Diastolic Dysfunction in a Pre-clinical Model of Diabetes Is Associated With Changes in the Cardiac Non-myocyte Cellular Composition

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Cohen, Miles J. De Blasio, Man K. S. Lee, Gabriella E. Farrugia, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-342544/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background: Diabetes is associated with a significantly elevated risk of cardiovascular disease and its specific pathophysiology remains unclear. Recent studies have changed our understanding of cardiac cellularity, with cellular changes accompanying diabetes yet to be examined in detail. This study aims to characterise the changes in the cardiac cellular landscape in murine diabetes to identify potential cellular protagonists in the diabetic heart. Methods: Diabetes was induced in male FVB/N mice by low-dose streptozotocin and a high-fat diet for 26-weeks. Cardiac function was measured by echocardiography at endpoint. Flow cytometry was performed on cardiac ventricles as well as blood, spleen, liver, and bone-marrow at endpoint from non-diabetic and diabetic mice. To validate flow cytometry results, immunofluorescence staining was conducted on left-ventricles of age-matched mice. Results Mice with diabetes exhibited hyperglycaemia and impaired glucose tolerance at endpoint. Echocardiography revealed reduced E:A and e’:a’ ratios in diabetic mice indicating diastolic dysfunction. Systolic function was not different between the experimental groups. Detailed examination of cardiac cellularity found resident mesenchymal cells (RMCs) were elevated as a result of diabetes, due to a marked increase in cardiac fibroblasts, while smooth muscle cells were reduced in proportion. Moreover, we found increased levels of Ly6C hi monocytes in both the heart and in the blood. Consistent with this, the proportion of bone-marrow haematopoietic stem cells were increased in diabetic mice. Conclusions: Murine diabetes results in distinct changes in cardiac cellularity. These changes—in particular increased levels of fibroblasts—offer a framework for understanding how cardiac cellularity changes in diabetes. The results also point to new cellular mechanisms in this context, which may further aid in development of pharmacotherapies to allay the progression of cardiomyopathy associated with diabetes. Cardiac & Cardiovascular Systems Cardiac cellularity diabetes flow cytometry echocardiography fibroblast Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Diabetes mellitus is a leading cause of death worldwide, with a total global prevalence exceeding 450 million individuals [1]. In 2015, diabetes was attributed to 12.8% of total all-cause mortality worldwide, providing a substantial socioeconomic burden and health concern [2, 3]. Diabetes is associated with a significantly elevated risk of cardiovascular death and hospitalisation for heart failure (HF) [4, 5]. However, there remains no specific treatment for HF or its development in individuals with diabetes. HF in diabetes is often accompanied by impaired cardiac output, cardiac fibrosis, cardiomyocyte hypertrophy, cell death, and oxidative stress [6]. Diabetes also involves chronic and systemic inflammation [7, 8] with monocytosis and neutrophilia [7–9]. Despite extensive efforts to characterise diabetes-induced HF, inherent cellular mechanisms underpinning cardiac dysfunction in diabetes remain to be ascertained. The mammalian heart consists of a diverse range of cell types [10]. Cardiac non-myocytes—comprised of endothelial cells (ECs), resident mesenchymal cells (RMCs) and leukocytes—outnumber myocytes, and are critical for maintaining homeostasis of the heart [10, 11]. While a number of recent studies have provided valuable new insight into the disparate roles of non-myocytes in cardiac homeostasis [10, 12] and pathological remodelling [13–15], the cellular dynamics of non-myocytes during development of diabetes-induced heart failure remains unexplored. Using a recently published murine model of diabetes-induced cardiomyopathy [16], this study aimed to determine the difference in cardiac non-myocyte cellular proportions compared to non-diabetic mice. Here, we show that experimental diabetes impacts multiple cellular compartments in the heart, providing a framework for understanding the cellular dynamics and mechanisms driving development of diabetes-induced heart failure. Research Design and Methods Animal experiments All animal-related experiments were approved by the Alfred Research Alliance (ARA) Animal Ethics Committee (Ethics number: E/1681/2016/B) and were performed in accordance with the National Health and Medical Research Council of Australia. FVB/N mice were sourced from the ARA Animal Services (provided in three separate cohorts). Mice had access to food and water ad libitum and were housed at 22°C on a 12 h light/dark cycle. Male 6-week-old FVB/N mice were randomly allocated into the non-diabetic (ND, n=7) citrate vehicle control group fed standard chow diet, or diabetes mellitus (diabetes, n=19) which was induced by the combination of low-dose streptozotocin (STZ; cat# AG-CN2-0046, AdipoGen Life Sciences, NSW, Australia) and high-fat-diet (HFD; SF04-001, Specialty Feeds, WA, Australia, 43% total calculated digestible energy from lipids). STZ was administered by three consecutive daily intraperitoneal (i.p.) injections (55mg/kg body weight in 0.1mol/L citric acid vehicle, pH 4.5 [cat# 251275, Sigma-Aldrich, USA]). Mice administered STZ were subsequently fed a HFD ad libitum for 26-weeks, as previously described [16]. Blood glucose levels were measured fortnightly via saphenous vein bleeds using a glucometer (Accu-Chek® Performa II, Roche Diagnostics, NSW, Australia). Intraperitoneal glucose and insulin tolerance tests were conducted at endpoint (26-weeks of diabetes) to assess glucose clearance and insulin resistance, as previously described [16]. Whole-body composition analysis was performed at endpoint using an Echo-MRI™ 4-in-1 700 Analyser (EchoMRI, Houston, TX, USA) to assess percentage fat mass and total lean mass. Percentage glycated haemoglobin (% HbA 1c ) was also measured at endpoint to assess long-term blood glucose levels (Cobas b 101 POC system, Roche Diagnostics, NSW, Australia). Mice were euthanised by administration of Ketamine/Xylazine (85/8.5mg/kg, i.p.) and subsequent cardiac exsanguination. As previously described [10–12], the thoracic cavity was exposed and right atrium was cut to allow for cardiac perfusion through the left-ventricular apex (PBS, 0.9mM CaCl 2 , 200mM KCl), after which the heart was excised and ventricles were used for flow cytometry. Echocardiography Echocardiography was conducted in mice under anaesthesia (Ketamine/Xylazine/Atropine [KXA], 80/8/0.96mg/kg, i.p.) at 26-weeks post diabetes (32-weeks of age) using a Philips iE33 ultrasound machine with a 15-MHz linear-array transducer. Analysis was conducted at the Baker Heart and Diabetes Institute and quality control was completed by technicians at the Preclinical Cardiology Microsurgery & Imaging Platform (PCMIP). Doppler flow echocardiography was used to assess cardiac transmitral flow velocity in each phase of diastole, where the early phase (E wave) and the late phase (A wave) were measured to determine the E:A ratio. Similarly, tissue Doppler was performed to examine the tissue motion of the mitral annulus (early phase = e’, late phase = a’ wave). M-mode echocardiography was conducted to assess left ventricle (LV) systolic function. Variables obtained from M-mode analysis included LV end-diastolic dimension (LVEDD) and LV end-systolic dimension (LVESD) to calculate fractional shortening (%FS = [(LVEDD-LVESD)/LVEDD] × 100). Flow cytometry Blood, spleen and bone marrow Whole blood was obtained by cardiac puncture at endpoint and stained using a leukocyte-specific antibody panel (Supplementary Table 3). Bone marrow from the tibia and femur were flushed using PBS without Mg 2+ and Ca 2+ into 50mL centrifuge tubes. Spleens were manually dissociated and passed through a 35µM filter into 50mL centrifuge tubes to obtain a single cell suspension as previously described [7]. Blood, spleen and bone marrow were then subjected to red blood cell (RBC) lysis for 15 minutes at 4°C using an ammonium chloride based commercial lysis buffer (1X dilution, 555899, Becton Dickinson, USA). After RBC lysis, the remaining stained cells were washed twice in ‘Fx buffer’ (1 X HBSS [Gibco™, NY, USA], 2% FCS). Between each wash, cells were centrifuged at 400× g for 5 minutes at 4°C. Cells were then resuspended in 200µl of Fx buffer containing 4′,6-diamidino-2-phenylindole (DAPI [0.1µg/mL]); and filtered through 35µM mesh into 5ml polystyrene round-bottom tubes (352052, Falcon®, NY, USA) for flow cytometry. Gating strategies for each of the above cell suspensions are provided in Supplementary Figures 3-5. For normalisation of flow cytometry data, 20µl of blood was used to measure total white blood cell count using a Sysmex XS-1000i Hematology Analyzer. Heart High-dimensional flow-cytometry was performed on cardiac ventricles (comprising the LV, ventricular septum and right ventricle) from ND and mice with diabetes. Following perfusion, hearts were minced using curved scissors (14077-09, Walton, USA) as previously described [10], and transferred to 5ml microfuge tubes for enzymatic digestion at 37°C (2 mg/mL collagenase type IV [LS004188, Worthington