Insulin deficiency inhibits vaccine-mediated antibody and germinal center B-cell formation in mice

Insulin deficiency inhibits vaccine-mediated antibody and germinal center B-cell formation in mice | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Insulin deficiency inhibits vaccine-mediated antibody and germinal center B-cell formation in mice View ORCID Profile Christopher J. Genito , View ORCID Profile Pablo Ariel , View ORCID Profile Mark T. Heise , View ORCID Profile Lance R. Thurlow doi: https://doi.org/10.1101/2025.09.24.677144 Christopher J. Genito 1 Department of Biomedical Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher J. Genito For correspondence: cgenito{at}unc.edu thurlow{at}email.unc.edu Pablo Ariel 2 Department of Pathology and Laboratory Medicine, School of Medicine, University of North Carolina at Chapel Hill Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pablo Ariel Mark T. Heise 3 READDI AViDD Center, University of North Carolina at Chapel Hill 4 Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill 5 Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mark T. Heise Lance R. Thurlow 1 Department of Biomedical Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill 5 Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lance R. Thurlow For correspondence: cgenito{at}unc.edu thurlow{at}email.unc.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Individuals with diabetes are at increased risk for severe outcomes from vaccine-preventable infections and often mount weaker immune responses to vaccination. The diabetes-related factors underlying this impaired immunity remain unclear, but defining them is critical for improving vaccine strategies in this vulnerable population. Here, we isolated insulin deficiency as a contributing factor to decreased vaccine-mediated immune responses using a mouse model. Following immunization with an alum-adjuvanted protein subunit vaccine, insulin-deficient mice exhibited reduced antigen-specific IgG antibody responses, decreased B-cell and T-cell numbers, and lower germinal center B-cell counts within the vaccine-draining lymph node. Three-dimensional whole-organ light sheet microscopy combined with virtual reality-assisted analysis further revealed significantly smaller germinal center volumes in insulin-deficient mice compared with controls. These findings indicate that insulin deficiency can significantly constrain germinal center responses and impair antibody production from vaccination. Our results provide foundational evidence that diabetes-associated metabolic changes can significantly and negatively influence the quality of vaccine-induced immunity and highlight insulin deficiency as a potential physiological factor in this process. This work establishes a framework for defining the mechanisms of diabetes-related immune suppression and guiding the design of more effective vaccines tailored to the unique immunological requirements of people with diabetes. INTRODUCTION Individuals with diabetes face a significantly higher risk for hospitalization and death from vaccine-preventable infections like influenza and COVID-19( 1 , 2 ). This elevated risk underscores the importance of vaccination to protect people with diabetes. However, the protection of this vulnerable population through immunization is complicated by reduced vaccine-mediated immune responses in both children and adults with diabetes, including responses from influenza( 3 ), COVID-19( 4 , 5 ), hepatitis B( 6 ), pneumococcal( 7 ), and MMR vaccines( 8 ). These deficits include lower production of protective antibodies( 3 , 5 – 14 ), reduced antibody longevity( 4 ), and impaired cellular responses( 3 , 14 – 16 ). Although the mechanisms remain unclear, poor glycemic control has been linked to weaker vaccine responses( 3 , 16 , 17 ), and uncontrolled diabetes is associated more broadly with immune dysfunction( 18 , 19 ). Type 1 and type 2 diabetes have been associated with decreased vaccine efficacy, suggesting a potentially shared mechanism. Both type 1 and type 2 diabetes are caused by impairments in insulin signaling, whether by insulin deficiency or insulin resistance. Insulin deficiency defines type 1 diabetes and can also arise in advanced stages of type 2 diabetes. Insulin therapy can restore some immune competence( 20 ), suggesting that insulin signaling itself may be an important determinant of the immune response to vaccines. We therefore sought here to isolate the effect of insulin deficiency on vaccine responses in a defined and experimentally tractable model of uncontrolled diabetes. Antibody responses elicited by immunization are major contributors to protection