Noninvasive Imaging of Myeloid Cell Dynamics: GPR84 PET Differentiates EAE and Tracks Therapy Response

Noninvasive Imaging of Myeloid Cell Dynamics: GPR84 PET Differentiates EAE and Tracks Therapy Response | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Noninvasive Imaging of Myeloid Cell Dynamics: GPR84 PET Differentiates EAE and Tracks Therapy Response Samantha T. Reyes, Renesmee C. Kuo, Isaac M. Jackson, Mausam Kalita, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9374449/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Multiple sclerosis is a disabling immune-mediated neurological disorder characterized by demyelinating lesions in the central nervous system (CNS) that drive progressive motor dysfunction, cognitive decline, and vision loss, and is the leading cause of non-traumatic disability in young adults. Pro-inflammatory myeloid cells, including microglia and macrophages, are abundant innate immune infiltrates in active lesions and are key mediators of disease onset and progression, making them attractive targets for non-invasive imaging. GPR84 is an immune-metabolic G protein-coupled receptor that is specifically expressed on myeloid cells with low basal expression in healthy tissue, involved in macrophage/microglia response to inflammation, and is upregulated in an inflammatory environment. This makes it a promising target for imaging innate immune activation via positron emission tomography (PET). We identified GPR84 as a biomarker of innate immune activation in EAE mice using bulk qPCR quantification. Subsequently, we developed 11 C-MGX-38 and evaluated it alongside 18 F-MGX-110S to assess their ability to detect innate immune activation in EAE mice and monitor therapeutic response to fingolimod. GPR84-PET demonstrated robust signal increases in the CNS of EAE mice compared to controls. Both radiotracers successfully detected treatment response in fingolimod-treated mice compared to vehicle-treated mice, with the fluorinated radiotracer showing higher sensitivity for delineating both disease severity and alterations in peripheral signal. These findings indicate that GPR84-PET is a promising method for non-invasive, cell-specific monitoring of innate immune activation in EAE mice, with 18 F-MGX-110S emerging as the leading candidate for potential clinical translation and possible applications in MS patient stratification, therapeutic monitoring, and drug development due to its superior sensitivity and half-life. multiple sclerosis EAE PET imaging GPR84 innate immune activation treatment response myeloid cell Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Multiple sclerosis (MS) is a lifelong, immune-mediated neurological disorder in which demyelinating CNS lesions drive progressive motor dysfunction, cognitive decline, and vision loss [ 1 ]. MS is typically diagnosed in young adults between ages 15–45 [ 2 ], disproportionally affects females, and substantially impairs quality of life for patients, families, and caregivers; in fact, MS is the leading cause of non-traumatic disability [ 1 , 3 ]. Approximately 85% of MS patients present with relapsing-remitting MS (RR-MS), which is characterized by periods of disease progression followed by recovery whilst the remaining 15% are diagnosed with either primary progressive (PP-MS) or progressive-relapsing (PR-MS) MS, which both lack periods of recovery [ 1 ]. Many disease-modifying therapies (DMTs) have been developed for MS that can modify or slow clinical disease progression [ 4 ]; however, no tools exist to identify the pathogenic cells (e.g., myeloid cells, B cells, and T cells) that drive disease progression. The ability to do so would help expedite therapy selection and enable real-time efficacy monitoring. Microglia and macrophages have been identified as the predominant pathogenic cells in MS lesions within the CNS [ 5 , 6 ]. Under homeostatic conditions, microglia, the resident immune cells of the brain, protect the brain by clearing debris, pruning synapses, and promoting myelin repair. Macrophages, in contrast, are peripheral immune cells not normally found in the brain. In a pro-inflammatory immune environment, maladaptive immune responses, including macrophage egress and infiltration into the CNS, contribute to pathological damage in MS. Although the role of the adaptive immune system in MS pathogenesis is undeniable, with auto-reactive B and T cell infiltration promoting degeneration and demyelination [ 6 – 8 ], a growing body of evidence supports that the innate immune system is significantly implicated in MS pathophysiology across all disease types [ 9 ]. Myeloid cells (i.e., dendritic cells, macrophages, microglia, monocytes and neutrophils) are innate immune cells and central mediators of MS progression and remission [ 9 ], with reactive macrophages and microglia constituting the dominant pro-inflammatory innate immune infiltrates in the CNS of both acute and chronic active states of MS [ 9 , 10 ]. Clinically, microglial activation has been shown to occur in pre-active MS lesions and in normal-appearing white matter both during and preceding leukocyte infiltration into the CNS [ 11 ]. A murine model of MS, experimental autoimmune encephalomyelitis (EAE), demonstrates infiltration of activated macrophages in the CNS, predominantly in the spinal cord, recapitulating the infiltration seen in post-mortem CNS tissue from MS patients [ 12 ]. Studies in EAE mice have also shown that depleting myeloid cells or preventing infiltration prevents or delays disease onset [ 13 ]. Together, these studies highlight that pro-inflammatory myeloid cells are key players in MS disease onset and progression, and have potential to serve as clinically meaningful biomarkers in MS. Since activated macrophages and microglia emerge early in MS, persist throughout disease progression, and critically influence both progression and remission, they are an attractive target for non-invasive imaging to enhance patient stratification, disease staging, therapeutic monitoring, and targeted drug development. Innate immune activation encompasses highly dynamic processes with both protective and harmful responses exhibiting diverse spatial and temporal patterns. A noninvasive imaging tool for innate immune responses is essential to define disease-specific molecular signatures, as these responses vary across pathological contexts. Current clinical standard practices for MS diagnosis and monitoring include blood tests such as erythrocyte sedimentation rate and vitamin D, cerebrospinal fluid (CSF) analysis to check for the presence of oligoclonal bands, IgG index, and myelin basic protein, and magnetic resonance imaging (MRI) with and without contrast to identify lesion presence and monitor activity over time [ 14 , 15 ]. While these methods provide clinically validated information relating to MS, they do not provide molecular information in the CNS and do not reveal the extent of innate immune activation in both the CNS and throughout the whole body in living subjects. This knowledge gap provides an opportunity for developing new approaches to understand the molecular signature of innate immune activation in MS and how DMTs can dampen activated cells. Positron emission tomography (PET) imaging allows for spatial-temporal imaging of biomarkers in the whole body of living subjects and can enable longitudinal whole-body monitoring of pro-inflammatory myeloid cells. The most widely evaluated PET radiotracer for imaging neuroinflammation is translocator protein 18 kDa (TSPO). TSPO is located on the outer mitochondrial membrane and is involved in modulating mitochondrial bioenergetics through regulating calcium signaling, membrane potential, production of reactive oxygen species, and mitophagy – all processes essential for cell survival and inflammatory responses [ 16 , 17 ]. Although TSPO is intimately associated with inflammation, it lacks specificity to innate immune cells, as it is present on many cell types including microglia, astrocytes, endothelial cells, macrophages, skeletal muscle cells, and kidney cells. Additionally, TSPO expression does not change per myeloid cell under inflammatory conditions, but rather, numbers of TSPO-expressing cells are altered in regions of injury/pathology compared to baseline healthy tissue. TSPO thus cannot provide information on the functional phenotype (e.g., activation status) of individual myeloid cells, but instead provides an indication of where TSPO expressing cells are located [ 18 , 19 ]. TSPO-PET has been used to evaluate several inflammation-related diseases both clinically and preclinically. Clinical TSPO-PET imaging of patients with inflammatory disease such as Alzheimer’s Disease [ 20 ], MS [ 21 , 22 ], concussion [ 23 ] and long COVID [ 24 ]. Preclinically, TSPO-PET has been used extensively to investigate neuroinflammatory models like rodent models of MS [ 25 ] and AD [ 26 ], and infection [ 27 ]. While TSPO-PET imaging studies have provided robust information about the density and location of TSPO expressing cells, as well as mitochondrial bioenergetics, this imaging tool lacks the cell specificity required to identify pathogenic myeloid cell populations. There is thus a need to develop cell-specific PET radiotracers to specifically interrogate various cell populations in the context of inflammation. Our lab has identified G protein-coupled receptor 84 (GPR84), an immune metabolic G-protein coupled receptor (GPCR) that is specifically and significantly induced on myeloid cells (including microglia, macrophages, monocytes and neutrophils) under pro-inflammatory conditions and exhibits minimal expression in healthy tissue. GPR84 potentiates inflammation through multiple pathways (Scheme 1). Unlike TSPO, GPR84 provides superior cellular specificity for activated myeloid cells, making it a potentially suitable target for imaging pro-inflammatory innate immune responses [ 28 ] with improved interpretability. In this study, we characterize GPR84 as a biomarker of disease-associated myeloid cells in EAE mice using both bulk RNA quantification and qPCR. We also evaluate our newly developed GPR84-specific radiotracers 11 C-MGX-38 and 18 F-MGX-110S for their ability to detect innate immune activation, as well as monitor therapeutic response to an FDA-approved DMT used for treatment of MS, fingolimod (FTY720, Gilenya), using the mouse EAE model of MS. We demonstrate that GPR84-PET shows a robust increase in CNS signal in EAE mice compared to controls. It also detects treatment response in fingolimod-treated EAE mice versus vehicle-treated EAE mice, with the fluorinated tracer providing higher sensitivity for delineating both disease severity and peripheral signal. Materials and methods Bulk RNA quantification re-analysis We re-analyzed previously our published data [ 29 ] to identify potential biomarkers of innate immune activation in EAE mice as a target for novel radiotracer development. Briefly, bulk transcriptomic RNA quantification of lumbar spinal cords was generated from 15 C57BL/6 mice either healthy (n = 5) or induced with EAE and stratified into low disease progression (n = 5) and high disease progression (n = 5). Gene expression counts and disease metadata were imported from CSV files and organized into a Seurat object. Genes with a mean basal expression of ≤ 20 counts in healthy mice were retained to focus on low-abundance, potentially disease-specific transcripts, and the count matrix was log-normalized to account for differences in sequencing depth. Average expression of each gene per disease group was calculated, and fold-change analysis was performed sequentially (low versus healthy, high versus low) to identify progressively upregulated genes (fold-change ≥ 1 in both comparisons); these were ranked by absolute expression in the high disease group. Cell surface and myeloid marker databases were constructed by integrating Gene Ontology annotations (plasma membrane and myeloid differentiation/activation terms), UniProt localization queries, BioMart/Ensembl transmembrane or myeloid-related annotations, and literature-curated canonical markers. Progressively upregulated genes intersecting both databases were prioritized as candidate cell surface myeloid biomarkers, and their expression patterns across disease stages were visualized using a heatmap. Animals Female 8-10-week-old C57BL/6J mice (Jackson Laboratory, strain #: C57BL6J, RRID: 1MSR_JAX:000664) were housed in a non-barrier, temperature-controlled environment (humidity 40–60%) under a 12 h light/dark schedule with unrestricted access to food and water. A total of 237 mice were used for this study. Treatment groups were randomly assigned by cage and co-housed in the same room as vehicle and control groups. All animal care and procedures complied with the Animal Welfare Act and were in accordance with institutional guidelines. Permission to perform all animal experiments was granted by the Stanford Administrative Panel on Laboratory Animal Care (APLAC), which is accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care International (AAALAC International). EAE induction EAE was induced in female C57BL/6J WT mice using MOG35-55 emulsified in CFA (Hooke Laboratories, #EK-2110). All vehicle and therapy-treated mice received two pertussis toxin i.p. injections (110 ng) 2 and 24 h after MOG induction. Naïve littermates were used as additional controls. EAE mice were weighed and scored daily from day 8 onward using a standard scoring protocol for levels of paresis/paralysis [ 30 ]. qPCR of lumbar spinal cords with CD11b isolation Bulk spinal cords were combined (n = 2/tube) and stored on ice in MACS Tissue Storage Solution (Miltenyi) and then transferred to a C-Tube containing an enzymatic mix detailed in Multi Dissociation Kit 1 protocol (Miltenyi). Samples were then placed on a gentleMACS Octo Dissociator with Heaters (Miltenyi). After dissociation, the homogenates were de-myelinated with a 25% standard isotonic percoll gradient. For bulk qPCR, the single cell suspension was frozen in TRIzol (Invitrogen). To isolate CD11b positive and negative cell populations, the cells were incubated using CD11b (Microglia) MicroBeads (Miltenyi) and passed through an LS Separation column following manufacturer's instructions (Miltenyi). The positive and negative fractions were then frozen on Trizol. RNA was extracted per the manufacturer's protocol (Invitrogen). cDNA synthesis was performed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Gene expression of GPR84 (Qiagen GeneGlobe ID: PPM04873A-200) was assessed by RT-PCR on an Applied Biosystems QuantStudio 6, with GAPDH serving as the housekeeping gene. Each sample was run in triplicate, and fold change was calculated using the 2^ΔΔCt method. Undetectable transcripts were assigned a Ct (cycle threshold) of 38 [ 29 ], and samples with high inter-replicate variation (SD > 0.70) were excluded. The sample size (N) is provided in Table S1 . DMT administration Mice were randomly assigned to the following groups: naïve, EAE, vehicle-treated EAE, and fingolimod-treated EAE. Fingolimod was administered daily starting from 8 days post disease-induction until 14–16 days post-induction. Fingolimod (Cayman Chemicals, 3 mg/kg, #10006292) was administered via oral gavage. Vehicle-treated EAE mice received water via oral gavage. Radiosynthesis of 11 C-MGX-38 and 18 F-MGX-110S 11 C-MGX-38 Precursor (0.5 mg) was dissolved in DMF (0.5 mL) and loaded into a glass reactor. 1.0 M NaOH in water (6 µL) was added to the reactor just prior to the delivery of 11 C-MeI and the reactor was cooled to -10ºC; the cell was then sealed and the reaction heated to 70ºC for 5 minutes. The reaction mixture was cooled to 40ºC and diluted with 1 mL of 40:60 H 2 O : MeCN. The crude reaction mixture was purified via semipreparative HPLC (column: Phenomenex Gemini 5 mm C18 110 Å, 250 × 10 mm; mobile phase A: H 2 O with 0.1% trifluoroacetic acid (TFA) by volume; mobile phase B: MeCN with 0.1% TFA by volume; program: 40–60% B in 15 min at 5 mL/min; product retention time: 8.7 min). Purified 11 C-MGX-38 was collected in a round-bottom flask containing 20 mL H 2 O. The solution was passed through a Sep-pak plus lite C18 (conditioned with 5 mL EtOH and 10 mL H 2 O) and the cartridge was rinsed with H 2 O (6 mL). Purified 11 C-MGX-38 product was eluted with EtOH (0.5 mL) followed by saline (4.5 mL). Purity and identity of final product were confirmed via analytical HPLC (column: Phenomenex Gemini 5 mm C18 110 Å, 250 × 4.6 mm; mobile phase A: H 2 O with 0.1% TFA by volume; mobile phase B: MeCN with 0.1% TFA by volume; program: 50–95% B in 15 minutes at 1 mL/min; product retention time: 6.8 minutes). Resulting molar activity was 11555 ± 6383 mCi/µmol (n = 6). 18 F-MGX-110S 18 F-MGX-110S was synthesized following previously published methods [ 31 ]. Briefly, radiosynthesis of 18 F-MGX-110S was achieved by achieved by copper-mediated radiofluorination of a Bpin precursor in a mixture of dimethylacetamide at 130°C for 20 min. Resulting molar activity was 6670 ± 210 mCi/µmol (n = 5). In vitro radioactive cell binding studies: GPR84-transfected HEK-293 cells and control HEK-293 cells were plated at 3x10 5 cells per well in medium (DMEM with 10% FBS and 1% anti-anti, Thermo Fisher Scientific) into three 12-well plates 24 h prior to the cell binding assay. Once cells reached ~ 90% confluency, the radiotracer was prepared. Additionally, a solution of the GPR84 antagonist GLPG1205, 35 µM in DMSO was prepared, and the DMEM medium warmed. The radiotracer was diluted in DMEM without FBS or antibiotic to yield 30mL of solution where the amount of radioactivity was 50 µCi in 1mL. The 12-well plates were incubated for 40 minutes at 37ºC and 5% CO 2 in air. Cells were subsequently washed with PBS, trypsinized to remove from the surface of the plates, resuspended in DMEM + 10% FBS, and 500µL volumes pipetted into gamma counting tubes. Radioactivity bound to cells was measured in a gamma counter (Revvity), and cell numbers were recorded using Trypan blue to distinguish live and dead cells and an automatic cell counter to normalize radioactivity levels to cell count. PET/CT imaging Mice were anesthetized with isoflurane gas (2.0–3.0% for induction and 1.0–2.0% for maintenance) and intravenously injected with 18 F-MGX-110S (55–206 µCi) or 11 C-MGX-38 (133–578 µCi). For each radiotracer, mice either underwent either a 60-minute dynamic scan or a 10-min static PET as dynamic PET was used to understand tracer kinetics for selecting static time frame. Static images were acquired within 40–55 minutes post-injection of 18 F-MGX-110S or 35-minutes post-injection of 11 C-MGX-38 on days 14, 15 or 16 after EAE induction using a GNEXT scanner (Sofie Biosciences) in list mode format. In the case of a single group of N = 3 naïve mice, the scanner failed mid-acquisition and the timepoint was repeated at 66 minutes. CT images were acquired after each PET scan to provide an anatomical reference frame in addition to scatter and attenuation correction for PET data. Isotropic resolution was achieved using OSEM3D/MAP reconstruction algorithms with 24 subsets, 3 iterations, and a matrix size of 240 × 240 × 191. PET imaging analysis PET images were analyzed using VivoQuant 4.0 (InVicro) following previously published methods [ 32 ]. Briefly, the PET was overlaid and matched to the CT and decay-corrected for each dose administered. Manual ROIs were drawn on the CT for lumbar and thoracic/cervical spinal cords. A semi-automated atlas was used for the brain analysis. Analysis was conducted blind for the ROI drawing, but not for the dose corrections and %ID/g calculations as researchers used the mouse scores to assign into groups: High scoring disease (High EAE), Low scoring disease (Low EAE), vehicle-treated EAE (Veh-EAE), fingolimod-treated EAE (Tx EAE) and naïve. Refer to Tables S2-4 for N in each group for each radiotracer. Ex vivo gamma counting Immediately after PET, mice were euthanized under anesthesia (with inhaled isoflurane [2–3%]) and cardiac puncture performed. Mice were subsequently perfused with 20–30 mL of PBS and tissues of interest (blood, brain, kidney, lumbar spinal cord [LSC], thoracic/cervical spinal cord [TSC], liver, cecum, spleen, femur, bone marrow, muscle, adipose and tail) were individually harvested, weighed wet and gamma-counted (Hidex automatic gamma counter, Hidex). Gamma counting standards (3–10 µCi/standard) were used to accurately decay-correct biodistribution results, which were calculated as %ID/g using the weight of each dissected organ. Refer to Table S5 for N for each organ and radiotracer. Ex vivo autoradiography studies A separate, representative cohort of EAE and naïve mice were used to evaluate 11 C-MGX-38 and 18 F-MGX-110S in lumbar spinal cord via digital ex vivo autoradiography. 