Biochem, NJ, USA], 1 mg/mL Dispase II [04942078001, Roche, NSW, Australia] in 0.9 mM CaCl 2 in PBS). Cardiac non-myocyte cells were triturated three times at 15-minute intervals using a Pasteur pipette to mechanically aid enzymatic digestion for a total of 45 minutes. Digested non-myocyte cardiac cells were then filtered through 75µM nylon mesh into a 15mL tube containing 10 mL of cold PBS (0.9 mM CaCl 2 ) and subjected to centrifugation (200 g , 15 minutes, 4°C – no breaks) for debris clearance. The majority of the supernatant was aspirated and the remaining volume (~1 mL) was washed with a further 1 mL of Fx buffer supplemented with 0.9 mM CaCl 2 . Cells were pelleted at 400× g (4 minutes, 4°C) and resuspended in 200µl of Fx Buffer with Ca 2+ to yield the single cell suspension of non-myocyte cardiac cells. Cells were then stained using the antibody panel designed for examining the non-myocyte fraction of the heart (Supplementary Table 4). Cells were strained through a 35µm filter and flow cytometry was performed on a BD LSR Fortessa™ X-20 Special Order system located at the Baker Heart and Diabetes Institute. Histological analysis Age and sex-matched, fresh-frozen LV samples embedded in Optimal Cutting Temperature (OCT) compound were acquired from a separate cohort of ND and mice with diabetes [16] for histological analysis. LV sections were cut (10µm) on a cryostat (CM1950, Leica Biosystems) for staining (ND: n = 11, diabetes: n = 11). LV sections were co-stained with GATA4 (1:100, 14-9980-80, eBioScience™, Invitrogen, Australia) and PCM1 (1:100, 19856-1-AP, Proteintech Group, USA) antibodies to delineate the cell abundance of RMCs (PCM1 - GATA4 + cells) as recently reported [12]. Serial sections were stained with DACH1 (1:100, 10914-1-AP, ProteinTech, USA) to quantify EC abundance [10, 12]. All immunofluorescence sections were counterstained with DAPI to identify total cell nuclei. Immunofluorescence micrographs of each LV sample were acquired at a 20X objective and tiled (3x3 fields of view) on a Nikon A1R confocal laser scanning microscope. Quantified values of immunofluorescence signal were normalised to total nuclei (DAPI + ). Statistical analysis Flow cytometry data was analysed using FlowJo (v10.7.1) software. Raw cardiac flow cytometry data was normalised to the mean of the ND values within each batch, such that the mean of each ND cell type is equal to 1. Raw blood flow cytometry data was normalised to total white-blood cell count obtained from the hematology analyser, then subsequently batch normalised as aforementioned. Immunofluorescence micrographs were analysed by QuPath software (v0.2.3), using the cell count function to quantify nuclei. Echocardiography data was analysed using RadiAnt DICOM viewer software (v2020.2), after which quantification was performed in accordance with the PCMIP guidelines. All data was illustrated and analysed statistically using Graphpad Prism (v8.1.2). Comparison of experimental groups was conducted using an unpaired t -test, whereby statistical significance was determined as P < 0.05. Results The STZ-HFD model recapitulates primary features of diabetes The presence of diabetes was confirmed by a range of physiological tests prior to euthanasia. Consistent with our previous report [16], mice with diabetes exhibited significantly elevated blood glucose at endpoint (Supplementary Table 1). This was corroborated by measurement of glycated haemoglobin (% HbA 1c ) at endpoint, which was significantly increased in mice with diabetes ( P < 0.0001; Supplementary Table 1). In this study however, mice exhibiting diabetes did not gain more weight than their ND counterparts (Supplementary Table 1). This was recapitulated by the EchoMRI body composition analysis, showing no differences in lean or fat mass (Supplementary Table 1) between experimental groups. Impaired glucose tolerance was evident in mice with diabetes, indicating reduced clearing efficiency of systemic glucose, presented as the area under the curve (AUC, P < 0.0001, Supplementary Table 1). In contrast, there was no difference in the AUC from the insulin tolerance test between ND and mice with diabetes (Supplementary Table 1). STZ-HFD mice exhibit LV diastolic dysfunction, but not systolic dysfunction Echocardiography measurements of LV diastolic and systolic function were recorded in vivo , to determine the degree of cardiac functional impairment in mice with diabetes relative to their ND counterparts. Pulsed-wave Doppler echocardiography was conducted to measure mitral blood flow velocity during the early (E-wave) and late (A-wave) filling phases of diastole (Supplementary Figure 1A). Heart rate (HR) tended to be elevated in mice with diabetes, but this did not reach statistical significance ( P =0.07; Supplementary Figure 1B). Although no differences were detected in the peak E wave (Supplementary Figure 1C), the peak A wave velocity was significantly elevated in mice with diabetes compared to ND mice ( P < 0.05; Supplementary Figure 1D). Consequently, a significant reduction in E:A ratio (a hallmark feature of diastolic dysfunction) was observed in diabetic hearts vs. ND ( P < 0.05 Supplementary Figure 1E). There were no differences in other measurements of diastolic function including deceleration time or isovolumic relaxation time (IVRT) between experimental groups (Supplementary Figure 1F, 1G, respectively). To accompany transmitral blood flow, tissue Doppler echocardiography was used to assess the velocity of the mitral valve itself in each phase of diastole (e’ = early phase, a’ late phase, Supplementary Figure 1H-L). Although the peak e’ velocity was only modestly reduced ( P = 0.054, Supplementary Figure 1I) and the peak a’ velocity exhibited a minor increase ( P = 0.072, Supplementary Figure 1J), the e’:a’ ratio was significantly lower in mice with diabetes compared to ND mice ( P < 0.05, Supplementary Figure 1K). There were no detectable changes in the E:e’ ratio between cohorts (Supplementary Figure 1L). M-Mode echocardiography was also performed to assess the difference in ventricular wall thickness and systolic function in mice with diabetes. The anterior wall thickness at diastole (AWd), LV end-diastolic dimension (LVEDD) and posterior wall thickness at diastole (PWd) were not different between groups (Supplementary Table 2). Surprisingly, fractional shortening (% FS) was significantly elevated in mice with diabetes compared with ND mice ( P < 0.05; Supplementary Table 2), which is likely explained by the minor increase in LV end-systolic dimension in diabetic hearts (LVESD; P = 0.07, Supplementary Table 2). Diabetes alters the cardiac non-myocyte cellular composition To assess differences in cardiac cellularity associated with diabetes-induced HF, we performed flow cytometric analysis of murine cardiac ventricles at study endpoint. Examination of metabolically active, viable single-cells (Supplementary Figure 2) revealed significant differences in the proportion of endothelial cells (ECs; 0.26-fold decrease) and resident mesenchymal cells (RMCs; 2-fold increase) indicating diabetes alters cardiac cellularity (Figure 1A-B). Conversely, leukocytes were at similar levels in ND and mice with diabetes (Figure 1A-B). Next, we sought to validate the shifts in EC and RMC populations in diabetes observed by flow cytometry, with immunohistochemical analysis (Figure 1C-D). To achieve this, we stained left ventricular sections of both cohorts with an antibody cocktail of GATA4 and PCM1 (Figure 1C) or DACH1 (Figure 1D), which we have previously employed to quantify proportions of RMCs and ECs [12]. These analyses revealed that RMC (PCM1 - GATA4 + ) cell counts were significantly elevated in diabetic heart sections compared to ND counterparts ( P < 0.05, Figure 1C). Using the same approach for ECs, serial sections stained with DACH1 indicated no differences in EC abundance between experimental groups (Figure 1D), suggesting that the proportional difference observed by flow cytometry is driven by the increased RMCs. Considering the proportion of RMCs were markedly elevated in the diabetic heart, a range of RMC subtypes were investigated from the initial RMC gate (Supplementary Figure 2). Fibroblasts were significantly increased in diabetic hearts compared to ND (2.36-fold, P < 0.0001, Figure 2B). In contrast, the proportion of smooth muscle cells (SMCs), were reduced in the diabetic cohort compared to ND controls (0.27-fold, P < 0.05, Figure 2B). No major changes were observed in total mural cells, pericyte or Schwann cell populations (Figure 2B). While we did not detect any changes in total resident leukocyte proportions in diabetic mouse hearts compared to ND (Figure 1B), diabetes has been previously associated with cardiac inflammation and systemic monocytosis [7, 17, 18]. To develop an overview of leukocyte diversity and abundance in diabetic hearts, we identified an array of leukocytes including myeloid and lymphoid cell populations and their subsets (Figure 3A). There were no differences in cardiac leukocyte subsets between cohorts, except Ly6C hi monocytes, which were significantly increased in the myocardium of mice with diabetes (1.8-fold, Figure 3B). Circulating Ly6C hi monocytes are elevated in diabetes To confirm systemic monocytosis, we quantified circulating leukocytes and their broad subtypes by flow cytometry. As shown previously [7][19][16], monocytes, particularly the Ly6C hi subset, were