against most vaccine-preventable infections and are often used as key correlates of protection( 21 ). Following immunization, activation of naïve B-cells within B-cell follicles of vaccine-draining lymph nodes (LN) leads to the formation of germinal centers (GCs). Germinal center B-cells (GCBs) seed the B-cell response that gives rise to affinity-matured protective antibodies. While evidence exists that insulin plays a role in B-cell development, few studies have explored the effect of insulin deficiency. When insulin is inhibited from binding its cellular receptor, multipotent progenitors are skewed towards myeloid rather than a lymphocyte lineage differentiation( 22 ). Insulin likely plays a role in B-cell function as well, as activated B-cells express the insulin receptor( 23 ). Downstream of insulin signaling, PI3K/Akt activation and glucose transporter 1 (GLUT1) expression play important roles in B-cell activation( 24 ). Considering these findings, how impaired insulin signaling impacts B-cell activation and antibody production from vaccination remains a key unknown factor. A better understanding of the interplay between insulin deficiency and B-cell responses after immunization would be beneficial to inform vaccination strategies that may increase protection of people with diabetes from severe infections. Current strategies for subunit vaccines in clinical use and in preclinical development typically consist of a protein antigen in combination with an adjuvant to increase the strength of the immune response. For example, multiple COVID-19 vaccines are comprised of recombinant antigens based on the SARS-CoV-2 spike protein and the adjuvant alum( 25 ). Vaccine adjuvants, including alum, can enhance antibody responses to vaccines and facilitate the development of GCBs( 26 ). However, there is limited clinical evidence detailing how effective these vaccine strategies are specifically for people with diabetes, or how diabetes-related immune suppression or insulin deficiency affect immune responses to adjuvanted vaccines. In the present study, we evaluated the ability of an alum-adjuvanted subunit vaccine to induce antibodies and GCB responses in insulin-deficient mice. Alum was the first FDA-approved adjuvant and currently remains one of the most used vaccine adjuvants. We used the well-characterized model protein antigen vaccine, ovalbumin (Ova), to test the impact of insulin deficiency on immune responses. We also used the streptozotocin (STZ) model of insulin-deficiency in mice, which specifically oblates the insulin-producing pancreatic β cells. Using the STZ model allowed us to isolate the effects of insulin deficiency in the context of an immune system that is otherwise free of genetic or confounding metabolic defects. Mouse models also allow the enumeration and spatial analysis of GCBs within the vaccine-draining LN at a level of detail that is not feasible for study in humans. We used novel light sheet microscopy and virtual reality-based three-dimensional analysis to ensure the enumeration and volumetric determination of all GCs within the entirety of the lymph node. This modeled approach to vaccination in the context of diabetes led to several key findings of how insulin deficiency affects immune cell, GC, and antibody levels following vaccination. We found that insulin-deficient mice were almost completely unable to produce antibodies in response to vaccination. This correlated with a defect in GCB formation and smaller GC volume in the vaccine-draining lymph nodes of insulin-deficient animals following immunization. Our study demonstrates a clear connection between insulin deficiency and decreased antibody responses from vaccination. These findings are foundational to an understanding of how diabetes affects the immune response to vaccination. METHODS Animals Male and female C57BL/6J mice (The Jackson Laboratory) were used in this study under an IACUC-approved protocol and housed in an AAALAC-accredited facility. Vaccine preparation and administration Vaccines were formulated using ovalbumin (Ova) protein purified of endotoxin (Worthington Biochemical Corporation) and alum adjuvant in the form of 2% aluminum hydroxide gel (Alhydrogel, InvivoGen). Immediately before immunization, Ova was combined with alum in PBS to a final concentration of 40 µg/mL Ova and 1% alum. Mice were immunized once intramuscularly in the hind limb (gastrocnemius muscle) with 25 µL, corresponding to 1 µg Ova. Generation of insulin-deficient mice Mice were made insulin deficient by administration of streptozotocin (STZ). STZ was dissolved in sodium citrate buffer (0.1 M, pH 4) and injected peritoneally each day for 