Pancreatic islet-autonomous insulin and smoothened-mediated signalling modulate identity changes of glucagon+ α-cells

Cigliola, Valentina; Ghila, Luiza; Thorel, Fabrizio; van Gurp, Léon; Baronnier, Delphine; Oropeza, Daniel; Gupta, Simone; Miyatsuka, Takeshi; Kaneto, Hideaki; Magnuson, Mark A.; Osipovich, Anna B.; Sander, Maike; Wright, Christopher E. V.; Thomas, Melissa K.; Furuyama, Kenichiro; Chera, Simona; Herrera, Pedro L.

doi:10.1038/s41556-018-0216-y

Article
Published: 22 October 2018

Pancreatic islet-autonomous insulin and smoothened-mediated signalling modulate identity changes of glucagon⁺ α-cells

Valentina Cigliola¹^na1^nAff2,
Luiza Ghila ORCID: orcid.org/0000-0001-7173-6312^1,3^na1,
Fabrizio Thorel¹^na1,
Léon van Gurp¹,
Delphine Baronnier¹,
Daniel Oropeza¹,
Simone Gupta⁴,
Takeshi Miyatsuka⁵,
Hideaki Kaneto⁶,
Mark A. Magnuson ORCID: orcid.org/0000-0002-8824-6499⁷,
Anna B. Osipovich⁷,
Maike Sander⁸,
Christopher E. V. Wright⁹,
Melissa K. Thomas⁴,
Kenichiro Furuyama¹,
Simona Chera^1,3 &
…
Pedro L. Herrera ORCID: orcid.org/0000-0003-0771-9504¹

Nature Cell Biology volume 20, pages 1267–1277 (2018)Cite this article

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Abstract

The mechanisms that restrict regeneration and maintain cell identity following injury are poorly characterized in higher vertebrates. Following β-cell loss, 1–2% of the glucagon-producing α-cells spontaneously engage in insulin production in mice. Here we explore the mechanisms inhibiting α-cell plasticity. We show that adaptive α-cell identity changes are constrained by intra-islet insulin- and Smoothened-mediated signalling, among others. The combination of β-cell loss or insulin-signalling inhibition, with Smoothened inactivation in α- or δ-cells, stimulates insulin production in more α-cells. These findings suggest that the removal of constitutive ‘brake signals’ is crucial to neutralize the refractoriness to adaptive cell-fate changes. It appears that the maintenance of cell identity is an active process mediated by repressive signals, which are released by neighbouring cells and curb an intrinsic trend of differentiated cells to change.

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Main

Half a century of research into cell identity determination and maintenance has revealed that adult cells are not terminally differentiated but maintain some plasticity potential even in higher organisms^1,2,3. Spontaneous adult cell-type interconversion is considered a rare event that is highly regulated, often activated exclusively after injury and whose efficiency correlates with mechanisms preserving a specific cell identity^2,4. Conversely, our knowledge of the intricate mechanisms that maintain adult cell identity is still limited.

Cell-fate allocation and maintenance^1,5,6 result from the activity of transcriptional regulators and epigenetic modifiers that control the constitutive expression of identity genes that become ‘locked’ due to autoregulatory feed-back loops, stable chromatin modifications^7,8,9 or through the action of regulatory signals from the microenvironment in which cells reside. The plasticity potential of a given cell depends on the level of redundancy in which these complex mechanisms operate and on the physiological needs of the corresponding tissue.

Changes in adult cell identity, especially if triggered by stress responses, are a basis for in situ regenerative medicine^10,11. In the pancreas of adult mice, following near-total β-cell ablation, 1–2% of glucagon-expressing α-cells and somatostatin-expressing δ-cells spontaneously express insulin, leading to significant β-cell mass regeneration and normoglycaemia^12,13. The mechanisms controlling this insulin expression are unknown. We have previously shown that in α-cells, inhibition of the transcription factor Arx and the dimethyltransferase Dnmt1 causes transdifferentiation into insulin⁺ cells irrespective of β-cell loss¹⁴. However, nothing is known about the control of α-cell identity by extrinsic signals. Here we define the cellular mechanisms that regulate the expression of insulin in glucagon⁺ α-cells after near-total β-cell ablation or insulin action inhibition. We focus on local signals that act as constitutive brakes that limit cell reprogramming. We identified Smoothened- and insulin-signalling pathways in α-cells, and surprisingly also in δ-cells, as regulators of α-cell identity and conversion into insulin-producing cells.

Results

Conversion of α-cells is driven by local signals

To elucidate the signals that lead to insulin production in α-cells following β-cell loss in mice, we set up a series of islet transplantation experiments (Fig. 1a). To prevent allograft rejection, we used severe combined immunodeficient (SCID) mice¹⁵ as hosts for islets isolated from immunocompetent donor mice. In different experimental conditions, islet donor and/or recipient mice also bore the RIP-DTR transgene, which allowed for β-cell ablation following diphtheria toxin administration in either engrafted or pancreatic islets, or both¹³. As a readout for α-cell conversion to insulin production, we assessed the percentage of α-cells that co-expressed glucagon and insulin in the engrafted and/or pancreatic islets of the host. Indeed, cell lineage tracing experiments have shown that these bihormonal cells appearing after β-cell loss are reprogrammed α-cells¹³.

**Fig. 1: Insulin production engaged by α-cells after β-cell ablation in islets transplanted under the kidney capsule.**

We first transferred wild type (WT) islets (~700) under the kidney capsule of RIP-DTR SCID hosts to ensure normoglycaemia after diphtheria toxin-induced pancreatic β-cell loss (Fig. 1a, Experiment 1 (Exp. 1)). The reverse experiment, in which RIP-DTR islets were transferred into WT normoglycaemic SCID host mice, was also performed (Fig. 1a, Exp. 2). Ten days post-diphtheria toxin administration, β-cells were efficiently ablated in engrafted RIP-DTR islets (Supplementary Fig. 1a). We also transplanted islets from RIP-DTR mice into RIP-DTR SCID hosts (Fig. 1a, Exp. 3). In this experiment, diphtheria toxin treatment led to hyperglycaemia because of β-cell loss in both host and grafted islets (Fig. 1c, red lines). One month after β-cell ablation, insulin production in α-cells was observed in all three experiments, but only in β-cell-ablated islets (Fig. 1b,d), irrespective of their location (pancreatic or extra-pancreatic; Supplementary Table 1a) and glycaemia. Conversion of α-cells was also observed after β-cell ablation in RIP-DTR islets engrafted on the iris (anterior chamber of the eye) of normoglycaemic WT mice (not shown).

The percentage of α-cells producing insulin in β-cell-ablated islets (regardless of their location) was two-fold higher in RIP-DTR SCID mice (1–2% versus 3–4% in SCIDs; refs ^12,13). This suggests that the genetic background (SCID) influences α-cell plasticity.