significantly elevated in the blood of mice with diabetes (2.2-fold, 2.3-fold, respectively; P < 0.05 for both; Figure 4B). Numbers of circulating neutrophils and Ly6C lo monocytes were also marginally elevated in diabetic mice compared to their ND counterparts ( P = 0.09, P = 0.054 respectively; Figure 4B,). By contrast, numbers of circulating lymphocytes (B and T-cells) did not differ between cohorts (Figure 4C). Systemic monocytosis likely occurs via extramedullary myelopoiesis To identify the potential sources of the observed monocytosis in this model, we performed flow cytometry of the bone marrow and spleen. Within the bone marrow, LSKs (haematopoietic stem and progenitor cells; [Lin - Sca1 + cKit + ]) were significantly increased in mice with diabetes (1.8-fold, P < 0.01, Figure 5A). However, bone-marrow derived common myeloid progenitors (CMP) and granulocyte-myeloid progenitors (GMP) were not different between experimental groups (Figure 5A). Monocytes (both Ly6C hi and Ly6C lo ) were significantly increased in the spleen in mice with diabetes compared to their ND controls (1.7-fold, 1.3-fold respectively, P < 0.05, Figure 5B). These data suggest that the increased proportion of bone-marrow LSKs could be influencing these cells to mobilise to the spleen to undergo extramedullary myelopoiesis (Figure 5C). Discussion The relationship between diabetes and HF remains poorly understood. Diabetes-associated cardiac remodelling— encompassing myocyte hypertrophy, fibrosis, oxidative stress and apoptosis [13, 14] is well established. However, how the cardiac non-myocyte networks change in diabetes and contribute to this remodelling is unclear. Using a recently characterised mouse model of diabetes-induced cardiomyopathy [20], we aimed to determine how diabetes affects cardiac non-myocyte proportions and abundance. We revealed that with diabetes-associated diastolic dysfunction, proportions of cardiac fibroblasts are significantly increased in the myocardium. We also noted increased levels of Ly6C hi monocytes and decreased levels of SMCs in diabetic hearts. Numerous studies have implicated cardiac fibroblasts in diabetic cardiomyopathy, however their precise role in diabetes in vivo is still unknown. Cardiac fibroblasts are the primary cell type involved in deposition of extracellular matrix (ECM) in both states of acute injury or chronic stress [21]. However, in these contexts, fibroblast gene expression and phenotype are distinct [13]. For example, in myocardial infarction (MI), fibroblasts rapidly differentiate into activated fibroblasts and myofibroblasts—both well-established drivers of cardiac ECM deposition [16, 22, 23]. Conversely, we have recently reported that myofibroblasts are absent during the development of chronic fibrosis in angiotensin II-induced cardiac remodelling [24, 25]. Observations from the present study reveal that fibroblasts are the predominant non-myocyte cell type most dramatically affected by diabetes—suggesting an important role for fibroblasts in the development of diabetes-induced HF. Indeed, chronic hyperglycaemia is known to up-regulate various pro-fibrotic genes in the diabetic heart as a whole, including Col1a1, Postn, Timp-2 and Ccn2 [26]. Furthermore, diabetes is associated with fibroblast-to-myofibroblast differentiation and ECM deposition [7, 8]. However, further research such as single-cell sequencing or targeted cell depletion experiments are needed to further elucidate the precise role of the cardiac fibroblast in diabetes and the regulatory mechanisms that drive these changes. In the current study, we also observed an increase in monocyte numbers in the heart in diabetic mice, which is likely the result of increased systemic inflammation. In the non-injured heart, circulating leukocytes, such as monocytes primarily reside in the vascular lumens of cardiac capillaries [27–29], therefore reflecting changes occurring in the circulation. Systemic monocytosis is reported in both type-1 diabetes (T1D) and insulin resistant obese mice (i.e. leptin mutant ob/ob mice and diet-induced obese mice) [30]. Consistent with systemic inflammation, the diabetic heart exhibits upregulation of pro-inflammatory cytokines such as TNFα, MCP-1 and IL-1β [7, 8]. Corresponding to the monocytosis, we also noted increased progenitor cells and splenic monocytes—the major site of secondary myelopoiesis [31]. Monocytosis is a well-established feature of diabetes and obesity/insulin resistance [32, 33]. However, in this model we only detected a significant increase in haematopoietic stem and progenitor cells (HSPCs), but not common myeloid progenitors (CMPs) or granulocyte-macrophage progenitors (GMPs) in the bone marrow. Given HSPCs can migrate to secondary myelopoietic sites, such as the spleen, to increase monocyte numbers [34, 35], our findings suggest that this may be the primary mode of monocytosis observed in our model (Figure 5C). In contrast to the diabetes-induced increases in cardiac fibroblasts and monocyte numbers, we observed a decrease in SMC proportions. This was unexpected given hyperglycaemia has previously been associated with inhibition of aortic vascular SMC apoptosis in T1D patients, and in STZ-induced T1D mice [36–38]. Conversely, metabolic syndrome and hypercholesterolaemia are both associated with increased apoptosis in aortic VSMCs of mice and humans [39, 40]. Therefore, the precise mechanism leading to the reduction in SMC proportions in the hearts of STZ-HFD mice warrants further investigation. Although there are a number of studies examining the role of individual cardiac cell types in diabetes, to our knowledge this is the first study to consider the entire cardiac non-myocyte network to understand differences in tissue cellularity. While novel technologies such as single-cell RNA sequencing have been successfully applied to tissues such as the pancreas [41], kidney [13, 42] and liver [43, 44] in diabetes, detailed interrogation of the cellular heterogeneity in these tissue systems are lacking in this context. This study invites future research to consider cellular plasticity in diabetes to better understand the development of its associated pathologies. Study Limitations While this study provides a basis for providing new understanding of the cardiac cellular dynamics in the context of diabetes, a number of limitations are noteworthy. First, cardiomyocytes were not considered in this study, as they are too large in diameter to pass through the flow cytometer available in our laboratories. Although cardiomyocytes are detectable by histology (PCM1 + GATA4 + cells), they are often multi-nucleated, thus counting nuclei abundance is unlikely to yield accurate information. Second, we only examined male mice in our study. Given that cardiac pathology is sex-specific in mice [11, 12] and in humans [16, 45], cardiac cellular composition and gene expression are sexually-dimorphic [46]. Future work should examine the impact of biological sex in the development of diabetic cardiomyopathy. Third, the STZ-HFD model used in this study did not yield a population of mice with elevated fat mass and body weight as expected [47, 48]. Adiposity and obesity are important comorbidities contributing to pathology in experimental and clinical T2D [46], albeit obesity is not essential for development of T2D [47, 48]. Importantly however, in this study mice with diabetes exhibited hyperglycaemia, impaired glucose tolerance and LV diastolic dysfunction, which are clinically relevant features of HF associated with diabetes. Lastly, we were unable to ascertain whether the observed differences are attributed to the combination of STZ and HFD, or one of these individual insults. Further work using this model should consider the effect of STZ and HFD alone in addition to the combination of STZ-HFD to delineate the role of both factors in the development of diabetic HF. Conclusion Here we have profiled the differences in the cardiac non-myocyte network, observing that the cellular landscape of the heart changes in a murine model of diabetes. These results offer a framework for understanding the cellular mechanisms that may drive pathological remodelling of the heart during the development of diabetes-induced HF. Targeting the molecular pathways that drive these non-myocyte cellular changes may offer new therapeutic avenues to address the cardiac complications associated with diabetes. Declarations Acknowledgements We wish to acknowledge the Monash Micro-Imaging (MMI) facility for provision of instrumentation and training. Disclosures None. Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Author contributions CDC, MJD, RHR, ARP, conceived the experiments, GD, MJD, AJM and MKSL provided intellectual input. CDC, ARP, MJD, GEF, CK, TLG, MCF, DP, MD performed experiments. HK, DD performed echocardiography imaging and assisted with quality control of echocardiography analysis. CDC wrote the manuscript under ARP and RHR’s guidance. RHR, ARP, MJD, GD edited the manuscript. All co-authors read and approved the final manuscript. Funding CDC, CK and TLG are supported by the La Trobe University Postgraduate Research Scholarship (LTUPRS), Research Training Program Fees Off-set (RTP-Fo) Scholarship. CDC is supported by a Baker Institute ‘Bright Sparks’ Scholarship. TLG is supported by Defence Science Institute (DSI) RhD Grant. This work was supported by a project grant to RHR and MJD from the National Health and Medical Research Council (NHMRC) of Australia (APP1158013), and an NHMRC fellowship to RHR (APP1059960) and in part by an infrastructure grant from the Victorian Government of Australia. This work was also supported by an NHMRC Ideas Grant (GNT1188503) to ARP. Competing interests None. Ethics approval All animal-related experiments were approved by the Alfred Research Alliance (ARA) Animal Ethics Committee (Ethics number: E/1681/2016/B) and were in accordance with NHMRC guidelines. 