Dual-modality imaging enables longitudinal biodistribution profiling of intracerebroventricular CAR-T therapy in orthotopic glioma

Dual-modality imaging enables longitudinal biodistribution profiling of intracerebroventricular CAR-T therapy in orthotopic glioma | 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 Dual-modality imaging enables longitudinal biodistribution profiling of intracerebroventricular CAR-T therapy in orthotopic glioma Chunzhao Li, Peng Zhang, Xiaobin Zhao, Rui Feng, Na Xian, Gangxiong Huang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9126411/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 May, 2026 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted 15 You are reading this latest preprint version Abstract Locoregional CAR-T delivery is increasingly explored for glioblastoma to improve intracranial tumor exposure; however, organ-level biodistribution kinetics after intracranial administration remain poorly quantified in vivo, limiting route-informed optimization and preclinical risk assessment. Here, we report a dual-modality cell labeling and tracking strategy based on indocyanine green–conjugated iron nanoparticles (ICG-NPs) for in vivo assessment of B7-H3-targeting CAR-T cell (TX103) biodistribution using second near-infrared window (NIR-II) fluorescence imaging and magnetic resonance imaging (MRI). Using a heparin–protamine-assisted protocol, TX103 cells were labeled with high efficiency (83.1%) without detectable changes in viability, CAR expression, immunophenotype (including activation/exhaustion marker profile and CXCR3 expression), or cytotoxic function. In vitro imaging demonstrated a linear correlation between NIR-II fluorescence intensity and labeled cell numbers (R² = 0.973, p < 0.001), while MRI provided complementary anatomical context at higher cell densities. In an orthotopic glioma mouse model, longitudinal MRI and NIR-II imaging captured route-dependent differences in tumor-associated localization and whole-body biodistribution following intracerebroventricular and intravenous administration. Furthermore, NIR-II signal intensity correlated with CD3⁺ T-cell density across organs (R² = 0.552, p < 0.001), supported by multi-organ pathological validation. Collectively, we establish a biocompatible, materials-enabled dual-modality workflow that links intracranial anatomical localization with longitudinal whole-body biodistribution readouts for preclinical CAR-T tracking in solid tumor models. Glioblastoma CAR-T NIR-II MRI cell tracking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Glioblastoma (GBM) is the most common type of primary malignant brain tumor and is notorious for its limited response to current therapeutic options[ 1 ]. Despite aggressive standard-of-care therapies, the median survival for GBM patients is around 15 months[ 2 ]. Chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a promising treatment strategy, with encouraging preclinical and early-phase clinical outcomes reported in recent years, particularly with locoregional delivery approaches[ 3 – 7 ]. However, durable benefit is achieved in only a subset of patients, highlighting persistent barriers to therapeutic optimization[ 5 , 7 ]. This heterogeneity reflects multiple biological and treatment-related determinants, among which insufficient tumor trafficking and systemic off-target exposure of transferred cells are increasingly recognized as key variables that influence both therapeutic interpretation and risk assessment in cellular immunotherapy[ 8 , 9 ]. Therefore, a detailed understanding of in vivo CAR-T cell trafficking and biodistribution following local administration is critical for rational development and optimization of CAR-T therapy in GBM[ 10 ]. A major challenge for CAR-T therapy in GBM is the restrictive blood–brain barrier, which can limit the access of intravenously infused cells to intracranial tumors[ 11 , 12 ]. Accordingly, locoregional delivery approaches have been increasingly adopted in translational studies and clinical trials to enhance intracranial tumor exposure[ 13 , 14 ]. However, local administration does not necessarily confine transferred cells to the central nervous system. CAR-T cells delivered into the cerebrospinal fluid may redistribute via cerebrospinal fluid drainage and lymphatic outflow pathways and subsequently access peripheral compartments[ 15 ]. Despite its importance, the systemic exposure profile and organ-level biodistribution kinetics following intracranial CAR-T delivery remain incompletely characterized, complicating interpretation of therapeutic heterogeneity and preclinical evaluation of potential off-target organ exposure[ 16 ]. Therefore, a practical, non-invasive framework that links intracranial localization with longitudinal whole-body biodistribution and kinetics is needed to support route-informed optimization of CAR-T strategies for GBM[ 16 ]. Multiple imaging approaches have been explored to characterize post-infusion migration, distribution, and