5 days consecutively at 90 mg/kg for female mice and 65 mg/kg for male mice. Mice were then given 7 days for diabetes onset before vaccination. Diabetes was confirmed if the blood glucose of each mouse reached ≥ 300 mg/dL before the day of immunization; otherwise, these mice were excluded from the study. ELISA Serum from mice was collected two weeks after immunization and analyzed for Ova-specific IgG antibody responses by ELISA. High-binding ELISA microplates (Grenier Bio-One) were coated with 500 ng/well Ova protein overnight at 4°C and then blocked for 1 h with dilution buffer (5% milk in PBS with 0.05% Tween20). Serum samples were diluted 20-fold in dilution buffer with additional 3-fold serial dilutions and added to wells for 2 h. HRP-conjugated goat anti-IgG (H+L, human adsorbed, SouthernBiotech) was added at a 1:1000 dilution for 1 h. Plates were washed between each step three times with PBS + 0.05% Tween20, and then finally developed with 3,3’,5,5’-tetramethylbenzidine for 30 min. Developing was stopped with 50 uL 1M H 2 SO 4 . Optical density (OD) of developed plates was read at 450 nm with 570 nm background correction. Titrations of OD vs. dilution were fit using GraphPad Prism 10 nonlinear fit (variable with four parameters). The background (bottom parameter) was set to the average value of negative control wells from the ELISA where no serum was added. Antibody titers were determined to be the interpolated dilution at which OD = 1. A positive control sample was included in each assay to account for day-to-day variation. Seroconversion was considered at any titer over the limit of detection (20, corresponding with the initial 20-fold sample dilution). Area under the curve (AUC) was also calculated for ELISA titration curves using Prism 10. Spectral flow cytometry Inguinal lymph nodes and spleens were removed from mice two weeks after immunization and processed for analysis by spectral flow cytometry. Tissues were collected into pre-weighed tubes with RPMI media supplemented with L-glutamine, 25 mM HEPES, 10% FBS, and 1% penicillin/streptomycin on ice. Tissues were then pressed through a 70 µm nylon filter. Spleen samples were treated for 2 min with ACK red blood cell lysis buffer at room temperature. Cells were then pelleted at 500 xg for 5 min, resuspended in media, and passed through an additional 40 µm nylon filter. A subset of isolated cells were stained with trypan blue and live cells were counted with a hemocytometer. Cells were stained on ice with viability dye (50 ng/mL Pacific Blue-conjugated succinimidyl ester, Life Technologies) in PBS for 20 min, washed with flow cytometry staining buffer (Invitrogen), and blocked with TruStain FcX (BioLegend). Fluorescent antibody stain was performed for 30 min (antibodies are listed in Table S1 ) and cells were then fixed with BD Cytofix for 30 min. Cells were analyzed using a Cytek Aurora spectral flow cytometer with spectral unmixing performed using SpectroFlo software (Cytek). Gating and population analysis was performed using FCS Express 7 (De Novo Software). Light sheet microscopy Draining lymph nodes were fixed in 4% paraformaldehyde in PBS overnight at 4°C, then prepped, stained, and cleared for three-dimensional imaging using the Adipo-Clear method( 27 ), including embedding in 1% agarose. Antibodies used for staining are listed in Table S1 . Samples were imaged using a light sheet microscope (LaVision BioTech UltraMicroscope II, Miltenyi Biotec) with immersion in dibenzyl ether. Emission filters (for lasers) were Chroma ET525/50m (488 nm), ET600/50m (561 nm), and ET690/50m (647 nm). Images were acquired using an Olympus MVPLAPO 2X/0.5 objective (with corrected dipping cap, 6 mm working distance) coupled to a zoom body and an Andor Zyla 5.5 sCMOS camera. Imaging parameter details are listed in Table S2 . The 488 nm laser channel was used to capture autofluorescence. Three-dimensional image analysis in virtual reality Lymph nodes were virtually dissected from the surrounding adipose tissue by manually drawing a boundary at the 2D interface in XY planes and compiling a 3D surface using Imaris 10.2 (Oxford Instruments). The dissected LNs were then imported into syGlass v2.1 immersive virtual reality software operated using Meta Quest 3 mixed reality headset. Germinal centers were identified as GL7 + cells within follicles of B220 + or IgD + cells using the “average” projection. GC borders were established by manually setting a threshold which clearly separated the GC from background fluorescence in the GL7 channel (detailed settings in Table S3 ). GCs were masked in syGlass and masks were then imported into Imaris for volume determination. Volume