30–40 minutes after intravenous injection of 11 C-MGX-38 (800 µCi) or 18 F-MGX-110S (150 µCi), mice were euthanized under anesthesia (with inhaled isoflurane (2–3%)) and perfused with PBS (20 mL), and CNS tissues were harvested for autoradiography. Following gamma counting, lumbar spinal cords were frozen in optimal cutting temperature (OCT), and cryosectioned into 60 µm ( 11 C-MGX-38) or 40 µm ( 18 F-MGX-110S) thick sections and placed on superfrost plus slides (Thermo Fisher). The air dried slides were exposed to a digital storage phosphor screen (Amersham Biosciences) for 10 half-lives and imaged via digital autoradiography using a Typhoon phosphorimager (Amersham Biosciences). The slides were then stained with H&E (Hematoxylin Gills 3, Thermo Fisher Scientific, NC9964763; Eosin-Y Richard-Allan Scientific, Thermo Fisher Scientific, #22-110-637) for anatomical reference and visualization of the distribution of immune cells. Plasma free fraction (f p ) To assess f p , n = 3 mice were injected per tracer with 1.0-1.7 mCi of either 11 C-MGX-38 or 18 F-MGX-110S, iv. After 35-45-minutes post-injection, 500 µL of blood was collected from the heart and placed into an EDTA-coated blood tube and centrifuged at 500 rcf for 10 minutes. 200 µL of resulting plasma was placed into a Centrifree ultrafiltration unit (Millipore Sigma) and centrifuged at 1000 rcf for 30 min, following manufacturer’s instructions. The resulting fractions of bound and free radiotracer were placed on the gamma counter to calculate the f p for each tracer. Statistics and reproducibility Statistical analyses were performed using Prism (version 11, GraphPad Software). For comparisons across multiple groups, one-way ANOVA followed by Dunnett’s multiple comparisons test versus naive controls was used when variances were similar. When variance differed significantly between groups (Brown–Forsythe test), Welch’s ANOVA followed by Dunnett T3 multiple comparisons was performed. All ANOVA multiple comparison’s tests were compared to naïve unless otherwise noted. For datasets of 2 groups, a Welch’s t test was performed. Error bars represent standard deviation (SD). Exact tests used are indicated in figure legends. PET/CT and ex vivo gamma counting results are from six experiments for 18 F-MGX-110S and 11 C-MGX-38. Results Bulk RNA quantification identifies GPR84 as a promising myeloid cell surface marker of innate immune activation in EAE mice. To assess and identify potential new candidate biomarkers of innate immune activation in the context of EAE pathology, we re - analyzed our previously published dataset [ 29 ] of bulk RNA quantification of mouse lumbar spinal cords from naïve mice compared to mice with mild-moderate and severe EAE symptoms (Fig. 1 A). Specifically, we sought to identify markers that align with ideal characteristics of PET biomarkers, including having high cell specificity, be upregulated under inflammatory conditions, have low basal expression in healthy CNS and be located on the cell surface (Fig. 1 B). Based on these criteria, we shortlisted 5 targets (Fig. 1 C) and performed further investigation of cell specificity based on literature searches, readily available online resources (e.g., Barres Lab Brain RNAseq Library [ 33 ] and Human Protein Atlas [ 34 ]). Of the five biomarkers that met these criteria, we selected GPR84 as the most suitable for further investigation: the other four were eliminated due either to having high basal expression (clec7a had low basal expression in mice, but high basal CNS expression in humans), or having poor cell-specificity (TLR2, CD300lf and CD40 all were expressed on cancer cells[ 35 – 37 ]). GPR84 was found to be upregulated in lumbar spinal cords of EAE mice compared to naïve mice (Fig. 1 C-F). From the bulk RNA quantification, Gpr84 transcripts were significantly (p < 0.0001) upregulated in high and low EAE mice compared to naïve animals (Fig. 1 D). qPCR in a separate cohort was performed to validate these findings and showed that Gpr84 transcripts were significantly (p < 0.0001) upregulated in high and low EAE mice compared to naïve animals (p < 0.01, p < 0.05, respectively) (Fig. 1 E). Furthermore, Gpr8 4 was expressed significantly more highly (p < 0.001) on CD11b + cells than CD11b − cells (Fig. 1 F), confirming myeloid cell-specificity in the CNS. Successful radiosynthesis of 11 C-MGX-38 has high yield and shows effective binding to GPR84 . Developing a novel BBB-penetrant radiotracer requires careful consideration of many physico-chemical properties. A scoring system called CNO Multiparameter Optimization (CNS-MPO) [ 38 , 39 ] was used to predict 11 C-MGX-38 penetrance through an intact BBB. This molecule had appropriate characteristics to passively cross BBB including favorable lipophilicity (logD between 2–3), molecular weight (< 500) and polar surface area ( 98% purity of radiotracer (Fig. 2 A&B). In vitro cell studies demonstrated significantly increased (p < 0.0001) 11 C-MGX-38 binding to hGPR84-expressing HEK293 cells relative to parental control HEK293 cells (Fig. 2 C). Moreover, incubation of hGPR84 + HEK293 cells in the presence of the GPR84 antagonist GLPG1205 resulted in a significant (p < 0.0001) reduction in radiotracer binding (Fig. 2 C). We further compared 11 C-MGX-38 to a recently developed GPR84-PET radiotracer, 18 F-MGX-110S (Fig. 2 D), synthesized as previously reported [ 31 ] and also exhibits favorable physico-chemical properties for BBB-penetrance (Table 1 ). The f p of both radiotracers was calculated with 18 F-MGX-110S having a higher f p compared to 11 C-MGX-38 (42.36% and 16.16%, respectively) (Table S6). Table 1 Physico-chemical properties of 11 C-MGX-38 and 18 F-MGX-110S Molecular mass (g/mol) 11 C-MGX-38 18 F-MGX-110S 370.18 331.12 cLogP 2.08 0.188 LogD (experimental) 2.6 2.06 tPSA 57.23 60.36 CNS MPO score 5.9 / 6.0 6.0/6.0 Ki (nM) 10.23 9.49 GPR84-PET reveals increased signal in spinal cords of EAE mice compared to controls. To evaluate the utility of GPR84-PET for imaging pro-inflammatory myeloid cells in EAE mice compared to naïve animals, two GPR84-PET radiotracers were used. Time activity curves for 11 C-MGX-38 show a high peak and steady washout over 60-minutes (Fig. 3 A). 18 F-MGX-110S time activity curves exhibit a more rapid washout than 11 C-MGX-110S (Fig. 3 B). 11 C-MGX-38 and 18 F-MGX-110S both showed significantly higher signal in the lumbar (p < 0.0001, p < 0.05, respectively) and thoracic/cervical regions (p < 0.01, p < 0.01, respectively) of the spinal cord of EAE mice compared to naïve (Fig. 3 ) (in Legend). Time-activity curves (TACs) of the brain stem also show elevated signal in EAE mice compared to naïve (Fig. S1 ) Ex vivo gamma counting reveals superior sensitivity of 18 F-MGX-110S, confirms spinal cord GPR84-PET findings and identifies elevated signal in brain and adipose. To determine absolute binding quantification in organs of interest, mice were perfused after the scan, or 45–55 minutes after injection of radiotracer. Perfusion is necessary to remove radiotracer circulating in the blood and evaluate signal in each organ of interest due to specific binding alone. 11 C-MGX-38 gamma counting signal mirrored the PET imaging quantification in the spinal cord, revealing significantly higher binding in high EAE mice compared to naive (Fig. 4 A, p < 0.01). 18 F-MGX-110S showed higher sensitivity in detecting pathological changes in the spinal cord of both high and low scoring EAE mice compared to naive (Fig. 4 B, p < 0.01). Further, 18 F-MGX-110S also showed a significant increase in signal in the whole brain of high scoring EAE mice compared to naïve (Fig. 4 B, p < 0.01). Peripherally, both GPR84 tracers showed elevated signal in brown adipose tissue of high scoring EAE mice compared to naive (Fig. 4 A&B, p < 0.01). Only 18 F-MGX-110S demonstrated a significant %ID/g increase in spleen (p < 0.001), muscle (p < 0.001), bone marrow (p < 0.05) and blood (p < 0.001) (Fig S2 A&C) in high scoring EAE mice compared to naïve. We also compared the sensitivity of our GPR84 radiotracers to the TSPO radiotracer 18 F-GE-180, using our previously published EAE data [ 1 ]. With PET imaging, both 11 C-MGX-38 and 18 F-MGX-110S demonstrated similar signal-to-background ratios of high EAE-to-naïve in the spinal cord (1.6 vs 1.3), outperforming 18 F-GE-180 (Fig. 4 C). However, ex vivo gamma counting revealed a clear advantage for 18 F-MGX-110S, which achieved EAE-to-naïve ratios of 9.4 and 11.8 in the lumbar and thoracic/cervical spinal cords, respectively. In contrast, 11 C-MGX-38 yielded ratios of 2.0 and 1.8, comparable to those of 18 F-GE-180 (Fig. 4 C).Finally, both GPR84-radiotracers have high peripheral signal which was measured ex vivo to find that 11 C-MGX-38 has higher liver signal than that observed for 18 F-MGX-110S, while 18 F-MGX-110S has elevated signal in the cecum (Fig. S3). Ex vivo autoradiography (ARG) of representative lumbar spinal cord sections demonstrated that 18 F-MGX-110S clearly distinguished both high and low EAE mice from naïve (Fig. 5 ), while 11 C-MGX-38 showed elevated signal only in high EAE compared to naïve, providing further evidence that 18 F-MGX-110S detects changes in EAE disease severity with superior sensitivity than 11 C-MGX-38. GPR84-PET of EAE mice detects in vivo response to FDA-approved therapy. A major hindrance in MS management is the absence of tools to objectively guide therapy selection and monitor treatment response. GPR84-PET may address this gap by quantifying innate immune activation across the whole body, offering a non-invasive clinical measure. To evaluate this preclinically, we treated EAE mice with fingolimod, an FDA-approved therapy for MS, starting at 8-days post-EAE induction (Tx-EAE mice). GPR84-PET imaging was performed between days 14–16 post-induction (Fig. 6 A) when the vehicle-treated EAE mice (Veh-EAE) were at peak of disease (hindlimb paralysis). GPR84-PET imaging showed significant changes in signal between groups for each radiotracer (Fig. 6 B&C). 11 C-MGX-38 and 18 F-MGX-110S both showed significantly higher %ID/g in the LSC and TSC in the vehicle-treated mice compared to those treated with fingolimod (p < 0.05 & p < 0.01, respectively, Fig. 6 B&C). 11 C-MGX-38 ex vivo gamma counting revealed significantly higher %ID/g in the Veh-EAE mice compared to the Tx-EAE mice in all tissues examined, except in the spleen (Fig. 6 B, Fig. S2 A) which although not significant, trended higher. 18 F-MGX-110S also had significantly higher %ID/g in all tissues examined except for the brain, which also trended higher (Fig. 6 C, Fig. S2 B). Mirroring the EAE-to-naïve comparison, PET imaging showed similar sensitivity to spinal cord signal with both 11 C-MGX-38 and 18 F-MGX-110S when comparing vehicle-treated to treatment-EAE mice (1.2 vs. 1.5, Fig. S2 C). Ex vivo gamma counting, however, again highlighted the superior sensitivity of 18 F-MGX-110S, with vehicle-to-treatment EAE ratios of 7.4 and 12.0 in the lumbar and thoracic/cervical spinal cords, respectively, compared to 2.6 and 2.7 for 11 C-MGX-38 (Fig. S2 C). These findings further establish 18 F-MGX-110S as the lead candidate for clinical translation, demonstrating utility not only in tracking disease severity but also in monitoring therapeutic response. Discussion and Conclusions Understanding the molecular signatures of CNS disease is fundamental to advancing our knowledge of disease progression, accelerating therapy development, and enabling objective monitoring of treatment efficacy in the clinic. PET imaging is a powerful tool to non-invasively image living subjects and has the potential to transform how neuroinflammatory diseases are diagnosed and managed. For decades, TSPO-PET has been widely used for imaging neuroinflammation. While TSPO is a biomarker of inflammation and mitochondrial bioenergetics, its utility is constrained by two key limitations: broad cellular expression across many cell types including immune cells, muscle cells, and endothelial cells, and a lack of upregulation under CNS inflammatory conditions. Together, these features make TSPO-PET image interpretation difficult and reduces its efficacy as a pharmacodynamic endpoint in clinical trials. There is therefore a pressing need for new radiotracers that are cell specific and can provide insight into innate immune activation. To address this need, we performed bulk RNA quantification and selected Gpr84 as an ideal candidate for a novel biomarker for PET imaging of innate immune activation. GPR84 was selected due to its cell specificity and phenotypic relevance to activated myeloid lineage cells, its cell surface localization, and its absence of expression on cancer cells. With GPR84 established as a suitable target, we assessed candidate GPR84 antagonists against physicochemical criteria required for passive BBB penetration and suitability for rapid 11 C-radiolabeling. Developing a new radiotracer that passively penetrates the BBB is challenging, as the BBB is designed to exclude foreign substances. In order for a ligand to passively cross the BBB, certain physico-chemical properties must be met: these include low molecular mass, total polar surface area below 90Å 2 and appropriate lipophilicity (2 < logP < 4 or 1.5 < logD 7.4 <3.5) [ 40 – 42 ]. A scoring system called CNS MPO is commonly used to predict BBB penetrance of a molecule. We were able to select a molecule with a MPO score of 5.9/6 (Table 1 ) with a methoxy group to allow rapid 11 C-labelling without changing the structure for our first generation GPR84-PET radiotracer. In parallel, 18 F-MGX-110S was developed and examined as a lead fluorinated molecule for GPR84-PET. With the ultimate goal of clinical translation, two molecules were evaluated in the EAE murine model of MS to determine the ability of GPR84-PET to detect changes in inflammation in EAE mice in the presence or absence of treatment, compared to naïve animals. Our study highlights GPR84-PET as a promising tool for probing innate immune activation in EAE with potential for clinical translation to MS, offering substantial advantages over TSPO-PET. Unlike TSPO, GPR84 is highly selective for microglia and macrophages, which are the primary cellular drivers of CNS innate immunity; GPR84 is also robustly upregulated under inflammatory conditions. This translates to PET images that are easier to interpret and more sensitive to disease-relevant changes. We evaluated two GPR84 radiotracers, 11 C-MGX-38 and 18 F-MGX-110S, in EAE and naïve mice. Both demonstrated elevated spinal cord signal in EAE mice relative to naïve animals, consistent with the known pathology of this model, in which lesions and infiltrating immune cells predominately localize to the spinal cord before spreading to the brain [ 12 ]. Ex vivo gamma counting further revealed that 18 F-MGX-110S exhibited a greater sensitivity to subtle changes in pathology than 11 C-MGX-38, with spinal cord signal that tracked closely with disease severity and a significant increase in brain binding which is an important feature for capturing the full spatial extent of neuroinflammation. Peripherally, both radiotracers detected significantly elevated signal in the brown adipose tissue, a tissue known to increase levels of GPR84 during inflammation [ 43 ]. However, only 18 F-MGX-110S detected significantly elevated signal in the spleen, muscle, bone marrow and blood of high scoring EAE mice compared to naïve mice. GPR84-PET can hence provide a potential means to understand innate immune response not only in the CNS, but in the periphery as well. This can provide insights into whole body inflammation as well as therapeutic response. The comparatively reduced sensitivity of 11 C-MGX-38 may reflect its lower f p , which is the percentage of the radiotracer that is freely circulating in the blood, rather than bound by plasma proteins. A decreased f p decreases radiotracer availability, potentially shortening circulation time and limiting target engagement. Further, the slightly higher lipophilicity and apparent slower washout kinetics of 11 C-MGX-38 may contribute to higher signal in the naïve mice post-perfusion, reducing the sensitivity of EAE-to-naïve. There are several limitations to GPR84-PET in EAE mice with both 11 C-MGX-38 and 18 F-MGX-110S that merit consideration. Firstly, regardless of radiotracer, quantification of the spinal cord, in particular the LSC, is subjected to partial volume effect, as some portions of the spinal cord have volume less than the resolution of the scanner, which is around 1mm. Furthermore, the compact anatomy of mice positions organs closely, increasing the risk of signal spillover from peripheral metabolism and off-target binding into spinal cord. In the case of 18 F-MGX-110S, progressive signal accumulation in the cecum over time (Fig S3) may have contributed spillover into the adjacent spinal cord, potentially attenuating the apparent difference between high EAE and naïve animals. Similarly, the high liver signal (Fig. S3) observed with 11 C-MGX-38 is a potential source of spillover into the spinal cord. These limitations were overcome through ex vivo gamma counting and autoradiography where we observed a significant increase in signal in spinal cord of EAE mice compared to naïve. Importantly, the anatomical differences between mice and humans mean that spillover from the liver or cecum into the spinal cord is unlikely to pose a comparable concern in the clinical setting, where greater inter-organ distances substantially reduce this risk. Fingolimod is an FDA-approved sphingosine-1-phosphate (S1P) receptor modulator that reduces neuroinflammation by sequestering lymphocytes in lymph nodes, thereby limiting their infiltration into the CNS [ 44 ]. While its primary mechanism targets adaptive immunity, fingolimod also exerts direct effects on innate immune cells (e.g. microglia and macrophages) by modulating S1P receptor signaling, reducing pro-inflammatory cytokine release, and attenuating microglial activation [ 45 ]. As GPR84 is highly upregulated on activated microglia and macrophages, GPR84-PET was investigated to evaluate its utility to monitor the therapeutic response of fingolimod as an immunomodulatory DMT in the murine EAE model of MS. In our treatment studies, both radiotracers successfully detected decreased PET signal in the spinal cord associated with fingolimod treatment, distinguishing vehicle-treated EAE mice from treated animals. Ex vivo gamma counting mirrored the PET signal in the spinal cord for both radiotracers. 