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Supplementary Files CCOHENstzhfdcardiaccellularitySUPPcollated.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 10 Apr, 2021 Review # 3 received at journal 07 Apr, 2021 Review # 1 received at journal 31 Mar, 2021 Review # 2 received at journal 31 Mar, 2021 Reviewer # 2 agreed at journal 18 Mar, 2021 Reviewer # 3 agreed at journal 18 Mar, 2021 Reviewers invited by journal 18 Mar, 2021 Reviews received at journal 18 Mar, 2021 Editor assigned by journal 18 Mar, 2021 Reviewer # 1 agreed at journal 18 Mar, 2021 Submission checks completed at journal 18 Mar, 2021 Editor invited by journal 18 Mar, 2021 First submitted to journal 17 Mar, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-342544","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":17569335,"identity":"b4cb19f4-eba6-47a9-b996-80f795c30e0b","order_by":0,"name":"Charles D. Cohen","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Charles","middleName":"D.","lastName":"Cohen","suffix":""},{"id":17569336,"identity":"c72e29cc-2565-4bbc-b323-631ff84c818d","order_by":1,"name":"Miles J. De Blasio","email":"","orcid":"","institution":"Monash University MIPS: Monash Institute of Pharmaceutical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Miles","middleName":"J.","lastName":"De Blasio","suffix":""},{"id":17569337,"identity":"30e0e646-c0e3-4659-8a31-a19db7d041c5","order_by":2,"name":"Man K. S. Lee","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Man","middleName":"K. S.","lastName":"Lee","suffix":""},{"id":17569338,"identity":"974b4334-8d6b-4a80-8f97-16e891f42111","order_by":3,"name":"Gabriella E. Farrugia","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gabriella","middleName":"E.","lastName":"Farrugia","suffix":""},{"id":17569339,"identity":"1f5d3899-6f27-481b-a12b-7cdaa9750657","order_by":4,"name":"Darnel Prakoso","email":"","orcid":"","institution":"Monash University MIPS: Monash Institute of Pharmaceutical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Darnel","middleName":"","lastName":"Prakoso","suffix":""},{"id":17569340,"identity":"464293be-372e-4821-9ab4-eb507aa1a616","order_by":5,"name":"Crisdion Krstevski","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Crisdion","middleName":"","lastName":"Krstevski","suffix":""},{"id":17569341,"identity":"242fca89-7dfb-4098-a0c1-f2a3bc1bc2e2","order_by":6,"name":"Minh Deo","email":"","orcid":"","institution":"Monash University MIPS: Monash Institute of Pharmaceutical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Minh","middleName":"","lastName":"Deo","suffix":""},{"id":17569342,"identity":"762bccdf-0590-4931-9d12-6164ce51b9d1","order_by":7,"name":"Daniel G. Donner","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"G.","lastName":"Donner","suffix":""},{"id":17569343,"identity":"7251d59a-58b9-47a6-ac86-aa7ba3a00b80","order_by":8,"name":"Helen Kiriazis","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Helen","middleName":"","lastName":"Kiriazis","suffix":""},{"id":17569344,"identity":"eec455a5-0424-4a95-90dc-2c48edcef12e","order_by":9,"name":"Michelle C. Flynn","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"C.","lastName":"Flynn","suffix":""},{"id":17569345,"identity":"cdb304c2-0c4e-4278-9d46-7d8f6a7c5750","order_by":10,"name":"Taylah L. Gaynor","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Taylah","middleName":"L.","lastName":"Gaynor","suffix":""},{"id":17569346,"identity":"450ba5d4-9169-498a-8eff-f8730527b4ea","order_by":11,"name":"Andrew J. Murphy","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"J.","lastName":"Murphy","suffix":""},{"id":17569347,"identity":"caee2936-f40e-4123-98f9-7dd7eff2b765","order_by":12,"name":"Grant R. Drummond","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Grant","middleName":"R.","lastName":"Drummond","suffix":""},{"id":17569348,"identity":"d1e2421a-da16-4b68-9eed-77f4e8247eeb","order_by":13,"name":"Alexander R. Pinto","email":"","orcid":"","institution":"Baker Heart and Diabetes Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"R.","lastName":"Pinto","suffix":""},{"id":17569349,"identity":"a5c8d611-6ffe-447a-b08f-f787122df029","order_by":14,"name":"Rebecca Helen Ritchie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYFACHijNDEIGDHJgDmMDkDhAUAszWIsxCVoYQFoYGBIbCGkxb+89+OBHjR2QwX/wc0GBTfpaoMjHnzsY5PhuJGDVInPmXLJhz7FkBpnDzMzSMwzScrcBRaR5zzAYS+LQIiGRYybN2MDMIMHMzMbMY3A4d9uNHANpxjaGxA24tMi/Mf/N2FAP0/I/3exGjvHPn20M9Ti1SPCYMTM2HIZpOZAA1GImwdvGkGCASwtPXrJkz7HjPEAtxtI8BsmG286cMbPmbZMwnHnmAXYt7GcPfvhRUy0nwX/w4WeeP3byZsd7jG/+bLOR5zuO3RYY4EEXkMCrfBSMglEwCkYBfgAAhGhUgjhV3NkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8610-0058","institution":"Monash University MIPS: Monash Institute of Pharmaceutical Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"Helen","lastName":"Ritchie","suffix":""}],"badges":[],"createdAt":"2021-03-18 11:52:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-342544/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-342544/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":7324599,"identity":"f921fbb3-893c-4a82-b6ff-1a5f05ff7cd0","added_by":"auto","created_at":"2021-03-24 19:14:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82189,"visible":true,"origin":"","legend":"Differences in the abundance of major non-myocyte cell classes in the diabetic heart. (A) Flow cytometry contour plot displaying gating of major non-myocyte cell types for quantification of cell type proportion (summarised in B). For full gating strategy see Supplementary Figure 2. Endothelial cells (ECs; CD31+), resident mesenchymal cells (RMCs; CD31-CD45-) and leukocytes (Leuks; CD45+). (B) Proportions of major cell types in non-diabetic (ND; n = 7) and diabetic (T2D; n = 19) mouse hearts. Individual sample values are shown with mean ± SEM. (C) Immunohistochemical analysis of the abundance of RMCs in ND and diabetic mouse heart left ventricles. Left and middle panels show representative confocal micrographs of mouse heart tissue stained for PCM1 and GATA4. PCM1+GATA4+ and PCM1 GATA4+ nuclei correspond to nuclei of cardiomyocytes (CM) and RMCs respectively. Nuclei are counterstained with DAPI. Right panel (box-plot) summarises proportion of nuclei corresponding to RMCs in ND (n = 9) vs. T2D (n = 10) enumerated from micrographs. Whiskers of box-and-whisker plot indicate max and min. (D) As for C, heart left ventricle sections were stained with Dach1 to identify nuclei corresponding to endothelial cells in ND (n = 10) and T2D (n = 9) left ventricles. *P \u003c 0.05, **** P \u003c 0.0001 (Student’s unpaired t-test). Scale bar = 100µM.","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-342544/v1/9e74c6b9c4d6ce4784f0b2e0.jpg"},{"id":7323799,"identity":"ca1e7d2d-d934-4ce3-818e-2e17165ef39d","added_by":"auto","created_at":"2021-03-24 19:08:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87548,"visible":true,"origin":"","legend":"Differences in resident mesenchymal cell (RMC) subtypes in the diabetic heart.\n(A) Flow cytometry contour plots display gating strategy for cardiac RMCs and subsets (fibroblasts, SMCs, pericytes and Schwann cells) for quantifying RMC proportions (summarised in B). For full gating strategy see Supplementary Figure 2. (B) Proportions of RMC sub-classes in ND (n = 7) and diabetic (n = 19) mouse ventricles. Fibro: Fibroblast; Mural: Mural cells; SMCs: smooth muscle cells. Data is displayed as mean ± SEM. *P \u003c 0.05, **** P \u003c 0.0001 (Student’s unpaired t-test).\n","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-342544/v1/6d4ae491338bbcd7f6c173d8.jpg"},{"id":7324315,"identity":"48d0ce9c-ca50-491b-a3a6-cb3cf4cb640d","added_by":"auto","created_at":"2021-03-24 19:11:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91708,"visible":true,"origin":"","legend":"Ly6Chi monocytes, but not resident leukocytes are increased in the diabetic heart. \n(A) Flow cytometry contour plots display gating strategy for quantifying cardiac leukocytes (summarised in B). For full gating strategy see Supplementary Figure 2. (B) Proportions of leukocyte sub-types in ND (n = 7) and T2D (n = 19) mouse ventricles. Data is displayed as mean ± SEM. *P \u003c 0.05 (Student’s unpaired t-test).\n","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-342544/v1/208eb4e3440aaa3956ab08c4.jpg"},{"id":7324598,"identity":"b40da90b-9a5b-4f34-afe8-6d0d6208cb36","added_by":"auto","created_at":"2021-03-24 19:14:04","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50852,"visible":true,"origin":"","legend":"Mice with diabetes exhibit systemic monocytosis.\n(A) Flow cytometry contour plots display gating strategy for circulating leukocytes in whole blood (summarised in B and C). For full gating strategy see Supplementary Figure 2. (B-C) Proportions of circulating myeloid leukocytes and lymphocytes in ND (n = 7) vs. T2D (n = 19) mice. Monocytes (Monos.), Ly6Chi monocytes (Ly6Chi), Neutrophils (Neuts.) and Ly6Clo monocytes (Ly6Clo). Data is displayed as mean ± SEM. *P \u003c 0.05 (Student’s unpaired t-test).