was determined using the continuous border around identified GCs. Statistical analysis Statistical analyses were performed using GraphPad Prism 10. Antibody titers, cell counts, and GC volumes were log-transformed before statistical analysis. Comparisons between three or more groups were corrected for multiple comparisons as denoted in figure legends. Preliminary power analysis determined group sizes of n=10 had power of >0.8 to determine a 2.5-fold difference in antibody titer and n=4 had >0.8 power to determine 2-fold differences in germinal center volume by light sheet microscopy. RESULTS Minimal antibodies are elicited after vaccination in insulin-deficient mice Insulin deficiency was modeled in mice to determine its effect on immunological outcomes from vaccination. Mice were made deficient in insulin production via STZ administration and immunized intramuscularly one week later with model vaccine antigen Ova and clinically relevant vaccine adjuvant alum ( Fig. 1A ). Two weeks after immunization, Ova-specific serum antibodies were measured by ELISA. Both titer ( Fig. 1B ) and area under the curve analysis of ELISA readouts (AUC, Fig. S1 ) showed that control animals reached significantly higher antibody responses than both insulin-deficient and vaccine-naïve animals. Insulin-deficient animals did not achieve anti-Ova IgG titers or ELISA AUC values that were significantly higher than naïve animals. In fact, only 2 of 13 (15%) of insulin-deficient mice displayed antibody seroconversion to the vaccine, defined by an IgG titer above the limit of detection ( Fig. 1C ). In contrast, 14 of 15 (93%) of control mice displayed seroconversion. We concluded that insulin-deficient mice were unable to form significant levels of antigen-specific IgG in response to an alum-adjuvanted vaccine. Download figure Open in new tab Figure 1. Antigen-specific IgG antibody levels in insulin-deficient mice after immunization with alum-adjuvanted vaccine. (A) Mice were made insulin-deficient using streptozotocin (STZ) and immunized with ovalbumin (Ova) adjuvanted with alum. Created in BioRender. Genito, C. ( 2025 ) https://BioRender.com/xs9cetx . (B) Two weeks after immunization, Ova-specific IgG antibody titers were quantified by ELISA for control (Ctrl; n = 15), STZ-treated (n = 13), and naïve (n = 16) mice. Data is presented as quartiles with a line at the median. Statistical comparisons were made using nonparametric Kruskal-Wallis test with Dunn’s correction for multiple comparisons. (C) Percentage of mice in each group that displayed seroconversion to the vaccine, defined as an IgG ELISA titer above the limit of detection. Statistical comparisons were made using Fisher’s exact test with Bonferroni’s correction for multiple comparisons. *** p < 0.001, **** p < 0.0001, ns = not significant. Data is pooled from three independent experiments. Low lymphocyte numbers are observed within lymphoid tissue after vaccination in insulin-deficient mice Vaccination with alum adjuvant causes swelling associated with responding immune cells proliferating in the draining lymph node (LN)( 28 ). We observed >2-fold smaller LN diameter from insulin-deficient mice vaccinated with Ova+alum compared to control animals ( p < 0.0001, Fig. 2AB ). In control animals, there was ∼3-fold increase ( p = 0.001) in the number of live cells in the LN after vaccination compared to naïve animals ( Fig. 2C ). In contrast, this number was slightly decreased in vaccinated insulin-deficient animals. We did not observe significant increases in weight or cellularity in spleens after vaccination, but there was a trend for fewer live cells and decreased weight for spleens from vaccinated insulin-deficient animals compared to vaccinated controls ( Fig. S2 ). Download figure Open in new tab Figure 2. Draining lymph node size is decreased in vaccinated insulin-deficient mice. Draining inguinal lymph nodes (LN) were removed 2 weeks after vaccination with ovalbumin adjuvanted with alum from mice made insulin-deficient through streptozotocin administration (STZ) or from control animals (Ctrl). (A) Representative images of removed LNs. Scale bar = 3 mm. (B) LN size is quantified by measuring the semi-major elliptical axis (largest diameter) of each LN. Data are represented by mean + 95% CI and statistically compared by Student’s t -test (Ctrl n = 7, STZ n = 5). (C) Cells were isolated from LNs and live cell counts were determined by staining with trypan blue for Ctrl (naïve n = 13, vaccinated n = 11) and STZ-treated (naïve n = 10, vaccinated n = 9) mice. Data are represented by geometric mean + 95% CI and statistically compared by ANOVA with Tukey’s correction for multiple