11 C-MGX-38 detected ex vivo signal decrease in all organs examined except in the spleen and 18 F-MGX-110S detected ex vivo signal decrease in all organs examined except in the brain. There has been considerable effort to explore biomarkers of innate immune activation and develop radiotracers specific to activated myeloid cells, with several relevant targets under active investigation. Among these are colony-stimulating factor 1 receptor (CSF1R) and the purinergic ion channel P2X7 receptor (P2X7R), Both targets are upregulated on myeloid cells under neuroinflammatory conditions. 11 C-CPPC, a radiotracer targeting CSF1R, has been evaluated both preclinically and clinically across a range of neuroinflammatory diseases [ 46 , 47 ], demonstrating increased brain signal in EAE mice compared to controls. However, PET quantification of naive mice showed nearly zero signal in the brain and in EAE mice there less than 0.75%ID/g in the brain stem, suggesting limited BBB penetration in this species, but more favorable VT measurements have been reported in clinical studies [ 48 ]. 11 C-SMW139, a radiotracer targeting P2X7R, similarly demonstrated elevated binding in EAE rats compared to naïve controls, though CNS penetration was also limited with PET quantitation of 0.05%ID/mL in brain and spinal cord after 15 minutes post-injection [ 49 ]. 11 C-SMW139 was also recently used to image MS patients, where radiotracer binding was not consistently elevated in MS lesions, warranting further clinical investigation in a larger patient population [ 50 ]. The utility of P2X7R as a myeloid-selective biomarker is further complicated by its expression at baseline on oligodendrocytes in addition to microglia [ 33 ], as well as on peripheral immune cells including B and T cells [ 51 , 52 ]. A limitation of both biomarkers is their expression on cancer cells [ 52 , 53 ], which may confound imaging interpretation in oncological contexts. GPR84, on the other hand, appears to be highly specific for myeloid lineage cells with no expression on cancer cells. In rodent models of MS, both our GPR84 radiotracers exhibit higher %ID/g (2–7%ID/g in the spinal cords of EAE mice) via PET imaging, however, they have not yet been clinically translated to compare in human. Overall, these findings support both 11 C-MGX-38 and 18 F-MGX-110S as viable candidates for clinical translation as non-invasive tools for monitoring CNS innate immune activation. With greater cellular specificity and higher sensitivity than TSPO-PET, GPR84-PET is well-positioned for translational application across preclinical and clinical settings from longitudinal disease monitoring to pharmacodynamic evaluation of emerging immunotherapies. While both tracers show promise for imaging innate immune activation in the context of MS-like pathology, 18 F-MGX-110S emerges as the leading candidate for clinical translation due to its superior sensitivity in both the CNS and periphery and longer half-life. Abbreviations AAALAC Association for the Assessment and Accreditation of Laboratory Animal Care AD Alzheimer's Disease ANOVA Analysis of Variance APLAC Administrative Panel on Laboratory Animal Care ARG Autoradiography BBB Blood-Brain Barrier Bpin Boronic Acid Pinacol Ester cDNA Complementary Deoxyribonucleic Acid CFA Complete Freund's Adjuvant cLogP Calculated Logarithm of Partition Coefficient CNS Central Nervous System CNS-MPO Central Nervous System Multiparameter Optimization CSF Cerebrospinal Fluid CSF1R Colony-Stimulating Factor 1 Receptor CT Computed Tomography Ct Cycle Threshold DMEM Dulbecco's Modified Eagle Medium DMF Dimethylformamide DMSO Dimethyl Sulfoxide DMT Disease-Modifying Therapy EAE Experimental Autoimmune Encephalomyelitis EDTA Ethylenediaminetetraacetic Acid EtOH Ethanol FBS Fetal Bovine Serum FDA Food and Drug Administration fp Plasma Free Fraction GAPDH Glyceraldehyde-3-Phosphate Dehydrogenase GPCR G Protein-Coupled Receptor GPR84 G Protein-Coupled Receptor 84 H&E Hematoxylin and Eosin HEK293 Human Embryonic Kidney 293 Cells HPLC High-Performance Liquid Chromatography %ID/g Percent Injected Dose per Gram IgG Immunoglobulin G i.p. Intraperitoneal kDa Kilodalton Ki Inhibition Constant LogD Distribution Coefficient (logarithm) LSC Lumbar Spinal Cord MACS Magnetic-Activated Cell Sorting MAP Maximum A Posteriori MeCN Acetonitrile MOG35-55 Myelin Oligodendrocyte Glycoprotein Peptide 35–55 MRI Magnetic Resonance Imaging mRNA Messenger Ribonucleic Acid MS Multiple Sclerosis nM Nanomolar OCT Optimal Cutting Temperature OSEM3D Ordered Subset Expectation Maximization 3D P2X7R Purinergic Receptor P2X7 PBS Phosphate-Buffered Saline PET Positron Emission Tomography PP-MS Primary Progressive Multiple Sclerosis PR-MS Progressive-Relapsing Multiple Sclerosis qPCR Quantitative Polymerase Chain Reaction rcf Relative Centrifugal Force RNA Ribonucleic Acid ROI Region of Interest RR-MS Relapsing-Remitting Multiple Sclerosis RT-PCR Reverse Transcription Polymerase Chain Reaction S1P Sphingosine-1-Phosphate SD Standard Deviation TAC Time-Activity Curve TFA Trifluoroacetic Acid tPSA Topological Polar Surface Area TSC Thoracic/Cervical Spinal Cord TSPO Translocator Protein 18 kDa VT Volume of Distribution WT Wild Type µCi Microcurie Declarations Data Availability The data that support the findings of this study and the code to re-analyze the bulk qPCR data are available from the corresponding author upon reasonable request. Funding This work was funded by NIH/NINDS 1R21 AG07556501 (MLJ), Stanford University Wu Tsai Translate Grant (MLJ) and SNMMI Predoctoral Molecular Imaging Scholar Program (STR). Conflicts of Interest M.L.J., M.K., IMJ and SCN are co-inventors on patent no. PCT/US2024/024901 “Method for detecting innate immune action in vivo using GPR84-PET.” Author Contributions STR contributed to Conceptualization, Methodology, Investigation, Formal Analysis, Writing: Original Draft, and Writing: Review & Editing. RCK contributed to Conceptualization, Methodology, Investigation, and Formal Analysis and Writing: Review & Editing. IMJ contributed to Conceptualization, Methodology, Investigation, Formal Analysis and Writing: Original Draft. MK, PM, SM, MS, BZ, JG, DD, AS, and SCN contributed to Investigation. HWC, NS, and PJ contributed to Investigation and Formal Analysis. TW contributed to Formal Analysis. EMD contributed to Writing: Review & Editing. GG contributed to Conceptualization, Methodology, Formal Analysis, and Writing: Review & Editing. GM contributed to Conceptualization, Methodology and Writing: Review & Editing. MLJ contributed to Conceptualization, Methodology, Writing: Original Draft, and Writing: Review & Editing. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the Stanford University Cyclotron and Radiochemistry Facility (CRF) and Stanford Center for Innovation in In vivo Imaging (Sci3) for their support with this work. References Goldenberg MM. Multiple Scler Rev Pharm Ther. 2012;37:175–84. Goodin DS. The epidemiology of multiple sclerosis. Handb Clin Neurol [Internet]. Elsevier; 2014 [cited 2026 Mar 27]. pp. 231–66. https://doi.org/10.1016/B978-0-444-52001-2.00010-8 Dobson R, Giovannoni G. Multiple sclerosis – a review. Eur J Neurol. 2019;26:27–40. https://doi.org/10.1111/ene.13819 . Vargas DL, Tyor WR. Update on Disease-Modifying Therapies for Multiple Sclerosis. J Investig Med SAGE Publications. 2017;65:883–91. https://doi.org/10.1136/jim-2016-000339 . Singh S, Metz I, Amor S, van der Valk P, Stadelmann C, Brück W. Microglial nodules in early multiple sclerosis white matter are associated with degenerating axons. Acta Neuropathol (Berl). 2013;125:595–608. https://doi.org/10.1007/s00401-013-1082-0 . Hemmer B, Kerschensteiner M, Korn T. Role of the innate and adaptive immune responses in the course of multiple sclerosis. Lancet Neurol. 2015;14:406–19. https://doi.org/10.1016/S1474-4422(14)70305-9 . Owens GP, Bennett JL, Gilden DH, Burgoon MP. The B cell response in multiple sclerosis. Neurol Res Taylor Francis. 2006;28:236–44. https://doi.org/10.1179/016164106X98099 . Dendrou CA, Fugger L, Friese MA. Immunopathology of multiple sclerosis. Nat Rev Immunol Nat Publishing Group. 2015;15:545–58. https://doi.org/10.1038/nri3871 . Prado C, Herrada AA, Hevia D, Goiry LG, Escobedo N. Role of innate immune cells in multiple sclerosis. Front Immunol [Internet] Front. 2025. https://doi.org/10.3389/fimmu.2025.1540263 . [cited 2026 Mar 27];16. Dendrou CA, Fugger L, Friese MA. Immunopathology of multiple sclerosis. Nat Rev Immunol Nat Publishing Group. 2015;15:545–58. https://doi.org/10.1038/nri3871 . Myeloid cell distribution and activity in multiple sclerosis. Histol Histopathol. ES: Sercrisma Int s l. 2016;357–70. https://doi.org/10.14670/HH-11-699 . Berard JL, Wolak K, Fournier S, David S. Characterization of relapsing–remitting and chronic forms of experimental autoimmune encephalomyelitis in C57BL/6 mice. Glia. 2010;58:434–45. https://doi.org/10.1002/glia.20935 . Suppression of experimental allergic encephalomyelitis. in Lewis rats after elimination of macrophages. J Exp Med. 1990;172:1025–33. https://doi.org/10.1084/jem.172.4.1025 . Dobson R, Giovannoni G. Multiple sclerosis – a review. Eur J Neurol. 2019;26:27–40. https://doi.org/10.1111/ene.13819 . Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17:162–73. https://doi.org/10.1016/S1474-4422(17)30470-2 . Gatliff J, East D, Crosby J, Abeti R, Harvey R, Craigen W, et al. TSPO interacts with VDAC1 and triggers a ROS-mediated inhibition of mitochondrial quality control. Autophagy. 2014;10:2279–96. https://doi.org/10.4161/15548627.2014.991665 . Gatliff J, East DA, Singh A, Alvarez MS, Frison M, Matic I, et al. A role for TSPO in mitochondrial Ca2 + homeostasis and redox stress signaling. Cell Death Dis. 2017;8:e2896–2896. https://doi.org/10.1038/cddis.2017.186 . Nutma E, Fancy N, Weinert M, Tsartsalis S, Marzin MC, Muirhead RCJ, et al. Translocator protein is a marker of activated microglia in rodent models but not human neurodegenerative diseases. Nat Commun. 2023;14:5247. https://doi.org/10.1038/s41467-023-40937-z . Nutma E, Gebro E, Marzin MC, Van Der Valk P, Matthews PM, Owen DR, et al. Activated microglia do not increase 18 kDa translocator protein (TSPO) expression in the multiple sclerosis brain. Glia. 2021;69:2447–58. https://doi.org/10.1002/glia.24052 . Yokokura M, Terada T, Bunai T, Nakaizumi K, Takebayashi K, Iwata Y, et al. Depiction of microglial activation in aging and dementia: Positron emission tomography with [11C]DPA713 versus [11C](R)PK11195. J Cereb Blood Flow Metab. 2017;37:877–89. https://doi.org/10.1177/0271678X16646788 . Airas L, Rissanen E, Rinne JO. Imaging neuroinflammation in multiple sclerosis using TSPO-PET. Clin Transl Imaging. 2015;3:461–73. https://doi.org/10.1007/s40336-015-0147-6 . Polvinen E, Matilainen M, Nylund M, Sucksdorff M, Airas LM. TSPO-Detectable Chronic Active Lesions Predict Disease Progression in Multiple Sclerosis. Neurol Neuroimmunol Neuroinflammation. 2023;10:e200133. https://doi.org/10.1212/NXI.0000000000200133 . Rubin LH, Du Y, Sweeney SE, O’Toole R, Thomas CL, Zandi AG, et al. Imaging Brain Injury in Former National Football League Players. JAMA Netw Open. 2023;6:e2340580. https://doi.org/10.1001/jamanetworkopen.2023.40580 . Visser D, Golla SSV, Palard-Novello X, Verfaillie SCJ, Verveen A, Koch DW, et al. Varying Levels of Inflammatory Activity in Brain and Body of Patients with Persistent Fatigue and Difficulty Concentrating After COVID-19: A TSPO PET Study. J Nucl Med. 2025. https://doi.org/10.2967/jnumed.124.269297 . jnumed.124.269297. Abourbeh G, Thézé B, Maroy R, Dubois A, Brulon V, Fontyn Y, et al. Imaging Microglial/Macrophage Activation in Spinal Cords of Experimental Autoimmune Encephalomyelitis Rats by Positron Emission Tomography Using the Mitochondrial 18 kDa Translocator Protein Radioligand [18F]DPA-714. J Neurosci. 2012;32:5728–36. https://doi.org/10.1523/JNEUROSCI.2900-11.2012 . Rejc L, Gómez-Vallejo V, Joya A, Arsequell G, Egimendia A, Castellnou P, et al. Longitudinal evaluation of neuroinflammation and oxidative stress in a mouse model of Alzheimer disease using positron emission tomography. Alzheimers Res Ther. 2022;14:80. https://doi.org/10.1186/s13195-022-01016-5 . Vicente-Rodríguez M, Singh N, Turkheimer F, Peris-Yague A, Randall K, Veronese M, et al. Resolving the cellular specificity of TSPO imaging in a rat model of peripherally-induced neuroinflammation. Brain Behav Immun. 2021;96:154–67. https://doi.org/10.1016/j.bbi.2021.05.025 . Kalita M, Park JH, Kuo RC, Hayee S, Marsango S, Straniero V, et al. PET Imaging of Innate Immune Activation Using 11 C Radiotracers Targeting GPR84. JACS Au. 2023;3:3297–310. https://doi.org/10.1021/jacsau.3c00435 . Chaney AM, Cropper HC, Jain P, Wilson E, Simonetta F, Johnson EM, et al. PET imaging of TREM1 identifies CNS-infiltrating myeloid cells in a mouse model of multiple sclerosis. Sci Transl Med. 2023;15:eabm6267. https://doi.org/10.1126/scitranslmed.abm6267 . Stevens MY, Cropper HC, Lucot KL, Chaney AM, Lechtenberg KJ, Jackson IM, et al. Development of a CD19 PET tracer for detecting B cells in a mouse model of multiple sclerosis. J Neuroinflammation [Internet]. 2020. https://doi.org/10.1186/s12974-020-01880-8 . [cited 2021 Apr 29];17. Kalita M, Kuo RC, Straniero V, Reyes ST, Pandrala M, Lanzini A, et al. Illuminating proinflammatory myeloid cells with PET tracers targeting GPR84. Proc Natl Acad Sci U S A; 2026. Reyes ST, Azevedo EC, Cropper HC, Nagy S, Deal EM, Chaney AM, et al. Imaging CD19 + B Cells in an Experimental Autoimmune Encephalomyelitis Mouse Model using Positron Emission Tomography. J Vis Exp. 2023;64133. https://doi.org/10.3791/64133 . Brain RNA-S. – Brain RNA-Seq [Internet]. [cited 2026 Mar 30]. https://brainrnaseq.org/ . Accessed 30 Mar 2026. The Human Protein Atlas [Internet]. [cited 2026 Mar 30]. https://www.proteinatlas.org/ . Accessed 30 Mar 2026. Di Lorenzo A, Bolli E, Tarone L, Cavallo F, Conti L. Toll-Like Receptor 2 at the Crossroad between Cancer Cells, the Immune System, and the Microbiota. Int J Mol Sci. 2020;21:9418. https://doi.org/10.3390/ijms21249418 . Xu Z, Jin Y, Zhang X, Xia P, Wen X, Ma J, et al. Pan-cancer analysis identifies CD300 molecules as potential immune regulators and promising therapeutic targets in acute myeloid leukemia. Cancer Med. 2022;12:789–807. https://doi.org/10.1002/cam4.4905 . Zhou Y, Richmond A, Yan C. Harnessing the potential of CD40 agonism in cancer therapy. Cytokine Growth Factor Rev. 2024;75:40–56. https://doi.org/10.1016/j.cytogfr.2023.11.002 . Wager TT, Hou X, Verhoest PR, Villalobos A. Moving beyond Rules: The Development of a Central Nervous System Multiparameter Optimization (CNS MPO) Approach To Enable Alignment of Druglike Properties. ACS Chem Neurosci. 2010;1:435–49. https://doi.org/10.1021/cn100008c . Wager TT, Hou X, Verhoest PR, Villalobos A. Central Nervous System Multiparameter Optimization Desirability: Application in Drug Discovery. ACS Chem Neurosci. 2016;7:767–75. https://doi.org/10.1021/acschemneuro.6b00029 . Pike VW. PET radiotracers: crossing the blood–brain barrier and surviving metabolism. Trends Pharmacol Sci. 2009;30:431–40. https://doi.org/10.1016/j.tips.2009.05.005 . Pike VW. Considerations in the Development of Reversibly Binding PET Radioligands for Brain Imaging. Curr Med Chem. 2016;23:1818–69. https://doi.org/10.2174/0929867323666160418114826 . Lindberg A, Chassé M, Varlow C, Pees A, Vasdev N. Strategies for designing novel positron emission tomography (PET) radiotracers to cross the blood–brain barrier. J Label Compd Radiopharm. 2023;66:205–21. https://doi.org/10.1002/jlcr.4019 . Suzuki M, Takaishi S, Nagasaki M, Onozawa Y, Iino I, Maeda H, et al. Medium-chain Fatty Acid-sensing Receptor, GPR84, Is a Proinflammatory Receptor. J Biol Chem. 2013;288:10684–91. https://doi.org/10.1074/jbc.M112.420042 . Chun J, Hartung H-P. Mechanism of Action of Oral Fingolimod (FTY720) in Multiple Sclerosis. Clin Neuropharmacol. 2010;33:91–101. https://doi.org/10.1097/WNF.0b013e3181cbf825 . Di Dario M, Colombo E, Govi C, De Feo D, Messina MJ, Romeo M, et al. Myeloid cells as target of fingolimod action in multiple sclerosis. Neurol Neuroimmunol Neuroinflammation. 2015;2:e157. https://doi.org/10.1212/NXI.0000000000000157 . Mills KA, Du Y, Coughlin JM, Foss CA, Horti AG, Jenkins K et al. Exploring [11C]CPPC as a CSF1R-targeted PET Imaging Marker for Early Parkinson’s Disease Severity. medRxiv. 2024;2023.05.28.23290647. https://doi.org/10.1101/2023.05.28.23290647 Horti AG, Naik R, Foss CA, Minn I, Misheneva V, Du Y, et al. PET imaging of microglia by targeting macrophage colony-stimulating factor 1 receptor (CSF1R). Proc Natl Acad Sci U S A. 2019;116:1686–91. https://doi.org/10.1073/pnas.1812155116 . Coughlin JM, Du Y, Lesniak WG, Harrington CK, Brosnan MK, O’Toole R, et al. First-in-human use of 11C-CPPC with positron emission tomography for imaging the macrophage colony-stimulating factor 1 receptor. EJNMMI Res. 2022;12:64. https://doi.org/10.1186/s13550-022-00929-4 . Beaino W, Janssen B, Kooijman E, Vos R, Schuit RC, O’Brien-Brown J, et al. PET imaging of P2X7R in the experimental autoimmune encephalomyelitis model of multiple sclerosis using [11C]SMW139. J Neuroinflammation. 2020;17:300. https://doi.org/10.1186/s12974-020-01962-7 . Hagens MHJ, Golla SSV, Janssen B, Vugts DJ, Beaino W, Windhorst AD, et al. The P2X7 receptor tracer [11C]SMW139 as an in vivo marker of neuroinflammation in multiple sclerosis: a first-in man study. Eur J Nucl Med Mol Imaging. 2020;47:379–89. https://doi.org/10.1007/s00259-019-04550-x . Burnstock G. P2X ion channel receptors and inflammation. Purinergic Signal. 2016;12:59–67. https://doi.org/10.1007/s11302-015-9493-0 . Schmidt S, Isaak A, Junker A. Spotlight on P2X7 Receptor PET Imaging: A Bright Target or a Failing Star? Int J Mol Sci. 2023;24:1374. https://doi.org/10.3390/ijms24021374 . Cersosimo F, Lonardi S, Ulivieri C, Martini P, Morrione A, Vermi W, et al. CSF-1R in Cancer: More than a Myeloid Cell Receptor. Cancers. 