To explore the effect of global 50% β-cell mass loss, we co-transplanted WT and RIP-DTR islets at a 1:1 ratio into WT SCID hosts, either at two separate locations or mixed together at a single location (Supplementary Fig. 1b, Exp. 4). To distinguish the two types of engrafted islets, we labelled α-cells with yellow fluorescent protein (YFP) in RIP-DTR islets (using Glucagon-rtTA; TetO-Cre; R26-YFP; RIP-DTR mice as donors, in which doxycycline (DOX) triggers the irreversible expression of YFP in α-cells¹³) and β-cells with mCherry in WT islets (using islets from Insulin-mCherry mice, whose insulin-producing cells constitutively express mCherry). In the two-spot transplantation experiment, after diphtheria toxin treatment, no glucagon⁺ cell was insulin⁺ in unablated WT mCherry⁺ islets, whereas 4% of YFP⁺ α-cells produced insulin in β-cell-ablated RIP-DTR (Supplementary Fig. 1c and Supplementary Table 1a, Exp. 4). This further confirms that α-cells start insulin expression only in massively ablated islets. Similarly, no evidence of α-cell conversion after diphtheria toxin-mediated β-cell ablation was found in WT islets when mixed with RIP-DTR islets, whereas the proportion of YFP-labelled α-cells producing insulin in RIP-DTR islets was similar to the two-spot experiment (Supplementary Fig. 1c–d; Supplementary Table 1a, Exp. 4). This suggests that no signals act locally between adjacent β-cell-ablated (RIP-DTR) and unablated (WT) islets, neither from β-cell-ablated RIP-DTR islets that would promote conversion in WT islets with an intact β-cell mass nor from WT islets that would restrict α-cell conversion in nearby RIP-DTR islets.

We next explored the effect of 50% β-cell ablation occurring in every individual islet, either in the pancreas or after transplantation. We used hemizygous RIP-DTR females, in which about 50% of the β-cells express DTR¹³. In these settings (Exp. 5, Supplementary Table 1a), mice remained normoglycaemic after diphtheria toxin treatment and no evidence of α-cell conversion was observed (Supplementary Table 1a). Partial β-cell loss is thus insufficient to trigger α-cell conversion, even in the more permissive genetic backgrounds (that is, SCID mice).

In summary, these experiments reveal that α-cell conversion occurs only in islets undergoing massive β-cell loss, in an autonomous manner, irrespective of their location or glycaemia. The existence of signals from a putative systemic ‘β-cell mass sensor’ (Exps. 1–3) or acting at short distance (Exp. 4) is not supported, given that bihormonal cells were only detected in massively β-cell-ablated islets, in hyper- and normoglycaemic mice, and not in islets retaining approximately 50% of their β-cell mass (Exp. 5). Instead, our findings suggest that massive β-cell injury may lead to the release of local signals acting as modulators of α-cell change or maintenance.

Loss of β-cells facilitates insulin expression in α-cells

Why do most α-cells not express insulin after massive β-cell loss? To explore whether all α-cells can produce insulin, we induced Pdx1 (a β-cell-specific transcription factor) in αPdx1OE mice. In this quintuple transgenic line (Fig. 2a), the administration of DOX causes irreversible expression of Pdx1 and YFP in α-cells (Fig. 2b). The in vivo activity of Pdx1 in α-cells leads to the suppression of glucagon¹⁶ (Fig. 2c–e and Supplementary Table 1b,c) and to insulin expression in a small subset (3%) of α-cells (Supplementary Table 1d). Yet most Pdx1-expressing α-cells produce insulin after β-cell loss induced by treatment with diphtheria toxin or streptozotocin (STZ, a glucose analogue toxic for β-cells) (Fig. 2f–h and Supplementary Table 1d), suggesting that all α-cells can produce insulin. αPdx1OE mice remained hyperglycaemic after β-cell loss, which implies that the insulin-producing α-cells are probably not fully functional (not shown).

**Fig. 2: Pdx1 expression inhibits glucagon production in adult α-cells.**

We also used DOX to induce another β-cell-specific transcription factor Nkx6.1 in α-cells (Fig. 2i–l). This led to Pdx1 induction, glucagon inhibition and insulin expression in most α-cells, but only after β-cell loss (Fig. 2k,l).

In summary, in intact islets, β-cell transcription factor activity in α-cells is insufficient to trigger insulin protein production. However, results reveal an intrinsic ability of α-cells to produce insulin when combined with β-cell injury. Thus, β-cell loss enhances the capacity of α-cells to produce insulin.

Antagonistic response of α-cells to β-cell loss

To understand how massive β-cell loss influences the α-cell population, we performed RNA-Seq on native α-cells, α-cells one month after diphtheria toxin-induced β-cell ablation (αDT) and native β-cells. We also profiled α-cells expressing Pdx1, that is, α-cells from Pdx1OE mice (αPdx1) that lack glucagon expression and α-cells from Pdx1OE mice one month after diphtheria toxin-induced β-cell loss (αPdx1 + diphtheria toxin) that co-express glucagon and insulin (Fig. 3a and Supplementary Fig. 2). Principal component analyses revealed that αDT, αPdx1 and αPdx1 + diphtheria toxin cells retain a strong α-like gene signature, indicating that they are significantly different from native β-cells (Supplementary Fig. 3a). We filtered all differentially expressed genes between native α- and β-cells (1,682 and 1,258 genes, respectively; false discovery rate (FDR) < 0.01; fold change (FC) > 2; Supplementary Table 2a), assessed how they were impacted in α-cells in all conditions (αDT, αPdx1 and αPdx1 + diphtheria toxin; Supplementary Table 2) and categorized them according to their modulation (up- or downregulated compared to native α-cells; Fig. 3b,c and Supplementary Fig. 3b,c). Fifty-nine α-cell genes were downregulated and 140 β-cell genes were upregulated in the α-cells that did not express insulin after diphtheria toxin administration (Fig. 3b and Supplementary Fig. 3c), which indicates that α-cells acquire some β-cell identity traits following β-cell loss. Concomitantly, 67 α-cell-enriched genes were further upregulated, reinforcing the α-cell signature. This dual response was also observed in α-cells expressing Pdx1 in islets with intact β-cell mass: 238 β-cell genes were induced (Ins1, Ins2, Gjd2, …), 153 α-cell genes were downregulated (Gcg, MafB, …) and 81 α-cell genes were upregulated. Some of the induced β-cell genes (Ins1 and G6pc2) are direct Pdx1 targets. Thirty-five β-cell-enriched genes were induced by diphtheria toxin or Pdx1 (Fig. 3b).

**Fig. 3: Diphtheria toxin-mediated β-cell loss facilitates β-cell gene expression and elicits dual responses in α-cells.**

Although they were not expressed at levels comparable to that of native β-cells, 40% of the β-cell-enriched genes (503 out of 1,258) were upregulated in α-cells expressing Pdx1 when β-cells were ablated (Fig. 3b; αPdx1 + diphtheria toxin). Interestingly, Pdx1 was expressed in these α-cells at levels similar to those observed in β-cells (Fig. 3c). Within these 503 upregulated β-cell genes, 233 (46%) were not significantly induced by diphtheria toxin or Pdx1 alone (Fig. 3d). Similarly, 238 (66%) of the 361 downregulated α-cell genes in αPdx1 + diphtheria toxin cells, were not significantly modulated by Pdx1 activity alone or after β-cell loss (Fig. 3b,c and Supplementary Fig. 3c).