comparisons. * p < 0.05, ** p < 0.01, **** p < 0.0001 for all relevant comparisons. Data was pooled from 3 independent experiments. The number of immune cells quantified in the vaccine-draining LN were consistent with the overall cell counts: increased in control animals and slightly decreased in insulin-deficient animals after vaccination ( Fig. 2C , Fig. 3A ). Both B-cells and T-cells, the largest immune cell general subpopulations, were proportional in number to the total immune cell count in draining LNs ( Fig. 3BC , Fig. S3 ). There were no differences in B-cell or T-cell frequencies among total immune cells in the LN, signifying that there was no cell type-specific effect ( Fig. 3DE ). Total immune cell counts in the spleen largely reflected the total number of live cells ( Fig. S2 , Fig. S4A ). However, T-cells were more affected than B-cells by insulin deficiency in both number and frequency within the spleen ( Fig. S4BCDE ). Overall, it appeared that lymphocyte numbers in the relevant lymphoid organs were low after vaccination in insulin-deficient animals, potentially contracting in vaccine-draining LNs. This was consistent with significantly higher frequencies of dead immune cell populations within the LNs of vaccinated insulin-deficient mice ( Fig. S5A ). Download figure Open in new tab Figure 3. Lower immune cell counts in draining lymph nodes after vaccination in insulin-deficient mice. Total immune cell (A) , total B-cell (B) , and total T-cell (C) counts, as well as B-cell (D) and T-cell (E) frequencies among total immune cells, were quantified by flow cytometry in vaccine-draining inguinal lymph nodes 2 weeks after vaccination with ovalbumin adjuvanted with alum for insulin-deficient (STZ, naïve n = 10, vaccinated n = 9) and control (Ctrl, naïve n = 13, vaccinated n = 11) mice. Data is represented as geometric mean + 95% CI. Statistical comparisons were made using ANOVA with Tukey’s correction for multiple comparisons. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. Data was pooled from 3 independent experiments. To test if observed decreases in immune cells of insulin-deficient mice was due to STZ-related toxicity, we isolated immune cells from the spleens of naïve control animals and incubated them with a range of doses extending well above published plasma concentrations reported for STZ administration in mice( 29 ) ( Fig. S5B ). Concentrations up to 2-fold higher than the highest reported STZ plasma concentration did not affect the viability of splenocytes after 24 h of co-incubation. Indeed, toxicity at >150-fold higher than the highest reported plasma concentration was only moderate (∼30%). Importantly, the maximum plasma concentration in mice was reported for a 200 mg/kg injection of STZ, which was 2-3-fold higher than the injection doses used in this study. There were also no differences in the frequency of dead cells in the LNs of naïve STZ-treated animals compared to naïve controls ( Fig. S5A ). Overall, we concluded that decreases in cell counts observed in insulin-deficient mice were unlikely to be due to direct STZ-related toxicity on immune cells. Germinal center formation in response to vaccination is severely impacted in insulin-deficient mice The frequency and number of germinal center B-cells (GCBs) within the draining LN were significantly increased 2 weeks after vaccination in control animals ( Fig. 4 ). In stark contrast, the number nor frequency of GCBs were increased after vaccination in insulin-deficient mice, remaining at the level of naïve animals. Similar trends were observed in the spleen ( Fig. S6 ). Download figure Open in new tab Figure 4. Lower germinal center B-cell (GCB) counts after vaccination in insulin-deficient mice. GCB cell counts (A) and GCB frequency among B-cells (B) , were quantified by flow cytometry in vaccine-draining inguinal lymph nodes (LN) 2 weeks after vaccination with ovalbumin adjuvanted with alum for insulin-deficient (STZ, naïve n = 10, vaccinated n = 9) and control (Ctrl, naïve n = 13, vaccinated n = 11) mice. Data is represented as geometric mean + 95% CI. Statistical comparisons were made using ANOVA with Tukey’s correction for multiple comparisons. ** p < 0.01, **** p < 0.0001. Data was pooled from 3 independent experiments. Given no detectable increase in GCB number, we hypothesized that germinal centers were not being formed within the draining LN of insulin-deficient mice following vaccination. We used light-sheet microscopy to identify and accurately count any GCs within the whole draining LN two weeks after vaccination ( Fig. 5 ). GCs were able to be identified as GL7 + clusters of cells within B-cell follicles identified