2024;16:282. https://doi.org/10.3390/cancers16020282 . Schemes Scheme 1 is available in the Supplementary Files section Additional Declarations Competing interest reported. M.L.J., M.K., IMJ and SCN are co-inventors on patent no. PCT/US2024/024901 “Method for detecting innate immune action in vivo using GPR84-PET.” Supplementary Files ReyesJNISupplementalMaterial.docx Schematic1.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 10 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 10 Apr, 2026 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. 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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-9374449","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629531873,"identity":"22c5e7f8-41fc-4f6e-b516-6c48f9bfb906","order_by":0,"name":"Samantha T. Reyes","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Samantha","middleName":"T.","lastName":"Reyes","suffix":""},{"id":629531876,"identity":"1e5ea8ac-8230-43ea-a52c-a111965b9a57","order_by":1,"name":"Renesmee C. 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Jackson","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"M.","lastName":"Jackson","suffix":""},{"id":629531881,"identity":"18074999-70eb-4850-961b-6aab9c47b18f","order_by":3,"name":"Mausam Kalita","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Mausam","middleName":"","lastName":"Kalita","suffix":""},{"id":629531884,"identity":"310a1b80-9791-4e09-b1ff-19dccca933b3","order_by":4,"name":"Piper Mahn","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Piper","middleName":"","lastName":"Mahn","suffix":""},{"id":629531886,"identity":"cd6a0512-3d6e-46c6-ad10-25913855d93f","order_by":5,"name":"Spencer Mak","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Spencer","middleName":"","lastName":"Mak","suffix":""},{"id":629531888,"identity":"c19afb33-77bd-49db-b84d-87ab6e7dbb17","order_by":6,"name":"Mira Sundar","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Mira","middleName":"","lastName":"Sundar","suffix":""},{"id":629531889,"identity":"e8d46835-8ed2-4ece-9c7c-63d504e3e2e4","order_by":7,"name":"Bo Zhang","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zhang","suffix":""},{"id":629531890,"identity":"3a281a37-8254-4662-9eda-3b90082852e8","order_by":8,"name":"Jonathan Green","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Green","suffix":""},{"id":629531892,"identity":"27aaa2f3-057f-44de-b33f-f62e85a101ba","order_by":9,"name":"Hui-Wen Chan","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Hui-Wen","middleName":"","lastName":"Chan","suffix":""},{"id":629531894,"identity":"0869fa3a-ffbe-458e-b77d-54258f5172e3","order_by":10,"name":"Desiree D’Moore","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Desiree","middleName":"","lastName":"D’Moore","suffix":""},{"id":629531898,"identity":"987c4ddd-1df4-472b-b341-7e11d007ed9a","order_by":11,"name":"Nidhi Srikantam","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Srikantam","suffix":""},{"id":629531899,"identity":"a8550f8c-c4e1-4ef7-a57e-5fa02b8e6b78","order_by":12,"name":"Poorva Jain","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Poorva","middleName":"","lastName":"Jain","suffix":""},{"id":629531900,"identity":"2ef776fa-75cd-426a-8a44-4aae130279a6","order_by":13,"name":"Andrew Setiadi","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Setiadi","suffix":""},{"id":629531901,"identity":"fdf45753-72b1-46c1-b6bc-c89aac58ec62","order_by":14,"name":"Sydney C. Nagy","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Sydney","middleName":"C.","lastName":"Nagy","suffix":""},{"id":629531902,"identity":"11ddbbd3-70cb-4c92-a412-748046313800","order_by":15,"name":"Tahlia Wu","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Tahlia","middleName":"","lastName":"Wu","suffix":""},{"id":629531903,"identity":"e0946181-90d8-4334-b74d-38ba008ff6d9","order_by":16,"name":"Emily M. Deal","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"M.","lastName":"Deal","suffix":""},{"id":629531904,"identity":"ae0c188b-f87b-4f1e-8c6e-cee4d98b1700","order_by":17,"name":"Gayatri Gowrishankar","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Gayatri","middleName":"","lastName":"Gowrishankar","suffix":""},{"id":629531905,"identity":"6d05ee98-f437-423d-af11-26badcfdf3b8","order_by":18,"name":"Graeme Milligan","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Graeme","middleName":"","lastName":"Milligan","suffix":""},{"id":629531906,"identity":"08e14cce-7e51-4923-b845-ef033c12ddcd","order_by":19,"name":"Michelle L. James","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACdoYEECUH5lQwMDA2MDAY4NfCDNFizMAGJM8QqQUMEhuI1qLbzPDw042KuvT585ufPThQcU+2gb15mwQ+LWaHGZKlc84czt1wjM3c4MCZYuMGnmNlhLQkSOe2HcjdwMZgJv2xLSGxQSLHjKAtv3P/1aXLt7F/kzgI0iL/hqCWNOncBuYEhmM8ZhAtEjyEtVjnHDtsuOFYTpnEgTMJxm08acUWeLUc70m+nVNTJy/ffHybxIGKBNl+9sMbb+DTwsDAk4DKZ8OvHATYDxBWMwpGwSgYBSMbAAClcUvdpwo1FgAAAABJRU5ErkJggg==","orcid":"","institution":"Stanford University","correspondingAuthor":true,"prefix":"","firstName":"Michelle","middleName":"L.","lastName":"James","suffix":""}],"badges":[],"createdAt":"2026-04-10 04:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9374449/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9374449/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108072648,"identity":"61a90111-594a-4811-ba0e-3b01ce27ea5e","added_by":"auto","created_at":"2026-04-29 06:14:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":278785,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGPR84 is a cell surface marker that is upregulated in inflammatory conditions. A) \u003c/strong\u003eLumbar spinal cords were harvested from EAE and naïve mice and processed for bulk RNA quantification.\u003cstrong\u003e B) \u003c/strong\u003ePreviously published RNA quantification data was re-analyzed to define cell-surface microglial markers that are upregulated under inflammatory conditions. \u003cstrong\u003eC) \u003c/strong\u003eHeatmap showing the top 5 markers that meet criteria from part \u003cstrong\u003eA\u003c/strong\u003e. \u003cem\u003eGpr84\u003c/em\u003e was selected as candidate biomarker for innate immune activation due to its cell-specificity and functional relevance for activated myeloid-lineage cells. \u003cstrong\u003eD) \u003c/strong\u003eAnalysis of \u003cem\u003eGpr84\u003c/em\u003e transcript expression from bulk RNA quantification from parts \u003cstrong\u003eA-C\u003c/strong\u003e shows significant transcript upregulation in low and high EAE mice compared to naïve via ordinary one-way ANOVA comparing to healthy mice. \u003cstrong\u003eE) \u003c/strong\u003eqPCR in LSC of a different cohort of mice to validate bulk RNA quantification findings shows a significant fold change in high EAE compared to naïve via one-way ANOVA comparing to healthy mice. \u003cstrong\u003eF)\u003c/strong\u003e Spinal cords were dissociated and separated by CD11b\u003csup\u003e+\u003c/sup\u003e (myeloid lineage cells) and CD11b\u003csup\u003e-\u003c/sup\u003e cell types. qPCR was performed on the fractions showing \u003cem\u003eGpr84\u003c/em\u003e expression is significantly higher on\u0026nbsp; myeloid cells compared to CD11b\u003csup\u003e-\u003c/sup\u003e cells via Welch’s t test. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9374449/v1/eee7878f6e49470127a428a7.png"},{"id":108181387,"identity":"b8b04619-1743-4070-9b3d-b7f289173d19","added_by":"auto","created_at":"2026-04-30 08:58:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGPR84-PET radiotracers have high purity and and bind GPR84 effectively.\u003c/strong\u003e \u003cstrong\u003eA)\u003c/strong\u003e \u003csup\u003e11\u003c/sup\u003eC-MGX-38 was synthesized via a one step methylation reaction \u003cstrong\u003eB)\u003c/strong\u003e Identity and radiochemical purity of \u003csup\u003e11\u003c/sup\u003eC-MGX-38 (red) was confirmed via HPLC co-injection with reference standard (black). \u003cstrong\u003eC)\u003c/strong\u003e Cell binding study of \u003csup\u003e11\u003c/sup\u003eC-MGX-38 with hGPR84 expressing HEK293 cells to confirm binding to the receptor \u003csup\u003e11\u003c/sup\u003eC-MGX-38 demonstrated significantly higher binding in hGPR84\u003csup\u003e+\u003c/sup\u003eHEK293 cells (purple) at 40 minutes compared to parental control cells (blue). Binding was significantly decreased in hGPR84\u003csup\u003e+\u003c/sup\u003eHEK293 cells in the presence of GPR84 antagonist GLPG1205 (pink). Statistics performed using Tucky ordinary one-way ANOVA with multiple comparisons. \u003cstrong\u003eD)\u003c/strong\u003e \u003csup\u003e18\u003c/sup\u003eF-MGX-110S was synthesized as previously reported through copper mediated radiofluorination of a Bpin precursor. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9374449/v1/8627e36a5a027517b274eaa6.png"},{"id":108491070,"identity":"7365ee2f-e3d0-4631-958c-96f0bb3f0a55","added_by":"auto","created_at":"2026-05-05 09:51:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":319590,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGPR84-PET reveals elevated signal in the spinal cord of EAE mice compared to naïve.\u003c/strong\u003e \u003cstrong\u003eA)\u003c/strong\u003e Representative PET images are shown for \u003csup\u003e11\u003c/sup\u003eC-MGX-38. Regions with elevated signal include: adipose (white arrow), bladder (orange arrow), Liver (cyan arrow), lumbar (green arrow) and thoracic (pink arrow). Dynamic TACs show penetration into and steady wash out of the spinal cord. Quantification of 10-min static PET 35-min post-injection reveals significant increase in signal via ordinary one-way ANOVA. \u003cstrong\u003eB)\u003c/strong\u003e Representative PET images are shown for \u003csup\u003e18\u003c/sup\u003eF-MGX-110S. In addition to the regions labeled in \u003cstrong\u003eA\u003c/strong\u003e, the GI tract has elevated signal (yellow arrow). Dynamic TACs show penetration into and steady wash out of the spinal cord. Quantification of 10-min static PET 40-55-min post-injection reveals significant increase in signal via Brown-Forsythe and Welch ANOVA test. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9374449/v1/3ca72082e9618551c55fc07d.png"},{"id":108072654,"identity":"1802387c-d328-4497-8692-942da66fade8","added_by":"auto","created_at":"2026-04-29 06:14:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":191229,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEx vivo \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003egamma reveals \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF-MGX-110S has higher sensitivity than both \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eC-MGX-38 and \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF-GE-180 in EAE mouse CNS. A) \u003c/strong\u003e\u003csup\u003e11\u003c/sup\u003eC-MGX-38\u003cem\u003e s\u003c/em\u003etatistics calculated via ordinary one-way ANOVA. \u003cstrong\u003eB) \u003c/strong\u003e\u003csup\u003e18\u003c/sup\u003eF-MGX-110S statistics calculated with Brown-Forsythe and Welch ANOVA test. \u003cstrong\u003eC) \u003c/strong\u003eComparing the ratio of High EAE-to-Naïve for both GPR84-PET tracers \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S and TSPO-PET radiotracer \u003csup\u003e18\u003c/sup\u003eF-GE-180 with both PET and \u003cem\u003eex vivo \u003c/em\u003egamma counting. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9374449/v1/7ae0822f918e37aa7117f9f2.png"},{"id":108182474,"identity":"7278d0ac-d40c-4d99-beba-3f6d57e17aa2","added_by":"auto","created_at":"2026-04-30 08:59:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":195430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eex vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e autoradiography and H\u0026amp;E overlay corroborate \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eex vivo \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003egamma counting data\u003c/strong\u003e. \u003cstrong\u003eA) \u003c/strong\u003e\u003csup\u003e11\u003c/sup\u003eC-MGX-38 shows visually higher signal in the high EAE mouse LSC compared naïve LSC. \u003cstrong\u003eB) \u003c/strong\u003e\u003csup\u003e18\u003c/sup\u003eF-MGX-110S shows visually higher signal in both high- and low-scoring EAE mouse LSC compared to naïve LSC.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9374449/v1/8cf932dedc40f6e8dd637f50.png"},{"id":108182517,"identity":"6dc0aad0-e765-432f-aeea-062aee1d2579","added_by":"auto","created_at":"2026-04-30 08:59:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":286375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGPR84-PET detects response to treatment with fingolimod in EAE mice.\u003c/strong\u003e \u003cstrong\u003eA) \u003c/strong\u003eDesign of fingolimod treatment study in EAE mice. \u003cstrong\u003eB)\u003c/strong\u003e Representative PET images of \u003csup\u003e11\u003c/sup\u003eC-MGX-38 in EAE mice that received fingolimod (right) or vehicle (left). Quantification of 10-min static PET 35-min post-injection of EAE mice treated with fingolimod or vehicle. \u003cem\u003eEx vivo \u003c/em\u003egamma counting data mirrors PET signal in the spinal cord and shows significant signal depletion in the brain and adipose tissue of treated EAE mice vs vehicle mice. \u003cstrong\u003eC) \u003c/strong\u003eRepresentative PET images of \u003csup\u003e18\u003c/sup\u003eF-MGX-110S in EAE mice that received fingolimod (right) or vehicle (left). Quantification of 10-min static PET 40-55-min post-injection of EAE mice treated with fingolimod or vehicle. \u003cem\u003eEx vivo \u003c/em\u003egamma counting data shows high sensitivity in the spinal cord and shows significant signal decrease in the and adipose tissue of treated EAE mice vs vehicle mice via. All statistics use Welch’s t test. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9374449/v1/515c2ef8a8e4264b6f87d064.png"},{"id":108496545,"identity":"74505fd0-4a1b-4496-aec9-572c7514fc79","added_by":"auto","created_at":"2026-05-05 10:12:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1810963,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9374449/v1/8abce476-5a02-4a7a-9f43-70672cc66608.pdf"},{"id":108182001,"identity":"4fd43dd5-f8f9-4542-bb4c-49a6c35ec531","added_by":"auto","created_at":"2026-04-30 08:59:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1370748,"visible":true,"origin":"","legend":"","description":"","filename":"ReyesJNISupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9374449/v1/0b56285f545439b9435ede40.docx"},{"id":108072650,"identity":"1ca085da-f62f-41b7-9236-299b274cc199","added_by":"auto","created_at":"2026-04-29 06:14:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3563810,"visible":true,"origin":"","legend":"","description":"","filename":"Schematic1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9374449/v1/825b034cf6f543b28fbdf6f7.docx"}],"financialInterests":"Competing interest reported. M.L.J., M.K., IMJ and SCN are co-inventors on patent no. PCT/US2024/024901 “Method for detecting innate immune action in vivo using GPR84-PET.”","formattedTitle":"Noninvasive Imaging of Myeloid Cell Dynamics: GPR84 PET Differentiates EAE and Tracks Therapy Response","fulltext":[{"header":"Background","content":"\u003cp\u003eMultiple sclerosis (MS) is a lifelong, immune-mediated neurological disorder in which demyelinating CNS lesions drive progressive motor dysfunction, cognitive decline, and vision loss [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. MS is typically diagnosed in young adults between ages 15\u0026ndash;45 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], disproportionally affects females, and substantially impairs quality of life for patients, families, and caregivers; in fact, MS is the leading cause of non-traumatic disability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Approximately 85% of MS patients present with relapsing-remitting MS (RR-MS), which is characterized by periods of disease progression followed by recovery whilst the remaining 15% are diagnosed with either primary progressive (PP-MS) or progressive-relapsing (PR-MS) MS, which both lack periods of recovery [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Many disease-modifying therapies (DMTs) have been developed for MS that can modify or slow clinical disease progression [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; however, no tools exist to identify the pathogenic cells (e.g., myeloid cells, B cells, and T cells) that drive disease progression. The ability to do so would help expedite therapy selection and enable real-time efficacy monitoring.\u003c/p\u003e \u003cp\u003eMicroglia and macrophages have been identified as the predominant pathogenic cells in MS lesions within the CNS [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Under homeostatic conditions, microglia, the resident immune cells of the brain, protect the brain by clearing debris, pruning synapses, and promoting myelin repair. Macrophages, in contrast, are peripheral immune cells not normally found in the brain. In a pro-inflammatory immune environment, maladaptive immune responses, including macrophage egress and infiltration into the CNS, contribute to pathological damage in MS. Although the role of the adaptive immune system in MS pathogenesis is undeniable, with auto-reactive B and T cell infiltration promoting degeneration and demyelination [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], a growing body of evidence supports that the innate immune system is significantly implicated in MS pathophysiology across all disease types [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Myeloid cells (i.e., dendritic cells, macrophages, microglia, monocytes and neutrophils) are innate immune cells and central mediators of MS progression and remission [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], with reactive macrophages and microglia constituting the dominant pro-inflammatory innate immune infiltrates in the CNS of both acute and chronic active states of MS [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClinically, microglial activation has been shown to occur in pre-active MS lesions and in normal-appearing white matter both during and preceding leukocyte infiltration into the CNS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A murine model of MS, experimental autoimmune encephalomyelitis (EAE), demonstrates infiltration of activated macrophages in the CNS, predominantly in the spinal cord, recapitulating the infiltration seen in post-mortem CNS tissue from MS patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies in EAE mice have also shown that depleting myeloid cells or preventing infiltration prevents or delays disease onset [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Together, these studies highlight that pro-inflammatory myeloid cells are key players in MS disease onset and progression, and have potential to serve as clinically meaningful biomarkers in MS.