These results indicate that β-cell loss facilitates the expression of β-cell-enriched genes in α-cells. This correlates with the strong induction of insulin in most αPdx1 cells after β-cell ablation (Supplementary Fig. 3). Massive β-cell ablation decreases the α-cell expression of some α-cell-enriched genes; this is accentuated when combined with Pdx1 induction. Also, other α-cell-enriched genes were further upregulated, indicating that these conditions alter α-cell identity in antagonistic ways.

Insulin deprivation leads to α-cell identity changes

Our results suggest that massive β-cell loss is a requisite for spontaneous α-cell conversion. We therefore hypothesized that active insulin signalling in α-cells may prevent these cells from producing insulin. We assessed whether insulin signalling is compromised in α-cells after β-cell depletion on sorted Venus⁺ α-cells from glucagon-Venus, RIP-DTR mice, five days after β-cell ablation induction (Supplementary Fig. 3d,e). Massive β-cell death triggered a rapid downregulation of insulin-signalling genes in α-cells (Supplementary Fig. 3e). Interestingly, insulin-like growth factor 1 (Igf1), an activator of the insulin-signalling pathway, was upregulated in β-cell-depleted islets (Supplementary Fig. 3f), probably as a compensatory attempt to maintain insulin/IGF1 signalling in islets. This confirms that insulin signalling is active in α-cells under physiological conditions and is blunted after β-cell loss. Gene set enrichment analyses from RNA sequencing (RNA-Seq) indicate that insulin receptor signalling pathways (PKC activity and PI3K binding) are modulated in α-cells following β-cell ablation (Supplementary Fig. 3g).

To investigate whether local insulin deprivation alters α-cell identity, we genetically or pharmacologically impaired insulin/IGF1 receptor signalling in healthy mice. Through DOX administration to α-IR/IGF1R-KO mice, insulin and IGF1 receptors (insulin receptor and IGF1R) were downregulated and YFP was activated in α-cells (Fig. 4a,b). This led to Ins1, Nkx6.1 and Pdx1 transcript upregulation in α-cells, although insulin was not detected at the protein level (Fig. 4c,d). Therefore, the downregulation of insulin/IGF1 signalling, specifically in α-cells, initiates insulin gene transcription. We speculate that a more efficient inactivation of IR/IGF1R genes in α-cells, and perhaps in all non-β islet cell types, would lead to stronger upregulation of β-cell genes. Furthermore, the observed upregulation of IRS2 probably compensates in part the effects of IR/IGF1R downregulation.

**Fig. 4: Decreased insulin signalling predisposes α-cells to insulin production in islets with an intact β-cell mass.**

In parallel, we transiently blocked insulin action using the insulin receptor antagonist S961 (Novo Nordisk)^17,18,19, which induces hyperglycaemia and insulin resistance. Healthy Glucagon-rtTA; TetO-Cre; R26-YFP mice were treated with DOX to tag α-cells and then with S961 (Fig. 4e). Mice were either analysed during S961 treatment (analysis 1) or after treatment was stopped (analysis 2). When insulin action was inhibited^13,20, about 2% of the YFP⁺-traced α-cells expressed insulin (Fig. 4f,g, analysis 1 and Supplementary Table 1e), Pdx1 and Nkx6.1 (Supplementary Fig. 4a–c). Insulin (Ins1 and Ins2) transcripts were upregulated in α-cells sorted from S961-treated glucagon-Venus mice (Supplementary Fig. 4d,e). In contrast, when islets were analysed one month after S961 treatment was stopped (Fig. 4e, analysis 2; Supplementary Table 1e), insulin protein became undetectable in α-cells, which suggests that its production had ceased (Fig. 4f,g).

We next combined STZ-induced partial β-cell loss with the pharmacological inhibition of insulin^20,21. One month after STZ treatment, Glucagon-rtTA; TetO-Cre; R26-YFP mice received either Wortmannin (a PI3K inhibitor) or S961, to inhibit the residual insulin signalling (Fig. 4h). The proportion of α-cells that produce insulin increased following drug treatment compared to mice treated with only STZ (Fig. 4i and Supplementary Table 1e). Conversely, α-cell reprogramming was abrogated when STZ treatment was followed by insulin therapy (Fig. 4i and Supplementary Table 1e), confirming that insulin negatively modulates α-cell plasticity.

When combined with Pdx1 overexpression in αPdx1OE mice, the number of insulin-producing α-cells were greatly increased in all of the conditions described above (Supplementary Fig. 4f–k and Supplementary Table 1d). Importantly, the number of insulin-negative YFP⁺ α-cells increased again after S961 treatment was stopped (Supplementary Fig. 4j), which indicates that α-cells are maintained after discontinuing insulin production.

These observations suggest that β-cell death per se is not required for insulin gene expression in α-cells. Insulin-signalling deprivation promotes insulin production in α-cells in a reversible manner. Therefore, in homeostatic conditions, islet insulin signalling helps maintaining α-cell identity.

Constitutive Smo-mediated signalling restricts α-cell plasticity

Given that the vast majority of α-cells are apparently unaffected after β-cells loss, we hypothesized that signalling pathways less affected by β-cell injury could convey molecular cues restricting α-cell plasticity, even when insulin signalling is compromised.

Two observations suggest that Smoothened-mediated Hedgehog (SmoHh) signalling could act as an α-cell identity keeper. First, SmoHh components are expressed in α-, β- and δ-cells, and active signalling was reported in intact islets (Supplementary Fig. 5a–c; refs ^22,23). Second, SmoHh is linked to cell differentiation and maintenance in many organs, including the pancreas^23,24,25,26, where it regulates Pdx1 and Ins expression^27,28.

To test whether SmoHh controls α-cell plasticity, we generated Glucagon-rtTA; TetO-Cre; R26-YFP; Smoothened^fl/fl (loxP-flanked); RIP-DTR mice (α-Smo-KO) (Fig. 5a). One-month-old α-Smo-KO mice were DOX-treated to downregulate Smo in α-cells and tag them with YFP (Supplementary Fig. 5e,f). The downregulation of α-cell-Smo triggered the downregulation of Gcg and Arx (two α-cell-specific markers; Supplementary Fig. 5g), suggesting that active SmoHh in α-cells maintains α-cell identity.

**Fig. 5: *Smo* inactivation in α-cells facilitates their engagement in insulin production.**

Downregulation of α-cell-Smo did not trigger insulin production (Supplementary Fig. 5h). Conversely, Smo inactivation along with β-cells loss or S961 treatment (Fig. 5b) increased the percentage of insulin-expressing α-cells (Fig. 5c,d; Supplementary Table 1f). Hence, downregulation of SmoHh facilitates α-cell reprogramming when combined with insulin-signalling inhibition.

We next examined whether insulin-producing α-cells secrete insulin in response to glucose. As these cells are rare, we took advantage of their increased number in the islets of diphtheria toxin-treated α-Smo-KO mice. C-peptide was released in a glucose-responsive manner in the blood of α-Smo-KO mice, one month after β-cell ablation (Fig. 5e and Supplementary Table 1g). To determine the precise contribution of converted α-cells and of escaping β-cells (<0.5%), we assessed insulin secretion from α-cells in vitro. We isolated islets from α-Smo-KO^fl/fl and α-Smo-KO^WT/WT mice after β-cell loss (Supplementary Table 1h), sorted YFP⁺ α-cells and re-aggregated them to reconstitute highly purified ‘monotypic pseudoislets’ (Fig. 5f,g). We confirmed the increased insulin production in these α-cells by immunofluorescence (Fig. 5h,i and Supplementary Table 1i) and performed glucose-stimulated insulin secretion tests in vitro. Pseudoislets from α-Smo-KO mice secreted C-peptide in a glucose-dependent manner (Fig. 5j and Supplementary Table 1l). These results suggest that insulin-producing α-cells are functional and naturally secrete insulin in response to glucose.