by IgD or B220 markers ( Fig. S7 ). Light sheet microscopy allowed the construction of three-dimensional images within virtual reality-based software (syGlass). The three-dimensional borders of each GC were determined within the virtual reality environment for volume determination ( Fig. 6 , Video 1, Video 2 ). Surprisingly, we detected a similar number of GCs within the LNs of vaccinated insulin-deficient mice and vaccinated controls ( Fig. 6ABC ). However, GCs within insulin-deficient mice had significantly and drastically less volume ( Fig. 6DE ). The median GC volume was 10-fold less in the lymph nodes of insulin-deficient mice following vaccination compared to controls. We therefore concluded that germinal center formation in response to vaccination is severely impacted in insulin-deficient mice. Download figure Open in new tab Figure 5. Analysis of germinal centers by light sheet microscopy. (A) Three-dimensional rendering of draining inguinal lymph node (LN) 2 weeks after vaccination with ovalbumin adjuvanted with alum, imaged with light sheet microscopy (markers: red channel = B220, green channel = GL7; white channel = autofluorescence). (B) After manual identification of LN surface, the LN was virtually dissected from the surrounding adipose and connective tissue. (C) Representative two-dimensional slice of the 3D rendering in panel B. Germinal centers (GCs, green arrow) were identified as GL7 + clusters of cells within B220 + B-cell follicles (red arrow) within the LN. (D) Boundaries of identified GCs were established in virtual reality (syGlass) in the GL7 channel against a threshold background fluorescence. Cells included in the GC boundary are shown in yellow. (E) GC boundaries established in syGlass were imported into Imaris and processed as GC surfaces (yellow outline). The red outline denotes the LN surface. (F) Each opaque object (multicolor) is a 3D representation of a detected germinal center within the LN (translucent red outline). Scale bars represent 200 μm. Download figure Open in new tab Figure 6. Analysis of germinal center size after immunization in insulin-deficient mice. Germinal centers (GCs) within draining inguinal lymph nodes (LN) were analyzed by light sheet microscopy 2 weeks after vaccination with ovalbumin and alum adjuvant in mice made insulin deficient by administration of streptozotocin (STZ). (A,B) Representative 3D images of LNs (red outline) and GCs (multicolor, opaque) are shown for control (Ctrl) and STZ-treated mice. (C) The total number of GCs in each lymph node (n = 4 LNs per group). Data are represented as mean±SD. (D,E) Cumulative size distribution of GC volumes (Ctrl, n = 74; STZ, n = 59). Data is represented as quartiles with line at median (D) and a frequency of distributions (E). **** p < 0.0001, Kolmogorov-Smirnov non-parametric test for cumulative distributions. Data was pooled from 4 independent experiments. Scale bar = 200 μm. DISCUSSION Poorly controlled diabetes increases the risk of severe outcomes from infection( 1 , 2 ) and has also been associated with poorer immune responses to vaccination to protect from these infections( 3 , 4 , 6 – 17 ). Our study expands on findings by others that show a deleterious effect of insulin deficiency on antibody formation from vaccination( 30 – 33 ). Here, we demonstrate how insulin deficiency profoundly alters the GCB response to vaccination ( Fig. 4 ) as well as structural GC responses using advanced whole-LN imaging ( Fig. 5 , Fig. 6 ). Quantifying GCBs by flow cytometry or immunofluorescence is straightforward, but these approaches provide limited information on GC architecture, especially at the whole LN scale. Conventional 2D histology introduces variability, as only a few sections from a larger 3D volume are analyzed. Confocal z-stacks, while enabling 3D reconstructions, can only do so for relatively small volumes, and are very labor-intensive for whole-LN analyses. By contrast, light-sheet microscopy permits rapid and unbiased volumetric imaging of all GCs within an LN. Although this approach has been applied in other contexts, its use in diabetes research has been limited. This is particularly relevant because diabetes-associated microvascular changes may alter LN architecture and GC organization, making whole-LN 3D analysis critical for detecting differences in GC number and volume that 2D sampling might miss. Moreover, because GC boundaries are highly dynamic and not sharply defined, immersive virtual reality-assisted segmentation can accelerate boundary detection and spatial mapping compared to conventional tools. Using this approach, we found that vaccinated insulin-deficient mice generated similar