persistence of CAR-T cells[ 17 , 18 ]. MRI and Magnetic Particle Imaging (MPI) have been used to track CAR-T cells labeled with iron-based agents and to assess tumor-associated localization[ 17 – 19 ]. Nuclear imaging modalities such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) were utilized to image and quantify CAR-T cells labeled with nuclide probes or reporter genes[ 20 – 23 ]. However, applying these strategies to intracranial or locoregional delivery settings remains less established and is often constrained by modality-specific trade-offs in sensitivity, spatial resolution, imaging depth, and practical implementation[ 24 ]. Consequently, a framework that links intracranial localization, whole-body biodistribution, and distribution kinetics remains needed to support route-informed interpretation and optimization of CAR-T studies in glioma models[ 10 , 18 , 24 ]. In this context, second near-infrared window (NIR-II) fluorescence imaging provides a practical complementary modality for sensitive, real-time whole-body biodistribution assessment[ 25 , 26 ]. In this study, we establish a dual-modality cell-labeling and imaging workflow using a biologically compatible indocyanine green–conjugated iron oxide nanoparticle (ICG-NP) label to track a clinically manufactured B7-H3–targeting CAR-T product (TX103) in an orthotopic glioma model. We couple intracranial anatomical localization (MRI) with longitudinal systemic exposure profiling (NIR-II) using a biologically compatible labeling strategy validated by orthogonal pathology. Using this framework, we characterize route-dependent differences in tumor-associated localization and organ-level biodistribution following intracerebroventricular and intravenous administration, providing a practical approach for preclinical biodistribution profiling in intracranial tumor immunotherapy settings. Methods Synthesis and characterization analysis of ICG-NP ICG-NP was synthesized by conjugating ICG-NHS to Fe₃O₄-PEG-NH₂ nanoparticles. Briefly, 15 mg Fe₃O₄-PEG-NH₂ (1 mg/mL in 15 mL) was concentrated using a 100 kDa ultrafiltration tube (3000 g, 15 min), washed once with 0.1 M citrate buffer (CB), and resuspended in 14 mL CB. ICG-NHS (5 mg/mL in methanol) was added, the volume was adjusted to 15 mL with CB, and the mixture was incubated overnight at room temperature with continuous rotation. The reaction mixture was then purified by ultrafiltration (4500 rpm, 15 min) and washed three times with purified water, followed by resuspension to 15 mL. The core size of Fe₃O₄-PEG-NH₂ was measured by TEM (HT-7800, Hitachi, Japan). Hydrodynamic diameter (DLS) and zeta potential of Fe₃O₄-PEG-NH₂ and ICG-NP were measured using a Zetasizer Lab (Malvern Instruments, Malvern, UK). Excitation/absorption and emission spectra were assessed using a lifetime and steady-state spectrometer (FLS980, Edinburgh Instruments, UK). Cell lines The U87MG-Luc human glioma cell line was kindly provided by Fuzhou Tcelltech Biological Science and Technology Inc. (Fuzhou, China). Cell line identity was confirmed by short tandem repeat (STR) profiling (21 loci), showing a 100% match to U-87MG/U-87MG-Luc2 profiles in the Cellosaurus database and no evidence of cross-contamination. Cells were cultured in DMEM supplemented with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin at 37°C in a humidified 5% CO₂ incubator. Culture media and supplements were purchased from Gibco (Carlsbad, CA, USA) unless otherwise stated. Manufacturing of B7-H3-targeting CAR-T cells Human PBMCs were purchased from Shanghai Saily Biotechnology Co., Ltd. CAR design and construction were described previously[ 27 ]. Briefly, TX103 was generated using a humanized scFv derived from the 7E12 monoclonal antibody linked to intracellular 4-1BB and CD3-ζ domains. TX103 and untargeted-T cells (PBMC without CAR) were cultured in RPMI-1640 supplemented with 10% FBS, 1% L-glutamine, IL-2 (300 IU/mL; MedChem Express, Monmouth Junction, NJ, USA), and 1% penicillin–streptomycin at 37°C in 5% CO₂. Labeling CAR-T and untargeted-T cells with ICG-NP and heparin-protamine complex TX103 and untargeted-T cells were co-incubated with ICG-NP and heparin–protamine complex (HPC; 1× = 2 IU/mL heparin + 60 µg/mL protamine). Labeling conditions were screened across incubation times (0, 4, 8, 16, 20, and 24 h), ICG-NP concentrations (0, 50, 100, and 200 µg/mL), and HPC dilutions (0×–4×) based on viability and labeling efficiency. Viability and cell number were assessed by trypan blue staining using an automatic cell counter (C-100, RWD Life Science, USA). Labeling efficiency was evaluated by ICP-OES and flow cytometry. Flow Cytometry Flow cytometry was performed to assess the impact of ICG-NP labeling on CAR-T cell phenotype (CD45RA, CCR7), activation (CD69, 4-1BB), exhaustion (TIM3, PD1, LAG3), and the homing receptor CXCR3. Transduction efficiency of CAR-T cells was analyzed using PE-conjugated b7h3-mouse igG protein[ 27 ]. Cells were resuspended in FACS buffer (PBS supplemented with 