In this review, we discuss work that has led to the current understanding of determinants of pancreatic islet architecture, and how this architecture is maintained or disrupted during tissue remodeling in response to normal and pathological metabolic changes. We further discuss both empirical and modeling data that highlight the importance of islet architecture for islet function.The islets of Langerhans are highly organized structures that have species-specific, three-dimensional tissue architecture. Islet architecture is critical for proper hormone secretion in response to nutritional stimuli. Islet architecture is disrupted in all types of diabetes mellitus and in cadaveric islets for transplantation during isolation, culture, and perfusion, limiting patient outcomes. Moreover, recapitulating native islet architecture remains a key challenge for  These small clusters of endocrine cells reside in the pancreas, surrounded by exocrine tissue. In contrast to the exocrine tissue, which secretes digestive enzymes into the gut through the pancreatic duct system, the islets of Langerhans secrete endocrine hormones into the blood. Through four decades of research, it has become clear that islets are not just unstructured endocrine cell aggregates but rather highly organized micro-organs. They have species-specific three-dimensional architecture, which is critical to their proper function in response to nutritional stimuli. Islet architecture facilitates endocrine cell polarity and connection with the microvasculature to guarantee secretion of insulin into the capillaries, physical and electrical cell-cell coupling to assure synchronous hormone secretion, and directionality of intra-islet paracrine signaling and connection with the nervous system for feedback regulation.Recent research has focused on how three-dimensional islet architecture forms during development, and how it is maintained during the tissue remodeling processes that accompany islet adaptation to life events such as pregnancy and obesity. Research has also investigated how islet architecture is re-formed during regeneration after injury and how it is lost in diabetes. In this review, we discuss work that has led to the current understanding of determinants and dynamics of pancreatic islet architecture. We further discuss how islet architecture influences islet function and how mathematical modeling approaches can help to investigate this structure-function relationship.11 . Studies in a variety of animals models other than rodent, where physiological perturbations are introduced, will help clarify the relative contribution of developmental mechanisms and physiological demands on islet architectural differences between species.Several comparative studies involving species beyond mouse and human suggest that though islet endocrine cells can organize in many ways across species, this may be due more to differences in physiological demand rather than developmental mechanisms.30–33Pancreatic endocrine cell differentiation and islet formation during embryonic development have been thoroughly covered elsewhere and will not be discussed in detail here. For detailed review of islet development, see references.34–36 Until recently, the prevailing dogma maintained that Neurog3+ endocrine progenitors delaminate from the pancreatic epithelial cord and migrate through the mesenchyme as single cells, simultaneously undergoing differentiation and eventually clustering to form mature islet architecture away from the ducts.31 This idea was initially supported by the observation that islets are polyclonal in nature and thus come from multiple founder endocrine progenitors.37 However, recent work has provided compelling evidence that endocrine progenitors instead remain adjacent and attached to the epithelial cord throughout endocrine cell differentiation and most of islet formation.39 Further, there is evidence that islets initially develop as long interconnected chains along the ductal epithelium and only resolve into the rounded distinct structures of the adult through fission and subsequent engulfment of the islet by surrounding mesenchyme.40 Interestingly, it appears that human islets develop first with a mantle-core architecture like that of rodent islets, which is replaced by the intercalating α-β cell architecture of the human islet during the first months of postnatal life.42 Though there have been several hypotheses put forth to explain the formation of the classic core-mantle organization of the endocrine cell types in developing islets, an actual mechanism remains elusive.43In brief, islet morphogenesis begins with transient high expression of the transcription factor Neurogenin3 (Neurog3) in individual bi-potential progenitor cells of the pancreatic trunk epithelium around embryonic day (E)13.5 in mice, which designates them as endocrine progenitors.There is a large body of research that describes molecules which affect the development and maintenance of islet architecture. Here we summarize what is known about determinants of islet architecture in hopes of gaining insight into a more unified view of the underlying mechanisms driving islet morphogenesis and maintenance.in vitro into stereotypical core–mantle architecture when they are re-aggregated after dispersion, likely due to intrinsic differential cell–cell adhesion properties.44 Interestingly, however, when human islets are dissociated and allowed to re-aggregate, they tend to form inverted core–mantle architecture such that α cells form the core and β cells form the mantle, indicating that intrinsic cell–cell adhesion properties in human endocrine cells may not be sufficient to fully recapitulate in vivo architecture.45 This is further supported by the observation that human islets transplanted back into the in vivo environment undergo further architectural changes that resemble human like morphologies.7 It is becoming increasingly clear that cell–cell adhesion molecules are necessary for endocrine cell clustering and maintenance of islet cell type segregation during islet morphogenesis and throughout life. One family of these molecules, the Cadherins, has been shown to be important for islet endocrine cell clustering. Blocking normal localization of Cadherins in general in vivo via expression of a dominant-negative E-cadherin in β cells prevents normal clustering of endocrine cells at E18.5 in mice. However, deletion of single Cadherins leaves islet architecture intact suggesting that Cadherins can compensate for each other in this role.46–49 In addition to controlling cell clustering, there is evidence that Cadherins and cell adhesion molecules (CAM) are important for maintaining cell type segregation in the islet. It is thought that differential expression of Cadherins and CAMs between different cell types in the islets creates differential tissue surface tensions, which in turn guide self-segregation of the cells to their appropriate locations.50–52 Particularly, NCAM knockout mice show some intermixing of α and β cells starting at 4–5 weeks of age, while earlier developmental time points remain unaffected. This defect in islet architecture was attributed to a redistribution of Cadherins to the center of β cell rosette-like structures instead of lateral domains of individual β cells, supporting a role for correct adherens junction localization in maintenance of islet architecture.53 Unlike NCAM, the cell adhesion molecules Roundabout (Robo) receptors were shown to be necessary for the development of islet architecture as early as E18.5 in mice. When Robo1 and Robo2 genes are deleted selectively in β cells in either early or late stages of islet morphogenesis, endocrine cell type segregation is severely disrupted, while β cell identity remains unaffected.54 Importantly, deletion of Robo2 in α cells has no effect on islet architecture, implying that β cells themselves are the main endocrine determinant of islet architecture.54 This is further emphasized by the observation that eliminating α cells altogether had little effect on β cell clustering and overall islet formation.55 Importantly, Robo is the only identified factor to date that governs the development of islet architecture uncoupled from β cell differentiation.54Rodent endocrine cells can spontaneously self-organize 56 Thus, it is not surprising that the ECM and cell–matrix adhesions molecules are also implicated in islet morphogenesis. Specifically, αv integrins are differentially expressed in fetal and adult mouse pancreatic endocrine cells, and can control differential adhesion to ECM components present in the developing and adult pancreas.57 Supporting a role for integrins in controlling islet architecture, when human fetal pancreatic tissue explants were implanted in the kidney capsule of mice treated with an integrin blocking peptide, islets remained closely associated to the ducts, with their α and δ cells specifically oriented toward the duct.58 This bipolar islet architecture is reminiscent of the fetal islet architectural stage observed early during islet morphogenesis in humans, suggesting that differential adhesion of endocrine cells to matrix molecules may affect islet architecture development in humans.59 In support of this, a recent matrix-specific proteome analysis of human fetal and adult pancreatic tissue showed age-dependent differential expression of many ECM and matrix adhesion molecules implicating them as possible regulators of human islet architecture.60 Thus, changes in expression of ECM components and matrix adhesion molecules such as αv integrins throughout development likely govern islet architecture formation during development and possibly in adulthood.The extracellular matrix (ECM) is an important molecular and mechanical signaling hub that changes both its repertoire of signaling molecules and its relative stiffness throughout time and space to influence cell behaviors in a variety of developmental processes.62 In particular, the Rho GTPase family of cytoskeletal regulators has been shown to mediate this aspect of islet morphogenesis. β cell-specific expression of a dominant negative form of the Rho GTPase Rac1 in mice resulted in larger islets which remained close to the duct.63 Similarly, the Rho GTPase Cdc42 was shown to affect islet morphogenesis through its ability to modulate N-WASP, which ultimately controls the dynamics of junctional F-actin complexes between neighboring cells. Disrupting Cdc42 activity in mice results in improper delamination of newly formed β cells from the ductal epithelium and dedifferentiation of β cells in adult islets, both of which are partially rescuable by N-WASP knockout.62During islet morphogenesis, endocrine cells undergo active cytoskeletal remodeling in part to facilitate separation of islets into discrete units away from the ductal epithelium.64 This was attributed to abrogation of filopodial extensions and loss of directional migration, like the phenotypes observed in Rho GTPase mutant mice. Confoundingly, this same phenotype is not recapitulated when Rac1 is knocked down with a morpholino in zebrafish, which may be due to compensatory signaling from other small GTPases or to species-specific