\n","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-342544/v1/ec957652b2a446916cd5ff33.jpg"},{"id":7324313,"identity":"a4f0d775-92e3-41fb-b250-d51eae21a9cf","added_by":"auto","created_at":"2021-03-24 19:11:04","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":73216,"visible":true,"origin":"","legend":"Bone marrow and spleen myelopoiesis evident in mice with diabetes.\n(A) Quantified proportions of bone marrow progenitor cells. LSK cells (Lineage-, Sca-1+, cKit+; haematopoietic stem cells), common myeloid progenitors (CMPs) and granulocyte myeloid progenitors (GMPs). (B) Proportions of spleen monocytes in ND (n = 7) vs. T2D (n = 19) mice. (C) Proposed mechanism by which systemic monocytosis occurs in diabetic mice administered STZ-HFD. See Supplementary Figure 4 and 5 for full gating strategies for flow cytometry analysis. Data displayed as mean ± SEM. *P \u003c 0.05, **P \u003c 0.01 (Student’s unpaired t-test).\n","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-342544/v1/cf034b0063270c32313cde25.jpg"},{"id":13682438,"identity":"6ffbd223-d11e-4088-9748-6b060842823d","added_by":"auto","created_at":"2021-09-17 11:57:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":651571,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-342544/v1/426a4436-9e57-4d3a-8562-f149b3fa05f1.pdf"},{"id":7323804,"identity":"bc45af68-a595-4987-ba5d-52d179474766","added_by":"auto","created_at":"2021-03-24 19:08:05","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1502001,"visible":true,"origin":"","legend":"","description":"","filename":"CCOHENstzhfdcardiaccellularitySUPPcollated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-342544/v1/0e6cb8d9a6b546823a910271.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eDiastolic Dysfunction in a Pre-clinical Model of Diabetes Is Associated With Changes in the Cardiac Non-myocyte Cellular Composition\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetes mellitus is a leading cause of death worldwide, with a total global prevalence exceeding 450 million individuals [1]. In 2015, diabetes was attributed to 12.8% of total all-cause mortality worldwide, providing a substantial socioeconomic burden and health concern [2, 3].\u003c/p\u003e\n\u003cp\u003eDiabetes is associated with a significantly elevated risk of cardiovascular death and hospitalisation for heart failure (HF) [4, 5]. However, there remains no specific treatment for HF or its development in individuals with diabetes. HF in diabetes is often accompanied by impaired cardiac output, cardiac fibrosis, cardiomyocyte hypertrophy, cell death, and oxidative stress [6]. Diabetes also involves chronic and systemic inflammation [7, 8] with monocytosis and neutrophilia [7\u0026ndash;9]. Despite extensive efforts to characterise diabetes-induced HF, inherent cellular mechanisms underpinning cardiac dysfunction in diabetes remain to be ascertained.\u003c/p\u003e\n\u003cp\u003eThe mammalian heart consists of a diverse range of cell types [10]. Cardiac non-myocytes\u0026mdash;comprised of endothelial cells (ECs), resident mesenchymal cells (RMCs) and leukocytes\u0026mdash;outnumber myocytes, and are critical for maintaining homeostasis of the heart [10, 11]. While a number of recent studies have provided valuable new insight into the disparate roles of non-myocytes in cardiac homeostasis [10, 12] and pathological remodelling [13\u0026ndash;15], the cellular dynamics of non-myocytes during development of diabetes-induced heart failure remains unexplored. Using a recently published murine model of diabetes-induced cardiomyopathy [16], this study aimed to determine the difference in cardiac non-myocyte cellular proportions compared to non-diabetic mice. Here, we show that experimental diabetes impacts multiple cellular compartments in the heart, providing a framework for understanding the cellular dynamics and mechanisms driving development of diabetes-induced heart failure.\u003c/p\u003e"},{"header":"Research Design and Methods","content":"\u003cp\u003e\u003cem\u003eAnimal experiments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll animal-related experiments were approved by the Alfred Research Alliance (ARA) Animal Ethics Committee (Ethics number: E/1681/2016/B) and were performed in accordance with the National Health and Medical Research Council of Australia. FVB/N mice were sourced from the ARA Animal Services (provided in three separate cohorts). Mice had access to food and water \u003cem\u003ead libitum\u003c/em\u003e and were housed at 22\u0026deg;C on a 12 h light/dark cycle. Male 6-week-old FVB/N mice were randomly allocated into the non-diabetic (ND, n=7) citrate vehicle control group fed standard chow diet, or diabetes mellitus (diabetes, n=19) which was induced by the combination of low-dose streptozotocin (STZ; cat# AG-CN2-0046, AdipoGen Life Sciences, NSW, Australia) and high-fat-diet (HFD; SF04-001, Specialty Feeds, WA, Australia, 43% total calculated digestible energy from lipids). STZ was administered by three consecutive daily intraperitoneal (i.p.) injections (55mg/kg body weight in 0.1mol/L citric acid vehicle, pH 4.5 [cat# 251275, Sigma-Aldrich, USA]). Mice administered STZ were subsequently fed a HFD \u003cem\u003ead libitum\u003c/em\u003e for 26-weeks, as previously described [16]. Blood glucose levels were measured fortnightly via saphenous vein bleeds using a glucometer (Accu-Chek\u0026reg; Performa II, Roche Diagnostics, NSW, Australia). Intraperitoneal glucose and insulin tolerance tests were conducted at endpoint (26-weeks of diabetes) to assess glucose clearance and insulin resistance, as previously described [16]. Whole-body composition analysis was performed at endpoint using an Echo-MRI\u0026trade; 4-in-1 700 Analyser (EchoMRI, Houston, TX, USA) to assess percentage fat mass and total lean mass. Percentage glycated haemoglobin (% HbA\u003csub\u003e1c\u003c/sub\u003e) was also measured at endpoint to assess long-term blood glucose levels (Cobas b 101 POC system, Roche Diagnostics, NSW, Australia). Mice were euthanised by administration of Ketamine/Xylazine (85/8.5mg/kg, i.p.) and subsequent cardiac exsanguination. As previously described [10\u0026ndash;12], the thoracic cavity was exposed and right atrium was cut to allow for cardiac perfusion through the left-ventricular apex (PBS, 0.9mM CaCl\u003csub\u003e2\u003c/sub\u003e, 200mM KCl), after which the heart was excised and ventricles were used for flow cytometry.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEchocardiography\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEchocardiography was conducted in mice under anaesthesia (Ketamine/Xylazine/Atropine [KXA], 80/8/0.96mg/kg, i.p.) at 26-weeks post diabetes (32-weeks of age) using a Philips iE33 ultrasound machine with a 15-MHz linear-array transducer. Analysis was conducted at the Baker Heart and Diabetes Institute and quality control was completed by technicians at the Preclinical Cardiology Microsurgery \u0026amp; Imaging Platform (PCMIP). Doppler flow echocardiography was used to assess cardiac transmitral flow velocity in each phase of diastole, where the early phase (E wave) and the late phase (A wave) were measured to determine the E:A ratio. Similarly, tissue Doppler was performed to examine the tissue motion of the mitral annulus (early phase = e\u0026rsquo;, late phase = a\u0026rsquo; wave). M-mode echocardiography was conducted to assess left ventricle (LV) systolic function. Variables obtained from M-mode analysis included LV end-diastolic dimension (LVEDD) and LV end-systolic dimension (LVESD) to calculate fractional shortening (%FS = [(LVEDD-LVESD)/LVEDD] \u0026times; 100).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFlow cytometry\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBlood, spleen and bone marrow\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhole blood was obtained by cardiac puncture at endpoint and stained using a leukocyte-specific antibody panel (Supplementary Table 3). Bone marrow from the tibia and femur were flushed using PBS without Mg\u003csup\u003e2+\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e into 50mL centrifuge tubes. Spleens were manually dissociated and passed through a 35\u0026micro;M filter into 50mL centrifuge tubes to obtain a single cell suspension as previously described [7]. Blood, spleen and bone marrow were then subjected to red blood cell (RBC) lysis for 15 minutes at 4\u0026deg;C using an ammonium chloride based commercial lysis buffer (1X dilution, 555899, Becton Dickinson, USA). After RBC lysis, the remaining stained cells were washed twice in \u0026lsquo;Fx buffer\u0026rsquo; (1 X HBSS [Gibco\u0026trade;, NY, USA], 2% FCS). Between each wash, cells were centrifuged at 400\u0026times;\u003cem\u003eg\u003c/em\u003e for 5 minutes at 4\u0026deg;C. Cells were then resuspended in 200\u0026micro;l of Fx buffer containing 4\u0026prime;,6-diamidino-2-phenylindole (DAPI [0.1\u0026micro;g/mL]); and filtered through 35\u0026micro;M mesh into 5ml polystyrene round-bottom tubes (352052, Falcon\u0026reg;, NY, USA) for flow cytometry. Gating strategies for each of the above cell suspensions are provided in Supplementary Figures 3-5. For normalisation of flow cytometry data, 20\u0026micro;l of blood was used to measure total white blood cell count using a Sysmex XS-1000i Hematology Analyzer.