1% FBS and 1 µM EDTA) and stained with fluorochrome-conjugated monoclonal antibodies or corresponding isotype control for 15 minutes at 4°C in the dark (detailed information was provided in Supplementary Table 1). After two washes, cells were resuspended in 200 µL of FACS buffer and analyzed on a Beckman CytoFLEX S flow cytometer (Beckman Coulter, Miami, FL). Examples for the gating strategy used for flow cytometry analysis are given in Supplementary Fig. 1G. Data analysis was conducted using FlowJo (v10.8.1, BD Biosciences). Cell fluorescence staining Cell membrane staining was performed using the PKH26 Red Fluorescence Cell Linker Kit (Beijing Fluorescence Biotechnology, China). A total of 2 × 10⁷ TX103 CAR-T cells, both labeled and unlabeled, were treated with the PKH26 kit and DAPI. Multiplex fluorescence images were then captured using the Thunder Imager 3D Assay (Leica, Wetzlar, Germany). Functional assays The functional integrity of ICG-NP-labeled and unlabeled TX103 was evaluated using an interferon-γ release assay and a tumor cell killing assay. U87MG-Luc cells were seeded in 96-well plates and cocultured with either labeled or unlabeled TX103 for 16 hours at effector-to-target ratios of 5:1, 1:1, and 0.1:1. IFN-γ levels in culture supernatant were quantified using the Sensitivity Flex Set CBA (Human IFN-γ Flex Set 558269, BD, USA). For cytotoxicity assessments, tumor cells were stained with CFSE (Invitrogen, Carlsbad, C1157, CA, USA). Following co-culturing for 12–16 hours, cells were harvested and counterstained with DAPI. Finally, flow cytometry was performed to measure the percentage of non-viable tumor cells, identified as CFSE + DAPI + . Western blotting for B7-H3 expression Western blotting for B7-H3 expression U87MG and additional glioma cell lines were lysed in RIPA buffer supplemented with protease inhibitors. Equal amounts of protein were separated by SDS–PAGE, transferred onto PVDF membranes, and probed with anti–B7-H3 antibody (CST, 14058; 1:3,000). The following secondary antibody was used: anti–rabbit IgG secondary antibody and HRP (Abcam, ab6721; 1:5,000). The specific protein bands were visualized via enhanced chemiluminescence reagents (NCM Biotech, P10300) on an Amersham Imager 600 (GE). Animal experiment SPF NOG mice (male/female, 6–8 weeks; Charles River Laboratories; n = 27; severe immunodeficiency) were housed under SPF barrier conditions (IVC; 5 mice/cage) and monitored daily. All procedures were approved by the Animal Care and Use Committee of our institute (Ethical Number: IA21-2302-420303). The health of the mice was monitored daily, with particular attention to neurological signs, weight, and environmental conditions, including access to food and water. U87MG-Luc cells (1×10⁵ in 5 µL PBS) were stereotactically injected into the right basal ganglia (2 mm lateral to bregma, 3.5 mm below dura) under 3% isoflurane anesthesia using a 25 µL Hamilton syringe with fine-step stereotactic control. Ten days post-inoculation, mice were randomized into three groups (n = 9/group): (i) ICV-labeled TX103, (ii) ICV-labeled untargeted-T cells, and (iii) i.v.-labeled TX103. For i.v. administration, 5×10⁶ labeled TX103 cells in 100 µL PBS were injected via tail vein. For ICV administration, 5×10⁶ labeled TX103 or untargeted-T cells were injected into the left lateral ventricle (AP − 0.22 mm, ML 1 mm, DV 2.3–2.8 mm). Tumor engraftment was confirmed by BLI using an IVIS Spectrum In Vivo Imaging System (Caliper Life Sciences, Waltham, MA) on day 7 post-inoculation. Magnetic resonance imaging (MRI) MRI was performed using a 7.0T small-animal scanner (PharmaScan 70/16, Bruker, Germany) with a T2-weighted sequence for in vitro and in vivo imaging. Mice underwent baseline MRI on day 9 post-inoculation and follow-up scans on days 1, 4, and 7 after CAR-T administration (TR/TE = 2236/35 ms; FOV = 22×22 mm; matrix = 256×256; slice thickness = 0.5 mm; averages = 5; voxel size ≈ 0.0037 mm³). In vitro cell pellets were scanned using TR/TE = 2500/33 ms; FOV = 63×63 mm; matrix = 256×256; slice thickness = 0.35 mm; averages = 3; voxel size ≈ 0.0212 mm³. SNR was calculated as the mean ROI signal divided by the SD of background noise. Tumor volume was segmented using 3D Slicer v4.11.0. NIR-II fluorescence imaging NIR-II imaging was conducted to monitor the distribution of ICG-NP-labeled TX103 in vitro and in vivo . Regarding in vitro NIR-II imaging of TX103, labeled and unlabeled TX103 from each gradient were centrifuged to the bottom of Eppendorf tubes. For in vivo NIR-II imaging, mice were anesthetized and placed on the imaging platform following skin preparation. A high-resolution NIR-II camera (Cheetah 640, Xenics, Belgium) captured fluorescence images of the front and back of the mice. The exposure time was set to 1000 ms, and a high-pass filter (FEL1000, Thorlabs, USA) was used to detect the NIR-II signals. Continuous light at 792 nm served as the excitation light, with a power density of 100 mW/cm² at the imaging site. Imaging parameters were