differences in islet morphogenesis between mice and zebrafish.61Disruption of proteins that regulate Rho GTPases cause similar defects in islet morphogenesis. When PI3K, an upstream regulator of Rac1, is inhibited in zebrafish, islet cells fail to migrate and coalesce to form the primary islet normally found in zebrafish.Altogether, the body of work on cytoskeletal regulation during islet morphogenesis points to a role in the formation of discrete islet units and migratory behavior during neonatal islet development.65 However, it has yet to be determined whether these architectural phenotypes are due to the overgrown mesenchyme itself or to the mis-regulation of Hedgehog signaling in this mouse model. Confoundingly, it was found that when mesenchyme is ablated at different developmental time points from E9.5 to E16.5, using an inducible diphtheria toxin (DT)-based ablation driven by the mesenchyme marker Nkx3.2, pancreas endocrine mass is reduced but islet endocrine cell ratios and architecture remain intact, suggesting that islet architecture development is not completely dependent on signals from the mesenchyme.66 However, recent evidence suggests that pancreatic mesenchyme is not homogenous but rather composed of several subpopulations that may be spatially and functionally distinct in vivo.68 Thus, it is possible that subpopulations of mesenchymal cells not descended from the Nkx3.2 lineage may be present, and if so, could be sufficient for mesenchymal maintenance of islet architecture.67–69The pancreatic mesenchyme affects islet morphogenesis. When pancreatic mesenchyme is severely overgrown due to unregulated mesenchyme-specific Hedgehog signaling in mice, stereotypical islet architecture is replaced by a heterogeneous mix of architectures, in which some islets appear normal, some are intermixed, and some have inverted core–mantle organization.Hox6 is expressed specifically in the mesenchyme and upon its genetic deletion, mice show a large reduction in cell mass of all endocrine cell types, which can be rescued in explant culture by exogenous Wnt5a.70 Semaphorin 3a (Sema3a), which binds the Neuropilin 2 (Nrp2) receptor in islet endocrine cells, is expressed in a gradient within the pancreatic mesenchyme such that high levels are present at the edge of the organ, while lower levels are present toward the center. This Sema3a gradient is thought to attract endocrine cells away from the duct during development, as evidenced by cell migration assays and disrupted separation and migration of islets away from the ductal epithelium in −/ −Nrp2 mice.71 Whole-body knockout of the Robo Receptor ligands, Slit2 and Slit3, which are expressed in the pancreatic mesenchyme but not in the islet, results in severe defects in islet cell clustering at E18.5.72Specific factors from the mesenchyme that affect islet morphogenesis have been identified. In the pancreas, Wnt5a in mice results in elongated islets that remain closely opposed to the ductal epithelium, while over expression results in small, scattered islets.74 Similarly, EGFR is expressed throughout the pancreas, including the mesenchyme, while its ligand TGF-α is expressed in β cells in humans and mice. Whole body knockout of EGFR in mice shows defects in islet fission and migration away from the ducts.75 TGF-β is also expressed in the pancreatic mesenchyme and the endocrine compartment, and mouse embryonic pancreas organ explants treated with pan-TGF-β inactivating antibodies ceased islet budding.76 Some experiments have been performed to untangle the tissue specific roles of TGF-β in the pancreas, though these have not been entirely elucidated. For example, expression of a mesenchyme specific dominant negative TGF-βRII causes a hyper-proliferation of endocrine cells though no defects in morphogenesis per se are observed.77 Additionally, over expression of the TGFβ signaling inhibitor Smad7 in Pdx1 expressing cells from birth results in a 90% reduction of β cells and islets made of mostly α cells.78Some factors expressed in both mesenchyme and other pancreas tissue compartments affect islet morphogenesis, but their tissue-specific roles have yet to be untangled. Wnt5a is expressed throughout the fetal pancreas including the mesenchyme, while its receptor Fzd2 is restricted to the endocrine compartment by late developmental stages. Whole body knockout of 76 However, MMP2/9 double knockout mice undergo normal islet morphogenesis, and when TIMP1, a modulator of many MMPs, is knocked out or over expressed, islet morphogenesis proceeds normally. This supports the idea that MMPs are dispensable for islet morphogenesis in vivo, however a role for other redundant MMPs, or of MMPs not under regulation by TIMP1, cannot be ruled out.79 Indeed, it was shown that when MMP9 is deleted in mice, expression of MMP7 increases in the islets suggesting compensation by other MMPs is possible.80Both the EGFR and the TGF-β-dependent islet morphogenesis phenotypes described above were originally attributed to changes in activity of the matrix metalloproteinases MMP9 and MMP2, respectively. Accordingly, it was shown that treatment of embryonic pancreas explants with a specific or a general MMP inhibitor recapitulates the phenotype seen in the pan-TGF-β inactivating antibodies treatment.Though it remains unclear if the mesenchyme is fully required for normal islet architectural development, signals from the mesenchyme can and do influence islet architecture and islet morphogenesis more generally.82 However, β cells can themselves attract vasculature via the production of VEGF-A.83 An extreme example of this is seen in experiments in which acinar tissue is reprogrammed directly into β cells in mice pancreata in vivo by viral infection of the transcription factors Neurog3, Pdx1, and MafA.84 In these pancreata, the newly reprogrammed β cells, initially present as single β cells scattered across the tissue, secrete VEGF, and remodel the pancreatic vasculature by attracting new blood vessels to themselves.84 Strikingly, the newly reprogrammed β cells migrate toward each other over time and aggregate to form “islet-like” clusters, attracting both close vascular support and innervation.85Islet vasculature and endocrine cells reciprocate signals during islet morphogenesis. It was hypothesized that endothelial cells attract endocrine cells, causing the latter to migrate through the mesenchyme and coalesce around blood vessels to form the islets.86 It is possible that control of the correct amount of VEGF-A in β cells during development is mediated by the β2-adrenergic receptor, Adrb2, which suppresses VEGF-A production in β cells. Deletion of Adrb2 in pancreatic endoderm during mouse development results in increase in the proportion of small, hyper-vascularized, islets, though no change in overall islet architecture was seen.87 Partial reduction in islet vasculature down to approximately 30% of its normal level, on the other hand, has little effect on formation of islet architecture in either mice or zebrafish embryos.89 However, near complete elimination of islet vasculature in zebrafish embryos resulted in disrupted islet architecture.90 Interestingly, in experiments in adult mice, islet architecture was altered following β cell death in an inducible model of VEGF-A over expression but was restored after β cell regeneration following withdrawal of the VEGF-A induction.91Nevertheless, over expression of VEGF-A in β cells during the embryonic and postnatal periods in mice results in reduced endocrine cell migration from the duct, and almost completely abolishes islet morphogenesis, indicating that too much vasculature is deleterious to islet development.93 also mediates islet architecture. When sympathetic nerves are genetically ablated in mice, the ratio of islets with α cells found in their core increases compared to normally innervated controls.94 Since β cells in these mice show maturity defects, it is unclear if the disrupted cell type segregation is caused directly due to loss of morphogenetic signals from the nerves, or indirectly through the loss of β cell maturation (see next section). Additionally, islets in these mice remain closer to the ducts and have elongated morphologies reminiscent of islets in mouse models with cytoskeletal and cell migratory defects. It was proposed that these cell migration defects in islets of mice with genetically ablated sympathetic nerves are caused by directional adrenergic signaling from neurons to β cells, as β cells migrate toward sympathetic ganglia in coculture, but not when an adrenergic antagonist is added to the media.94 Additionally, the islet architecture defects in these mice are partially rescuable in vivo by treatment with a β-adrenergic agonist. Interestingly, it was shown that when just the intra-islet nerve fibers, which normally develop in the first week of life, are ablated, islet architecture remains intact, indicating that intra-islet but not extra-islet pancreatic innervation is dispensable for normal islet architecture.92Sympathetic pancreatic innervation, which closely follows vasculature during development,95–97 To date, virtually all experiments in which mouse β cells were prevented from reaching their mature identity or were forced to lose it after it had been acquired showed some degree of concomitant loss of canonical core-mantle architecture, marked by intermingled α cells in the islet core. Preventing β cells from assuming mature identity during development by genetic deletion of the key transcription factors Pdx1, MafA, or NeuroD1 results in partial or complete loss of canonical mouse islet architecture.98–101 Similar results are seen in transgenic mice over-expressing the Nkx2.2-repressor domain in mature β-cells.102 This was also seen with over expression of a dominant-negative form of HNF-1α, which was associated with reduced expression of E-cadherin in the immature β cells.103 Likewise, when expression of the β cell progenitor transcription factor, HNF6a is forced past its normal temporal domain (after E18.5), β cells fail to mature and islets lose endocrine cell type sorting.104 The loss of endocrine cell type sorting following continued expression of HNF6a may be due to the subsequent decrease in expression of CTGF, as mice with CTGF deletion show intermixing of endocrine cell types in adult islets.105 Additionally, when mTOR is deleted in β cells before architecture develops, β cells fail to mature and islets remain elongated and close to the ducts.106 Other factors for which genetic disruption resulted in both loss of β cell maturation and loss of canonical islet architecture include DICR1 (when deleted in embryonic β cells),48 Synaptotagmin,107 αCatenin,108 and, to some extent, BMPR1α.109One of the strongest determinants of islet architecture is β cell maturity. Between birth and weaning, rodent β cells undergo a maturation process that results in acquisition of the adult glucose stimulated insulin secretion response.MafA in adult mice β cells results in a delayed loss of β cell identity markers by 8 weeks of age. Lagging just behind this loss of maturation, at 12 weeks of age severe intermixing of endocrine cell types, is observed.110 Similarly, when