\u003c/p\u003e \u003cp\u003eSince activated macrophages and microglia emerge early in MS, persist throughout disease progression, and critically influence both progression and remission, they are an attractive target for non-invasive imaging to enhance patient stratification, disease staging, therapeutic monitoring, and targeted drug development. Innate immune activation encompasses highly dynamic processes with both protective and harmful responses exhibiting diverse spatial and temporal patterns. A noninvasive imaging tool for innate immune responses is essential to define disease-specific molecular signatures, as these responses vary across pathological contexts. Current clinical standard practices for MS diagnosis and monitoring include blood tests such as erythrocyte sedimentation rate and vitamin D, cerebrospinal fluid (CSF) analysis to check for the presence of oligoclonal bands, IgG index, and myelin basic protein, and magnetic resonance imaging (MRI) with and without contrast to identify lesion presence and monitor activity over time [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While these methods provide clinically validated information relating to MS, they do not provide molecular information in the CNS and do not reveal the extent of innate immune activation in both the CNS and throughout the whole body in living subjects. This knowledge gap provides an opportunity for developing new approaches to understand the molecular signature of innate immune activation in MS and how DMTs can dampen activated cells.\u003c/p\u003e \u003cp\u003ePositron emission tomography (PET) imaging allows for spatial-temporal imaging of biomarkers in the whole body of living subjects and can enable longitudinal whole-body monitoring of pro-inflammatory myeloid cells. The most widely evaluated PET radiotracer for imaging neuroinflammation is translocator protein 18 kDa (TSPO). TSPO is located on the outer mitochondrial membrane and is involved in modulating mitochondrial bioenergetics through regulating calcium signaling, membrane potential, production of reactive oxygen species, and mitophagy \u0026ndash; all processes essential for cell survival and inflammatory responses [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although TSPO is intimately associated with inflammation, it lacks specificity to innate immune cells, as it is present on many cell types including microglia, astrocytes, endothelial cells, macrophages, skeletal muscle cells, and kidney cells. Additionally, TSPO expression does not change per myeloid cell under inflammatory conditions, but rather, numbers of TSPO-expressing cells are altered in regions of injury/pathology compared to baseline healthy tissue. TSPO thus cannot provide information on the functional phenotype (e.g., activation status) of individual myeloid cells, but instead provides an indication of where TSPO expressing cells are located [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTSPO-PET has been used to evaluate several inflammation-related diseases both clinically and preclinically. Clinical TSPO-PET imaging of patients with inflammatory disease such as Alzheimer\u0026rsquo;s Disease [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], MS [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], concussion [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and long COVID [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Preclinically, TSPO-PET has been used extensively to investigate neuroinflammatory models like rodent models of MS [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and AD [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and infection [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. While TSPO-PET imaging studies have provided robust information about the density and location of TSPO expressing cells, as well as mitochondrial bioenergetics, this imaging tool lacks the cell specificity required to identify pathogenic myeloid cell populations. There is thus a need to develop cell-specific PET radiotracers to specifically interrogate various cell populations in the context of inflammation.\u003c/p\u003e \u003cp\u003eOur lab has identified G protein-coupled receptor 84 (GPR84), an immune metabolic G-protein coupled receptor (GPCR) that is specifically and significantly induced on myeloid cells (including microglia, macrophages, monocytes and neutrophils) under pro-inflammatory conditions and exhibits minimal expression in healthy tissue. GPR84 potentiates inflammation through multiple pathways (Scheme 1). Unlike TSPO, GPR84 provides superior cellular specificity for activated myeloid cells, making it a potentially suitable target for imaging pro-inflammatory innate immune responses [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] with improved interpretability.\u003c/p\u003e \u003cp\u003eIn this study, we characterize GPR84 as a biomarker of disease-associated myeloid cells in EAE mice using both bulk RNA quantification and qPCR. We also evaluate our newly developed GPR84-specific radiotracers \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S for their ability to detect innate immune activation, as well as monitor therapeutic response to an FDA-approved DMT used for treatment of MS, fingolimod (FTY720, Gilenya), using the mouse EAE model of MS. We demonstrate that GPR84-PET shows a robust increase in CNS signal in EAE mice compared to controls. It also detects treatment response in fingolimod-treated EAE mice versus vehicle-treated EAE mice, with the fluorinated tracer providing higher sensitivity for delineating both disease severity and peripheral signal.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBulk RNA quantification re-analysis\u003c/h2\u003e \u003cp\u003eWe re-analyzed previously our published data [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] to identify potential biomarkers of innate immune activation in EAE mice as a target for novel radiotracer development. Briefly, bulk transcriptomic RNA quantification of lumbar spinal cords was generated from 15 C57BL/6 mice either healthy (n\u0026thinsp;=\u0026thinsp;5) or induced with EAE and stratified into low disease progression (n\u0026thinsp;=\u0026thinsp;5) and high disease progression (n\u0026thinsp;=\u0026thinsp;5). Gene expression counts and disease metadata were imported from CSV files and organized into a Seurat object. Genes with a mean basal expression of \u0026le;\u0026thinsp;20 counts in healthy mice were retained to focus on low-abundance, potentially disease-specific transcripts, and the count matrix was log-normalized to account for differences in sequencing depth. Average expression of each gene per disease group was calculated, and fold-change analysis was performed sequentially (low versus healthy, high versus low) to identify progressively upregulated genes (fold-change\u0026thinsp;\u0026ge;\u0026thinsp;1 in both comparisons); these were ranked by absolute expression in the high disease group. Cell surface and myeloid marker databases were constructed by integrating Gene Ontology annotations (plasma membrane and myeloid differentiation/activation terms), UniProt localization queries, BioMart/Ensembl transmembrane or myeloid-related annotations, and literature-curated canonical markers. Progressively upregulated genes intersecting both databases were prioritized as candidate cell surface myeloid biomarkers, and their expression patterns across disease stages were visualized using a heatmap.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnimals\u003c/h3\u003e\n\u003cp\u003eFemale 8-10-week-old C57BL/6J mice (Jackson Laboratory, strain #: C57BL6J, RRID: 1MSR_JAX:000664) were housed in a non-barrier, temperature-controlled environment (humidity 40\u0026ndash;60%) under a 12 h light/dark schedule with unrestricted access to food and water. A total of 237 mice were used for this study. Treatment groups were randomly assigned by cage and co-housed in the same room as vehicle and control groups. All animal care and procedures complied with the Animal Welfare Act and were in accordance with institutional guidelines. Permission to perform all animal experiments was granted by the Stanford Administrative Panel on Laboratory Animal Care (APLAC), which is accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care International (AAALAC International).\u003c/p\u003e\n\u003ch3\u003eEAE induction\u003c/h3\u003e\n\u003cp\u003eEAE was induced in female C57BL/6J WT mice using MOG35-55 emulsified in CFA (Hooke Laboratories, #EK-2110). All vehicle and therapy-treated mice received two pertussis toxin i.p. injections (110 ng) 2 and 24 h after MOG induction. Na\u0026iuml;ve littermates were used as additional controls. EAE mice were weighed and scored daily from day 8 onward using a standard scoring protocol for levels of paresis/paralysis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eqPCR of lumbar spinal cords with CD11b isolation\u003c/h3\u003e\n\u003cp\u003eBulk spinal cords were combined (n\u0026thinsp;=\u0026thinsp;2/tube) and stored on ice in MACS Tissue Storage Solution (Miltenyi) and then transferred to a C-Tube containing an enzymatic mix detailed in Multi Dissociation Kit 1 protocol (Miltenyi). Samples were then placed on a gentleMACS Octo Dissociator with Heaters (Miltenyi). After dissociation, the homogenates were de-myelinated with a 25% standard isotonic percoll gradient. For bulk qPCR, the single cell suspension was frozen in TRIzol (Invitrogen). To isolate CD11b positive and negative cell populations, the cells were incubated using CD11b (Microglia) MicroBeads (Miltenyi) and passed through an LS Separation column following manufacturer's instructions (Miltenyi). The positive and negative fractions were then frozen on Trizol. RNA was extracted per the manufacturer's protocol (Invitrogen). cDNA synthesis was performed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Gene expression of GPR84 (Qiagen GeneGlobe ID: PPM04873A-200) was assessed by RT-PCR on an Applied Biosystems QuantStudio 6, with GAPDH serving as the housekeeping gene. Each sample was run in triplicate, and fold change was calculated using the 2^ΔΔCt method. Undetectable transcripts were assigned a Ct (cycle threshold) of 38 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and samples with high inter-replicate variation (SD\u0026thinsp;\u0026gt;\u0026thinsp;0.70) were excluded. The sample size (N) is provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eDMT administration\u003c/h3\u003e\n\u003cp\u003eMice were randomly assigned to the following groups: na\u0026iuml;ve, EAE, vehicle-treated EAE, and fingolimod-treated EAE. Fingolimod was administered daily starting from 8 days post disease-induction until 14\u0026ndash;16 days post-induction. Fingolimod (Cayman Chemicals, 3 mg/kg, #10006292) was administered via oral gavage. Vehicle-treated EAE mice received water via oral gavage.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRadiosynthesis of \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S\u003c/h2\u003e \u003cp\u003e \u003csup\u003e \u003cb\u003e11\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eC-MGX-38\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePrecursor (0.5 mg) was dissolved in DMF (0.5 mL) and loaded into a glass reactor. 1.0 M NaOH in water (6 \u0026micro;L) was added to the reactor just prior to the delivery of \u003csup\u003e11\u003c/sup\u003eC-MeI and the reactor was cooled to -10\u0026ordm;C; the cell was then sealed and the reaction heated to 70\u0026ordm;C for 5 minutes. The reaction mixture was cooled to 40\u0026ordm;C and diluted with 1 mL of 40:60 H\u003csub\u003e2\u003c/sub\u003eO : MeCN. The crude reaction mixture was purified via semipreparative HPLC (column: Phenomenex Gemini 5 mm C18 110 \u0026Aring;, 250 \u0026times; 10 mm; mobile phase A: H\u003csub\u003e2\u003c/sub\u003eO with 0.1% trifluoroacetic acid (TFA) by volume; mobile phase B: MeCN with 0.1% TFA by volume; program: 40\u0026ndash;60% B in 15 min at 5 mL/min; product retention time: 8.7 min). Purified \u003csup\u003e11\u003c/sup\u003eC-MGX-38 was collected in a round-bottom flask containing 20 mL H\u003csub\u003e2\u003c/sub\u003eO. The solution was passed through a Sep-pak plus lite C18 (conditioned with 5 mL EtOH and 10 mL H\u003csub\u003e2\u003c/sub\u003eO) and the cartridge was rinsed with H\u003csub\u003e2\u003c/sub\u003eO (6 mL). Purified \u003csup\u003e11\u003c/sup\u003eC-MGX-38 product was eluted with EtOH (0.5 mL) followed by saline (4.5 mL). Purity and identity of final product were confirmed via analytical HPLC (column: Phenomenex Gemini 5 mm C18 110 \u0026Aring;, 250 \u0026times; 4.6 mm; mobile phase A: H\u003csub\u003e2\u003c/sub\u003eO with 0.1% TFA by volume; mobile phase B: MeCN with 0.1% TFA by volume; program: 50\u0026ndash;95% B in 15 minutes at 1 mL/min; product retention time: 6.8 minutes). Resulting molar activity was 11555 \u0026plusmn; 6383 mCi/\u0026micro;mol (n\u0026thinsp;=\u0026thinsp;6).\u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cb\u003e18\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eF-MGX-110S\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e18\u003c/sup\u003eF-MGX-110S was synthesized following previously published methods [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Briefly, radiosynthesis of \u003csup\u003e18\u003c/sup\u003eF-MGX-110S was achieved by achieved by copper-mediated radiofluorination of a Bpin precursor in a mixture of dimethylacetamide at 130\u0026deg;C for 20 min. Resulting molar activity was 6670\u0026thinsp;\u0026plusmn;\u0026thinsp;210 mCi/\u0026micro;mol (n\u0026thinsp;=\u0026thinsp;5).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIn vitro radioactive cell binding studies:\u003c/h3\u003e\n\u003cp\u003eGPR84-transfected HEK-293 cells and control HEK-293 cells were plated at 3x10\u003csup\u003e5\u003c/sup\u003e cells per well in medium (DMEM with 10% FBS and 1% anti-anti, Thermo Fisher Scientific) into three 12-well plates 24 h prior to the cell binding assay. Once cells reached\u0026thinsp;~\u0026thinsp;90% confluency, the radiotracer was prepared. Additionally, a solution of the GPR84 antagonist GLPG1205, 35 \u0026micro;M in DMSO was prepared, and the DMEM medium warmed. The radiotracer was diluted in DMEM without FBS or antibiotic to yield 30mL of solution where the amount of radioactivity was 50 \u0026micro;Ci in 1mL. The 12-well plates were incubated for 40 minutes at 37\u0026ordm;C and 5% CO\u003csub\u003e2\u003c/sub\u003e in air. Cells were subsequently washed with PBS, trypsinized to remove from the surface of the plates, resuspended in DMEM\u0026thinsp;+\u0026thinsp;10% FBS, and 500\u0026micro;L volumes pipetted into gamma counting tubes. Radioactivity bound to cells was measured in a gamma counter (Revvity), and cell numbers were recorded using Trypan blue to distinguish live and dead cells and an automatic cell counter to normalize radioactivity levels to cell count.\u003c/p\u003e\n\u003ch3\u003ePET/CT imaging\u003c/h3\u003e\n\u003cp\u003eMice were anesthetized with isoflurane gas (2.0\u0026ndash;3.0% for induction and 1.0\u0026ndash;2.0% for maintenance) and intravenously injected with \u003csup\u003e18\u003c/sup\u003eF-MGX-110S (55\u0026ndash;206 \u0026micro;Ci) or \u003csup\u003e11\u003c/sup\u003eC-MGX-38 (133\u0026ndash;578 \u0026micro;Ci). For each radiotracer, mice either underwent either a 60-minute dynamic scan or a 10-min static PET as dynamic PET was used to understand tracer kinetics for selecting static time frame. Static images were acquired within 40\u0026ndash;55 minutes post-injection of \u003csup\u003e18\u003c/sup\u003eF-MGX-110S or 35-minutes post-injection of \u003csup\u003e11\u003c/sup\u003eC-MGX-38 on days 14, 15 or 16 after EAE induction using a GNEXT scanner (Sofie Biosciences) in list mode format. In the case of a single group of N\u0026thinsp;=\u0026thinsp;3 na\u0026iuml;ve mice, the scanner failed mid-acquisition and the timepoint was repeated at 66 minutes. CT images were acquired after each PET scan to provide an anatomical reference frame in addition to scatter and attenuation correction for PET data. Isotropic resolution was achieved using OSEM3D/MAP reconstruction algorithms with 24 subsets, 3 iterations, and a matrix size of 240 \u0026times; 240 \u0026times; 191.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePET imaging analysis\u003c/h2\u003e \u003cp\u003ePET images were analyzed using VivoQuant 4.0 (InVicro) following previously published methods [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Briefly, the PET was overlaid and matched to the CT and decay-corrected for each dose administered. Manual ROIs were drawn on the CT for lumbar and thoracic/cervical spinal cords. A semi-automated atlas was used for the brain analysis. Analysis was conducted blind for the ROI drawing, but not for the dose corrections and %ID/g calculations as researchers used the mouse scores to assign into groups: High scoring disease (High EAE), Low scoring disease (Low EAE), vehicle-treated EAE (Veh-EAE), fingolimod-treated EAE (Tx EAE) and na\u0026iuml;ve. Refer to Tables S2-4 for N in each group for each radiotracer.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eEx vivo\u003c/span\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003egamma counting\u003c/span\u003e\u003c/p\u003e \u003cp\u003eImmediately after PET, mice were euthanized under anesthesia (with inhaled isoflurane [2\u0026ndash;3%]) and cardiac puncture performed. Mice were subsequently perfused with 20\u0026ndash;30 mL of PBS and tissues of interest (blood, brain, kidney, lumbar spinal cord [LSC], thoracic/cervical spinal cord [TSC], liver, cecum, spleen, femur, bone marrow, muscle, adipose and tail) were individually harvested, weighed wet and gamma-counted (Hidex automatic gamma counter, Hidex). Gamma counting standards (3\u0026ndash;10 \u0026micro;Ci/standard) were used to accurately decay-correct biodistribution results, which were calculated as %ID/g using the weight of each dissected organ. Refer to Table S5 for N for each organ and radiotracer.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eEx vivo\u003c/span\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eautoradiography studies\u003c/span\u003e\u003c/p\u003e \u003cp\u003eA separate, representative cohort of EAE and na\u0026iuml;ve mice were used to evaluate \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S in lumbar spinal cord via digital \u003cem\u003eex vivo\u003c/em\u003e autoradiography. 