numbers of GCs in vaccine-draining LNs compared to controls ( Fig. 6C ), but they were 10-times smaller ( Fig. 6DE ), in agreement with 10-fold lower GCB numbers ( Fig. 4A ). In insulin-deficient mice, GCB numbers did not increase with vaccination ( Fig. 4A ), suggesting that without insulin, a critical growth factor, the proliferative potential of GCBs and expansion of the GC may be impaired. Reduced LN size ( Fig. 2 , Fig. 6 ) may also impose spatial limitations on GC expansion. Together, these data demonstrate that insulin deficiency constrains GC formation at both the cellular and structural level, revealing a previously unexplored mechanism by which impaired metabolic control may compromise vaccine responsiveness. Our study importantly incorporates clinically relevant contexts by using a vaccine formulated with alum and administered through the intramuscular route, consistent with many licensed human vaccines. Despite this, insulin-deficient mice mounted weaker antibody and GCB responses ( Fig. 1 , Fig. 4 , Fig. 6 ), indicating that standard vaccination strategies may be insufficient under conditions of metabolic dysregulation. Some clinical analyses of “simple” subunit vaccines without adjuvants show comparable immune responses elicited in people with or without diabetes( 14 , 34 , 35 ). However, more advanced vaccine platforms, including adjuvanted protein subunit and mRNA vaccines, have repeatedly revealed impaired immunogenicity in people with diabetes( 4 , 5 , 14 ). These discrepancies raise the critical question of whether platforms that enhance responses in the general population are equally effective in individuals with diabetes. Given that people with diabetes consistently experience worse outcomes following infection, developing vaccines that are specifically effective in this population is of pressing importance. Using a model of STZ-induced diabetes in mice is advantageous to isolate the effects of insulin deficiency on the immune system. Other mouse models contain confounding immunological factors, like autoimmune defects in the non-obese diabetic (NOD) mice, or obesity-related or other hormone-related ( e.g. , leptin) effects in other mouse models of diabetes. While insulin deficiency specifically impairs immune function in the STZ model( 36 ), it has been historically suspected that STZ-related toxicity may also impact long-term splenic, thymic, and bone marrow immune cell viability in mice. In our model, mice were given a low dose of STZ to reduce the chance of toxicity outside of pancreatic β cells. STZ was not directly toxic to splenic immune cells at physiological doses ( Fig. S5B ). We also did not observe any significant decreases in live B-cell and T-cell populations or increases in dead immune cells within the LNs of naïve STZ-treated mice ( Fig. 3 , Fig. S5A ). Vaccines were given a week after the last STZ dose to ensure STZ presence did not influence immune cell activation by immunization. STZ is rapidly cleared by the kidneys and has a plasma half-life of less than 10 min in rodents( 37 ). Our study therefore did not contain evidence to directly link STZ toxicity to the observed deficiencies in the immune response to vaccination. Nonetheless, a partial effect of direct STZ action on immune cells was not definitively ruled out. Sufficient insulin signaling could be directly and/or indirectly required for B-cell activation. While insulin signaling has direct cellular involvement in T-cell expansion, metabolism, chemotaxis, and effector functions, the direct activity of insulin in B-cell activation and development remains unclear( 24 ). Here, we observed lower cell counts in both the T-cell and B-cell populations within the vaccine-draining LN after immunization in insulin-deficient mice compared to controls ( Fig. 3 ). Cell-to-cell contact and cytokine production from T-cells, follicular dendritic cells, and other immune cells are important for GCB development within lymphoid tissues. In particular, T follicular helper cells (TFHs) are crucial mediators of GCB survival, activation, and development. Functional inhibition of TFHs could very well affect GCB responses. However, whether TFHs rely on insulin signaling for their function to the same extent as conventional T-cells remains undescribed by established studies. Another important consideration is that insulin may support immune function by preserving a favorable milieu for immune cell activation. Systemic physiological changes incurred by insulin deficiency potentially have significant effects on immune function in response to vaccination. For example, hyperglycemia can impair T-cell function including cellular trafficking, control