the maturation associated transcription factor Insm1 is deleted in β cells in the adult mouse, β cells lose maturity markers, and α cells are subsequently found mixed into the islet core.111 Interestingly, even in islets of healthy adult mice, a sub-population of immature β cells located in the periphery has been reported to migrate into the islet core concomitant with gaining mature gene expression and glucose responsiveness,112 suggesting a connection between the degree of β cell maturation and their location within the islet.Even when β cells reach maturity and architecture has formed normally, loss of β cell maturity in adulthood still results in loss of normal islet architecture. For example, deletion of However, it remains to be determined if β cell maturation is required for correct islet architecture, and/or if correct islet architecture itself is required for proper β cell maturation.Once islet architecture has been established, it does not remain static throughout life. Rather, various physiological stresses like obesity, diabetes, and pregnancy result in architectural remodeling of the adult islets. This plasticity of adult islet architecture suggests that there may be a functional role for islet architecture remodeling. However, whether these changes are adaptive or pathological remains largely unknown. The following sections will discuss what is known about architectural changes in islets in adulthood.113 Similarly, during pregnancy, islets undergo β cell expansion, adapting to the transient insulin resistance in the pregnant female, while subsequent to pregnancy a retraction of β cells to pre-pregnancy levels occurs.114 Concomitant with this β cell expansion, transient architectural remodeling may occur.115 Short-term induction of islet mass expansion in mice by acute administration of the insulin receptor antagonist S961 or glucagon receptor antagonists causes large increases in islet size, but the expanded islets retain their typical mantle-core architecture.117 Thus, in successful compensatory islet mass expansion, islets retain their three-dimensional architecture. How islets maintain correct spatial architecture while undertaking the massive tissue remodeling required for compensatory expansion remains an open question.During times of increased metabolic demand, such as in mild obesity, the islets of Langerhans expand to compensate for the increased need for insulin, while retaining their correct spatial endocrine cell type positioning.118 There is evidence that the observed disruption in islet architecture found in models of T2D is in part due to transdifferentiation of β cells that already populate the islet core, rather than an invasion of α and δ cells from the islet mantle.119 For instance, in several mouse models of T2D that display disrupted islet architecture, including db/db, GIRKO, and metabolically stressed FoxO1-knockout mice, a majority of the loss in β cell mass is due to β cell dedifferentiation rather than β cell death, and in some cases, these dedifferentiated β cells partially transdifferentiate to other endocrine cell types.119 Similarly, diabetic insulin-knockout mice also show islet architecture abnormalities, with strong β cell dedifferentiation observed after 34 days of severe diabetes.120 These observations suggest that under physiological stresses like diabetes, mechanisms that maintain islet architecture during compensatory expansion may be perturbed. Interestingly, most dedifferentiated β cells in these models do not undergo a full transdifferentiation to other endocrine cell types. This could suggest that in diabetes, architecture is disrupted because incomplete lineage transition may not allow for expression of molecules regulating cell sorting appropriate to their “new” cell type. Other rodent models of diabetes in which islet architecture is altered include diabetic Goto-Kakizaki rats, mice with a β cell-specific deletion of the gene fumarate, and mice with mutated version of KATP channel (βV59M).5 Finally, islet architecture in T2D can be disrupted also by deposition of human islet amyloids.121Islet architecture is disrupted in mouse models of type 2 diabetes (T2D), yet how and why this happens remains unknown.Kir6.2 activating mutations in mice led to massive β cell identity loss and expansion of α cells in the islet core. When hyperglycemia was rescued with insulin or sulfonylurea pellets, the islet architectural defects and α cell mass increase were rescued, indicating that restoration of β cell identity could restore normal islet architecture in the adult.122 Similarly, work from Cheng and colleagues suggests that in diabetic db/db mice, treatment with a fasting-mimicking diet rescues diabetes induced defects in islet architecture, although these changes were not specifically quantified.123 It remains unknown if the rescue of islet architecture in these models is due to a passive or active sorting processes, i.e., whether incompletely transdifferentiated β cells in the core of the islet are restoring their identity, or rather new non-β endocrine cells are actively self-segregating from the β cell core when normoglycemia is restored.In some contexts, architectural disruptions in the adult mouse due to T2D are reversible. For example, Brereton and colleagues showed that hyperglycemia induced by 115 Nevertheless, in studies of human islet architectural defects in early and late stage T1D patients, it appears that immune cell invasion within the core of the islet is highly variable between donors, yet architectural defects are ubiquitous. Thus, these defects are likely caused by additional factors other than massive immune infiltration.125 As T1D progresses, β cell mass decreases while α cell and δ cell masses are less affected, and by late stages of T1D almost all hormone-positive cells within T1D stain positive for glucagon or somatostatin.126 Additionally, many glucagon-positive cells in human T1D islets also stain positive for β cell markers like Nkx6.1, which could possibly be due to β to α cell transdifferentiation.124–126 Hormone-negative, immune cell marker-negative clusters of cells are also found interspersed throughout human T1D islets, which are thought to be degenerative β cells.125 Thus, it is plausible that, as in T2D, transdifferentiation and loss of β cell identity and maturity are also contributing factors to the abnormal architectures observed in T1D.In mouse models of type 1 diabetes (T1D) where β cells are largely killed by an immune attack, the mechanism of islet architecture disruption may differ from that of models of T2D where β cell death is likely not a large contributing factor in the early stages of disease development. In non-obese diabetic mice (NOD), a model of immune mediated T1D, islet architecture appears to be largely disrupted due to β cell core collapse after immune cell invasion. This likely clouds the ability to see any real architectural changes since the resulting islets are mostly comprised of non-β endocrine cells intermixed with lymphocytes.127–129 Thorel and colleagues reported that in acute β cell ablation with DT where all β cells were destroyed, 10% of the β cell mass can be regenerated with periodic insulin treatment, mainly by transdifferentiation from α cells.128 However, in this model, islet architecture appears to remain disrupted even 10 months after the start of regeneration, though this was not specifically quantified.128 On the other hand, Nir and colleagues performed less acute β cell ablation via a β cell specific DT protocol that only destroys 70–80% of β cells.129 In this model, β cell regeneration was derived mostly from replication of surviving β cells, and the resultant islets had a near-perfect architecture 23 weeks into the regeneration process.129 These seemingly contradicting results may be due to the different origin of the regenerating β cells (transdifferentiation versus existing mature β cells). However, this may not be the only explanation, as when Alloxan treated mice were infected with a virus that forced expression of MafA and Pdx1 in α cells to convert them to β cells, normal architecture was restored though this was also not specifically quantified.127 The difference in ability to restore normal islet architecture upon restoration of normoglycemia could be due to the difference in the source of new β cells in each of these models. In the acute DT and Alloxan ablation model, new β cells come from transdifferentiated α cells. The new β cells that formed from forced expression of MafA and Pdx1 in the Alloxan model were shown to be fully mature and likely relatively transcriptionally similar to a normal β cell, while the α cells transdifferentiated in the acute DT model were less committed to the β cell fate.128 In the more mild DT model from Nir and colleagues, a pool of surviving β cells provides the source for new β cells.129 Taking these three models together, this suggests that β cells arising from naturally transdifferentiating α cells, as is the case in the acute DT ablation model, may be less committed to the β cell lineage and thus not express the necessary factors important for endocrine cell type segregation. However, when new β cells that are more similar to fully mature β cells are generated, like in the case of forced expression of Pdx1 and MafA in the Alloxan treated mice or from the pool of existing β cells as is the case in the milder DT model, they may be transcriptionally closer to native β cells and thus express the full array of factors necessary for generating correct islet architecture.Three models of β cell ablation, namely, severe ablation of all β cells by β cell-targeted diphtheria toxin (DT) expression, a less acute β cell targeted DT that destroys 70–80% of β cells, and Alloxan injection, provide insight into how β cell regeneration might affect islet architecture. In all three of these ablation models, islet architecture is disrupted, yet each differs in its ability to regenerate normal islet architecture after restoration of normoglycemia.130 During the culture period after islet isolation, human islets lose preferential homotypic interactions between β cells and display a more random cell arrangement.7 However, 2 weeks after transplantation of isolated human islets to the kidney capsule or in the liver of mice, islets regain more human-like architecture, with subunits of β cells surrounded by α cells that line newly formed blood vessels.7 Even more, when dispersed human islet cells are allowed to reaggregate in vitro, they form an α cell core with a β cell mantle, yet after 2 weeks post kidney capsule or liver transplantation, they display normal human islet architecture with β cell cores and α cell mantles lining the vessels.7 It is yet unknown what causes these changes in islet architecture in culture and after transplant, but a possible mechanism may be related to interactions between endocrine cells and matrix proteins secreted from vessels.7Islet architecture and tissue integrity are disrupted in human cadaver islets during isolation and culture prior to islet transplantation, as well as after infusion into the portal vein during islet transplantation treatments in patient with