30\u0026ndash;40 minutes after intravenous injection of \u003csup\u003e11\u003c/sup\u003eC-MGX-38 (800 \u0026micro;Ci) or \u003csup\u003e18\u003c/sup\u003eF-MGX-110S (150 \u0026micro;Ci), mice were euthanized under anesthesia (with inhaled isoflurane (2\u0026ndash;3%)) and perfused with PBS (20 mL), and CNS tissues were harvested for autoradiography. Following gamma counting, lumbar spinal cords were frozen in optimal cutting temperature (OCT), and cryosectioned into 60 \u0026micro;m (\u003csup\u003e11\u003c/sup\u003eC-MGX-38) or 40 \u0026micro;m (\u003csup\u003e18\u003c/sup\u003eF-MGX-110S) thick sections and placed on superfrost plus slides (Thermo Fisher). The air dried slides were exposed to a digital storage phosphor screen (Amersham Biosciences) for 10 half-lives and imaged via digital autoradiography using a Typhoon phosphorimager (Amersham Biosciences). The slides were then stained with H\u0026amp;E (Hematoxylin Gills 3, Thermo Fisher Scientific, NC9964763; Eosin-Y Richard-Allan Scientific, Thermo Fisher Scientific, #22-110-637) for anatomical reference and visualization of the distribution of immune cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePlasma free fraction (f\u003csub\u003ep\u003c/sub\u003e)\u003c/h2\u003e \u003cp\u003eTo assess f\u003csub\u003ep\u003c/sub\u003e, n\u0026thinsp;=\u0026thinsp;3 mice were injected per tracer with 1.0-1.7 mCi of either \u003csup\u003e11\u003c/sup\u003eC-MGX-38 or \u003csup\u003e18\u003c/sup\u003eF-MGX-110S, iv. After 35-45-minutes post-injection, 500 \u0026micro;L of blood was collected from the heart and placed into an EDTA-coated blood tube and centrifuged at 500 rcf for 10 minutes. 200 \u0026micro;L of resulting plasma was placed into a Centrifree ultrafiltration unit (Millipore Sigma) and centrifuged at 1000 rcf for 30 min, following manufacturer\u0026rsquo;s instructions. The resulting fractions of bound and free radiotracer were placed on the gamma counter to calculate the f\u003csub\u003ep\u003c/sub\u003e for each tracer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistics and reproducibility\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using Prism (version 11, GraphPad Software). For comparisons across multiple groups, one-way ANOVA followed by Dunnett\u0026rsquo;s multiple comparisons test versus naive controls was used when variances were similar. When variance differed significantly between groups (Brown\u0026ndash;Forsythe test), Welch\u0026rsquo;s ANOVA followed by Dunnett T3 multiple comparisons was performed. All ANOVA multiple comparison\u0026rsquo;s tests were compared to na\u0026iuml;ve unless otherwise noted. For datasets of 2 groups, a Welch\u0026rsquo;s t test was performed. Error bars represent standard deviation (SD). Exact tests used are indicated in figure legends. PET/CT and ex vivo gamma counting results are from six experiments for \u003csup\u003e18\u003c/sup\u003eF-MGX-110S and \u003csup\u003e11\u003c/sup\u003eC-MGX-38.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eBulk RNA quantification identifies GPR84 as a promising myeloid cell surface marker of innate immune activation in EAE mice.\u003c/b\u003e To assess and identify potential new candidate biomarkers of innate immune activation in the context of EAE pathology, we re\u003cb\u003e-\u003c/b\u003eanalyzed our previously published dataset [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] of bulk RNA quantification of mouse lumbar spinal cords from na\u0026iuml;ve mice compared to mice with mild-moderate and severe EAE symptoms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Specifically, we sought to identify markers that align with ideal characteristics of PET biomarkers, including having high cell specificity, be upregulated under inflammatory conditions, have low basal expression in healthy CNS and be located on the cell surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Based on these criteria, we shortlisted 5 targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) and performed further investigation of cell specificity based on literature searches, readily available online resources (e.g., Barres Lab Brain RNAseq Library [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and Human Protein Atlas [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]). Of the five biomarkers that met these criteria, we selected GPR84 as the most suitable for further investigation: the other four were eliminated due either to having high basal expression (clec7a had low basal expression in mice, but high basal CNS expression in humans), or having poor cell-specificity (TLR2, CD300lf and CD40 all were expressed on cancer cells[\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]). GPR84 was found to be upregulated in lumbar spinal cords of EAE mice compared to na\u0026iuml;ve mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-F). From the bulk RNA quantification, \u003cem\u003eGpr84\u003c/em\u003e transcripts were significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) upregulated in high and low EAE mice compared to na\u0026iuml;ve animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). qPCR in a separate cohort was performed to validate these findings and showed that \u003cem\u003eGpr84\u003c/em\u003e transcripts were significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) upregulated in high and low EAE mice compared to na\u0026iuml;ve animals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Furthermore, \u003cem\u003eGpr8\u003c/em\u003e4 was expressed significantly more highly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on CD11b\u003csup\u003e+\u003c/sup\u003e cells than CD11b\u003csup\u003e\u0026minus;\u003c/sup\u003e cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), confirming myeloid cell-specificity in the CNS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSuccessful radiosynthesis of\u003c/b\u003e \u003csup\u003e\u003cb\u003e11\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC-MGX-38 has high yield and shows effective binding to GPR84\u003c/b\u003e. Developing a novel BBB-penetrant radiotracer requires careful consideration of many physico-chemical properties. A scoring system called CNO Multiparameter Optimization (CNS-MPO) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] was used to predict \u003csup\u003e11\u003c/sup\u003eC-MGX-38 penetrance through an intact BBB. This molecule had appropriate characteristics to passively cross BBB including favorable lipophilicity (logD between 2\u0026ndash;3), molecular weight (\u0026lt;\u0026thinsp;500) and polar surface area (\u0026lt;\u0026thinsp;90\u0026Aring;\u003csup\u003e2\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Synthesis of \u003csup\u003e11\u003c/sup\u003eC-MGX-38 yielded\u0026thinsp;\u0026gt;\u0026thinsp;98% purity of radiotracer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026amp;B). \u003cem\u003eIn vitro\u003c/em\u003e cell studies demonstrated significantly increased (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u003csup\u003e11\u003c/sup\u003eC-MGX-38 binding to hGPR84-expressing HEK293 cells relative to parental control HEK293 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Moreover, incubation of hGPR84\u003csup\u003e+\u003c/sup\u003e HEK293 cells in the presence of the GPR84 antagonist GLPG1205 resulted in a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) reduction in radiotracer binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). We further compared \u003csup\u003e11\u003c/sup\u003eC-MGX-38 to a recently developed GPR84-PET radiotracer, \u003csup\u003e18\u003c/sup\u003eF-MGX-110S (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), synthesized as previously reported [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and also exhibits favorable physico-chemical properties for BBB-penetrance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The f\u003csub\u003ep\u003c/sub\u003e of both radiotracers was calculated with \u003csup\u003e18\u003c/sup\u003eF-MGX-110S having a higher f\u003csub\u003ep\u003c/sub\u003e compared to \u003csup\u003e11\u003c/sup\u003eC-MGX-38 (42.36% and 16.16%, respectively) (Table S6).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysico-chemical properties of \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMolecular mass (g/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003csup\u003e11\u003c/sup\u003eC-MGX-38\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-MGX-110S\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e370.18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e331.12\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecLogP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogD (experimental)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etPSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNS MPO score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.9 / 6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.0/6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi (nM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGPR84-PET reveals increased signal in spinal cords of EAE mice compared to controls.\u003c/b\u003e To evaluate the utility of GPR84-PET for imaging pro-inflammatory myeloid cells in EAE mice compared to na\u0026iuml;ve animals, two GPR84-PET radiotracers were used. Time activity curves for \u003csup\u003e11\u003c/sup\u003eC-MGX-38 show a high peak and steady washout over 60-minutes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). \u003csup\u003e18\u003c/sup\u003eF-MGX-110S time activity curves exhibit a more rapid washout than \u003csup\u003e11\u003c/sup\u003eC-MGX-110S (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S both showed significantly higher signal in the lumbar (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively) and thoracic/cervical regions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively) of the spinal cord of EAE mice compared to na\u0026iuml;ve (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (in Legend). Time-activity curves (TACs) of the brain stem also show elevated signal in EAE mice compared to na\u0026iuml;ve (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEx vivo\u003c/b\u003e \u003cb\u003egamma counting reveals superior sensitivity of\u003c/b\u003e \u003csup\u003e\u003cb\u003e18\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eF-MGX-110S, confirms spinal cord GPR84-PET findings and identifies elevated signal in brain and adipose.\u003c/b\u003e To determine absolute binding quantification in organs of interest, mice were perfused after the scan, or 45\u0026ndash;55 minutes after injection of radiotracer. Perfusion is necessary to remove radiotracer circulating in the blood and evaluate signal in each organ of interest due to specific binding alone. \u003csup\u003e11\u003c/sup\u003eC-MGX-38 gamma counting signal mirrored the PET imaging quantification in the spinal cord, revealing significantly higher binding in high EAE mice compared to naive (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). \u003csup\u003e18\u003c/sup\u003eF-MGX-110S showed higher sensitivity in detecting pathological changes in the spinal cord of both high and low scoring EAE mice compared to naive (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Further, \u003csup\u003e18\u003c/sup\u003eF-MGX-110S also showed a significant increase in signal in the whole brain of high scoring EAE mice compared to na\u0026iuml;ve (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Peripherally, both GPR84 tracers showed elevated signal in brown adipose tissue of high scoring EAE mice compared to naive (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026amp;B, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Only \u003csup\u003e18\u003c/sup\u003eF-MGX-110S demonstrated a significant %ID/g increase in spleen (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), muscle (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bone marrow (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and blood (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA\u0026amp;C) in high scoring EAE mice compared to na\u0026iuml;ve. We also compared the sensitivity of our GPR84 radiotracers to the TSPO radiotracer \u003csup\u003e18\u003c/sup\u003eF-GE-180, using our previously published EAE data [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With PET imaging, both \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S demonstrated similar signal-to-background ratios of high EAE-to-na\u0026iuml;ve in the spinal cord (1.6 vs 1.3), outperforming \u003csup\u003e18\u003c/sup\u003eF-GE-180 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). However, \u003cem\u003eex vivo\u003c/em\u003e gamma counting revealed a clear advantage for \u003csup\u003e18\u003c/sup\u003eF-MGX-110S, which achieved EAE-to-na\u0026iuml;ve ratios of 9.4 and 11.8 in the lumbar and thoracic/cervical spinal cords, respectively. In contrast, \u003csup\u003e11\u003c/sup\u003eC-MGX-38 yielded ratios of 2.0 and 1.8, comparable to those of \u003csup\u003e18\u003c/sup\u003eF-GE-180 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).Finally, both GPR84-radiotracers have high peripheral signal which was measured \u003cem\u003eex vivo\u003c/em\u003e to find that \u003csup\u003e11\u003c/sup\u003eC-MGX-38 has higher liver signal than that observed for \u003csup\u003e18\u003c/sup\u003eF-MGX-110S, while \u003csup\u003e18\u003c/sup\u003eF-MGX-110S has elevated signal in the cecum (Fig. S3). \u003cem\u003eEx vivo\u003c/em\u003e autoradiography (ARG) of representative lumbar spinal cord sections demonstrated that \u003csup\u003e18\u003c/sup\u003eF-MGX-110S clearly distinguished both high and low EAE mice from na\u0026iuml;ve (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), while \u003csup\u003e11\u003c/sup\u003eC-MGX-38 showed elevated signal only in high EAE compared to na\u0026iuml;ve, providing further evidence that \u003csup\u003e18\u003c/sup\u003eF-MGX-110S detects changes in EAE disease severity with superior sensitivity than \u003csup\u003e11\u003c/sup\u003eC-MGX-38.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003eGPR84-PET of EAE mice detects\u003c/b\u003e\u003cb\u003ein vivo\u003c/b\u003e\u003cb\u003eresponse to FDA-approved therapy.\u003c/b\u003e A major hindrance in MS management is the absence of tools to objectively guide therapy selection and monitor treatment response. GPR84-PET may address this gap by quantifying innate immune activation across the whole body, offering a non-invasive clinical measure. To evaluate this preclinically, we treated EAE mice with fingolimod, an FDA-approved therapy for MS, starting at 8-days post-EAE induction (Tx-EAE mice). GPR84-PET imaging was performed between days 14\u0026ndash;16 post-induction (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) when the vehicle-treated EAE mice (Veh-EAE) were at peak of disease (hindlimb paralysis). GPR84-PET imaging showed significant changes in signal between groups for each radiotracer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u0026amp;C). \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S both showed significantly higher %ID/g in the LSC and TSC in the vehicle-treated mice compared to those treated with fingolimod (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u0026amp; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, respectively, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u0026amp;C). \u003csup\u003e11\u003c/sup\u003eC-MGX-38 \u003cem\u003eex vivo\u003c/em\u003e gamma counting revealed significantly higher %ID/g in the Veh-EAE mice compared to the Tx-EAE mice in all tissues examined, except in the spleen (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA) which although not significant, trended higher. \u003csup\u003e18\u003c/sup\u003eF-MGX-110S also had significantly higher %ID/g in all tissues examined except for the brain, which also trended higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). Mirroring the EAE-to-na\u0026iuml;ve comparison, PET imaging showed similar sensitivity to spinal cord signal with both \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S when comparing vehicle-treated to treatment-EAE mice (1.2 vs. 1.5, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC). \u003cem\u003eEx vivo\u003c/em\u003e gamma counting, however, again highlighted the superior sensitivity of \u003csup\u003e18\u003c/sup\u003eF-MGX-110S, with vehicle-to-treatment EAE ratios of 7.4 and 12.0 in the lumbar and thoracic/cervical spinal cords, respectively, compared to 2.6 and 2.7 for \u003csup\u003e11\u003c/sup\u003eC-MGX-38 (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC). These findings further establish \u003csup\u003e18\u003c/sup\u003eF-MGX-110S as the lead candidate for clinical translation, demonstrating utility not only in tracking disease severity but also in monitoring therapeutic response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Discussion and Conclusions","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003cp\u003eUnderstanding the molecular signatures of CNS disease is fundamental to advancing our knowledge of disease progression, accelerating therapy development, and enabling objective monitoring of treatment efficacy in the clinic. PET imaging is a powerful tool to non-invasively image living subjects and has the potential to transform how neuroinflammatory diseases are diagnosed and managed. For decades, TSPO-PET has been widely used for imaging neuroinflammation. While TSPO is a biomarker of inflammation and mitochondrial bioenergetics, its utility is constrained by two key limitations: broad cellular expression across many cell types including immune cells, muscle cells, and endothelial cells, and a lack of upregulation under CNS inflammatory conditions. Together, these features make TSPO-PET image interpretation difficult and reduces its efficacy as a pharmacodynamic endpoint in clinical trials. There is therefore a pressing need for new radiotracers that are cell specific and can provide insight into innate immune activation. To address this need, we performed bulk RNA quantification and selected \u003cem\u003eGpr84\u003c/em\u003e as an ideal candidate for a novel biomarker for PET imaging of innate immune activation. GPR84 was selected due to its cell specificity and phenotypic relevance to activated myeloid lineage cells, its cell surface localization, and its absence of expression on cancer cells. With GPR84 established as a suitable target, we assessed candidate GPR84 antagonists against physicochemical criteria required for passive BBB penetration and suitability for rapid \u003csup\u003e11\u003c/sup\u003eC-radiolabeling.\u003c/p\u003e \u003cp\u003eDeveloping a new radiotracer that passively penetrates the BBB is challenging, as the BBB is designed to exclude foreign substances. In order for a ligand to passively cross the BBB, certain physico-chemical properties must be met: these include low molecular mass, total polar surface area below 90\u0026Aring;\u003csup\u003e2\u003c/sup\u003e and appropriate lipophilicity (2\u0026thinsp;\u0026lt;\u0026thinsp;logP\u0026thinsp;\u0026lt;\u0026thinsp;4 or 1.5\u0026thinsp;\u0026lt;\u0026thinsp;logD\u003csub\u003e7.4\u003c/sub\u003e \u0026lt;3.5) [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A scoring system called CNS MPO is commonly used to predict BBB penetrance of a molecule. We were able to select a molecule with a MPO score of 5.9/6 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with a methoxy group to allow rapid \u003csup\u003e11\u003c/sup\u003eC-labelling without changing the structure for our first generation GPR84-PET radiotracer. In parallel, \u003csup\u003e18\u003c/sup\u003eF-MGX-110S was developed and examined as a lead fluorinated molecule for GPR84-PET. With the ultimate goal of clinical translation, two molecules were evaluated in the EAE murine model of MS to determine the ability of GPR84-PET to detect changes in inflammation in EAE mice in the presence or absence of treatment, compared to na\u0026iuml;ve animals.