kept constant throughout all experiments. After each imaging session, brains and vital organs (heart, liver, spleen, lungs, and kidneys) from mice in each group were harvested. Before formalin fixation, NIR-II images of the samples were collected to validate the in vivo results. The average gray value of the NIR-II images was measured using ImageJ (1.8.0_112, National Institutes of Health, USA). Data were log10-transformed to normalize the distribution and facilitate linear regression analysis. Quantification of NIR-II exposure metrics For longitudinal biodistribution profiling, regions of interest (ROIs) were manually delineated in ImageJ on all images acquired under identical imaging settings. Organ-level NIR-II exposure was quantified as the mean fluorescence intensity (mean gray value) within each organ ROI. For statistical analyses, organ-level intensities were log10-transformed and reported as log10-transformed organ-level NIR-II exposure [log10(I)]. To describe intracranial relative exposure over time, a brain-to–whole-body exposure fraction (%) was calculated as: Brain-to–whole-body exposure fraction (%) = 100 × I_brain / ΣI_organs, where I_brain denotes the mean gray value within the brain ROI and ΣI_organs denotes the sum of mean gray values across all delineated organ ROIs from the same mouse at the same time point. Tissue section pathology and imaging Tissues were fixed in formalin, paraffin-embedded, and sectioned at 4 µm for IHC and H&E staining. For IHC, sections underwent antigen retrieval and blocking, followed by incubation with anti-CD3 antibody (rabbit, 1:100, abcam ab243874) as previously described, and secondary antibody (1:500, ZB-2301). After DAB development and hematoxylin counterstaining, slides were scanned using Pannoramic MIDI (3DHISTECH). CD3⁺ T-cell density (cells/mm²) was quantified in QuPath v0.6.0 using automated positive cell detection. Prussian blue staining Prussian blue staining was used to detect iron deposition. After dewaxing and hydration, sections were stained with freshly prepared Prussian blue working solution (equal parts Reagents A and B, DJ0001, LEAGENE Biotechnology) for 15–30 min, rinsed with distilled water, and counterstained with Nuclear Fast Red for 5–10 min. Slides were dehydrated using alcohol, cleared in xylene, mounted with Neutral Balsam, and scanned using the Pannoramic MIDI. Statistical analysis Statistical analyses were performed using IBM SPSS Statistics 24. In vitro assays were conducted in triplicate and are presented as mean ± SD. Data normality was assessed by the Shapiro–Wilk test and homogeneity of variance by Levene’s test. Two-group comparisons were performed using an unpaired Student’s t-test or Mann–Whitney U test, as appropriate. Multi-group comparisons used one-way ANOVA with Tukey’s post hoc test or Kruskal–Wallis with Dunn’s test (Bonferroni correction). Linear regression was used to assess associations between fluorescence intensity and cell number in vitro . To account for within-animal clustering when multiple organs were sampled from the same mouse, a linear mixed-effects model (random-intercept) was applied with Mouse ID as a random effect and NIR-II fluorescence intensity as the dependent variable. Statistical significance was defined as *p < 0.05, **p < 0.01, and ***p < 0.001. Results Characterization of ICG-NP To enable dual-modality tracking of CAR-T cell–associated signals in vivo , we synthesized an indocyanine green–conjugated iron oxide nanoparticle probe (ICG-NP) and characterized its physicochemical and optical properties. ICG-NP was synthesized by conjugating ICG with PEG-Fe₃O₄ (-NH₂) nanoparticles through an amide reaction (Fig. 1 A). The TEM revealed a uniform size distribution of ICG-NP, with a diameter of approximately 20 nm (Fig. 1 B). DLS measurements showed a hydrodynamic size of 69.42 nm for ICG-NP (Fig. 1 C), and the zeta potential was − 36.82 mV (Fig. 1 D). Spectroscopic analysis confirmed that the probe retained NIR-II fluorescence, with the highest emission at 924 nm, and displayed robust signals within the 900–1200 nm near-infrared range (Fig. 1 E-F). These characteristics supported the use of ICG-NP for subsequent CAR-T cell labeling and imaging studies. Optimization of a viable and efficient ICG-NP labeling protocol for TX103 We next optimized ICG-NP labeling conditions for TX103 (clinically available B7-H3 CAR-T cell product [NCT05241392]) to maximize uptake while preserving viability, because biological compatibility is essential for interpreting downstream trafficking and biodistribution readouts. Because heparin–protamine complex (HPC) can enhance iron nanoparticle internalization by T cells [ 30 ], we tested HPC concentrations and selected 1× HPC based on minimal cytotoxicity (Supplementary Fig. 1A). TX103 cells were then co-incubated with different concentrations of ICG-NP plus 1× HPC; viability remained comparable to controls at 50 and 100 µg/mL (Fig. 2 A; Supplementary Fig. 1B–D). CAR expression remained unchanged under either