of infection, and memory responses( 38 ), though the specific effect of hyperglycemia on B-cells is much less described. The interaction of insulin deficiency and the immune response to vaccines is likely multifaceted, and further study would be needed to elucidate the role of insulin signaling in immune cell functions, intercellular interactions, and maintaining a favorable metabolic environment for vaccine-mediated immunity. Because it isolates insulin deficiency as a factor, our model therefore did not explore how other factors and co-morbidities related to diabetes like obesity, cardiovascular disease, and impaired wound healing may impact vaccine effectiveness. However, using this focused model demonstrated that insulin deficiency can potentially have a profound effect on GCB responses and antibody formation from vaccination. The overall finding of this study is that the ability of an alum-adjuvanted protein subunit vaccine to elicit antibody responses was markedly impaired in a mouse model of insulin deficiency. In particular, both GCB numbers and GC volume were reduced in vaccine-draining lymph nodes. Importantly, our work establishes a virtual reality-assisted whole-LN approach for identifying and analyzing GCs in three dimensions, providing a powerful tool for investigating how diabetes and other diseases alter spatial immunological processes within lymphoid tissues. This study is a step to understanding the role of insulin signaling in acquired immunity and gives context to future study on disease states where insulin signaling is impaired through resistance, most pertinent in type 2 diabetes and obesity. It nonetheless remains unclear how insulin resistance may specifically affect immune responses to vaccination and to what extent it might differ from insulin deficiency. However, these findings highlight insulin deficiency as a potential contributor to the defective vaccine responses observed in people with diabetes and underscore the need to define how diabetes-specific physiological factors shape vaccine-induced immunity. Elucidating the mechanisms underlying impaired GC formation and antibody production will be essential for optimizing vaccine design and tailoring immunization strategies for this vulnerable population. Such efforts may inform adjuvant selection, dosing strategies, or even the development of next-generation vaccines that account for the unique immunological considerations of people with diabetes. Author contributions CJG, MTH, and LRT designed the studies. CJG and PA designed the data analysis methods. CJG carried out the experiments and analyzed the data. CJG and LRT wrote the original draft of the manuscript. All authors discussed the results and prepared the final manuscript. Guarantor statement LRT and CJG are the guarantors of this work and, as such, have full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Conflict of interest statement The authors report no conflicts of interest. Funding NIH NIAID R01 AI173004 (L.R.T.). The UNC Flow Cytometry Core Facility (RRID:SCR_019170) is supported in part by P30 CA016086 Cancer Center Core Support Grant to the UNC Lineberger Comprehensive Cancer Center. The Microscopy Services Laboratory, Department of Pathology and Laboratory Medicine, is supported in part by P30 CA016086 Cancer Center Core Support Grant to the UNC Lineberger Comprehensive Cancer Center. Research reported in this publication was supported in part by the North Carolina Biotech Center Institutional Support Grant 2016-IDG-1016. FIGURE LEGENDS Video 1. 3D analysis of germinal centers in draining inguinal lymph node from vaccinated control mouse. Video 2. 3D analysis of germinal centers in draining inguinal lymph node from vaccinated insulin-deficient mouse. ACKNOWLEDGMENTS We would like to acknowledge Dr. Ramiro Diz and the UNC Chapel Hill Flow Cytometry Core Facility; the UNC Chapel Hill Respiratory TRACTS Core Laboratory; Dr. Jake Dillard, Dr. Caitlyn Molloy, Dr. Nancie MacIver, and the MacIver Laboratory at UNC Chapel Hill; Nathan Spencer at syGlass, Inc.; and Benjamin Darwitz and the Thurlow Laboratory for advice and technical support. Graphical abstract: Created in BioRender. Genito, C. ( 2025 ) https://BioRender.com/zy9qgfb . Funder Information Declared National Institute of Allergy and Infectious Diseases , R01 AI173004 National Cancer Institute , P30 CA016086 North Carolina Biotechnology Center , 2016-IDG-1016 Footnotes https://doi.org/10.5281/zenodo.17155425 REFERENCES 1. ↵ Allard R , Leclerc P , Tremblay C , Tannenbaum TN . Diabetes and the Severity of Pandemic Influenza A (H1N1) Infection . 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