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHeart\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHigh-dimensional flow-cytometry was performed on cardiac ventricles (comprising the LV, ventricular septum and right ventricle) from ND and mice with diabetes. Following perfusion, hearts were minced using curved scissors (14077-09, Walton, USA) as previously described [10], and transferred to 5ml microfuge tubes for enzymatic digestion at 37\u0026deg;C (2 mg/mL collagenase type IV [LS004188, Worthington Biochem, NJ, USA], 1 mg/mL Dispase II [04942078001, Roche, NSW, Australia] in 0.9 mM CaCl\u003csub\u003e2 \u003c/sub\u003ein PBS). Cardiac non-myocyte cells were triturated three times at 15-minute intervals using a Pasteur pipette to mechanically aid enzymatic digestion for a total of 45 minutes. Digested non-myocyte cardiac cells were then filtered through 75\u0026micro;M nylon mesh into a 15mL tube containing 10 mL of cold PBS (0.9 mM CaCl\u003csub\u003e2\u003c/sub\u003e) and subjected to centrifugation (200\u003cem\u003eg\u003c/em\u003e, 15 minutes, 4\u0026deg;C \u0026ndash; no breaks) for debris clearance. The majority of the supernatant was aspirated and the remaining volume (~1 mL) was washed with a further 1 mL of Fx buffer supplemented with 0.9 mM CaCl\u003csub\u003e2\u003c/sub\u003e. Cells were pelleted at 400\u0026times;\u003cem\u003eg\u003c/em\u003e (4 minutes, 4\u0026deg;C) and resuspended in 200\u0026micro;l of Fx Buffer with Ca\u003csup\u003e2+\u003c/sup\u003e to yield the single cell suspension of non-myocyte cardiac cells. Cells were then stained using the antibody panel designed for examining the non-myocyte fraction of the heart (Supplementary Table 4). Cells were strained through a 35\u0026micro;m filter and flow cytometry was performed on a BD LSR Fortessa\u0026trade; X-20 Special Order system located at the Baker Heart and Diabetes Institute.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHistological analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAge and sex-matched, fresh-frozen LV samples embedded in Optimal Cutting Temperature (OCT) compound were acquired from a separate cohort of ND and mice with diabetes [16] for histological analysis. LV sections were cut (10\u0026micro;m) on a cryostat (CM1950, Leica Biosystems) for staining (ND: \u003cem\u003en\u003c/em\u003e = 11, diabetes: \u003cem\u003en\u003c/em\u003e = 11). LV sections were co-stained with GATA4 (1:100, 14-9980-80, eBioScience\u0026trade;, Invitrogen, Australia) and PCM1 (1:100, 19856-1-AP, Proteintech Group, USA) antibodies to delineate the cell abundance of RMCs (PCM1\u003csup\u003e-\u003c/sup\u003eGATA4\u003csup\u003e+\u003c/sup\u003e cells) as recently reported [12]. Serial sections were stained with DACH1 (1:100, 10914-1-AP, ProteinTech, USA) to quantify EC abundance [10, 12]. All immunofluorescence sections were counterstained with DAPI to identify total cell nuclei. Immunofluorescence micrographs of each LV sample were acquired at a 20X objective and tiled (3x3 fields of view) on a Nikon A1R confocal laser scanning microscope. Quantified values of immunofluorescence signal were normalised to total nuclei (DAPI\u003csup\u003e+\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFlow cytometry data was analysed using FlowJo (v10.7.1) software. Raw cardiac flow cytometry data was normalised to the mean of the ND values within each batch, such that the mean of each ND cell type is equal to 1. Raw blood flow cytometry data was normalised to total white-blood cell count obtained from the hematology analyser, then subsequently batch normalised as aforementioned. Immunofluorescence micrographs were analysed by QuPath software (v0.2.3), using the cell count function to quantify nuclei. Echocardiography data was analysed using RadiAnt DICOM viewer software (v2020.2), after which quantification was performed in accordance with the PCMIP guidelines. All data was illustrated and analysed statistically using Graphpad Prism (v8.1.2). Comparison of experimental groups was conducted using an unpaired \u003cem\u003et\u003c/em\u003e-test, whereby statistical significance was determined as \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eThe STZ-HFD model recapitulates primary features of diabetes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe presence of diabetes was confirmed by a range of physiological tests prior to euthanasia. Consistent with our previous report [16], mice with diabetes exhibited significantly elevated blood glucose at endpoint (Supplementary Table 1). This was corroborated by measurement of glycated haemoglobin (% HbA\u003csub\u003e1c\u003c/sub\u003e) at endpoint, which was significantly increased in mice with diabetes (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.0001; Supplementary Table 1). In this study however, mice exhibiting diabetes did not gain more weight than their ND counterparts (Supplementary Table 1). This was recapitulated by the EchoMRI body composition analysis, showing no differences in lean or fat mass (Supplementary Table 1) between experimental groups. Impaired glucose tolerance was evident in mice with diabetes, indicating reduced clearing efficiency of systemic glucose, presented as the area under the curve (AUC, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.0001, Supplementary Table 1). In contrast, there was no difference in the AUC from the insulin tolerance test between ND and mice with diabetes (Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSTZ-HFD mice exhibit LV diastolic dysfunction, but not systolic dysfunction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEchocardiography measurements of LV diastolic and systolic function were recorded \u003cem\u003ein vivo\u003c/em\u003e, to determine the degree of cardiac functional impairment in mice with diabetes relative to their ND counterparts. Pulsed-wave Doppler echocardiography was conducted to measure mitral blood flow velocity during the early (E-wave) and late (A-wave) filling phases of diastole (Supplementary Figure 1A). Heart rate (HR) tended to be elevated in mice with diabetes, but this did not reach statistical significance (\u003cem\u003eP\u003c/em\u003e=0.07; Supplementary Figure 1B). Although no differences were detected in the peak E wave (Supplementary Figure 1C), the peak A wave velocity was significantly elevated in mice with diabetes compared to ND mice (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05; Supplementary Figure 1D). Consequently, a significant reduction in E:A ratio (a hallmark feature of diastolic dysfunction) was observed in diabetic hearts vs. ND (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 Supplementary Figure 1E). There were no differences in other measurements of diastolic function including deceleration time or isovolumic relaxation time (IVRT) between experimental groups (Supplementary Figure 1F, 1G, respectively).\u003c/p\u003e\n\u003cp\u003eTo accompany transmitral blood flow, tissue Doppler echocardiography was used to assess the velocity of the mitral valve itself in each phase of diastole (e\u0026rsquo; = early phase, a\u0026rsquo; late phase, Supplementary Figure 1H-L). Although the peak e\u0026rsquo; velocity was only modestly reduced (\u003cem\u003eP\u003c/em\u003e = 0.054, Supplementary Figure 1I) and the peak a\u0026rsquo; velocity exhibited a minor increase (\u003cem\u003eP\u003c/em\u003e = 0.072, Supplementary Figure 1J), the e\u0026rsquo;:a\u0026rsquo; ratio was significantly lower in mice with diabetes compared to ND mice (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Supplementary Figure 1K). There were no detectable changes in the E:e\u0026rsquo; ratio between cohorts (Supplementary Figure 1L).\u003c/p\u003e\n\u003cp\u003eM-Mode echocardiography was also performed to assess the difference in ventricular wall thickness and systolic function in mice with diabetes. The anterior wall thickness at diastole (AWd), LV end-diastolic dimension (LVEDD) and posterior wall thickness at diastole (PWd) were not different between groups (Supplementary Table 2). Surprisingly, fractional shortening (% FS) was significantly elevated in mice with diabetes compared with ND mice (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Supplementary Table 2), which is likely explained by the minor increase in LV end-systolic dimension in diabetic hearts (LVESD; \u003cem\u003eP\u003c/em\u003e = 0.07, Supplementary Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDiabetes alters the cardiac non-myocyte cellular composition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess differences in cardiac cellularity associated with diabetes-induced HF, we performed flow cytometric analysis of murine cardiac ventricles at study endpoint. Examination of metabolically active, viable single-cells (Supplementary Figure 2) revealed significant differences in the proportion of endothelial cells (ECs; 0.26-fold decrease) and resident mesenchymal cells (RMCs; 2-fold increase) indicating diabetes alters cardiac cellularity (Figure 1A-B). Conversely, leukocytes were at similar levels in ND and mice with diabetes (Figure 1A-B).