labeling condition, with consistently high CAR positivity across groups (Fig. 2 B; Supplementary Fig. 2A). Labeling efficiency and iron uptake were higher with 100 µg/mL ICG-NP for 20 h than with 50 µg/mL for 24 h (83.1% vs. 78.5%), supported by flow cytometry, ICP-OES, and Prussian blue staining (Fig. 2 C–D; Supplementary Fig. 1E–F). Functionally, labeled TX103 retained cytotoxic activity, with tumor-killing capacity comparable to or slightly higher than controls despite modestly reduced IFN-γ release at higher E/T ratios (Fig. 2 E–F). Confocal imaging further indicated that ICG-NPs were predominantly localized intracellularly around the perinuclear region rather than on the cell surface, supporting stable labeling (Fig. 2 G). Therefore, 100 µg/mL ICG-NP with 1× HPC for 20 h was selected for subsequent experiments. Viability of TX103 under different labeling conditions, including varying ICG-NP concentrations in the presence of 1× heparin–protamine complex (HPC), assessed by trypan blue staining after 4, 8, 16, 20, and 24 h of in vitro labeling. Unlabeled TX103 (0 µg/mL) served as control. Statistical comparisons versus the 0 µg/mL control are indicated above the corresponding curves. CAR expression in unlabeled and labeled TX103 cells assessed by flow cytometry. (C) Labeling efficiency of TX103 exposed to 50 or 100 µg/mL ICG-NP compared with unlabeled TX103, assessed by flow cytometry. (D) Iron uptake per TX103 measured by inductively coupled plasma optical emission spectrometry (ICP-OES) under 100 µg/mL ICG-NP + 1× HPC for 20 h versus 50 µg/mL ICG-NP + 1× HPC for 24 h. ICG-NP labeling preserves key immunophenotypic readouts of TX103 To assess biological compatibility, we evaluated whether ICG-NP labeling altered TX103 immunophenotype, activation/exhaustion marker profiles, or homing receptor expression by flow cytometry. The distribution of naïve/TSCM (CCR7⁺CD45RA⁺), central memory (CCR7⁺CD45RA⁻), and terminal effector (CCR7⁻CD45RA⁺) subsets was comparable between unlabeled and labeled TX103 (Fig. 3 A; Supplementary Fig. 2B), and activation markers (CD69, 4-1BB) were unchanged (Fig. 3 B; Supplementary Fig. 2C). PD-1 and LAG-3 expression remained low and unaffected by labeling, while TIM-3 was uniformly high across groups (96.9% ± 1.03%) without concomitant PD-1/LAG-3 upregulation, consistent with an activation-associated rather than exhausted phenotype (Fig. 3 C–E; Supplementary Fig. 2D)[ 28 ]. CXCR3 expression was also preserved (Fig. 3 F; Supplementary Fig. 2E). Collectively, these data indicate that ICG-NP labeling does not measurably perturb key immunophenotypic readouts of TX103. High in vitro detection capacity of the ICG-NP-labeled CAR-T Cells by MRI and NIR-II Imaging We then defined the in vitro detectability and quantitative range of labeled TX103 by NIR-II imaging and MRI using cell pellets with graded cell numbers. Compared with unlabeled TX103, labeled TX103 produced stronger signals in both NIR-II imaging and MRI (Fig. 4 A–B; Supplementary Fig. 3A). NIR-II imaging also exhibited a linear correlation with cell numbers from 1×10 3 to 2×10 5 [R 2 = 0.973, p < 0.001], and its in vitro detection threshold was as low as 1000 cells [p = 0.008, 95% CI = 43.767–161.85] (Fig. 4 A). Under the current in vitro conditions, MRI detection required higher labeled cell numbers to achieve reliable signal changes. The minimum cell number required for reliable MRI detection was 5×10 5 labeled TX103, which significantly reduced T2 relaxation time (P < 0.001, Fig. 4 B) and increased SNR (p < 0.001, Fig. 4 C). Together, these data indicate that NIR-II imaging provides a sensitive, semi-quantitative readout across a broad range, while MRI offers complementary anatomical context for subsequent in vivo analyses. MRI-based assessment of tumor-associated localization of cell-associated signals in an orthotopic glioma model At day 7 post-inoculation, bioluminescence imaging confirmed successful tumor engraftment in all mice (Supplementary Fig. 4A), and B7-H3 expression in U87MG cells was confirmed by Western blotting (Supplementary Fig. 4B). Baseline MRI and NIR-II scans were acquired on day 9, followed by infusion of ICG-NP–labeled TX103 or untargeted-T cells via intracerebroventricular (ICV) or intravenous (i.v.) routes (Fig. 5 A–B; Supplementary Fig. 4C). The i.v.-delivered TX103 and ICV-delivered untargeted-T cells served as negative controls due to their minimal expected tumor-associated localization[ 6 ]. MRI showed a tumor-associated signal pattern in the ICV-TX103 group (Fig. 5 B), supported by human CD3 immunohistochemistry and Prussian blue staining (Fig. 6 A–B). In contrast, MRI and pathology showed no or minimal tumor-associated signals in the i.v.