δ-cells restrict α-cell plasticity

A percentage of δ-cells produce insulin following β-cell loss¹². As we observed that SmoHh-mediated signalling is active in δ-cells (Supplementary Fig. 5a–c), we investigated its putative role in blocking δ-cell plasticity by downregulating it in Somatostatin-rtTA; TetO-Cre; R26-YFP; Smoothened^fl/fl; RIP-DTR mice (δ-Smo-KO) (Fig. 6a and Supplementary Fig. 5i,j).

**Fig. 6: *Smo* inactivation in δ-cells leads to enhanced conversion of α-cells β-cells.**

In δ-Smo-KO mice, DOX induces δ-cells Smo downregulation along with YFP-labelling (Supplementary Fig. 5i,j and Supplementary Table 1m).

Downregulation of Smo did not affect δ-cell-specific gene expression or induced insulin production (Supplementary Fig. 5j and Supplementary Table 1n). After β-cell ablation, the percentage of δ-cells engaging insulin expression was similar in the control and δ-Smo-KO mice (Supplementary Fig. 5k and Supplementary Table 1n). Yet, surprisingly, the number of glucagon⁺insulin⁺ bihormonal cells was ten-fold higher in mice with Smo downregulated in δ-cells compared to WT mice (Fig. 6b–d; Supplementary Table 1o). These bihormonal cells were not YFP⁺-traced, indicating that they were not reprogrammed δ-cells (Fig. 6c). This suggests a potent δ-cell-mediated non-α-cell-autonomous regulation of α-cell plasticity.

To further explore this non-cell-autonomous regulation of α-cell identity, we generated α + δ-Smo-KO mice (Glucagon-rtTA; Somatostatin-rtTA; TetO-Cre; R26-YFP; Smoothened^fl/fl; RIP-DTR), to inactivate Smo in α- and δ-cells (Fig. 6e). In this situation, the glucagon⁺ and insulin⁺ bihormonal cells were YFP⁺-traced (Fig. 6f,g and Supplementary Table 1p), thus suggesting their α-cell origin.

Pharmacological inhibition of SmoHh with the Hh-signalling inhibitor GANT61 also increased the number of insulin-producing α-cells after β-cell loss (Supplementary Fig. 5l,m).

These observations suggest that active SmoHh-mediated signalling in δ-cells is a non-cell-autonomous intra-islet inhibitory signal that restricts α-cell plasticity in β-cell-ablated islets.

Co-ablation of δ- and β-cells enhances α-cell conversion

To further confirm that δ-cells act as negative regulators of α-cell plasticity, we generated Somatostatin-Cre; R26-YFP; R26-iDTR; RIP-DTR mice, to co-ablate β- and δ-cells simultaneously (Supplementary Fig. 5n). The loss of β- and δ-cells doubled the proportion of glucagon⁺ and insulin⁺ bihormonal cells (Supplementary Fig. 5n and Supplementary Table 1q). This result is compatible with a δ-cell-mediated restriction of α-cell plasticity.

Discussion

Maintenance of adult cell identity is a highly dynamic process that depends on the tight convergence of diverse signals whose complexity might be correlated with the regenerative capacity of tissues. The pressure to preserve cell identity is probably stronger in highly specialized cells implicated in vital metabolic processes. Endodermal cells may be intrinsically different, always in a ‘regenerative state’, because they are exposed to external insults¹. Any uncontrolled phenotypic instability could be detrimental and result in the onset of disease.

Our results show that the near-total loss of β-cells triggers simultaneous signals with antagonistic effects on α-cells: they activate insulin production and favour regeneration while also increasing α-cell marker expression, seemingly enforcing the α-cell fate and opposing identity changes.

We have identified paracrine repressive signals that maintain α-cell identity. We show here that α-cell identity is tightly maintained under physiological conditions through the constant repressive influence of local insulin and SmoHh signalling, originating from proximate β- and δ-cells; the inhibition of proximate β- and δ-cells leads to a substantial increase in insulin⁺ α-cell numbers. Thus, in addition to regulating α-cell function through somatostatin and insulin, δ- and β-cells ensure α-cell fate maintenance in a non-α-cell-autonomous manner. Even with dual Smo-Ins downregulation, α-cell conversion is only partially improved, which suggests that α-cell conversion is restricted by the synergistic influence of multiple signals. Recent studies on transcription factors expressed in β-cells (such as Pdx1, Nkx6.1, Nkx2.2 and Pax6) suggest that they are directly involved in repressing non-β-cell genes^{29,30,31,32,33,34,35}. The extracellular repressive signals that lock α-cells in their state may therefore be transmitted via such transcriptional repressors.

The maintenance of cell identity detected in the critical, physiologically relevant islets may be much more widespread in differentiated cells, similar to a natural ‘tendency’ or ‘capacity’ of adult mature cells, and appears to be subtler than the complete iPSC reprogramming, with several levels of control of switching of cell phenotype.

This has remarkable implications for our comprehension of how the cell identity–differentiation equilibrium is established.

Our results indicate that spontaneous insulin production in α-cells is not simply due to uncontrolled stress-induced insulin gene dysregulation, but is dynamically regulated, representing a meaningful compensatory response to cope with situations of insulin insufficiency.

Importantly, Lee et al. reported the appearance of insulin-resistant α-cells during diabetes³⁶. We propose that these insulin-resistant α-cells would be more susceptible to changes in their identity, allowing them to better cope with insulin deficiency. In agreement with such a speculation, bihormonal cells have been reported in the pancreas of diabetic patients^37,38. The similarity of insulin-producing α-cells to native β-cells and their level of maturity were not addressed in this study and should be investigated.

In the context of this study, α-cell recruitment into insulin production, encompassing the reduction of glucagon expression, would also be beneficial for diabetics by limiting glucagon secretion and hepatic glucose mobilization, without major metabolic defects caused by α-cell deficit^39,40.

In conclusion, we have found that the stability of cell identity is heavily context-dependent and not ‘carved in stone’, with several levels of control on the switching of cell phenotype. A physiological input from signalling pathways inside the islet keeps the identity of the cells in homeostasis. The endocrine-cell plasticity detected in this critical organ may be much more widespread in differentiated cells, akin to the natural tendencies of adult mature cells. Maintaining cell identity is therefore an active process of repressive signals released from surrounding neighbour cells, blocking an intrinsic tendency of specialized differentiated cells to modify their phenotype and functional characteristics (Supplementary Fig. 6).