T1D.in vitro from human pluripotent stem cells were reported to re-organize after transplantation under the kidney of mouse recipients. These transplants were described as either having typical rodent-like islet architecture131 or human-like architecture.132 Similarly, transplantation of clusters of earlier stage progenitor cells derived from human pluripotent stem cells gave either rodent-like133 or human-like architecture.134 Fetal human pancreatic islets transplanted into mice were reported to mature into human-like islet morphology.135 However, to date, no systematical morphological quantifications were reported of the architecture of stem cell-derived islet-like clusters after transplantation, and it is evident that either of the grafts do not show the overall morphology of bona fide islets. These discrepancies will be resolved with more thorough quantification of islet architecture, which was not in the scope of the above works.In some cases, human islet-like clusters derived 136 This is because one of the most important mechanisms through which β cells synchronize GSIS is via electrical coupling using Connexin36 (Cx36) gap junctions that form between touching β cells, which allow for exchange of the cations that ultimately control these oscillations.137–139 In addition to clustering between β cells, the spatial organization of heterogeneous β cell subpopulations within an islet is also important for normal GSIS. Electrically coupled β cells within an islet display varying levels of excitability and metabolic rates which help to suppress calcium oscillations throughout the islet at low glucose and regulate calcium oscillation dynamics at high glucose.140 These β cell subpopulations characterized by varying levels of excitability and metabolic rate display a nonrandom spatial organization across the islet that is likely important for GSIS dynamics.4 Specifically, it was shown that glucose induced Ca2+ wave initiation sites, an intrinsic and stable feature of an islet, occur in areas with both low excitability and metabolic rate and thus a faster intrinsic oscillation frequency, a quality that has been suggested to promote wave origination.141 Recent work using in vivo imaging of Ca2+ dynamics in the Robo βKO mouse model has further highlighted the importance of 3D cell organization on islet function beyond simple clustering. Robo βKO islets show normal endocrine cell clustering and maintain intrinsic β cell identify and function yet display extensive endocrine cell type intermixing resulting in decreased β cell homotypic interactions.142 These features make this model uniquely suited for testing the effects of 3D endocrine cell organization on islet function, uncoupled from β cell pathology. Intravital Ca2+ imaging revealed that Robo βKO mice display a decrease in synchronous oscillations between intra-islet β cells, that is reminiscent of the reduced oscillations synchronicity phenotype seen in islets from mice heterozygous for the Cx36 gene (–+/Cx36).142 However, unlike +/–Cx36 islets, Robo βKO islets show no change in Cx36 gene expression or Cx36 plaque area, and Cx36 gap junctions still localize to their normal domain at the β cell borders. This indicates that disruption in synchronous oscillatory behavior in Robo βKO islets is likely due to changes in islet architecture itself rather than decreased expression or mis-localization of gap junction machinery.Islet architecture is important for normal islet function. Homotypic interactions between β cells, allowed for by β cell clustering within an islet, are required for the normal synchronous oscillations between β cells which underly controlled glucose stimulated insulin secretion (GSIS). This is evidenced by the observation that dispersed β cells with no cell contacts display heterogeneous uncoordinated oscillations at both basal and elevated glucose levels, which results in high insulin secretion at the basal glucose level and low uncoordinated insulin secretion at the high glucose levels.143–148 Furthermore, the spatial architecture of the islet facilitates direct receptor-ligand contacts between adjacent β cells that influence GSIS dynamics independent of electrical coupling. Indeed, dissociated wild type β cells have worse GSIS response than that of intact islets lacking Cx36 gap junctions.149 One such mechanism of non-electrical regulation of GSIS dictated by cell-cell contacts is that of EphA-ephrinA binding between β cells.150 Finally, islet architecture also influences β cells electrical coupling to neighboring δ cells, which can add another layer of regulation to hormone secretion.151In addition to electrical β cell-β cell coupling, islet architecture also influences directionality of diffusible paracrine and autocrine signals within the islet, and these in turn affect islet function.in vitro differentiation that causes changes in islet architecture resembling those found during human islet morphogenesis.152 This transition to a mature human-like islet architecture was not accompanied by an increase in β cell mass yet was still correlated with lower basal insulin release and increased glucose stimulated insulin secretion compared to islets that had not undergone this maturation stage. Further, a pseudo-time analysis of single cell RNAseq data that looked at iPSC derived islets during the functional in vitro maturation step, during in vivo engraftment which causes further maturation, and in adult islets, found that Robo1 and Robo2 levels were highly dynamic across these stages. Specifically, Robo1 and Robo2 were expressed at high levels during the attainment of mature-like architecture and lower in the adult islet, consistent with their role in regulating the development of normal islet architecture important for controlled GSIS.152Further highlighting the importance of 3D islet organization, a recent report found that induced pluripotent stem cell (iPSC) derived islets become more functionally mature when subjected to a maturation step during 153 Though this remains to be rigorously tested, if it proves true this is likely a very important function of synchronous oscillations within islets. Peripheral insulin pulsatility is important for mounting a robust response to insulin in the liver, for keeping peripheral tissues insulin sensitive, and for allowing the readily releasable pool of insulin granules time to replenish during sustained glucose stimulation.153–157 Pulsatile insulin levels are also known to be disrupted early in diabetes disease progression, often before overt diabetes has set in.157 Thus, it is an interesting possibility that the architectural defects seen during diabetes progression may contribute to the perturbations in pulsatile insulin secretion observed in mouse models of diabetes and pre-diabetic and diabetic patients.It has been hypothesized that insulin secretion is not only synchronous within an individual islet but across all islets in the pancreas and thus is responsible for pulsatile insulin levels in circulating blood observed in humans and mice.158 Hoang and colleagues further expanded on these models by including α cells and using experimentally derived parameters to define the intrinsic ability of endocrine cells to oscillate, including effects from autocrine and paracrine interactions.159 Using this model, they tested how artificial arrangements of α and β cells oscillate at varying glucose levels and found that both phase and synchronicity were dependent on specific architectures. Even more, they found that the specific cell ratios of mouse and human islets are likely important for handling dynamic glucose changes in their respective architectures. When diabetes induced changes to islet architecture were simulated by removing β cells at random within islets, synchronization of β cells decreased at high glucose levels as β cells were removed, even when islets were allowed to re-aggregate after 50% of β cell loss.159 In another example of modeling the relationship between islet architecture and function, Sterk and colleagues used mathematical modeling and confocal imaging of mouse pancreatic tissue slices to show that the origin of calcium waves are primarily triggered by a specific region of the islet.160 As more sophisticated methods to quantify and model human islet architecture emerge,161 it will be interesting to see what new information will be gained on the role of islet architecture in islet function.Computational modeling also provides support for the idea that islet architecture affects islet function. Nitalla and colleagues created models of oscillating β cell clusters to demonstrate that higher levels of coupling between β cells allow for higher resistance to pathological perturbations like loss of β cells from diabetes.in vitro from human pluripotent stem cells, and perhaps also to target islet architecture as another layer of control in helping islets adapt to different diabetogenic stresses, and/or improve cadaveric islet culture for optimal transplantation results. Several important questions still need to be answered toward these goals (Accumulating data indicate that islet architecture is important for optimal islet function. Through mouse genetics and mathematical modeling in human islets, we are beginning to understand what parts of islet architecture are important for which functional aspect of islet biology, from optimal hormone response to stimuli, to β cell maturation and identity. At the same time, new data and models allow us to decipher different cellular and microenvironmental determinants controlling islet architecture. In the future, these could be used to optimize and impose correct architecture on islet-like clusters made se goals . It will


We read with interest the article by Stefanou et al. about a 47yo male who developed Guillain–Barre syndrome (GBS) followed by acute, demyelinating encephalomyelitis (ADEM) after a vaccination with the Ad26.COV2.S vaccine (Johnson & Johnson) . For GBSWe do not agree with the statement that “neurological adverse events following immunisation against SARS-CoV-2 have been shown to be rare” . There aWe also do not agree with the statement that “neurological adverse reactions following immunisation against SARS-CoV-2 have been shown less frequent in patients undergoing vaccination against SARS-CoV-2 as compared to patients with COVID-19” [Furthermore, we are not convinced that the index patient had ADEM. Since the patient had received IVIG prior to taking the images provided in figure 1 and since IVIG can be complicated by hyperintensities within neuronal structures , 6, it iWe also should be told why the left peduncular lesion did not manifest clinically and it should be explained why the patient had a T6 sensory level and not a C5 level since there was an enhancing lesion at C4/5.Overall, the interesting report has some limitations that call into question some of the results and their interpretation. Mild to severe adverse reaction to any of the available SARS-CoV-2 vaccines are more frequent than usually propagated. Anti-SARS-CoV-2 vaccinations are not safe for everyone, and IVIG may be responsible for further deterioration of a GBS developing 4 weeks after the vaccination.