\u003c/p\u003e\n\u003cp\u003eNext, we sought to validate the shifts in EC and RMC populations in diabetes observed by flow cytometry, with immunohistochemical analysis (Figure 1C-D). To achieve this, we stained left ventricular sections of both cohorts with an antibody cocktail of GATA4 and PCM1 (Figure 1C) or DACH1 (Figure 1D), which we have previously employed to quantify proportions of RMCs and ECs [12]. These analyses revealed that RMC (PCM1\u003csup\u003e-\u003c/sup\u003eGATA4\u003csup\u003e+\u003c/sup\u003e) cell counts were significantly elevated in diabetic heart sections compared to ND counterparts (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Figure 1C). Using the same approach for ECs, serial sections stained with DACH1 indicated no differences in EC abundance between experimental groups (Figure 1D), suggesting that the proportional difference observed by flow cytometry is driven by the increased RMCs.\u003c/p\u003e\n\u003cp\u003eConsidering the proportion of RMCs were markedly elevated in the diabetic heart, a range of RMC subtypes were investigated from the initial RMC gate (Supplementary Figure 2). Fibroblasts were significantly increased in diabetic hearts compared to ND (2.36-fold,\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.0001, Figure 2B). In contrast, the proportion of smooth muscle cells (SMCs), were reduced in the diabetic cohort compared to ND controls (0.27-fold,\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.05, Figure 2B). No major changes were observed in total mural cells, pericyte or Schwann cell populations (Figure 2B).\u003c/p\u003e\n\u003cp\u003eWhile we did not detect any changes in total resident leukocyte proportions in diabetic mouse hearts compared to ND (Figure 1B), diabetes has been previously associated with cardiac inflammation and systemic monocytosis [7, 17, 18]. To develop an overview of leukocyte diversity and abundance in diabetic hearts, we identified an array of leukocytes including myeloid and lymphoid cell populations and their subsets (Figure 3A). There were no differences in cardiac leukocyte subsets between cohorts, except Ly6C\u003csup\u003ehi\u003c/sup\u003e monocytes, which were significantly increased in the myocardium of mice with diabetes (1.8-fold, Figure 3B).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCirculating Ly6C\u003csup\u003ehi\u003c/sup\u003e monocytes are elevated in diabetes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo confirm systemic monocytosis, we quantified circulating leukocytes and their broad subtypes by flow cytometry. As shown previously [7][19][16], monocytes, particularly the Ly6C\u003csup\u003ehi\u003c/sup\u003e subset, were significantly elevated in the blood of mice with diabetes (2.2-fold, 2.3-fold, respectively; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 for both; Figure 4B). Numbers of circulating neutrophils and Ly6C\u003csup\u003elo\u003c/sup\u003e monocytes were also marginally elevated in diabetic mice compared to their ND counterparts (\u003cem\u003eP \u003c/em\u003e= 0.09, \u003cem\u003eP\u003c/em\u003e = 0.054 respectively; Figure 4B,). By contrast, numbers of circulating lymphocytes (B and T-cells) did not differ between cohorts (Figure 4C).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSystemic monocytosis likely occurs via extramedullary myelopoiesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo identify the potential sources of the observed monocytosis in this model, we performed flow cytometry of the bone marrow and spleen. Within the bone marrow, LSKs (haematopoietic stem and progenitor cells; [Lin\u003csup\u003e-\u003c/sup\u003eSca1\u003csup\u003e+\u003c/sup\u003ecKit\u003csup\u003e+\u003c/sup\u003e]) were significantly increased in mice with diabetes (1.8-fold, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, Figure 5A). However, bone-marrow derived common myeloid progenitors (CMP) and granulocyte-myeloid progenitors (GMP) were not different between experimental groups (Figure 5A). Monocytes (both Ly6C\u003csup\u003ehi\u003c/sup\u003e and Ly6C\u003csup\u003elo\u003c/sup\u003e) were significantly increased in the spleen in mice with diabetes compared to their ND controls (1.7-fold, 1.3-fold respectively, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, Figure 5B). These data suggest that the increased proportion of bone-marrow LSKs could be influencing these cells to mobilise to the spleen to undergo extramedullary myelopoiesis (Figure 5C).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe relationship between diabetes and HF remains poorly understood. Diabetes-associated cardiac remodelling\u0026mdash; encompassing myocyte hypertrophy, fibrosis, oxidative stress and apoptosis [13, 14] is well established. However, how the cardiac non-myocyte networks change in diabetes and contribute to this remodelling is unclear. Using a recently characterised mouse model of diabetes-induced cardiomyopathy [20], we aimed to determine how diabetes affects cardiac non-myocyte proportions and abundance. We revealed that with diabetes-associated diastolic dysfunction, proportions of cardiac fibroblasts are significantly increased in the myocardium. We also noted increased levels of Ly6C\u003csup\u003ehi\u003c/sup\u003e monocytes and decreased levels of SMCs in diabetic hearts.\u003c/p\u003e\n\u003cp\u003eNumerous studies have implicated cardiac fibroblasts in diabetic cardiomyopathy, however their precise role in diabetes \u003cem\u003ein vivo\u003c/em\u003e is still unknown. Cardiac fibroblasts are the primary cell type involved in deposition of extracellular matrix (ECM) in both states of acute injury or chronic stress [21]. However, in these contexts, fibroblast gene expression and phenotype are distinct [13]. For example, in myocardial infarction (MI), fibroblasts rapidly differentiate into activated fibroblasts and myofibroblasts\u0026mdash;both well-established drivers of cardiac ECM deposition [16, 22, 23]. Conversely, we have recently reported that myofibroblasts are absent during the development of chronic fibrosis in angiotensin II-induced cardiac remodelling [24, 25]. Observations from the present study reveal that fibroblasts are the predominant non-myocyte cell type most dramatically affected by diabetes\u0026mdash;suggesting an important role for fibroblasts in the development of diabetes-induced HF. Indeed, chronic hyperglycaemia is known to up-regulate various pro-fibrotic genes in the diabetic heart as a whole, including \u003cem\u003eCol1a1, Postn, Timp-2\u003c/em\u003e and \u003cem\u003eCcn2\u003c/em\u003e [26]. Furthermore, diabetes is associated with fibroblast-to-myofibroblast differentiation and ECM deposition [7, 8]. However, further research such as single-cell sequencing or targeted cell depletion experiments are needed to further elucidate the precise role of the cardiac fibroblast in diabetes and the regulatory mechanisms that drive these changes.\u003c/p\u003e\n\u003cp\u003eIn the current study, we also observed an increase in monocyte numbers in the heart in diabetic mice, which is likely the result of increased systemic inflammation. In the non-injured heart, circulating leukocytes, such as monocytes primarily reside in the vascular lumens of cardiac capillaries [27\u0026ndash;29], therefore reflecting changes occurring in the circulation. Systemic monocytosis is reported in both type-1 diabetes (T1D) and insulin resistant obese mice (i.e. leptin mutant \u003cem\u003eob/ob\u003c/em\u003e mice and diet-induced obese mice) [30]. Consistent with systemic inflammation, the diabetic heart exhibits upregulation of pro-inflammatory cytokines such as TNF\u0026alpha;, MCP-1 and IL-1\u0026beta; [7, 8]. Corresponding to the monocytosis, we also noted increased progenitor cells and splenic monocytes\u0026mdash;the major site of secondary myelopoiesis [31]. Monocytosis is a well-established feature of diabetes and obesity/insulin resistance [32, 33]. However, in this model we only detected a significant increase in haematopoietic stem and progenitor cells (HSPCs), but not common myeloid progenitors (CMPs) or granulocyte-macrophage progenitors (GMPs) in the bone marrow. Given HSPCs can migrate to secondary myelopoietic sites, such as the spleen, to increase monocyte numbers [34, 35], our findings suggest that this may be the primary mode of monocytosis observed in our model (Figure 5C).\u003c/p\u003e\n\u003cp\u003eIn contrast to the diabetes-induced increases in cardiac fibroblasts and monocyte numbers, we observed a decrease in SMC proportions. This was unexpected given hyperglycaemia has previously been associated with inhibition of aortic vascular SMC apoptosis in T1D patients, and in STZ-induced T1D mice [36\u0026ndash;38]. Conversely, metabolic syndrome and hypercholesterolaemia are both associated with increased apoptosis in aortic VSMCs of mice and humans [39, 40]. Therefore, the precise mechanism leading to the reduction in SMC proportions in the hearts of STZ-HFD mice warrants further investigation.\u003c/p\u003e\n\u003cp\u003eAlthough there are a number of studies examining the role of individual cardiac cell types in diabetes, to our knowledge this is the first study to consider the entire cardiac non-myocyte network to understand differences in tissue cellularity. While novel technologies such as single-cell RNA sequencing have been successfully applied to tissues such as the pancreas [41], kidney [13, 42] and liver [43, 44] in diabetes, detailed interrogation of the cellular heterogeneity in these tissue systems are lacking in this context. This study invites future research to consider cellular plasticity in diabetes to better understand the development of its associated pathologies.