Methods

Mice

Transgenic mice were described in earlier work^{12,13,16,41,42,43,44}. Sst^rtTA knock-in mice were genetically engineered as follows: a Sst^rtTA.LCA allele was made using a targeting vector made by BAC recombineering starting with BAC clone RP23-274H19 (purchased from CHORI). Both the general strategy and vectors were previously described⁴⁵. The targeting vector, pSst.rtTA.LCA, contained a 5′ homology arm of 7.3 kb and a 3′ homology arm of 3.6 kb flanking a Lox66 site, a modified region of the Sst gene containing the reverse tetracycline TransActivator (rtTA) and a Lox2272 site. The cassette contained both a PGK-puromycinΔthymidine kinase and EM7-kanamycin selectable markers flanked by two tandemly-oriented FRT sites. After electroporation of the targeting vector into 129S6-derived mouse ES cells, 192 puromycin-resistant clones were obtained, 24 of which had undergone the desired homologous recombination event. Clone 1A8 was used to derive chimeras that were then bred with C57BL/6J mice to obtain germline transmission. The FRT-flanked selectable markers were removed by interbreeding with a FlpE-expressing transgenic mouse⁴⁶.

Male and female mice were used for all experiments, except for transplantation experiments, for which only males were used as hosts. The animals were treated with care and respect. This study is compliant with all relevant ethical regulations regarding animal research and all experiments have been approved and performed according to the guidelines of the Direction générale de la santé du Canton de Genève (license numbers: GE/103/14; GE/111/17 and GE/121/17). The number of mice used was limited by the availability of the required complex genotype. The mice were randomly selected for treatments and control. The sample sizes were in the range of the published literature and exclusion criteria were transgene set, general health status and occasional spontaneous death during the experiments.

Diphtheria toxin, DOX, STZ, S961, wortmannin and insulin treatments

Diphtheria toxin (Sigma) was administrated by intra-peritoneal injections as previously described¹³. DOX (1 mg ml⁻¹; Sigma) was added to the drinking water. STZ was injected intraperitoneally as a single dose (200 mg per kg body weight). S961 (Novo Nordisk) was given via ALZET osmotic pumps implanted subcutaneously (40 nmol per week). Wortmannin (Sigma) was intraperitoneally injected daily for five consecutive days (1 mg per kg body weight). Mice received a subcutaneous insulin pellet (Linbit) when glycaemia exceeded 25 mM.

Islet isolation, FACS, RNA extraction and qPCR

Islet isolation, cell sorting, RNA preparation and qPCR were performed as described in earlier work¹². All qPCRs were performed in triplicate. Primers are listed in Supplementary Table 3. Components of the insulin signalling pathway were evaluated with the PAMM-030Z and qPCR were performed as previously reported¹².

Non-quantitative RT-PCR

Total RNA was isolated from the liver, uterus and duodenum and used as controls. Tissue RNA was prepared using the Qiagen RNeasy Mini Kit. Islets cells were FAC-sorted as previously described¹² and RNA prepared using the Qiagen RNeasy Micro Kit. The Qiagen QuantiTect Reverse Transcription Kit was used to prepare cDNA starting from 1 µg of RNA. Amplification conditions and primers were as previously reported^47,48. Primers for actin were unpublished and are reported in Supplementary Table 3.

RNA sequencing and quality control

Islet isolation and cell isolation by FACS were performed using previously published protocols¹². The gating strategy used for FACS sorting is provided in Supplementary Fig. 2. The experimental design for sample collection is reported in Fig. 2a. Briefly, purified cells were obtained as follows: (1) native α-cells from three-month-old Glucagon-rtTA; TetO-Cre; Rosa26YFP; RIP-DTR mice, (2) αDT: α-cells from Glucagon-rtTA; TetO-Cre; Rosa26YFP; RIP-DTR mice one month after diphtheria toxin treatment, (3) αPdx1OE: α-cells ectopically expressing PDX1 from three-month-old Glucagon-rtTA; TetO-Cre; R26YFP; CAG-Pdx1; RIP-DTR mice, (4) αPdx1OE + DT: α-cells expressing PDX1 from Glucagon-rtTA; TetO-Cre; R26YFP; CAG-Pdx1; RIP-DTR mice one month after diphtheria toxin treatment and (5) native β-cells from 3-month-old RIP-Cre and Rosa26YFP mice.

Extracted RNA was assessed for quality by an Agilent bioanalyzer. Libraries were prepared (according to Illumina’s standard protocols), multiplexed and sequenced on an Illumina platform with paired-end 100-bp reads. The sequencing quality control was done with FASTQC v.0.10.1, followed by sequence alignment to the mouse reference genome (UCSC mm10) using the TopHat v2.0.9 (default parameters). Biological quality control and summarization were done with the RSeQC v2.4, the PicardTools v1.92 and the SamTools v0.1.18. In brief, RNA sequencing quality for each sample was assessed for overall coverage, base composition, presence of adaptors and 3′ bias, among other factors. A subset of samples were excluded from further analyses due to poor sequencing quality, 3′ bias, contamination or outlier status for technical or biological reasons, including outliers on the first principal component analysis across the entire sample set or samples with PDX-1 expression levels exceeding 3 s.d. from the mean of each respective experimental group. Finally, 22 samples were included for all downstream analyses (n = 6 for native α-cells, n = 3 for αDT, n = 3 for αPdx1OE, n = 5 for αPdx1OE + diphtheria toxin and n = 5 for native β-cells). The raw count data was prepared with HTSeq v.0.6.1p1 (htseq-count, defaults parameters). The RNA-Seq data generated in this study have been deposited in the NCBI GEO database under accession number GSE109285.

Transcriptomic data analyses

The normalization and differential expression analysis was performed with the R/Bioconductor package edgeR package v.3.20.9. Briefly, genes expressed at very low levels were filtered out and genes that achieved ten counts in at least five samples were kept. The filtered data were normalized by the library size and DEGs were estimated with the negative binomial general model statistics. To identify DEGs, pairwise comparisons were performed (edgeR, GLM approach for the design matrix setup, the factors were combined together). To obtain reference gene expression profile datasets for native α- and β-cell phenotypes, DEGs between sorted α- and β-cells were analysed (FC > 2 FDR < 0.01). The 2,940 DEGs were used as reference set of genes to separate α- and β-cell-specific expression patterns (1,682 DEGs as α-cell genes, 1,258 DEGs as β-cell genes). Three additional comparisons were generated with respect to the reference α-cells: αDT versus α, αPdx1OE versus α and αPdx1OE + diphtheria toxin versus α (FDR < 0.05). All DEGs we identified are provided in Supplementary Table 2.

We considered upregulated β-cell genes or downregulated α-cell genes in the DEGs list as genes inducing a β-cell signature (‘induced β-cell signature’ category) and, reciprocally, upregulated α-cell genes as enhancing α-cell signature (‘enhanced α-cell signature’ category). DEGs from each condition were intersected with α-/β-cell reference genes and shown in Fig. 3b and Supplementary Table 2. The output data were displayed graphically as a principal component analysis-plot, heatmap, dendrogram or Venn diagram.

Pathway analysis

The pathway analyses were performed with gene set enrichment analysis (http://software.broadinstitute.org/gsea/index.jsp). Gene sets with significant enrichment in gene set enrichment analysis were identified among mm_GO of Gene Set Knowledgebase. All significant gene sets are shown in Supplementary Table 2.

Physiological studies

Pancreatic glucagon was measured as previously described⁴⁹.