-TX103 and ICV-untargeted-T cells groups (Fig. 5 B; Fig. 6 A–B). The ICV-TX103 group also exhibited smaller tumor volumes and better body-weight preservation within the study window (Fig. 6 C–D). Together, these findings are consistent with enhanced tumor-associated localization after ICV delivery[ 6 ], and indicate that ICG-NP labeling enables MRI-based assessment of intracranial localization in vivo . In vivo tracing of the distribution of ICG-NP-labeled CAR-T Cells using NIR-II imaging NIR-II imaging did not resolve distinct intratumoral CAR-T cell localization between the ICV-delivered TX103 and untargeted-T cells groups on day 1 or day 4 (Fig. 5 C), likely due to its limited resolution in brain structure. Nevertheless, given its strength in whole-body imaging[ 29 ] and broad detection range (Fig. 4 A), NIR-II imaging was further applied to analyze the in vivo biodistribution of CAR-T cells. Consistent with MRI findings (Fig. 5 B), NIR-II imaging showed higher brain-associated exposure after ICV-administered TX103 than after intravenous delivery (Fig. 5 C–E), suggesting greater intracranial exposure under the ICV route within the imaging window. In addition, ICV-delivered TX103 exhibited a slower decline in brain-associated fluorescence over time compared with ICV-delivered untargeted-T cells, with the brain-to–whole-body exposure fraction (%) decreasing from 49.6% to 36% over 7 days, whereas the corresponding ratio in the untargeted group declined from 51% to 11% (Supplementary Fig. 4F). These findings support tumor-associated retention of CAR-T cells. Beyond the central nervous system, the liver and spleen were identified as two major extra-CNS organs exhibiting marked fluorescence accumulation regardless of the administration route (Fig. 5 C–D). These distribution patterns were corroborated by CD3 immunohistochemistry and Prussian blue staining, supporting substantial T-cell infiltration and iron deposition in both organs (Fig. 6 A–B). In the spleen, abundant T-cell presence across all groups likely reflects physiological homing to secondary lymphoid organs[ 30 ], whereas in the liver, intravenous administration resulted in stronger fluorescence intensity and iron deposition compared with ICV delivery (Fig. 5 E, Fig. 6 B), consistent with first-pass hepatic distribution and Kupffer cell uptake[ 31 ]. To account for within-animal clustering across sampled organs, a linear mixed-effects model with Mouse ID as a random intercept showed a significant positive association between log10-transformed organ-level NIR-II exposure [log10(I)] and CD3⁺ T-cell density (β = 0.000614, 95% CI 0.000458–0.000771; p < 0.001; Fig. 6 E). In contrast, minimal fluorescence signal and negligible CD3⁺ T-cell infiltration were observed in the heart, lungs, and intestine (Fig. 5 C, Supplementary Fig. 4E), with no evidence of iron deposition or pathological abnormalities (Fig. 6 A–B, Supplementary Fig. 5). Collectively, these results demonstrate route-dependent organ-level exposure patterns and support this workflow for longitudinal biodistribution profiling in orthotopic glioma models. Discussion CAR-T therapy has shown promise for glioblastoma (GBM), including early clinical studies of locoregional administration [ 7 , 8 , 32 , 33 ]. However, heterogeneous responses and safety considerations underscore the need for practical approaches to characterize in vivo trafficking and organ-level exposure of transferred cells, which are important for preclinical interpretation and route-informed risk assessment[ 8 ]. In this context, an effective cell-labeling strategy should provide imaging-visible readouts while preserving key T-cell phenotypic and functional attributes. Here, we developed an indocyanine green–iron nanoparticle probe (ICG-NP) and established a dual-modality tracking workflow integrating NIR-II fluorescence imaging with MRI to enable non-invasive assessment of CAR-T cell distribution in orthotopic glioma models. Iron-based agents (including USPIOs[ 19 ], SPIONs[ 34 ], and ferumoxytol[ 18 ]) have been widely used to support MRI cell tracking[ 17 , 35 ], and ferumoxytol-labeled CAR-T cells have enabled multimodal imaging in solid tumor models[ 18 ]. In parallel, advances in NIR-II fluorescence imaging have expanded the feasibility of repeated, whole-body biodistribution readouts over time[ 36 ]. Building on these concepts, our data support a complementary framework in which MRI provides anatomical context for intracranial localization and tumor-associated signal evaluation (Fig. 5 B), whereas NIR-II enables longitudinal, whole-body profiling of distribution patterns (Fig. 5 C) [ 37 ]. The positive association between NIR-II signal intensity and CD3⁺ T-cell density across analyzed organs (Fig. 6 E), supported by multi-organ pathological analyses, further supports the utility of this workflow for semi-quantitative organ-level biodistribution profiling in vivo . Intracranial delivery strategies, including intracerebroventricular (ICV) administration, are increasingly adopted in glioma CAR-T studies to enhance intracranial exposure in the setting of blood–brain barrier constraints[ 4 , 7 , 38 – 40 ]. Importantly, intracranial delivery does not necessarily imply confinement to the central nervous system; rather, it can reshape exposure compartments and yield