\u003c/p\u003e \u003cp\u003eOur study highlights GPR84-PET as a promising tool for probing innate immune activation in EAE with potential for clinical translation to MS, offering substantial advantages over TSPO-PET. Unlike TSPO, GPR84 is highly selective for microglia and macrophages, which are the primary cellular drivers of CNS innate immunity; GPR84 is also robustly upregulated under inflammatory conditions. This translates to PET images that are easier to interpret and more sensitive to disease-relevant changes. We evaluated two GPR84 radiotracers, \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S, in EAE and na\u0026iuml;ve mice. Both demonstrated elevated spinal cord signal in EAE mice relative to na\u0026iuml;ve animals, consistent with the known pathology of this model, in which lesions and infiltrating immune cells predominately localize to the spinal cord before spreading to the brain [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. \u003cem\u003eEx vivo\u003c/em\u003e gamma counting further revealed that \u003csup\u003e18\u003c/sup\u003eF-MGX-110S exhibited a greater sensitivity to subtle changes in pathology than \u003csup\u003e11\u003c/sup\u003eC-MGX-38, with spinal cord signal that tracked closely with disease severity and a significant increase in brain binding which is an important feature for capturing the full spatial extent of neuroinflammation. Peripherally, both radiotracers detected significantly elevated signal in the brown adipose tissue, a tissue known to increase levels of GPR84 during inflammation [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, only \u003csup\u003e18\u003c/sup\u003eF-MGX-110S detected significantly elevated signal in the spleen, muscle, bone marrow and blood of high scoring EAE mice compared to na\u0026iuml;ve mice. GPR84-PET can hence provide a potential means to understand innate immune response not only in the CNS, but in the periphery as well. This can provide insights into whole body inflammation as well as therapeutic response. The comparatively reduced sensitivity of \u003csup\u003e11\u003c/sup\u003eC-MGX-38 may reflect its lower f\u003csub\u003ep\u003c/sub\u003e, which is the percentage of the radiotracer that is freely circulating in the blood, rather than bound by plasma proteins. A decreased f\u003csub\u003ep\u003c/sub\u003e decreases radiotracer availability, potentially shortening circulation time and limiting target engagement. Further, the slightly higher lipophilicity and apparent slower washout kinetics of \u003csup\u003e11\u003c/sup\u003eC-MGX-38 may contribute to higher signal in the na\u0026iuml;ve mice post-perfusion, reducing the sensitivity of EAE-to-na\u0026iuml;ve.\u003c/p\u003e \u003cp\u003eThere are several limitations to GPR84-PET in EAE mice with both \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S that merit consideration. Firstly, regardless of radiotracer, quantification of the spinal cord, in particular the LSC, is subjected to partial volume effect, as some portions of the spinal cord have volume less than the resolution of the scanner, which is around 1mm. Furthermore, the compact anatomy of mice positions organs closely, increasing the risk of signal spillover from peripheral metabolism and off-target binding into spinal cord. In the case of \u003csup\u003e18\u003c/sup\u003eF-MGX-110S, progressive signal accumulation in the cecum over time (Fig S3) may have contributed spillover into the adjacent spinal cord, potentially attenuating the apparent difference between high EAE and na\u0026iuml;ve animals. Similarly, the high liver signal (Fig. S3) observed with \u003csup\u003e11\u003c/sup\u003eC-MGX-38 is a potential source of spillover into the spinal cord. These limitations were overcome through \u003cem\u003eex vivo\u003c/em\u003e gamma counting and autoradiography where we observed a significant increase in signal in spinal cord of EAE mice compared to na\u0026iuml;ve. Importantly, the anatomical differences between mice and humans mean that spillover from the liver or cecum into the spinal cord is unlikely to pose a comparable concern in the clinical setting, where greater inter-organ distances substantially reduce this risk.\u003c/p\u003e \u003cp\u003eFingolimod is an FDA-approved sphingosine-1-phosphate (S1P) receptor modulator that reduces neuroinflammation by sequestering lymphocytes in lymph nodes, thereby limiting their infiltration into the CNS [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. While its primary mechanism targets adaptive immunity, fingolimod also exerts direct effects on innate immune cells (e.g. microglia and macrophages) by modulating S1P receptor signaling, reducing pro-inflammatory cytokine release, and attenuating microglial activation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. As GPR84 is highly upregulated on activated microglia and macrophages, GPR84-PET was investigated to evaluate its utility to monitor the therapeutic response of fingolimod as an immunomodulatory DMT in the murine EAE model of MS. In our treatment studies, both radiotracers successfully detected decreased PET signal in the spinal cord associated with fingolimod treatment, distinguishing vehicle-treated EAE mice from treated animals. \u003cem\u003eEx vivo\u003c/em\u003e gamma counting mirrored the PET signal in the spinal cord for both radiotracers. \u003csup\u003e11\u003c/sup\u003eC-MGX-38 detected \u003cem\u003eex vivo\u003c/em\u003e signal decrease in all organs examined except in the spleen and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S detected \u003cem\u003eex vivo\u003c/em\u003e signal decrease in all organs examined except in the brain.\u003c/p\u003e \u003cp\u003eThere has been considerable effort to explore biomarkers of innate immune activation and develop radiotracers specific to activated myeloid cells, with several relevant targets under active investigation. Among these are colony-stimulating factor 1 receptor (CSF1R) and the purinergic ion channel P2X7 receptor (P2X7R), Both targets are upregulated on myeloid cells under neuroinflammatory conditions. \u003csup\u003e11\u003c/sup\u003eC-CPPC, a radiotracer targeting CSF1R, has been evaluated both preclinically and clinically across a range of neuroinflammatory diseases [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], demonstrating increased brain signal in EAE mice compared to controls. However, PET quantification of naive mice showed nearly zero signal in the brain and in EAE mice there less than 0.75%ID/g in the brain stem, suggesting limited BBB penetration in this species, but more favorable VT measurements have been reported in clinical studies [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. \u003csup\u003e11\u003c/sup\u003eC-SMW139, a radiotracer targeting P2X7R, similarly demonstrated elevated binding in EAE rats compared to na\u0026iuml;ve controls, though CNS penetration was also limited with PET quantitation of 0.05%ID/mL in brain and spinal cord after 15 minutes post-injection [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. \u003csup\u003e11\u003c/sup\u003eC-SMW139 was also recently used to image MS patients, where radiotracer binding was not consistently elevated in MS lesions, warranting further clinical investigation in a larger patient population [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The utility of P2X7R as a myeloid-selective biomarker is further complicated by its expression at baseline on oligodendrocytes in addition to microglia [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], as well as on peripheral immune cells including B and T cells [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. A limitation of both biomarkers is their expression on cancer cells [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], which may confound imaging interpretation in oncological contexts. GPR84, on the other hand, appears to be highly specific for myeloid lineage cells with no expression on cancer cells. In rodent models of MS, both our GPR84 radiotracers exhibit higher %ID/g (2\u0026ndash;7%ID/g in the spinal cords of EAE mice) via PET imaging, however, they have not yet been clinically translated to compare in human.\u003c/p\u003e \u003cp\u003eOverall, these findings support both \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and \u003csup\u003e18\u003c/sup\u003eF-MGX-110S as viable candidates for clinical translation as non-invasive tools for monitoring CNS innate immune activation. With greater cellular specificity and higher sensitivity than TSPO-PET, GPR84-PET is well-positioned for translational application across preclinical and clinical settings from longitudinal disease monitoring to pharmacodynamic evaluation of emerging immunotherapies. While both tracers show promise for imaging innate immune activation in the context of MS-like pathology, \u003csup\u003e18\u003c/sup\u003eF-MGX-110S emerges as the leading candidate for clinical translation due to its superior sensitivity in both the CNS and periphery and longer half-life.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAAALAC Association for the Assessment and Accreditation of Laboratory Animal Care\u003c/p\u003e \u003cp\u003eAD Alzheimer's Disease\u003c/p\u003e \u003cp\u003eANOVA Analysis of Variance\u003c/p\u003e \u003cp\u003eAPLAC Administrative Panel on Laboratory Animal Care\u003c/p\u003e \u003cp\u003eARG Autoradiography\u003c/p\u003e \u003cp\u003eBBB Blood-Brain Barrier\u003c/p\u003e \u003cp\u003eBpin Boronic Acid Pinacol Ester\u003c/p\u003e \u003cp\u003ecDNA Complementary Deoxyribonucleic Acid\u003c/p\u003e \u003cp\u003eCFA Complete Freund's Adjuvant\u003c/p\u003e \u003cp\u003ecLogP Calculated Logarithm of Partition Coefficient\u003c/p\u003e \u003cp\u003eCNS Central Nervous System\u003c/p\u003e \u003cp\u003eCNS-MPO Central Nervous System Multiparameter Optimization\u003c/p\u003e \u003cp\u003eCSF Cerebrospinal Fluid\u003c/p\u003e \u003cp\u003eCSF1R Colony-Stimulating Factor 1 Receptor\u003c/p\u003e \u003cp\u003eCT Computed Tomography\u003c/p\u003e \u003cp\u003eCt Cycle Threshold\u003c/p\u003e \u003cp\u003eDMEM Dulbecco's Modified Eagle Medium\u003c/p\u003e \u003cp\u003eDMF Dimethylformamide\u003c/p\u003e \u003cp\u003eDMSO Dimethyl Sulfoxide\u003c/p\u003e \u003cp\u003eDMT Disease-Modifying Therapy\u003c/p\u003e \u003cp\u003eEAE Experimental Autoimmune Encephalomyelitis\u003c/p\u003e \u003cp\u003eEDTA Ethylenediaminetetraacetic Acid\u003c/p\u003e \u003cp\u003eEtOH Ethanol\u003c/p\u003e \u003cp\u003eFBS Fetal Bovine Serum\u003c/p\u003e \u003cp\u003eFDA Food and Drug Administration\u003c/p\u003e \u003cp\u003efp Plasma Free Fraction\u003c/p\u003e \u003cp\u003eGAPDH Glyceraldehyde-3-Phosphate Dehydrogenase\u003c/p\u003e \u003cp\u003eGPCR G Protein-Coupled Receptor\u003c/p\u003e \u003cp\u003eGPR84 G Protein-Coupled Receptor 84\u003c/p\u003e \u003cp\u003eH\u0026amp;E Hematoxylin and Eosin\u003c/p\u003e \u003cp\u003eHEK293 Human Embryonic Kidney 293 Cells\u003c/p\u003e \u003cp\u003eHPLC High-Performance Liquid Chromatography\u003c/p\u003e \u003cp\u003e%ID/g Percent Injected Dose per Gram\u003c/p\u003e \u003cp\u003eIgG Immunoglobulin G\u003c/p\u003e \u003cp\u003ei.p. Intraperitoneal\u003c/p\u003e \u003cp\u003ekDa Kilodalton\u003c/p\u003e \u003cp\u003eKi Inhibition Constant\u003c/p\u003e \u003cp\u003eLogD Distribution Coefficient (logarithm)\u003c/p\u003e \u003cp\u003eLSC Lumbar Spinal Cord\u003c/p\u003e \u003cp\u003eMACS Magnetic-Activated Cell Sorting\u003c/p\u003e \u003cp\u003eMAP Maximum A Posteriori\u003c/p\u003e \u003cp\u003eMeCN Acetonitrile\u003c/p\u003e \u003cp\u003eMOG35-55 Myelin Oligodendrocyte Glycoprotein Peptide 35\u0026ndash;55\u003c/p\u003e \u003cp\u003eMRI Magnetic Resonance Imaging\u003c/p\u003e \u003cp\u003emRNA Messenger Ribonucleic Acid\u003c/p\u003e \u003cp\u003eMS Multiple Sclerosis\u003c/p\u003e \u003cp\u003enM Nanomolar\u003c/p\u003e \u003cp\u003eOCT Optimal Cutting Temperature\u003c/p\u003e \u003cp\u003eOSEM3D Ordered Subset Expectation Maximization 3D\u003c/p\u003e \u003cp\u003eP2X7R Purinergic Receptor P2X7\u003c/p\u003e \u003cp\u003ePBS Phosphate-Buffered Saline\u003c/p\u003e \u003cp\u003ePET Positron Emission Tomography\u003c/p\u003e \u003cp\u003ePP-MS Primary Progressive Multiple Sclerosis\u003c/p\u003e \u003cp\u003ePR-MS Progressive-Relapsing Multiple Sclerosis\u003c/p\u003e \u003cp\u003eqPCR Quantitative Polymerase Chain Reaction\u003c/p\u003e \u003cp\u003ercf Relative Centrifugal Force\u003c/p\u003e \u003cp\u003eRNA Ribonucleic Acid\u003c/p\u003e \u003cp\u003eROI Region of Interest\u003c/p\u003e \u003cp\u003eRR-MS Relapsing-Remitting Multiple Sclerosis\u003c/p\u003e \u003cp\u003eRT-PCR Reverse Transcription Polymerase Chain Reaction\u003c/p\u003e \u003cp\u003eS1P Sphingosine-1-Phosphate\u003c/p\u003e \u003cp\u003eSD Standard Deviation\u003c/p\u003e \u003cp\u003eTAC Time-Activity Curve\u003c/p\u003e \u003cp\u003eTFA Trifluoroacetic Acid\u003c/p\u003e \u003cp\u003etPSA Topological Polar Surface Area\u003c/p\u003e \u003cp\u003eTSC Thoracic/Cervical Spinal Cord\u003c/p\u003e \u003cp\u003eTSPO Translocator Protein 18 kDa\u003c/p\u003e \u003cp\u003eVT Volume of Distribution\u003c/p\u003e \u003cp\u003eWT Wild Type\u003c/p\u003e \u003cp\u003e\u0026micro;Ci Microcurie\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study and the code to re-analyze the bulk qPCR data are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by NIH/NINDS 1R21 AG07556501 (MLJ), Stanford University Wu Tsai Translate Grant (MLJ) and SNMMI Predoctoral Molecular Imaging Scholar Program (STR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.L.J., M.K., IMJ and SCN are co-inventors on patent no. PCT/US2024/024901 \u0026ldquo;Method for detecting innate immune action in vivo using GPR84-PET.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSTR contributed to Conceptualization, Methodology, Investigation, Formal Analysis, Writing: Original Draft, and Writing: Review \u0026amp; Editing. RCK contributed to Conceptualization, Methodology, Investigation, and Formal Analysis and Writing: Review \u0026amp; Editing. IMJ contributed to Conceptualization, Methodology, Investigation, Formal Analysis and Writing: Original Draft. MK, PM, SM, MS, BZ, JG, DD, AS, and SCN contributed to Investigation. HWC, NS, and PJ contributed to Investigation and Formal Analysis. TW contributed to Formal Analysis. EMD contributed to Writing: Review \u0026amp; Editing. GG contributed to Conceptualization, Methodology, Formal Analysis, and Writing: Review \u0026amp; Editing. GM contributed to Conceptualization, Methodology and Writing: Review \u0026amp; Editing. MLJ contributed to Conceptualization, Methodology, Writing: Original Draft, and Writing: Review \u0026amp; Editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Stanford University Cyclotron and Radiochemistry Facility (CRF) and Stanford Center for Innovation in In vivo Imaging (Sci3) for their support with this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGoldenberg MM. Multiple Scler Rev Pharm Ther. 2012;37:175\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodin DS. The epidemiology of multiple sclerosis. Handb Clin Neurol [Internet]. Elsevier; 2014 [cited 2026 Mar 27]. pp. 231\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-444-52001-2.00010-8\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-444-52001-2.00010-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobson R, Giovannoni G. Multiple sclerosis \u0026ndash; a review. Eur J Neurol. 2019;26:27\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ene.13819\u003c/span\u003e\u003cspan address=\"10.1111/ene.13819\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVargas DL, Tyor WR. Update on Disease-Modifying Therapies for Multiple Sclerosis. J Investig Med SAGE Publications. 2017;65:883\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jim-2016-000339\u003c/span\u003e\u003cspan address=\"10.1136/jim-2016-000339\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh S, Metz I, Amor S, van der Valk P, Stadelmann C, Br\u0026uuml;ck W. Microglial nodules in early multiple sclerosis white matter are associated with degenerating axons. Acta Neuropathol (Berl). 2013;125:595\u0026ndash;608. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00401-013-1082-0\u003c/span\u003e\u003cspan address=\"10.1007/s00401-013-1082-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemmer B, Kerschensteiner M, Korn T. Role of the innate and adaptive immune responses in the course of multiple sclerosis. Lancet Neurol. 2015;14:406\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1474-4422(14)70305-9\u003c/span\u003e\u003cspan address=\"10.1016/S1474-4422(14)70305-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwens GP, Bennett JL, Gilden DH, Burgoon MP. The B cell response in multiple sclerosis. Neurol Res Taylor Francis. 2006;28:236\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1179/016164106X98099\u003c/span\u003e\u003cspan address=\"10.1179/016164106X98099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDendrou CA, Fugger L, Friese MA. Immunopathology of multiple sclerosis. Nat Rev Immunol Nat Publishing Group. 2015;15:545\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nri3871\u003c/span\u003e\u003cspan address=\"10.1038/nri3871\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrado C, Herrada AA, Hevia D, Goiry LG, Escobedo N. Role of innate immune cells in multiple sclerosis. Front Immunol [Internet] Front. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2025.1540263\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2025.1540263\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [cited 2026 Mar 27];16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDendrou CA, Fugger L, Friese MA. Immunopathology of multiple sclerosis. Nat Rev Immunol Nat Publishing Group. 2015;15:545\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nri3871\u003c/span\u003e\u003cspan address=\"10.1038/nri3871\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyeloid cell distribution and activity in multiple sclerosis. Histol Histopathol. ES: Sercrisma Int s l. 2016;357\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14670/HH-11-699\u003c/span\u003e\u003cspan address=\"10.14670/HH-11-699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerard JL, Wolak K, Fournier S, David S. Characterization of relapsing\u0026ndash;remitting and chronic forms of experimental autoimmune encephalomyelitis in C57BL/6 mice. Glia. 2010;58:434\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/glia.20935\u003c/span\u003e\u003cspan address=\"10.1002/glia.20935\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuppression of experimental allergic encephalomyelitis. in Lewis rats after elimination of macrophages. J Exp Med. 1990;172:1025\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1084/jem.172.4.1025\u003c/span\u003e\u003cspan address=\"10.1084/jem.172.4.1025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobson R, Giovannoni G. Multiple sclerosis \u0026ndash; a review. Eur J Neurol. 