C-peptide measurements and pseudoislets experiments

In vivo glucose challenge tests, islets isolation and FACS sorting of YFP⁺ cells were performed as described in earlier work^12,13. The gating strategy used for FACS sorting is provided in Supplementary Fig. 2.

For re-aggregation into pseudoislets, sorted islet cells from 3–8 mice were pooled and seeded on 96-well ultra-low adherent culture plates for 5–7 d (1,000 cells well⁻¹) at 37 °C in a 5% CO₂ incubator, in the following culture medium: Advanced DMEM/F12 (Invitrogen) supplemented with penicillin/streptomycin, 10 mM HEPES (Invitrogen), 2 mM GlutaMAX (Invitrogen), 10% fetal bovine serum, 10 mM nicotinamide (Sigma) and 1 mM N-acetyl-L-cysteine (Sigma). The culture medium was changed every second day.

For live imaging of cultured cells, images were captured manually on culture day seven using a Nikon Eclipse TE300 microscope.

To evaluate reprogramming events in pseudoislets, the histology of pseudoislets was examined in cryo-sections as described in earlier work^12,13.

Pseudoislets were hand-picked for each assay replicate and washed by incubation for 1 h at 37 °C in RPMI medium (Invitrogen) and then equilibrated by incubation for 1 h at 37 °C in Krebs Ringer Bicarbonate buffer containing 3 mM glucose (Sigma). The samples were then transferred into fresh Krebs Ringer Bicarbonate buffer containing 3 mM glucose (Low) for 1 h followed by incubation for another hour in Krebs Ringer Bicarbonate buffer containing 20 mM glucose (Hi) at 37 °C. The medium was collected after 1 h of incubation at each glucose concentration and stored at −80 °C for subsequent analyses. Mouse C-peptide concentration was quantified using Mouse C-peptide ELISA kit (Alpco).

Sorted α-cells (90,000–225,000 α-cells from 3–8 mice) were pooled for the three independent cohorts of experiments in each control and α-Smo-KO group, and re-aggregated into pseudoislets. In glucose-stimulated C-peptide secretion tests, the maximum number of pseudoislets (90–225 pseudoislets) that we could generate from the pooled α-cells was tested as a cohort and then pseudoislet number normalized C-peptide release.

Transplantations

Islet transplantations under the kidney capsule were performed as described in earlier work⁵⁰.

Immunofluorescence

Cryostat sections were 10 μm-thick. The antibodies used were: rabbit and guinea pig anti-Pdx1 (1/5000 and 1/750 respectively; C.W. Wright), rabbit anti-Nkx6.1 (1/800; BCBC), guinea pig anti-porcine insulin (1/400; DAKO), mouse anti-glucagon (1/1000; Sigma), mouse anti-somatostatin (1/200; BCBC) or goat anti-somatostatin (1/200; Santa Cruz), rabbit anti-GFP (1/400; Molecular Probes), mouse anti-mCherry (1/500; Abcam) and goat anti-Ihh (1/50; Santa Cruz). Secondary antibodies were coupled to Alexa 405, 488, 647 (Molecular Probes), Cy3, Cy5 (Jackson Immunoresearch) or TRITC (Southern Biotech). All antibodies are listed in Supplementary Table 4. The sections were examined with a confocal microscope (Leica TCS SPE). In all experiments cells were considered bihormonal (glucagon⁺insulin⁺, somatostatin⁺insulin⁺) or co-expressing markers (that is, insulin⁺YFP⁺) when one nucleus was clearly surrounded by both hormone/reporter staining.

Statistics and reproducibility

Error bars represent s.e.m. or s.d., as indicated in the figure legends. One representative biological replicate of an experiment is presented in the figures. All experiments were performed three or more times independently under identical or similar conditions, except when indicated in the figure legends. Statistical analyses were performed using the Prism v6.0 software and either unpaired t-tests or Mann-Whitney tests were applied for sample comparisons. Glycaemia was measured once on multiple time-points for each animal (Fig. 1c). The RNA-Seq experiment/reaction was performed once (Fig. 3, Supplementary Fig. 3a–c). Quantitative PCRs were performed once, using 3–5 individual biological samples as indicated in the figure legends; each biological sample was run in triplicate (Fig. 4 and Supplementary Figs. 3–5). Immunofluorescence for a particular antibody cocktail was performed once for each mouse with ≥3 cryo-sections/animal being stained at once and analysed (Figs. 2c,g,k,i; 4c,f; 5c; 6c,f and Supplementary Figs. 4b,g,j; 5h,j,k,l). The immunofluorescence reaction was repeated twice for Supplementary Fig. 5b.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The RNA-Seq data generated in this study have been deposited in the NCBI GEO database under accession number GSE109285. Source data for Figs. 1–6 and Supplementary Figs. 1,2,4–6 are provided as Supplementary Table 1 and RNA analyses are provided in Supplementary Table 2. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

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Acknowledgements

We are grateful to Y. Dor, A. Ruiz i Altaba and M. González Gaitán for carefully reading and discussing the manuscript; to O. Fazio, M. Urwyler, C. Gysler, B. Polat and R. Gangula for their technical help and to J.-P. Aubry-Lachainaye for FACS assistance. We acknowledge C. Delucinge-Vivier, M. Docquier, A. Efanov, P. Ebert, J. Calley, H. Wu, S.K. Syed and T. Wei for technical and expert assistance supporting RNA sequencing, quality control, statistical and bioinformatics analyses. M.S. was supported by R01DK068471 (NIH/NIDDK). S.C. is supported by grants from the Research Council of Norway (NFR no. 247577) and the Novo Nordisk Foundation (no. NNF15OC0015054). M.A.M. was supported by U01DK072473 (Beta Cell Biology Consortium). Work was funded by grants from the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Disease (Beta Cell Biology Consortium, grant no. U01DK089566 and Human Islet Research Network, grant nos UC4-DK104209 and UC4-DK108132), the Juvenile Diabetes Research Foundation (in part through joint research funding with Lilly Research Laboratories, grant nos 17-2011-276, and 2-SRA-2015-67-Q-R), the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 155005 (IMIDIA), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution, the Fondation privée des Hôpitaux Universitaires de Genève–Confirm, the Fondation Aclon and the Swiss National Science Foundation (grant no. NRP63, 310030_152965 and the Bonus of Excellence grant no.310030B_173319) to P.L.H.

Author information

Valentina Cigliola
Present address: Department of Cell Biology, Duke University Medical Center, Durham, NC, USA
These authors contributed equally: Valentina Cigliola, Luiza Ghila, Fabrizio Thorel.