One factor of uncertainty is the importance of tropical CH4 emissions into the global mix. Isotopic signatures of major sources remain poorly constrained, despite their usefulness in constraining the global methane budget. Here, a collection of new δ13C4CH signatures is presented for a range of tropical wetlands and rice fields determined from air samples collected during campaigns from 2016 to 2020. Long-term monitoring of δ13C4CH in ambient air has been conducted at the Chacaltaya observatory, Bolivia and Southern Botswana. Both long-term records are dominated by biogenic CH4 sources, with isotopic signatures expected from wetland sources. From the longer-term Bolivian record, a seasonal isotopic shift is observed corresponding to wetland extent suggesting that there is input of relatively isotopically light CH4 to the atmosphere during periods of reduced wetland extent. This new data expands the geographical extent and range of measurements of tropical wetland and rice δ13C4CH sources and hints at significant seasonal variation in tropical wetland δ13C4CH signatures which may be important to capture in future global and regional models.The atmospheric methane (CHThis article is part of a discussion meeting issue ‘Rising methane: is warming feeding warming? (part 2)’. In genely −53‰ . The dis6‰ shift . Biogeniuminants , wetlanduminants  and riceuminants . Thermogg events . Extreme4 isotopes to discern global sources of CH4 have demonstrated the need for better spatial resolution of δ13C4CH source signature data. This is needed to reduce significant biases from assumptions of geographically invariable δ13C4CH source signatures [et al. [et al. [et al. [δ13C4CH source signatures together, but it is clear from these studies that there are still large regions where little to no studies have taken place.Recent studies using global models of CHgnatures ,14. Much [et al. , Brownlo [et al.  and Feinδ13C4CH is primarily from background stations sampling the boundary layer at remote oceanic sites participating in the NOAA collaborative flask measurement programme. There is very little δ13C4CH isotopic information from inland tropical continental landmasses in South America, Africa or South Asia. This lack of regular measurements of δ13C4CH makes long-term and seasonal assessment of regional source input difficult to elucidate from the δ13C4CH record. Shorter term time-series records have been demonstrated to be useful in determining regional CH4 source input, such as natural gas leaks in central London [4 in Hungary [Long-term global monitoring of l London  or the i Hungary .4 source signatures for wetlands and rice fields from a range of field campaigns undertaken since 2016, creating a new working database from which global models can be populated with increased spatial resolution. We also show the value in long-term δ13C4CH sample collection at the Chacaltaya observatory, Bolivia, allowing us to analyse the data record for seasonal trends in CH4 input from the Amazonian region.In this work, we aim to extend the knowledge base of tropical CH. 2δ13C4CH stable isotope analysis.Air samples were collected as part of a range of field campaigns between 2016 and 2020, throughout two Natural Environmental Research Council (NERC) projects, MOYA (Methane Observations and Yearly Assessment) and ZWAMPS . Samples were either collected as part of dedicated ground sampling, or as part of airborne measurements investigating specific regional emission features. Air samples were subsequently returned to Royal Holloway for  (a)Ground-based sampling is targeted sampling with the intention of collecting emissions from an identified methane source. At each ground sampling location air is collected in either 3 L SKC Tedlar bags or 3 L SKC Flexfoil bags, using a battery-operated pump. Bags are filled at varying distances downwind of the targeted source being sampled, at a height of between 30 cm and 3 m height above ground. Ideally, upwind samples are also taken to provide a measure of the background airmass into which the source methane is mixing. The location of ground sample sites measured here is shown in a global context in  (i). 1Panicum repens, Cynodon dactylon, Sporobolus spicatus), attracts many types of herbivores and is grazed for most of the year.Botswana. There are two sites in Botswana's Okavango Delta. Ikoga, in Northern Botswana, is a village located approximately 170 km NW of Maun on the western side of the Panhandle of the Okavango Delta. Sampling took place at the water's edge of a lagoon at Ikoga Camp (an island during the wet season)  and at Nxaraga , located at the SW edge of Chief's Island in Moremi Game Reserve. This site is a seasonal floodplain and is typically flooded for six to eight months per year. The vegetation of the floodplain, which is dominated by C4 grasses  and was declared Ramsar Site No. 2107 in 2013. Rehabilitated and protected by a local conservation society (COSMO) the open grassland fringed by miombo woodland has regained its biodiversity supporting a wide spectrum of fauna and flora, including sedges and reeds.. 3Zambia. Wetland reed beds on the Ngwerere River were sampled . These are reed and papyrus-dominated wetlands north of Lusaka.. 4Uganda. Wetland marshes, fringing Lake Victoria. Samples were collected near Entebbe , on the northern coast of Lake Victoria. (ii). 1Reyes lies in the transition between the sub-Andean Amazon forests and Llanos de Moxos. Air samples were collected in July 2016 from a wetland close to the riverbed approximately 5 km SE of Reyes town  which represents the transition between sub-Andean Amazon forests and Moxos flooded plains.. 2Samples from the Moxos plains were taken in a 40 km transect between Trinidad city .. 3The northern edge of Laguna Cuitarama  during 2017 and 2019. In this area, the landscape has been modified by pre-Colombian cultures to raise beds, terraces, channels and dykes at large scale [Three wetland sampling sites were chosen in northeast Bolivia's Amazonian basin, in the Llanos de Moxos region of flooded plains and savannah. The region consists of savannahs dominated by mixed C4 and C3 grasses and graminoid, aquatic and marshland plants; different types of forest islands, open forest and low spiny shrubs . The reg (iii). 1Hong Kong. Yi O Rice Fields are located at the southwestern tip of the largely undeveloped Lantau island  30 km from downtown Hong Kong. The rice paddies are on the floor of an uninhabited steep-sided valley and are organically cultivated using standard Chinese long-grain rice varieties. Samples were taken above fallow, freshly planted and mature crops. A series of air samples were taken from managed reedbeds at the Mai Po Wetlands  at the mouth of the Sham Chun, Shan Pui and Kam Tin rivers that empty into Deep Bay along the Hong Kong S.A.R. border with Shenzhen.. 2Vietnam. Sampled rice fields are located approximately 10 km south of Ho Chi Minh City at the northeastern edge of the Mekong delta region, the primary rice cultivation area in Vietnam . Samples were collected post-harvest during the dry season (late March 2019). (b). 14 observable from the aircraft.Bolivia. Airborne sampling for isotopic analysis was performed on two flight campaigns. In March 2019 air samples were obtained flying within the planetary boundary layer above the large Llanos de Moxos flooded plains . Samples. 2Zambia and Uganda. In February 2019, air samples were collected in stainless-steel flasks during flights above three large wetland areas in Zambia; Bangweulu, Lukanga and Kafue and in Uganda at Lake Kyoga. These campaigns were completed using the BAE-146 FAAM (Facility for Airborne Atmospheric Measurement) aircraft as the sampling platform. Sampling strategy was to collect from a range of locations and altitudes above and downwind from the wetlands, with 12–19 samples collected for each wetland.The airborne sampling took place over wide-area wetlands in both South America and Africa. (c)δ13C4CH at sites in the Bolivian Andes and in Botswana. The sampling period from Bolivia was 6 years from 2013 to 2019 and the Botswana sampling was over 18 months from late 2016 to mid-2018. The Chacaltaya GAW Station (CHC) is located in the eastern branch of the Bolivian Andes  at 5240 m.a.s.l., 16°21′12′′ S 68°07′53′′ W. Chacaltaya is a mountain with a horizon open to the south and west facing the Altiplano (plateau of 3800 m.a.s.l.) and close to Titicaca Lake. To the east, the Amazon Basin starts at high peaks which progressively change to Yungas, sub-Andean Amazon forests and lowlands. Sampling occurred on a weekly basis from an inlet at 6 m height above ground level. The Botswana long-term site was at Modipane, a village located approximately 25 km east of the capital Gaborone in SE Botswana, which has a semi-arid environment, and is located approximately 75 km ESE of the Kalahari Desert. The sampling site at Modipane is located at the southeastern edge of the village, near to arable fields. Sampling occurred on a weekly basis using a hand-held sampling technique following the same protocol as in the ground-based sampling (§2a) with an inlet at approximately 3 m height.By contrast to the targeted wetland sampling, samples were collected for longer-term regional background measurements of methane mole fraction and  (d)4 scale. Samples are flushed through the Picarro for 120 s to ensure no contamination from previous sampling, and then analysed for 120 s. The mean of the 120 s of analysis (approx. 12 measurements) is calculated as the measured value. Precision is approximately 0.5 ppb for both Tedlar bags and stainless-steel flasks. Samples are then prepared for isotopic analysis (δ13C4CH) by continuous-flow isotope-ratio mass spectrometry using an Isoprime Trace Gas system [δ13C4CH are reported on the standard international isotope VPDB (Vienna Pee Dee Belemnite) scale. An internal secondary standard is analysed through the day to allow correction for instrumental drift.Samples collected in Tedlar bags and stainless-steel flasks are both treated to the same laboratory analysis procedure. First, the samples are analysed for methane mole fraction using a Picarro 1301 calibrated to the WMO X2004A CHs system . Samples (e)13C4CH isotopic source signature for the various source types a Keeling plot regression is used [δ13C4CH are plotted against the inverse of the CH4 mixing ratio to allow determination of the δ13C4CH value at the y-intercept which represents the δ13C4CH of the methane added to the natural background. This is represented mathematically in equation (2.1) and has been successfully applied in numerous studies to determine methane source signatures .2.1.2.113CCδ13C from background sites at Chacaltaya, Bolivia and from Modipane, Botswana were analysed using a Miller–Tans methodology [δ13C and CH4 mixing ratio record rather than assuming a fixed atmospheric background which would be the case in using a Keeling methodology for this purpose. Miller–Tans methodology allows investigation into both the bulk isotopic input of CH4 to a fixed site, as well as being able to interrogate the data for seasonal and longitudinal trends.δ13C is the measured isotopic composition of the methane, c is the mixing ratio of the methane, bg, obs and s refer to background, observations and source, respectively.For sites where multiple days or campaigns were conducted to sample the same source  a Miller–Tans approach was used  (2.2). Thodology  adapted . 