmine 9.77 1.22 1.17 1.03 4.55

49y DMSO 0.06 <0.02 <0.13* <0.88* <1.67*

Harmine 2.87 0.1 0.13 <0.63* <1.81*

55y DMSO 0.99 0.13 0.06 0.5 <1.64*

Harmine 7.06 0.48 1.03 1.16 <1.31*

57y DMSO 0.8 0.2 0.17 <0.27* 0.68

Harmine 2.96 0.49 0.91 1.71 2.44

42y_T2D DMSO 0.94 0.13 0.08 0.48 <1.72*

Harmine 7.9 1.34 1.19 1.93 <2.56*

59y_T2D DMSO 1.94 0.99 0.36 0.83 4.55

Harmine 9.43 1.17 1.34 <0.75* 5

*for populations where no Ki67+ cells were observed, the upper limits of replication rate was estimated by 1 over total number of cells detected.

Table S6. Percentage of Ki67+ cells with harmine or vehicle treatment. Related to Figure 5.