Authors and Affiliations

Department of Genetic Medicine and Development, iGE3 and Centre facultaire du diabète, Faculty of Medicine, University of Geneva, Geneva, Switzerland
Valentina Cigliola, Luiza Ghila, Fabrizio Thorel, Léon van Gurp, Delphine Baronnier, Daniel Oropeza, Kenichiro Furuyama, Simona Chera & Pedro L. Herrera
Department of Clinical Science and KG Jebsen Center for Diabetes Research, University of Bergen, Bergen, Norway
Luiza Ghila & Simona Chera
Lilly Research Laboratories, Lilly Corporate Center, Indianapolis, IN, USA
Simone Gupta & Melissa K. Thomas
Department of Metabolism and Endocrinology, Graduate School of Medicine , Juntendo University , Tokyo, Japan
Takeshi Miyatsuka
Department of Metabolic Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
Hideaki Kaneto
Departments of Molecular Physiology and Biophysics, Center for Stem Cell Biology, Vanderbilt University, Nashville, TN, USA
Mark A. Magnuson & Anna B. Osipovich
Department of Pediatrics and Cellular and Molecular Medicine, University of California, San Diego, CA, USA
Maike Sander
Department of Cell and Developmental Biology, Program in Developmental Biology and Center for Stem Cell Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
Christopher E. V. Wright

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Valentina Cigliola
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Luiza Ghila
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Fabrizio Thorel
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Contributions

V.C., F.T., L.G., S.C., D.B. and K.F. performed all of the experiments and most analyses. T.M., H.K, C.E.V.W. and M.S. provided transgenic lines. M.K.T., S.G., S.C., K.F. and L.V.G analysed RNA-Seq data. M.A.M. and A.B.O. generated the Sst-rtTA knock-in mouse line. K.F., F.T., V.C., L.G., S.C., D.O. and P.L.H. conceived the experiments and wrote the manuscript.

Corresponding author

Correspondence to Pedro L. Herrera.

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Integrated supplementary information

Supplementary Figure 1 Insulin production by α-cells after β-cell ablation in islets transplanted under the kidney capsule.

(a) Immunofluorescence staining of insulin (green) and glucagon (red) in RIP-DTR islets transplanted under the kidney capsule, either before (‘no DT’) or after (‘DT’) β-cell ablation. The scatter graph reports a quantification of the β-cell ablation efficiency, measured as the number of insulin-containing cells remaining per islet section in non-treated and DT-treated animals, 10 days later. (mean +/− s.e.m., n = 40 islet sections per condition. (b) Experimental design of Exp. #4, where islets were transplanted in the kidney capsule of immunocompromised mice (SCID) either mixed together or at 2 separated sites. (c) Proportion of YFP-traced α-cells expressing insulin 1-month post-DT in mixed RIP-DTR+WT islets compared to RIP-DTR islets from 2-sides graft. Data are shown as mean +/− s.d.; n = 3 biologically independent animals per condition. (d) Immunofluorescence staining of mCherry (magenta), YFP (green), insulin (red) in transplanted mixed RIP-DTR+WT islets 1-month post-DT. Scale bars: 20 µm. Experiment repeated independently 3 times. See Supplementary Table 1 as source data.

Supplementary Figure 2 FACS sorting. Gating strategy used for FACS sorting the different islet cell types used for RNA seq analyses.

Mice used to purify the different islet cell types are listed in the methods.

Supplementary Figure 3 α-cell transcriptome analysis and gene expression changes after β-cell loss.

(a) PCA plot of all samples analyzed by RNA-Seq. Native β-cells (‘β’, n = 5 mice) show a different profile compared to α-cell groups including native α-cells (‘α’, n = 6 mice), α-cells 1 month after DT-induced β-cell ablation (‘αDT’, n = 3 mice), α-cells overexpressing Pdx1 (‘αPdx1OE’, n = 3 mice), and α-cells overexpressing Pdx1 combined with β-cell ablation (‘αPdx1OE+DT’, n = 5 mice). (b) Heat map showing scaled expression (blue, high; white, low) of α- / β-cell-enriched genes differentially expressed in αDT, αPdx1OE, αPdx1OE+DT conditions compared to native α-cells. (c) Gene expression changes (log2 value) in αPdx1OE+DT (top), αPdx1OE (middle) and αDT (bottom) compared to native α-cells. DEGs indicated by red-colored background show induced β-cell signature upon the conditions, while DEGs indicated by green-colored background show refractory responses. * FDR < 0.05. n = 6 mice in α, n = 5 mice in αPdx1OE+DT, n = 3 mice in αPdx1OE, and n = 3 mice in αDT. (d) Transgenes required for constitutive labeling α-cells with the reporter Venus and experimental design for α-cell labeling and purification after β-cell loss. (e) RT-qPCR analyses for different components of the insulin/IGF1-R signaling pathway. Components of the pathway are downregulated in α-cells 5 days post-DT. Center indicates the mean. qPCR were performed in triplicates and data processing and statistical analyses were performed using the web-based software ‘RT2 Profiler PCR Array Data Analysis version 3.5’, from Qiagen. Quantification of the gene expression changes were performed using the 2^−ΔΔCt method. P-values were calculated using a Student’s t-test (two-tail distribution and equal variances between the two samples) on the 2^−ΔCt values. Genes with P<0.05 are shown. n = 3 mice. qPCR was performed once. (f) Experimental design for DT-mediated β-cell loss, islet isolation and qPCR. Igf1 mRNA is upregulated in islets after β-cell loss, likely as an attempt to sustain insulin signaling. Data shown as mean +/− sem; n = 3 mice with 3 technical replicates each. Center indicates the mean. Two-tailed unpaired t-test P = 0.0086. (g) Gene set enrichment analysis (GSEA) from RNA-Seq showing enrichment of the insulin signaling pathway components PKC (left) and PI3K (right) in α-cells after DT-mediated β-cell ablation (αDT) compared to native α-cells (α). P-values were calculated by an empirical phenotype-based permutation test. n = 6 mice in α, n = 3 mice in αDT. NES: normalized enrichment score. See Supplementary Table 2 as source data.

Supplementary Figure 4 Insulin production in α-cells under insulin signaling blockade.

(a) Transgenes for inducible α-cell lineage tracing and experimental design for α-cell lineage tracing and S961 treatment. (b-c) Immunofluorescence staining for the β-cell-specific transcription factors Pdx1 and Nkx6.1 in islets of mice treated for 1 month with S961. Experiments were repeated once, with n = 3 mice treated asynchronously according to their availability. (d) Mice and experimental design for α-cell labeling and sorting by FACS. (e) β-cell-specific genes are upregulated in α-cells upon S961 treatment. qPCRs were performed in triplicates. Data shown as mean +/− sem; n = 3 mice. Experiment performed once. Two-tailed unpaired t-test (P = 0.0017 INS1; P = 0.0010 INS2) (f) Transgenes required for inducible α-cell lineage tracing, simultaneous insulin receptor (IR) and IGF-1 receptor (IGF1R) downregulation, and ectopic Pdx1 induction in adult α-cells. (g) Impaired insulin/IGF1 signaling in α-cells through IR and IGF1R downregulation predisposes α-cells to insulin gene expression in mice with normal β-cell mass. (h) Fractions of α-cells expressing insulin after IR/IGF1R downregulation in α-cells and Pdx1OE. n = 3 mice. Two-tailed Mann Whitney test, P = 0.05. (i) Transgenes required for inducible Pdx1 expression in adult α-cells. (j) Wortmannin-mediated PI3K inhibition or S961-mediated insulin antagonism trigger insulin production in Pdx1OE α-cells. Insulin production in Pdx1OE α-cells is reversed when islets are analyzed 1 month after ending S961 treatment. (k) Fraction of converted α-cells found upon insulin signaling antagonism/inhibition. Black and red symbols: control and αPdx1OE mice, respectively. n = 6, 4, 3, 3 mice in DOX only, DOX+Wort, DOX+S961 and DOX+S961+STOP, respectively. Two-tailed Mann Whitney test, P = 0.0061 Pdx1 only vs Pdx1+wort, P = 0.0167 Pdx1 only vs Pdx1+S961, P = 0.0333 Pdx1 only vs Pdx1+S961then stop. All experiments were performed once with animals treated in an asynchronous way depending on their availability. Scale bars: 20 μm. See Supplementary Table 1d as source data.