distinct organ-level exposure profiles over time[ 15 ]. In our orthotopic glioma model, longitudinal NIR-II imaging revealed route-dependent peripheral exposure profiles, with prominent liver and spleen signals and differential hepatic signal intensity across routes (Fig. 5 C–E). These whole-body readouts were supported by CD3 immunohistochemistry and Prussian blue staining (Fig. 6 A–B), indicating that systemic exposure should be explicitly considered when interpreting intracranial CAR-T studies. Together, these observations highlight the value of whole-body biodistribution profiling for route-informed evaluation of CAR-T exposure in GBM models. Notably, we observed differences in clearance kinetics between labeled CAR-T and T cells (Fig. 4 E) and detectable cell-associated signals in cervical lymph nodes after ICV administration (Supplementary Fig. 4D). This pattern is consistent with reports that macromolecules and immune cells can drain from the central nervous system to deep cervical lymph nodes via meningeal lymphatic pathways[ 41 ]. These observations suggest that locally administered cells can be associated with peripheral exposure, potentially through cerebrospinal fluid drainage and systemic recirculation, and provide context for interpreting systemic readouts following intracranial delivery. Such information may aid the design and interpretation of preclinical CAR-T studies, particularly in settings where inefficient trafficking and off-target exposure remain important translational considerations. Conclusion In summary, we establish a biocompatible dual-modality imaging workflow for longitudinal, organ-level biodistribution profiling of CAR-T cell–associated signals in an orthotopic glioma model. By coupling MRI-based intracranial anatomical localization with NIR-II whole-body readouts and anchoring imaging signals with multi-organ pathology, this framework enables route-informed interpretation of peripheral exposure following intracranial CAR-T delivery. Declarations Declaration of competing interest No potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding This study was supported by the National Natural Science Foundation of China (NSFC) (81930048, 92059207, 92359301), CAS Youth Interdisciplinary Team (JCTD-2021-08), and Beijing Postdoctoral Research Foundation. Author Contribution Chunzhao Li: Investigation; Data curation; Formal analysis; Validation; Visualization; Writing-original draft. Na Xian: Resources; Investigation; Data curation; Formal analysis.Rui Feng: Resources; Investigation; Data curation; Formal analysis. Peng Zhang: Investigation; Methodology. Xiaobin Zhao: Data curation; Formal analysis. Nan Ji: Conceptualization; Supervision; Project administration; Writing - review & editing. Zhenhua Hu: Conceptualization; Supervision; Writing - review & editing. Yang Zhang: Conceptualization; Supervision; Writing-review & editing. Gangxiong Huang: Conceptualization; Supervision. Acknowledgement The authors thank AiMi Academic Services (www.aimieditor.com) for English language editing and review services. Data Availability Data will be made available on request. References Ostrom QT, Price M, Neff C, Cioffi G, Waite KA, Kruchko C, Barnholtz-Sloan JS (2023) CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2016–2020. Neuro Oncol 25:iv1–iv99. 10.1093/neuonc/noad149 Miller KD, Ostrom QT, Kruchko C et al (2021) Brain and other central nervous system tumor statistics, 2021. CA Cancer J Clin 71:381–406. 10.3322/caac.21693 Ahmed N, Brawley V, Hegde M et al (2017) HER2-Specific Chimeric Antigen Receptor-Modified Virus-Specific T Cells for Progressive Glioblastoma: A Phase 1 Dose-Escalation Trial. 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Sci Immunol 9:eadf2223. 10.1126/sciimmunol.adf2223 Guo Y, Hu J, Wang P et al (2023) In Vivo NIR-II Fluorescence Lifetime Imaging of Whole-Body Vascular Using High Quantum Yield Lanthanide-Doped Nanoparticles. Small (Weinheim an Der Bergstrasse, Germany). 19:e2300392. 10.1002/smll.202300392 Talbot LJ, Chabot A, Ross AB et al (2024) Redirecting B7-H3.CAR T Cells to Chemokines Expressed in Osteosarcoma Enhances Homing and Antitumor Activity in Preclinical Models. Clin Cancer Research: Official J Am Association Cancer Res 30:4434–4449. 10.1158/1078-0432.CCR-23-3298 Bernsmeier C, Singanayagam A, Patel VC, Wendon J, Antoniades CG (2015) Immunotherapy in the treatment and prevention of infection in acute-on-chronic liver failure. Immunotherapy 7:641–654. 10.2217/imt.15.27 Bagley SJ, Desai AS, Linette GP, June CH, O'Rourke DM (2018) CAR T-cell therapy for glioblastoma: recent clinical advances and future challenges. 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Light Sci Appl 13:96. 