2019;26:27\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ene.13819\u003c/span\u003e\u003cspan address=\"10.1111/ene.13819\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17:162\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1474-4422(17)30470-2\u003c/span\u003e\u003cspan address=\"10.1016/S1474-4422(17)30470-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGatliff J, East D, Crosby J, Abeti R, Harvey R, Craigen W, et al. TSPO interacts with VDAC1 and triggers a ROS-mediated inhibition of mitochondrial quality control. Autophagy. 2014;10:2279\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4161/15548627.2014.991665\u003c/span\u003e\u003cspan address=\"10.4161/15548627.2014.991665\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGatliff J, East DA, Singh A, Alvarez MS, Frison M, Matic I, et al. A role for TSPO in mitochondrial Ca2\u0026thinsp;+\u0026thinsp;homeostasis and redox stress signaling. Cell Death Dis. 2017;8:e2896\u0026ndash;2896. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/cddis.2017.186\u003c/span\u003e\u003cspan address=\"10.1038/cddis.2017.186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNutma E, Fancy N, Weinert M, Tsartsalis S, Marzin MC, Muirhead RCJ, et al. Translocator protein is a marker of activated microglia in rodent models but not human neurodegenerative diseases. Nat Commun. 2023;14:5247. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-023-40937-z\u003c/span\u003e\u003cspan address=\"10.1038/s41467-023-40937-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNutma E, Gebro E, Marzin MC, Van Der Valk P, Matthews PM, Owen DR, et al. Activated microglia do not increase 18 kDa translocator protein (TSPO) expression in the multiple sclerosis brain. Glia. 2021;69:2447\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/glia.24052\u003c/span\u003e\u003cspan address=\"10.1002/glia.24052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYokokura M, Terada T, Bunai T, Nakaizumi K, Takebayashi K, Iwata Y, et al. Depiction of microglial activation in aging and dementia: Positron emission tomography with [11C]DPA713 versus [11C](R)PK11195. J Cereb Blood Flow Metab. 2017;37:877\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0271678X16646788\u003c/span\u003e\u003cspan address=\"10.1177/0271678X16646788\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAiras L, Rissanen E, Rinne JO. Imaging neuroinflammation in multiple sclerosis using TSPO-PET. Clin Transl Imaging. 2015;3:461\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40336-015-0147-6\u003c/span\u003e\u003cspan address=\"10.1007/s40336-015-0147-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolvinen E, Matilainen M, Nylund M, Sucksdorff M, Airas LM. TSPO-Detectable Chronic Active Lesions Predict Disease Progression in Multiple Sclerosis. Neurol Neuroimmunol Neuroinflammation. 2023;10:e200133. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/NXI.0000000000200133\u003c/span\u003e\u003cspan address=\"10.1212/NXI.0000000000200133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubin LH, Du Y, Sweeney SE, O\u0026rsquo;Toole R, Thomas CL, Zandi AG, et al. Imaging Brain Injury in Former National Football League Players. JAMA Netw Open. 2023;6:e2340580. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamanetworkopen.2023.40580\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2023.40580\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisser D, Golla SSV, Palard-Novello X, Verfaillie SCJ, Verveen A, Koch DW, et al. Varying Levels of Inflammatory Activity in Brain and Body of Patients with Persistent Fatigue and Difficulty Concentrating After COVID-19: A TSPO PET Study. J Nucl Med. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2967/jnumed.124.269297\u003c/span\u003e\u003cspan address=\"10.2967/jnumed.124.269297\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. jnumed.124.269297.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbourbeh G, Th\u0026eacute;z\u0026eacute; B, Maroy R, Dubois A, Brulon V, Fontyn Y, et al. Imaging Microglial/Macrophage Activation in Spinal Cords of Experimental Autoimmune Encephalomyelitis Rats by Positron Emission Tomography Using the Mitochondrial 18 kDa Translocator Protein Radioligand [18F]DPA-714. J Neurosci. 2012;32:5728\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1523/JNEUROSCI.2900-11.2012\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.2900-11.2012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRejc L, G\u0026oacute;mez-Vallejo V, Joya A, Arsequell G, Egimendia A, Castellnou P, et al. Longitudinal evaluation of neuroinflammation and oxidative stress in a mouse model of Alzheimer disease using positron emission tomography. Alzheimers Res Ther. 2022;14:80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13195-022-01016-5\u003c/span\u003e\u003cspan address=\"10.1186/s13195-022-01016-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVicente-Rodr\u0026iacute;guez M, Singh N, Turkheimer F, Peris-Yague A, Randall K, Veronese M, et al. Resolving the cellular specificity of TSPO imaging in a rat model of peripherally-induced neuroinflammation. Brain Behav Immun. 2021;96:154\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbi.2021.05.025\u003c/span\u003e\u003cspan address=\"10.1016/j.bbi.2021.05.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalita M, Park JH, Kuo RC, Hayee S, Marsango S, Straniero V, et al. PET Imaging of Innate Immune Activation Using \u003csup\u003e11\u003c/sup\u003e C Radiotracers Targeting GPR84. JACS Au. 2023;3:3297\u0026ndash;310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/jacsau.3c00435\u003c/span\u003e\u003cspan address=\"10.1021/jacsau.3c00435\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaney AM, Cropper HC, Jain P, Wilson E, Simonetta F, Johnson EM, et al. PET imaging of TREM1 identifies CNS-infiltrating myeloid cells in a mouse model of multiple sclerosis. Sci Transl Med. 2023;15:eabm6267. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/scitranslmed.abm6267\u003c/span\u003e\u003cspan address=\"10.1126/scitranslmed.abm6267\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens MY, Cropper HC, Lucot KL, Chaney AM, Lechtenberg KJ, Jackson IM, et al. Development of a CD19 PET tracer for detecting B cells in a mouse model of multiple sclerosis. J Neuroinflammation [Internet]. 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12974-020-01880-8\u003c/span\u003e\u003cspan address=\"10.1186/s12974-020-01880-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [cited 2021 Apr 29];17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalita M, Kuo RC, Straniero V, Reyes ST, Pandrala M, Lanzini A, et al. Illuminating proinflammatory myeloid cells with PET tracers targeting GPR84. Proc Natl Acad Sci U S A; 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReyes ST, Azevedo EC, Cropper HC, Nagy S, Deal EM, Chaney AM, et al. Imaging CD19\u003csup\u003e+\u003c/sup\u003e B Cells in an Experimental Autoimmune Encephalomyelitis Mouse Model using Positron Emission Tomography. J Vis Exp. 2023;64133. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3791/64133\u003c/span\u003e\u003cspan address=\"10.3791/64133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrain RNA-S. \u0026ndash; Brain RNA-Seq [Internet]. [cited 2026 Mar 30]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://brainrnaseq.org/\u003c/span\u003e\u003cspan address=\"https://brainrnaseq.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 30 Mar 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe Human Protein Atlas [Internet]. [cited 2026 Mar 30]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 30 Mar 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Lorenzo A, Bolli E, Tarone L, Cavallo F, Conti L. Toll-Like Receptor 2 at the Crossroad between Cancer Cells, the Immune System, and the Microbiota. Int J Mol Sci. 2020;21:9418. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms21249418\u003c/span\u003e\u003cspan address=\"10.3390/ijms21249418\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Z, Jin Y, Zhang X, Xia P, Wen X, Ma J, et al. Pan-cancer analysis identifies CD300 molecules as potential immune regulators and promising therapeutic targets in acute myeloid leukemia. Cancer Med. 2022;12:789\u0026ndash;807. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cam4.4905\u003c/span\u003e\u003cspan address=\"10.1002/cam4.4905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Richmond A, Yan C. Harnessing the potential of CD40 agonism in cancer therapy. Cytokine Growth Factor Rev. 2024;75:40\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cytogfr.2023.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.cytogfr.2023.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWager TT, Hou X, Verhoest PR, Villalobos A. Moving beyond Rules: The Development of a Central Nervous System Multiparameter Optimization (CNS MPO) Approach To Enable Alignment of Druglike Properties. ACS Chem Neurosci. 2010;1:435\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/cn100008c\u003c/span\u003e\u003cspan address=\"10.1021/cn100008c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWager TT, Hou X, Verhoest PR, Villalobos A. Central Nervous System Multiparameter Optimization Desirability: Application in Drug Discovery. ACS Chem Neurosci. 2016;7:767\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acschemneuro.6b00029\u003c/span\u003e\u003cspan address=\"10.1021/acschemneuro.6b00029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePike VW. PET radiotracers: crossing the blood\u0026ndash;brain barrier and surviving metabolism. Trends Pharmacol Sci. 2009;30:431\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tips.2009.05.005\u003c/span\u003e\u003cspan address=\"10.1016/j.tips.2009.05.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePike VW. Considerations in the Development of Reversibly Binding PET Radioligands for Brain Imaging. Curr Med Chem. 2016;23:1818\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/0929867323666160418114826\u003c/span\u003e\u003cspan address=\"10.2174/0929867323666160418114826\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindberg A, Chass\u0026eacute; M, Varlow C, Pees A, Vasdev N. Strategies for designing novel positron emission tomography (PET) radiotracers to cross the blood\u0026ndash;brain barrier. J Label Compd Radiopharm. 2023;66:205\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jlcr.4019\u003c/span\u003e\u003cspan address=\"10.1002/jlcr.4019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuzuki M, Takaishi S, Nagasaki M, Onozawa Y, Iino I, Maeda H, et al. Medium-chain Fatty Acid-sensing Receptor, GPR84, Is a Proinflammatory Receptor. J Biol Chem. 2013;288:10684\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.M112.420042\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M112.420042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChun J, Hartung H-P. Mechanism of Action of Oral Fingolimod (FTY720) in Multiple Sclerosis. Clin Neuropharmacol. 2010;33:91\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/WNF.0b013e3181cbf825\u003c/span\u003e\u003cspan address=\"10.1097/WNF.0b013e3181cbf825\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Dario M, Colombo E, Govi C, De Feo D, Messina MJ, Romeo M, et al. Myeloid cells as target of fingolimod action in multiple sclerosis. Neurol Neuroimmunol Neuroinflammation. 2015;2:e157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/NXI.0000000000000157\u003c/span\u003e\u003cspan address=\"10.1212/NXI.0000000000000157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMills KA, Du Y, Coughlin JM, Foss CA, Horti AG, Jenkins K et al. Exploring [11C]CPPC as a CSF1R-targeted PET Imaging Marker for Early Parkinson\u0026rsquo;s Disease Severity. medRxiv. 2024;2023.05.28.23290647. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2023.05.28.23290647\u003c/span\u003e\u003cspan address=\"10.1101/2023.05.28.23290647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorti AG, Naik R, Foss CA, Minn I, Misheneva V, Du Y, et al. PET imaging of microglia by targeting macrophage colony-stimulating factor 1 receptor (CSF1R). Proc Natl Acad Sci U S A. 2019;116:1686\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1812155116\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1812155116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoughlin JM, Du Y, Lesniak WG, Harrington CK, Brosnan MK, O\u0026rsquo;Toole R, et al. First-in-human use of 11C-CPPC with positron emission tomography for imaging the macrophage colony-stimulating factor 1 receptor. EJNMMI Res. 2022;12:64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13550-022-00929-4\u003c/span\u003e\u003cspan address=\"10.1186/s13550-022-00929-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeaino W, Janssen B, Kooijman E, Vos R, Schuit RC, O\u0026rsquo;Brien-Brown J, et al. PET imaging of P2X7R in the experimental autoimmune encephalomyelitis model of multiple sclerosis using [11C]SMW139. J Neuroinflammation. 2020;17:300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12974-020-01962-7\u003c/span\u003e\u003cspan address=\"10.1186/s12974-020-01962-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagens MHJ, Golla SSV, Janssen B, Vugts DJ, Beaino W, Windhorst AD, et al. The P2X7 receptor tracer [11C]SMW139 as an in vivo marker of neuroinflammation in multiple sclerosis: a first-in man study. Eur J Nucl Med Mol Imaging. 2020;47:379\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00259-019-04550-x\u003c/span\u003e\u003cspan address=\"10.1007/s00259-019-04550-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurnstock G. P2X ion channel receptors and inflammation. Purinergic Signal. 2016;12:59\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11302-015-9493-0\u003c/span\u003e\u003cspan address=\"10.1007/s11302-015-9493-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt S, Isaak A, Junker A. Spotlight on P2X7 Receptor PET Imaging: A Bright Target or a Failing Star? Int J Mol Sci. 2023;24:1374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms24021374\u003c/span\u003e\u003cspan address=\"10.3390/ijms24021374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCersosimo F, Lonardi S, Ulivieri C, Martini P, Morrione A, Vermi W, et al. CSF-1R in Cancer: More than a Myeloid Cell Receptor. Cancers. 2024;16:282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers16020282\u003c/span\u003e\u003cspan address=\"10.3390/cancers16020282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Schemes","content":"\u003cp\u003eScheme 1 is available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"multiple sclerosis, EAE, PET imaging, GPR84, innate immune activation, treatment response, myeloid cell","lastPublishedDoi":"10.21203/rs.3.rs-9374449/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9374449/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMultiple sclerosis is a disabling immune-mediated neurological disorder characterized by demyelinating lesions in the central nervous system (CNS) that drive progressive motor dysfunction, cognitive decline, and vision loss, and is the leading cause of non-traumatic disability in young adults. Pro-inflammatory myeloid cells, including microglia and macrophages, are abundant innate immune infiltrates in active lesions and are key mediators of disease onset and progression, making them attractive targets for non-invasive imaging. GPR84 is an immune-metabolic G protein-coupled receptor that is specifically expressed on myeloid cells with low basal expression in healthy tissue, involved in macrophage/microglia response to inflammation, and is upregulated in an inflammatory environment. This makes it a promising target for imaging innate immune activation via positron emission tomography (PET). We identified GPR84 as a biomarker of innate immune activation in EAE mice using bulk qPCR quantification. Subsequently, we developed \u003csup\u003e11\u003c/sup\u003eC-MGX-38 and evaluated it alongside \u003csup\u003e18\u003c/sup\u003eF-MGX-110S to assess their ability to detect innate immune activation in EAE mice and monitor therapeutic response to fingolimod. GPR84-PET demonstrated robust signal increases in the CNS of EAE mice compared to controls. Both radiotracers successfully detected treatment response in fingolimod-treated mice compared to vehicle-treated mice, with the fluorinated radiotracer showing higher sensitivity for delineating both disease severity and alterations in peripheral signal. These findings indicate that GPR84-PET is a promising method for non-invasive, cell-specific monitoring of innate immune activation in EAE mice, with \u003csup\u003e18\u003c/sup\u003eF-MGX-110S emerging as the leading candidate for potential clinical translation and possible applications in MS patient stratification, therapeutic monitoring, and drug development due to its superior sensitivity and half-life.\u003c/p\u003e","manuscriptTitle":"Noninvasive Imaging of Myeloid Cell Dynamics: GPR84 PET Differentiates EAE and Tracks Therapy Response","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 06:14:38","doi":"10.21203/rs.3.rs-9374449/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T18:51:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T18:15:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T17:51:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49142548915196200626464437311624772088","date":"2026-04-25T13:48:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T14:22:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301750987219371761762956193355067360291","date":"2026-04-21T13:47:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75381325296575627993610188635085519600","date":"2026-04-20T19:52:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T13:34:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-15T14:21:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T11:10:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuroinflammation","date":"2026-04-10T04:21:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c55959fb-8976-4373-94cc-cda45fe063fa","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T18:51:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T18:15:10+00:00","index":26,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T17:51:59+00:00","index":25,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T18:55:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 06:14:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9374449","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9374449","identity":"rs-9374449","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}