Supplementary Figure 5 Hedgehog signaling modulation in α- and δ-cells.

(a) Hh signaling components are enriched in α- vs β-cells and their expression is unchanged 30 days upon DT-induced β-cell ablation, Pdx1OE or both. Log2FC are taken from the RNA-Seq presented in Fig. 3. (b) Indian Hedgehog ligand expression in islets of control mice. Immunofluorescence was repeated two times with similar results. (c) Non-quantitative RT-PCR of Hedgehog pathway’s components in sorted α-, δ- and β-cells. Liver, duodenum and uterus were used as controls. Experiments were repeated three times with similar results. Unprocessed scans of the blots are provided in Supplementary Fig. 7. (d) Transgenes required for simultaneous α-cell lineage tracing, Smo co-receptor downregulation and DT-induced β-cell ablation. (e) Experimental design. (f) Smo and (g) α-cell genes Arx and Glucagon are downregulated in α-cells with reduced Smo activity upon DOX treatment. n = 3 mice. qPCR was performed once. Two-tailed unpaired t-test; SMO: P = 0.0012, GCG: P = 0.0325 fl/+ vs +/+ P = 0.0141 fl/fl vs +/+ and P = 0.0177 fl/fl vs fl/+, ARX: P = 0.0214 fl/+ vs +/+, 0.0245 fl/fl vs +/+). Center indicates the mean. (h) Smo inactivation in α-cells in absence of β-cell loss or insulin signaling impairment does not lead to insulin production in intact islets. Immunofluorescence was repeated once, on 2 slides of 3 different mice. (i) Transgenes required to lineage trace δ-cells, experimental design and characterization of labeling specificity in the Sst-rtTA line. Only δ-cells express YFP. n = 5, 4, 4 for δ-, β- and α-cells, respectively. (j) Transgenes required for lineage tracing and Smo coreceptor inactivation in δ-cells, and for DT-induced β-cell ablation and experimental design. Smo is downregulated in purified δ-cells after DOX treatment. n = 3 mice. qPCRs were performed once. Two-tailed unpaired t-test, P = 0.0048. Downregulation of Smo in δ-cells does not alter the expression of the δ-cell genes Somatostatin and Hhex. n = 3 mice. qPCR was performed once. Center indicates the mean. Insulin protein is not expressed in δ-cells upon Smo inactivation in intact islets. Immunofluorescence was repeated once, on 3 slides of 3 different mice. (k) Experimental design for δ-cell lineage tracing and DT-induced β-cell loss in δ-Smo-KO mice. A fraction of δ-cells express insulin after β-cell ablation in δ-Smo-KO mice, but their percentage is not increased when compared to mice with intact Smo expression. n = 3, 3, 3 mice in no DT wt/wt, wt/fl, fl/fl respectively and n = 4, 4, 3 mice in DT wt/wt, wt/fl, fl/fl respectively. Immunofluorescence was repeated once, on 3 to 5 slides per mouse. Center in the graph indicates the mean. Two-tailed unpaired t-test. (l) Transgenes required for α-cell tracing and DT-induced β-cell ablation, experimental design and immunofluorescence staining of mouse islets 1 month after DT+GANT61 treatment. Immunofluorescence was repeated once, on 3 to 5 slides of n = 8 mice treated with DT+GANT61. A representative islet is shown. (m) The % of α-cells expressing insulin (n = 5 and 4 mice for DT+DMSO and DT+GANT61, respectively), the number of insulin-expressing cells per islet section (n = 8 mice for DT+DMSO and DT+GANT61), and the % of islets containing insulin⁺ cells (n = 8 mice and n = 9 mice for DT+DMSO and DT+GANT61, respectively) are increased in mice treated with GANT61 after DT-induced β-cell loss. Two-tailed unpaired t-test (P = 0.0236 panel left, P = 0.0085 middle, P = 0.0213 right graph. (n) Transgenes required for simultaneous β- and δ-cell ablation, experimental design and percentage of cells coexpressing insulin and glucagon after β- and δ-cell loss. n = 4, 5, 3 mice for β-, β+δ and δ-cell ablation, respectively. Center indicates the mean. Two tailed unpaired t-test, P = 0.0494. Scale bars: 10 μm. See Supplementary Table 1m,n,q as source data.

Supplementary Figure 6 Constitutive signals ensure maintenance of cell identity.

In physiological conditions, the identity of fully differentiated cells (such as α-cells) is maintained through a variety of constitutive signals preventing cell identity changes. Most of these brakes (f.i. insulin signaling) are preserved upon stress and injury (f.i. β-cell loss). However, few cells may undergo changes in cell identity as an attempt to replenish the compromised cell population (f.i. α-cells produce insulin to cope with insulin insufficiency). Due to the persistence of most constitutive brakes, this kind of natural cell interconversion events are rare and help explaining the poor regenerative capacity observed in higher vertebrates. Insulin-expressing α-cells may reacquire their initial fate upon restoration of physiological conditions (reversibility). Future therapies aimed at identifying and abrogating constitutive regeneration brakes should improve the intrinsic regenerative capacity of likely any organ.

Supplementary Figure 7

Unprocessed scans of the non-quantitative PCR blots provided in Supplementary Fig. 6.

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Supplementary Information

Supplementary Figures 1–7 and Supplementary Table legends.

Reporting Summary

Supplementary Table 1

Statistics source data

Supplementary Table 2

RNA analyses

Supplementary Table 3

Primers.

Supplementary Table 4

Antibodies.

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Cigliola, V., Ghila, L., Thorel, F. et al. Pancreatic islet-autonomous insulin and smoothened-mediated signalling modulate identity changes of glucagon⁺ α-cells. Nat Cell Biol 20, 1267–1277 (2018). https://doi.org/10.1038/s41556-018-0216-y

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Received: 05 December 2017
Accepted: 17 September 2018
Published: 22 October 2018
Issue date: November 2018
DOI: https://doi.org/10.1038/s41556-018-0216-y

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Abstract

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Main

Results

Conversion of α-cells is driven by local signals

Loss of β-cells facilitates insulin expression in α-cells

Antagonistic response of α-cells to β-cell loss

Insulin deprivation leads to α-cell identity changes

Constitutive Smo-mediated signalling restricts α-cell plasticity

δ-cells restrict α-cell plasticity

Co-ablation of δ- and β-cells enhances α-cell conversion

Discussion

Methods

Mice

Diphtheria toxin, DOX, STZ, S961, wortmannin and insulin treatments

Islet isolation, FACS, RNA extraction and qPCR

Non-quantitative RT-PCR

RNA sequencing and quality control

Transcriptomic data analyses

Pathway analysis

Physiological studies

C-peptide measurements and pseudoislets experiments

Transplantations

Immunofluorescence

Statistics and reproducibility

Reporting Summary

Data availability

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