3 (a)The calculated source signatures are grouped into two categories—individual source signatures δ13C scatter within the Keeling plots for the Llanos de Moxos and Lukanga (Zambia) may indicate that there are variable δ13C4CH inputs to the atmosphere from these large area wetlands δ13C4CH source signature of −57.1 ± 0.3‰ δ13C4CH at both Modipane, Botswana and at Chacaltaya, Bolivia are shown in electronic supplementary material, figure S2. The results of Miller–Tans analysis for these two sites shows regional bulk input of methane is dominated by biogenic sources. Bulk averaged input using all available data for Modipane is −55.4 ± 4.6‰ and −59.0 ± 1.3‰ for Chacaltaya. These results match well with their respective in-country seasonal wetland measurements of −56.3 ± 1.1‰ for Botswana  model back trajectory analysis from Chacaltaya observatory was conducted previously over a 5-year period . The bac (e)δ13C of methane likely reflect differences in wetland methane production, oxidation and emissions pathways throughout the year. The Bolivian Llanos de Moxos wetlands experience an extremely strong seasonal cycle in inundation, with inundated area changing by over an order of magnitude seasonally, as well as large interannual variability in inundation [δ13C of emitted CH4 [δ13C4CH are not limited to diffusive emissions; for example, Smith et al. [δ13C of CH4 emitted from macrophytes in the Orinoco River floodplain, correlated with strong seasonality in water level and methane emissions.Seasonal shifts in the undation ,46. The tted CH4 . Howeverh et al.  also obsδ13C4CH source signatures seen in the Bolivian data. Such very light values suggest some emissions come directly from biological sources without intervening methanotrophic oxidation during egress from water or soil, because methanotrophic removal selectively removes isotopically light carbon, causing selective enrichment in the remaining methane and resulting emissions. One possibility is that tree-mediated emissions in Amazonia, with a δ13C4CH source signature as light as −76.3 ± 0.9‰ [4 prior to emission may be an important control on the isotopic flux to the atmosphere from these expansive tropical wetlands.Variability in air mass origins does not explain the isotopically very negative 3 ± 0.9‰ , are a pδ13C might also reflect changes in the distribution of wetland types responsible for the methane emissions. For example, the permanently inundated wetlands may differ from the seasonally inundated wetlands in key environmental parameters including pH, vegetation type and nutrient status. These environmental parameters in turn control the relative proportion of acetoclastic versus hydrogenotrophic methanogenesis, and the resulting δ13C of CH4 produced [13C-enriched CH4 is expected where acetoclastic methanogenesis dominates, with more depleted CH4 found in nutrient poor environments where CO2 reduction dominates. For example, methane as depleted as −94‰ was observed in the porewater of an ombrotrophic peatland in Panama [δ13C could result from a changing relative contribution of different wetland types within a wetland complex throughout the year.The observed seasonal shifts in produced . More 13n Panama , and simn Panama . Thus, sδ13C [δ13C signature [δ13C of methane emitted from tropical wetland complexes is needed to determine the extent to which the bulk signature is affected. Although it is not possible from the data obtained here to determine fluxes, the flux-weighted δ13C signature is not anticipated to be overlysignificantly weighted towards the wet season, with data from Miller et al. [4 have been measured in the Amazon Basin in both the wet and dry seasons. Recent modelling by Tunnicliffe et al. [et al. [δ13C source signature for Chacaltya due to wetland methane is calculated as −61.5 ± 4.1‰ (equation 3.1). This compares well with the overall non-seasonally split δ13C source signature for Chacaltya determined from the long-term record of −59.0 ± 1.3‰.δ13Cdry, δ13Ctransition and δ13Cwet are taken from values in 4 (dry-flux), CH4 (transition-flux) and CH4 (wet-flux)) are the corresponding monthly average wetland fluxes derived for Amazon wetlands from 2010 to 2018 [Additionally, there may be seasonal shifts in the primary methane emissions pathways, with varying contributions from tree stem emissions, below-ground transport of dissolved gases, surface diffusion and ebullition. Diffusive emissions are much more likely to be highly oxidized, resulting in a more enriched δ13C . By contignature . Thus, gr et al.  indicatie et al.  estimate [et al.  monthly  to 2018 .The importance of understanding the seasonal variation of the isotopic signal emitted to the atmosphere will be key in coupling the subsurface processes driving the production and transportation of methane within the wetlands and the methane fluxes to the atmosphere from wetlands—a key component of the global methane budget.. 4δ13C4CH to determine the causes behind the recent global methane atmospheric growth. The results here show that rice emission δ13C4CH signatures appear to be very stable on an interannual basis and that generalized value of −61 ± 4‰ for rice is a reasonable model input. There appears to be good agreement in bulk wetland emissions from similar latitudes from both South America and Africa during the periods of peak wetland extent, with values of approximately −60 ± 5‰ as a recommended generalized value for the outer tropical wetlands from the wide-area aerial wetland sampling. The more papyrus rich tropical wetlands of the Okavango and Uganda appear to have an isotopically heavier δ13C4CH signature, with a bulk wetland signature of approximately −52 ± 2‰ measured above Ugandan wetlands indicating that relatively small  latitudinal and climatic changes in the tropics have a significant effect on δ13C4CH signatures. The values reported here for the bulk outer tropical wetland δ13C4CH signatures appear lighter than expected when comparing to the global map shown in Ganesan et al. [δ13C4CH signature records used as model inputs should not be overlooked. Continuing to fill in the unknowns in the tropical methane δ13C4CH source signature record remains a priority, as data is currently extrapolated over vast geographical ranges, especially in Central and Northern Africa and Australasia where data are incredibly limited.The importance of tropical wetlands to the global methane budget is well established, but the role that wetlands play in the current increase in global methane mixing ratios is still under debate. Continued work to understand the isotopic composition of wetland emissions is important to improve the accuracy of global models using n et al.  and alsoδ13C signature requires further investigation, ideally through targeted, longer-term campaigns to measure time series of emissions from tropical wetlands through several seasonal cycles. The long-term data from Chacaltaya shows strong seasonal isotopic differences, but the variability in the data cannot conclusively be tied to wetland emissions. Seasonal changes to soils and sediments would be expected to result in seasonal changes to the δ13C4CH source signatures of bulk atmospheric emissions from tropical wetlands, but there is no conclusive evidence to demonstrate the effect occurs or by how much. Improving the understanding of the latitudinal variability and seasonality of wetland emissions is key to being able to isotopically balance the δ13C4CH budget in global models. Further work investigating isotopic signatures from extensive permanent and seasonal wetlands over multiple years is now an important target to allow us to understand the large-scale seasonality of wetland emissions and the processes that control them.The influence of seasonality in the tropical wetland


Whilst bis–porphyrins are known to be effective chemical sensors able to differentiate various guests based on their chirality via induced CD, their ability to sense small differences in the shape and size of relatively large macrocycles, such as chiral cycHC[6] and cycHC[8], is scarcely examined. Both studied complexes exhibited characteristic induced CD signals in the region of porphyrin absorption upon complexation.In order to investigate the ability of bis(zinc octaethylporphyrin) ( Uponret band . This inbis–ZnOEP with n]cycHC[s were evaluated by 1H-NMR and UV-vis titrations using only - enantiomers of chiral macrocycles. A stepwise addition of cycHC[6] to the syn form of bis–ZnOEP in CD2Cl2 shows the shielding of the meso-α protons of bis–ZnOEP  for 1:1 binding with 1K = 198 ± 5 M−1. Therefore, a different mechanism of the binding of cycHC[8], as compared to cycHC[6], should be considered. Additionally, certain time-dependent changes during the UV-vis titration experiments with cycHC[8] were noted.However, the evaluation of the n]cycHC[ complexes in time was checked by measuring the UV-vis spectra of bis–ZnOEP immediately after the single addition of 2000 (or more) equivalents of cycHC[8] and cycHC[6] to provide sufficient abundance of the complex (see mole fractions on page S11) and compared with the data from the UV-vis titration first (pure bis–ZnOEP) and last points (complexed bis–ZnOEP) for the same macrocycles. The titration last points were measured approximately 1 h after the first addition and had the same equivalents of a guest as in the comparative single addition experiment . As less pronounced changes in time were observed for the complex of bis–ZnOEP with cycHC[6], we can conclude that the complex formation equilibrium is relatively fast within the measurement timeframe.The evaluation of the stability of periment A,B. In tcycHC[8] complex, the distinct differences were spotted between the final spectrum of titration and single addition of the same equivalents of cycHC[8]  band, and at 660 nm, the Q band  band A, which bis–ZnOEP with cycHC[8] was also observed in CD and fluorescence spectral data (bis–ZnOEP upon mixing with cycHC[8] at the starting point (0 h) proves the formation of a chiral complex  of bis–ZnOEP also fully support this conclusion  and Q bands of ZnOEP appearing at 577 and 628 nm, respectively  and in excess of cycHC[8] . The low binding constant of bis–ZnOEP to cycHC[8] and relatively small excess of cycHC[8] in the NMR study might hinder the formation of the assembled species, as indicated by other methods. The observed difference in time-dependent behavior of the To find a process responsible for the time-dependent changes, two VT experiments were performed. 1H-NMR of a samples containing 1:1 ratio of bis–ZnOEP and n]cycHC (12.4 Å) and cycHC[8] (11.9 Å) are similar, the diameters (d) and shapes obviously differ as a consequence of a differing number of monomers in n]cycHC[ (bis–ZnOEP aromatic rings (12 Å) is comparable in size to measures of n]cycHC may lead to increased steric hindrance and preference for the opened anti conformation. Hence, it is reasonable to assume that the interaction between bis–ZnOEP and cycHC[8] is directed by the macrocycle size and steric hindrance caused by the ethyl groups of bis–ZnOEP.As both macrocycles are chemically analogous, the observed differences in binding mechanisms must be related to structural inequalities, such as bulkiness, shapes, and the number of binding sites. While the heights of cycHC[8] could exhibit further aggregation while cycHC[6] does not. Although the binding of cycHC[6] proceeds through the tweezer-like complex—which is unsuitable for aggregation—an excess of both n]cycHC but not with cycHC[6] can arise from the difference in shape and position of additional binding sites. As one can deduce from the ICD spectra, the specific geometry of bis–ZnOEP·cycHC[8] complexes is close to parallel, while bis–ZnOEP·cycHC[6] exhibits a helical distortion. Therefore, it is reasonable to assume that the geometry of one complex is suitable for aggregate formation, whereas the other is not. However, we are currently undertaking further study to elucidate the detailed structure of observed complexes, mechanism of self-association, and chirogenic processes and will report our findings in due course.Nevertheless, a probable relation between the guest’s bulkiness and kinetics of bis–porphyrin’s spectral changes at μM concentrations cannot explicitly explain why bis–ZnOEP   cycHCcycHC[s have no bands in the same range  and mole fractions based on the obtained association constants for cycHC[6]. We examined