\u003c/p\u003e"},{"header":"Study Limitations","content":"\u003cp\u003eWhile this study provides a basis for providing new understanding of the cardiac cellular dynamics in the context of diabetes, a number of limitations are noteworthy. First, cardiomyocytes were not considered in this study, as they are too large in diameter to pass through the flow cytometer available in our laboratories. Although cardiomyocytes are detectable by histology (PCM1\u003csup\u003e+\u003c/sup\u003eGATA4\u003csup\u003e+\u003c/sup\u003e cells), they are often multi-nucleated, thus counting nuclei abundance is unlikely to yield accurate information. Second, we only examined male mice in our study. Given that cardiac pathology is sex-specific in mice [11, 12] and in humans [16, 45], cardiac cellular composition and gene expression are sexually-dimorphic [46]. Future work should examine the impact of biological sex in the development of diabetic cardiomyopathy. Third, the STZ-HFD model used in this study did not yield a population of mice with elevated fat mass and body weight as expected [47, 48]. Adiposity and obesity are important comorbidities contributing to pathology in experimental and clinical T2D [46], albeit obesity is not essential for development of T2D [47, 48]. Importantly however, in this study mice with diabetes exhibited hyperglycaemia, impaired glucose tolerance and LV diastolic dysfunction, which are clinically relevant features of HF associated with diabetes. Lastly, we were unable to ascertain whether the observed differences are attributed to the combination of STZ and HFD, or one of these individual insults. Further work using this model should consider the effect of STZ and HFD alone in addition to the combination of STZ-HFD to delineate the role of both factors in the development of diabetic HF.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHere we have profiled the differences in the cardiac non-myocyte network, observing that the cellular landscape of the heart changes in a murine model of diabetes. These results offer a framework for understanding the cellular mechanisms that may drive pathological remodelling of the heart during the development of diabetes-induced HF. Targeting the molecular pathways that drive these non-myocyte cellular changes may offer new therapeutic avenues to address the cardiac complications associated with diabetes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to acknowledge the Monash Micro-Imaging (MMI) facility for provision of instrumentation and training.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCDC, MJD, RHR, ARP, conceived the experiments, GD, MJD, AJM and MKSL provided intellectual input. CDC, ARP, MJD, GEF, CK, TLG, MCF, DP, MD performed experiments. HK, DD performed echocardiography imaging and assisted with quality control of echocardiography analysis. CDC wrote the manuscript under ARP and RHR\u0026rsquo;s guidance. RHR, ARP, MJD, GD edited the manuscript. All co-authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCDC, CK and TLG are supported by the La Trobe University Postgraduate Research Scholarship (LTUPRS), Research Training Program Fees Off-set (RTP-Fo) Scholarship. CDC is supported by a Baker Institute \u0026lsquo;Bright Sparks\u0026rsquo; Scholarship. TLG is supported by Defence Science Institute (DSI) RhD Grant. This work was supported by a project grant to RHR and MJD from the National Health and Medical Research Council (NHMRC) of Australia (APP1158013), and an NHMRC fellowship to RHR (APP1059960) and in part by an infrastructure grant from the Victorian Government of Australia. This work was also supported by an NHMRC Ideas Grant (GNT1188503) to ARP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal-related experiments were approved by the Alfred Research Alliance (ARA) Animal Ethics Committee (Ethics number: E/1681/2016/B) and were in accordance with NHMRC guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eRaghavan S, Vassy JL, Ho YL, Song RJ, Gagnon DR, Cho K, et al. Diabetes mellitus\u0026ndash;related all-cause and cardiovascular mortality in a national cohort of adults. Journal of the American Heart Association. 2019;8.\u003c/li\u003e\n\u003cli\u003eNg ACT, Delgado V, Borlaug BA, Bax JJ. 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The American Journal of Cardiology. 1972;30:595\u0026ndash;602.\u003c/li\u003e\n\u003cli\u003eRitchie RH, Dale Abel E. Basic mechanisms of diabetic heart disease. Circulation Research. 2020;126:1501\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eNagareddy PR, Murphy AJ, Stirzaker RA, Hu Y, Yu S, Miller RG, et al. Hyperglycemia promotes myelopoiesis and impairs the resolution of atherosclerosis. Cell Metabolism. 2013;17:695\u0026ndash;708.\u003c/li\u003e\n\u003cli\u003eNagareddy PR, Kraakman M, Masters SL, Stirzaker RA, Gorman DJ, Grant RW, et al. Adipose tissue macrophages promote myelopoiesis and monocytosis in obesity. Cell Metabolism. 2014;19:821\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eWong SL, Demers M, Martinod K, Gallant M, Wang Y, Goldfine AB, et al. Diabetes primes neutrophils to undergo NETosis, which impairs wound healing. 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World Journal of Diabetes. 2015;6:613.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiac cellularity, diabetes, flow cytometry, echocardiography, fibroblast","lastPublishedDoi":"10.21203/rs.3.rs-342544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-342544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eDiabetes is associated with a significantly elevated risk of cardiovascular disease and its specific pathophysiology remains unclear. Recent studies have changed our understanding of cardiac cellularity, with cellular changes accompanying diabetes yet to be examined in detail. This study aims to characterise the changes in the cardiac cellular landscape in murine diabetes to identify potential cellular protagonists in the diabetic heart.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eDiabetes was induced in male FVB/N mice by low-dose streptozotocin and a high-fat diet for 26-weeks. Cardiac function was measured by echocardiography at endpoint. Flow cytometry was performed on cardiac ventricles as well as blood, spleen, liver, and bone-marrow at endpoint from non-diabetic and diabetic mice. To validate flow cytometry results, immunofluorescence staining was conducted on left-ventricles of age-matched mice.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eMice with diabetes exhibited hyperglycaemia\u003csub\u003e \u003c/sub\u003eand impaired glucose tolerance at endpoint. Echocardiography revealed reduced E:A and e’:a’ ratios in diabetic mice indicating diastolic dysfunction. Systolic function was not different between the experimental groups. Detailed examination of cardiac cellularity found resident mesenchymal cells (RMCs) were elevated as a result of diabetes, due to a marked increase in cardiac fibroblasts, while smooth muscle cells were reduced in proportion. Moreover, we found increased levels of Ly6C\u003csup\u003ehi \u003c/sup\u003emonocytes in both the heart and in the blood. Consistent with this, the proportion of bone-marrow haematopoietic stem cells were increased in diabetic mice.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eMurine diabetes results in distinct changes in cardiac cellularity. These changes—in particular increased levels of fibroblasts—offer a framework for understanding how cardiac cellularity changes in diabetes. The results also point to new cellular mechanisms in this context, which may further aid in development of pharmacotherapies to allay the progression of cardiomyopathy associated with diabetes.\u003c/p\u003e","manuscriptTitle":"Diastolic Dysfunction in a Pre-clinical Model of Diabetes Is Associated With Changes in the Cardiac Non-myocyte Cellular Composition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-24 19:08:02","doi":"10.21203/rs.3.rs-342544/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2021-04-11T03:25:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-04-08T00:00:00+00:00","index":3,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"editorInvitedReview","content":"","date":"2021-04-01T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"editorInvitedReview","content":"","date":"2021-04-01T00:00:00+00:00","index":2,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2021-03-19T01:00:00+00:00","index":2,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-03-19T01:00:00+00:00","index":3,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-03-19T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-03-19T00:00:00+00:00","index":0,"fulltext":""},{"type":"editorAssigned","content":"","date":"2021-03-19T00:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-03-19T00:00:00+00:00","index":1,"fulltext":""},{"type":"checksComplete","content":"","date":"2021-03-18T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-03-18T23:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2021-03-18T00:43:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5999288a-7b94-4f67-8931-2e89061ff9e6","owner":[],"postedDate":"March 24th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":3187878,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2021-11-12T23:45:17+00:00","versionOfRecord":[],"versionCreatedAt":"2021-03-24 19:08:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-342544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-342544","identity":"rs-342544","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}