previously obtained crystal structures of bis–ZnOEP [n]cycHC ,  acycHC[6]  and weresoftware ,47. All ange see . The excis–ZnOEP ,52,53 anth ZnTPP  for thei1H-NMR experiments were measured using a QCI CryoProbe and DUL probe on a Bruker AVANCE III 800 MHz  spectrometer at a temperature of 298.15 K, except that time-dependent 1H-NMR and VT-NMR (253–298 K) were measured using 5 mm ID probe (Inverse Detect probe) on Agilent DD2 500 MHz spectrometer . All NMR titrations were performed in either CD2Cl2 or CH2Cl2 containing 10% CDCl3 to lock. Data was processed with MestreNova (Version 14.1.2) software. The UV-vis absorption spectra were recorded with Varian Cary® 50 UV-vis spectrophotometer . The CD spectra were recorded with a Jasco J-1500 circular dichroism spectrophotometer . All the fluorescence measurements were recorded by Hitachi F-7000 fluorescence spectrophotometer . All spectroscopic measurements were performed in CH2Cl2, and concentrations and spectral data are available in further detail in bis–ZnOEP with bulky chiral multidentate n]cycHC and cycHC[8] are chemically analogous and differ only in the number of binding sites, shape, and volume, bis–ZnOEP is able to differentiate between the two macrocycles by exhibiting a different behavior related to the self-assembly mechanism. Bis–ZnOEP forms a tweezer-like 1:1 complex with cycHC[6], which subsequently transforms into the opened 1:2 anti complex upon further addition of cycHC[6]. The evaluation of 1H-NMR and UV-vis titration data showed a negative cooperativity with the average association constants for both methods: 1K = 3200 M−1; 2K = 140 M−1. In contrast to cycHC[6], the interaction between bis–ZnOEP and cycHC[8] exhibited obscure host–guest interaction. This included the intermolecular binding itself and further time-dependent self-association, which prevented the evaluation of the corresponding association constants. This host–guest interaction includes the formation of the 1:1 syn complex between bis–ZnOEP and cycHC[8] followed by slow opening to the anti conformation, which is apparently driven by subsequent aggregate generation. Moreover, the specific cycHC[8]’s shape and larger number of binding sites leading to a different geometry of the complexes are associated with the presumed aggregate formation, which was not observed in the case of cycHC[6] complexes. Finally, this paper confirms that bis–ZnOEP is able to recognize n]cycHC[s and their enantiomers. Chiral cycHC[6] induces noticeably intense CD employing exciton coupling, therefore indicating a helical distortion of the whole bis–ZnOEP molecule, whilst chiral cycHC[8] induces only weak and monosignate CD, with exciton coupling absent apparently due to nearly parallel orientation of the porphyrin cores of bis–ZnOEP. In conclusion, this work clearly demonstrated an advanced sensory ability of bis–ZnOEP to recognize large macrocyclic systems, such as n]cycHC[s, and that the complexation-induced aggregation of porphyrins can lead to the formation of new chiral materials.In summary, the complex formation of


The amount of data available on chemical structures and their properties has increased steadily over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, Optical Chemical Structure Recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based. The DECIMER  project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50–100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information. Scientists build on the results of their peers. Knowledge and data arising from previous research is shared through scientific publications and increasingly through the deposition of data in repositories. To enable progress in core areas of chemistry, the availability of open data has a beneficial impact . Most ofThere has been a significant amount of development in the field of chemical data mining  with a cImage mining methods for chemical structure depictions and their conversion into a machine-readable file format is a comparatively small research area . The autOptical Chemical Structure Recognition (OCSR) software was built to parse chemical structure depictions. However, most of these softwares/tools are unable to handle whole page articles or scanned ones. In order to use these tools, it is necessary to segment the chemical structure depictions into separate images from printed literature and then use these segmented images as inputs. Also, the user should ensure that the image does not contain any other elements or artefacts other than a representation of a chemical structure in a segmented image. All of the available systems vary in their accuracy, OSRA  and MolVWith the advancements in computer vision, a few deep learning-based OCSR tools have been developed, e.g. by Staker et al. , the firThe DECIMER  project  is an enIn our work on DECIMER-Segmentation , the segThe DECIMER algorithm was primarily inspired by the successful AlphaGo Zero algorithm  developeDECIMER is a completely data-driven solution to chemical image recognition. Recent impressive applications of deep learning, such as the AlphaGo Zero example, all relied on the availability of very large to unlimited amounts of training data. In our case, one of the largest chemical databases on the planet, PubChem , was usehave a molecular weight of fewer than 1500 Daltons,not possess counter ions,only contain the elements C, H, O, N, P, S, F, Cl, Br, I, Se and B,not contain isotopes of Hydrogens ,have 3–40 bonds,not contain any charged groups including zwitterionic forms,only contain implicit hydrogens, except in functional groups,have less than 40 SMILES characters,no stereochemistry is allowed.The latest version of Pubchem was downloaded from their FTP site. All explicit hydrogens were removed using the CDK  and isomThe resulting main dataset contains 39 million molecules. The same rule set was used to generate a second dataset, but the molecules with charged groups including zwitterionic forms and stereochemistry were retained. Furthermore, the molecules containing tokens that were rare in the dataset were removed see “” sectionMolecular bitmap images were generated using the CDK Structure Diagram Generator (SDG). The CDK depiction generator enables the generation of production-quality 2D images. In this work, every molecule was randomly rotated and depicted as 8 Bit PNG images with a 299 × 299 resolution. It was made sure that each image contains only one structure.Gaussian BlurAverage BlurAdditive Gaussian NoiseSalt and PepperSaltPepperCoarse DropoutGamma ContrastSharpenEnhance BrightnessUsing the set of images from the second dataset and introducing image augmentations the third dataset was generated. The image augmentations were applied using the imgaug  python pOften, deep learning in chemistry is using SMILES as a textual representation of structures. Training Neural Networks (NNs) directly with SMILES, however, has pitfalls: In order to generate tokens, a set of rules has to be set up on how and where to split long strings of SMILES into smaller words. After training, invalid SMILES are often encountered in the predictions, which results in overall significantly reduced accuracy. To tackle this problem there are two new text representations named DeepSMILES  and SELFDataset 1: PNG images of chemical structure depictions plus corresponding canonical SMILES converted into SELFIES, without stereochemical information and charged groups.Dataset 2: PNG images of chemical structure depictions plus corresponding canonical SMILES converted into SELFIES, with stereochemical information and charged groups.Dataset 3: Augmented PNG images of chemical structure depictions plus corresponding canonical SMILES converted into SELFIES, with stereochemical information and charged groups.To summarize, the datasets used in this work are:Test datasets were selected from 10% of each dataset. To ensure that the chemical diversity of test and training data was similar, 10% of SMILES were selected as Test dataset using the RDKIT MaxMin algorithm. An overview of all the train and test datasets and the naming of subsets can be found in Table A Convolutional Neural Network (CNN) is used to parse the images, where the second last layer retains the features to be extracted for calculations. For training our model, we evaluated InceptionV3  and EffiEvery image was scaled to a resolution of 299 × 299 pixels and the pixel values were normalized to interval − 1 to 1, which corresponds to the format used on InceptionV3 and EfficientNet-B3. Then the image features were extracted into a vector format using the pre-trained weights of ImageNet  on IncepFor Inception V3 a feature vector size of 8 × 8 × 2048 and for EfficientNet-B3 a feature vector size of 10 × 10 × 1536 was obtained. The 8 × 8 × 2048 and 10 × 10 × 1536 dimensions are simply the shape of the last output layer in Inception-V3 and EfficientNet-B3 networks. Since Inception-V3 and EfficientNet-B3 are networks built for image classification, these features are then used in the final layer of these networks to classify the images.Next, these extracted feature vectors were saved into NumPy arrays.SELFIES were tokenized into a unique set of tokens and padded to fit the maximum length of SELFIES strings. Here the Keras  tokenizeTokens in Dataset 1: [C], [=C], [Branch1_1], [N], [Ring1], [O], [Branch1_2], [Expl=Ring1], [=N], [Branch2_1], [Branch1_3], [Ring2], [S], [F], [=O], [Branch2_2], [Cl], [Branch2_3], [#C], [Br], [P], [=S], [I], [=P], [Expl=Ring2], [B], [#N].Tokens in Dataset 2 & 3: [C], [=C], [Branch1_1], [Branch1_2], [Ring1], [N], [O], [=O], [=N], [Ring2], [Branch2_1], [S], [Branch1_3], [F], [Branch2_2], [Cl], [Branch2_3], [Br], [#C], [/C], [#N], [P], [C@Hexpl], [C@@Hexpl], [=N+expl], [=S], [=N-expl], [I], [O-expl], [N+expl], [\C], [/N], [/O], [C@expl], [B], [C@@expl], [\N], [Expl/Ring1], [\O], [NH+expl], [I-expl], [Expl\Ring1], [P+expl], [NH2+expl], [/Cl], [/S], [NH3+expl], [Cl-expl], [/F], [#N+expl], [C-expl], [\S], [N-expl], [=NH+expl], [=I], [S-expl], [\Cl], [S+expl], [#C-expl], [B-expl], [/Br].Complete list of rare tokens which were removed: [=B], [=Cl], [=Br], [#I], [=I], [#S], [Expl#Ring1], [#B], [#P], [=Br], [Expl#Ring2].Extracted feature vectors and tokenized data must be converted into TFRecords before training the models on Tensor Processing Units (TPU) . TFRecorUsing a custom python script all the datasets were converted into 75 MB chunks of TFRecords. Each TFRecord contains 128 Datapoints (128 image vectors + 128 tokenized strings).After generating the TFRecords locally, they were moved to a Google cloud storage bucket.Show, Attend and Tell [Attention is all you need [In this work, two different types of networks were evaluated. Initially, an encoder–decoder model was tested, which is based on the work by Google on their and Tell  publicatyou need  publicatThe encoder–decoder network used is an unaltered implementation by the TensorFlow team . The modThe network is trained using an Adam optimizer  with a lAttention is all you need. It uses four encoder–decoder layers and eight parallel attention heads. The attention has a dimension size of 512 and the feed-forward networks have a dimension size of 2048. Here the number of rows and columns corresponds to our image features extracted into a vector format, so for the InceptionV3, the feature vector size is 8 × 8 × 2048 and for the EfficientNet-B3 it is 10 × 10 × 1536. A drop out of 0.1 is used to avoid overfitting.The transformer model Fig.  used in The network is trained using an Adam optimizer with a custom learning rate scheduler according to . The losInitially, all the models were trained using an in-house server equipped with an Nvidia V100 Tesla with 32 GB GPU, 384 GB of RAM and two Intel(R) Xeon(R) Gold 6230 CPUs. The details regarding the scaling and performance were explained in our previous publication . For thiOn a TPU v3-8 (TPU version 3 with 8 nodes) the same model was trained with a batch size of 1024 which is distributed between 8 nodes, and it took on average 8 min and 41 s per epoch and for a complete convergence of the model, it took 8 h 41 min and 4 s. This is a reduction of 71.9% in computation time and we, therefore, decided to train all models with the TensorFlow distributed training API using the Tensor Processing Units v3-8.All the models were trained until their training loss converged, then each model was tested with a test data size of 10% of the training data. Throughout the process of selecting molecules for the test dataset, the RDKit  MaxMin aTest dataset evaluations were performed on the GPUs. Predicted SELFIES were decoded back to SMILES and then the Tanimoto Similarity Index was calculated for the original and predicted SMILES using PubChem fingerprints, included in the CDK. The Tanimoto Similarity Index provides useful information because it makes a difference whether a wrong prediction is completely wrong or provides a result very similar to the correct molecule was used because the predictions do not always correspond to the same molecule. The Tanimoto similarity thereby provides a quantitative measure of how well the network is able to “understand” graphical chemical structure representations.Apart from that for the predictions with the Tanimoto similarity index of 1.0, we additionally generated InChIs using the CDK to perform an isomorphism check and determined whether Tanimoto 1.0 predictions are a good proxy for structure identity.Models trained with augmentations were tested with augmented images and with images without any augmentation.Training large datasets such as the ones used here on deep neural networks take months even on GPUs, let alone regular CPUs. For performance measure, a dataset with 1 million molecules was trained for 50 epochs on an Nvidia Tesla V100 GPU and the same model was also trained on a TPU V3-8 (version 3 TPU with 8 nodes) and TPU V3-32 (version 3 TPU with 32 nodes).Training a model on a V3-8 TPU helped by increasing training speed up to 4 times compared to a V100 GPU and by using a V3-32 TPU a 16 times faster training speed was achieved, see Fig. To evaluate if testing accuracy could be improved by increasing the training dataset size, different subsets generated using dataset 1 were trained on TPU V3-8. The maximum length of SELFIES strings stayed the same throughout the training. As shown in Fig. It would take a considerable amount of time to examine the performance of the network using a bigger dataset. For the initial tests, Subset 1: a subset of 1 million was used, which was derived from Dataset 1. We split the dataset into 90% training data  and 10% test data  using the RDKIT MaxMin algorithm to ensure that the test data picked are diverse and resemble the training dataset.Correct extraction of the image features will result in an improved overall model at the end. In our previous work, the InceptionV3 model was used for image feature extraction. InceptionV3 is a state-of-the-art image classification network. A newer network, called EfficientNet, was created to enable better classification accuracy, and the results of noisy-student training using EfficientNet  were betTo compare the InceptionV3 feature extraction with EfficientNet-B3 feature extraction a subset of 1 million molecules was used. Using these models, the features were extracted and then used to train encoder–decoder based networks for 60 epochs until the training loss converged. The training time for the network that uses the features extracted using the InceptionV3 model was found to be shorter than the network which uses the EfficientNet-B3 model.After training, the models were tested with a test dataset. The predicted SELFIES were retranslated into SMILES strings and the Tanimoto similarity index was calculated between original SMILES and the retranslated SMILES. Here, no errors have occurred in translating SELFIES to SMILES. Table The Tanimoto 1.0 count indicated that the EfficientNet-B3 model led to a remarkable overall performance increase, so it was used for the entire work.In our previous work , the encFirst, the transformer network was tested with InceptionV3 based image feature extraction, then it was tested using the EfficientNet-B3 based image feature extraction. The extracted image features with tokenized SELFIES were used as inputs for the transformer. For this work, the same 1 million molecules subset was used with a 90:10 split for training and testing.The models were trained on TPU V3-8 until the training loss converged. The average time for transformer-based models was higher than the other, and the highest average training time was recorded for the EfficientNet-B3 Transformer network. Once the training was completed, the models were tested using the same test set. Table By comparing the Tanimoto 1.0 count, the transformer-based models clearly outperformed the encoder–decoder based models.With these results, it was decided to train all the other datasets using transformers with image features extracted using EfficientNet-B3 based image feature extraction.The work described in  indicateTo test these two image feature extraction methods and to see how well this helps us to achieve our main goal, a 1 million molecules image subset was used to train the transformer networks and the final models were evaluated using respective Images generated using the same test set. Table It is evident that the Image feature extraction using EfficientNet-B7 outperforms B3. We found, however, that most of the chemical structure depictions found on printed literature can easily fit the scale of 299 × 299, so to use the 600 × 600 scale the images should be upscaled. Upscaling will result in losing information which will be a major downside for this approach since the models majorly rely on the image features.Chemical structure depictions larger than 299 × 299 square pixels can be downscaled easily to be used in our models without losing any pixel information. Thus the size of the image was decided to be 299 × 299 and the feature extraction was performed using EfficientNet-B3.It may be possible in the future to use EfficientNet-B7 to extract image features for chemical image depictions with higher resolutions.To evaluate how the split percentage of training and test data affected the training efficiency, we use a small toy dataset (subset 1). The data was split into different sizes  reliably on segmented images from the scanned literature.The DECIMER software is fully open-source and hosted on GitHub. All data and trained models are openly available.


Fmr1 knockout (KO) mice that display the main phenotype of Fragile X syndrome (FXS), an intellectual disability syndrome and the best-known monogenic model of autism spectrum disorder (ASD). Altogether, seven minerals—Cu, Fe, K, Mg, Mn, Na, and P—were above the detection limit with the analysis revealing increased iron content in the heart of Fmr1 KO mice. In addition, levels of iron were higher in the cerebellum of the transgenic mouse when compared to wild type controls. These results implicate a role for dysregulated iron homeostasis in FXS tissues and suggest that defective iron-related mechanisms contribute to increased tissue vulnerability in FXS.Trace elements have important functions in several processes involved in cellular homeostasis and survival. Dysfunctional metal ion homeostasis can make an important impact on cellular defence mechanisms. We assessed the concentrations of 23 trace minerals in different tissues  of The online version contains supplementary material available at 10.1007/s10534-021-00320-1. Fmr1 knockout (KO) mouse recapitulates the human FXS phenotype. In most FXS patients, a CGG triple repeat expansion comprising > 200 repeats leads to transcriptional silencing of the FMR1 gene  after the age of 50 years (Loesch and Hagerman Fragile X syndrome (FXS) is an intellectual disability syndrome with a prevalence of around 1/4000 in males and 1/6000–8000 in females  to analyse trace elements, seven minerals were reliably detected in mouse tissues and iron content proved to be increased in the heart of the Fmr1 KO mouse.Metal ions are tightly linked to the redox state of a cell. Oxidative stress is elevated in the brain of Fmr1 KO mice  and their wild type (WT) littermates were used at the age of 4–5 months. Genotyping by tail-PCR was performed as previously described  between control and FXS tissue samples Table . All tisFmr1 KO cortex, WT liver and both Fmr1 KO and WT cerebellum). Briefly, weighed tissue samples were lyophilised, digested in 100–300 µl of 65% nitric acid  overnight at room temperature (RT), and heated for 20 min at 90 °C. Then an equivalent volume of 30% hydrogen peroxide  was added and samples were incubated for 30 min at RT, followed by 15 min at 70 °C. The average reduced volume was determined, and the samples were diluted with 1% nitric acid diluent using dilution factors as shown in Table The tissue concentrations of trace elements were measured by an established and fully validated method using ICP-MS as described previously  under routine multi-element operating conditions using helium as cell gas. The instrument was calibrated using 0, 5, 10, 50, and 100 ppb of certified multi-element standard calibration solutions  for the range of the elements and 200 ppb of Yttrium (Y89) was used as internal control . Three media blanks were used to determine detection limits. Conversion of readings in ppb was performed as follows: (μg/g) = /(tissue wet weight g). Samples were analysed in triplicate and median values were used for analyses. Results are expressed in micrograms of metal per gram of wet (µg/g wet wt). The concentration of 23 minerals and trace elements were assessed in mouse heart, liver, spleen, and brain tissues. Altogether, 7 trace elements  were above detection limits in our set of samples  was performed with all 35 metal variables (7 elements in 5 tissues). In the analysis, two WT mice were excluded due to several missing values and the missing values were replaced with the means when maximum of two tissues were missing. All statistical analyses, including linear correlations across all variables, were carried out using IBM SSPS Statistics. Data are shown as mean ± standard deviation. The criterion for significance was set to p < 0.05.Statistical significance between KO and WT mouse tissues with each variable was determined with the Student’s Fmr1 KO was found at the marginal level of significance (p = 0.052). The methodology suffered from low power and these statistical significances should be considered in the context of 35 individual comparisons  between KO and WT mice .Mean metal concentrations of WT and KO mouse tissues are presented in Table Fmr1 KO mouse. Subject mice by genotype and variables by the tissue of the variable are shown in Fig. To evaluate the complete dataset for pattern of variations, we used the PCA. Three components were extracted, explaining a total of 73% of the total variance; PC1 explained 35%, PC2 23%, and PC3 15%. PC1 correlated closely with brain and spleen variables. PC2 correlated with heart variables and PC3 was influenced mostly by liver variables. T-tests with respect to the genotype on each extracted principal component showed that KO and WT mice segregated across PC2 (p = 0.031), but not across PC1 (p = 0.9) or PC3 (p = 0.99), implicating altered biometal homeostasis in the heart of the Brain and liver metal levels measured in the present study were comparable to previously reported measurements in mouse and rat tissues  -oxidase activation, lipid peroxidation and protein oxidation  production, which responded to chelation of iron with desferrioxamine mesylate (DFO) . The excess iron exists as highly localized multifocal aggregates rather than a diffuse pattern in the tissue of FRDA patients and iron-mediated toxicity is not well understood Below is the link to the electronic supplementary material.