10.1038/s41377-024-01450-0 Additional Declarations No competing interests reported. Supplementary Files SupplementTable1.docx SupplementTable2.docx SUPFigure1.jpg supfigure2.jpg SUPFigure3.jpg SUPFigure4.jpg supfigure5.jpg SupplementFigureLegendNEW.docx GA.tif Graphical Abstract: Schematic of the ICG–iron nanoparticle (ICG-NP) labeling workflow for B7-H3–targeting CAR-T cells (TX103) and dual-modality in vivo tracking using MRI (tumor-associated localization) and NIR-II fluorescence imaging (whole-body biodistribution) in an orthotopic glioma model, with pathological validation. Cite Share Download PDF Status: Published Journal Publication published 02 May, 2026 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted Editorial decision: Revision requested 25 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 18 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 15 Mar, 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-9126411","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610168979,"identity":"167cb784-d6e1-4986-a89f-e9c5c7c1d8db","order_by":0,"name":"Chunzhao Li","email":"","orcid":"","institution":"Beijing Tian Tan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunzhao","middleName":"","lastName":"Li","suffix":""},{"id":610168982,"identity":"a43c246e-561a-4a55-9813-c1d03af2ec9a","order_by":1,"name":"Peng Zhang","email":"","orcid":"","institution":"Beijing Tian Tan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhang","suffix":""},{"id":610168985,"identity":"0d674389-2df9-4a36-9e0b-4be3cc0902d8","order_by":2,"name":"Xiaobin Zhao","email":"","orcid":"","institution":"Beijing Tian Tan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Zhao","suffix":""},{"id":610168987,"identity":"19c3d80b-b474-41ca-aef0-601a64c51062","order_by":3,"name":"Rui Feng","email":"","orcid":"","institution":"Fuzhou Tcelltech Biological Science and Technology Inc","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Feng","suffix":""},{"id":610168993,"identity":"a2a2d8d6-3acc-4554-9de0-bde866f819a1","order_by":4,"name":"Na Xian","email":"","orcid":"","institution":"Fuzhou Tcelltech Biological Science and Technology Inc","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Xian","suffix":""},{"id":610168997,"identity":"52306c69-4c2b-48e4-8c56-40281a21f8ee","order_by":5,"name":"Gangxiong Huang","email":"","orcid":"","institution":"Fuzhou Tcelltech Biological Science and Technology Inc","correspondingAuthor":false,"prefix":"","firstName":"Gangxiong","middleName":"","lastName":"Huang","suffix":""},{"id":610169000,"identity":"08df3499-e9e9-4d86-8ad0-d9af90d41adb","order_by":6,"name":"Zhenhua Hu","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Hu","suffix":""},{"id":610169001,"identity":"f2e1386f-3278-4647-9584-e16be30df94d","order_by":7,"name":"Yang Zhang","email":"","orcid":"","institution":"Beijing Tian Tan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zhang","suffix":""},{"id":610169002,"identity":"cc289360-37cf-4ce0-8410-59f8f22d5143","order_by":8,"name":"Nan Ji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACxmYQWXGAmYGBhyQtZw4w87ARqwWir+0AA/FamNt5D34unHeH3V6+9wDDxz21xDiML1l65rZnQIfxJTDOeHacGC08BtK82w6D/GLAzHPgGFFajH/zziFRi5k0bwNcSw1xWqx5jgH9cizH4OCMAwcIazHsP2N8m6fmTjJ78xnDBx8O1BGhpQFCJ4MIoBWHCWuRh9J2UJoIW0bBKBgFo2DEAQAopzQR7y5edwAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Tian Tan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Nan","middleName":"","lastName":"Ji","suffix":""}],"badges":[],"createdAt":"2026-03-15 05:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9126411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9126411/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00262-026-04403-1","type":"published","date":"2026-05-02T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":105282231,"identity":"08cae867-259e-4ded-93cb-f4c52dbade05","added_by":"auto","created_at":"2026-03-24 10:27:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":188419,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of ICG-NP.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic representation of ICG conjugation to PEG-Fe₃O₄(-NH₂) nanoparticles. (B) TEM images of ICG-NP (magnification, 200,000×). (C) DLS analysis of the hydrodynamic diameter of ICG-NP. (D) Zeta potential measurements of ICG-NP. (E) Absorption spectra of ICG-NP. (F) Emission spectra of ICG-NP in the NIR-II range. For (C) and (D), measurements were performed in triplicate\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9126411/v1/0580678e4de7047065015f96.jpg"},{"id":105282255,"identity":"a1c33954-4b3d-4072-a158-31a9c775d626","added_by":"auto","created_at":"2026-03-24 10:27:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":650714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOptimization of labeling B7-H3-targeting CAR-T cells (TX103) with ICG-NP.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Viability of TX103 under different labeling conditions, including varying ICG-NP concentrations in the presence of 1× heparin–protamine complex (HPC), assessed by trypan blue staining after 4, 8, 16, 20, and 24 h of in vitro labeling. Unlabeled TX103 (0 μg/mL) served as control. Statistical comparisons versus the 0 μg/mL control are indicated above the corresponding curves.\u003c/p\u003e\n\u003cp\u003e(B) CAR expression in unlabeled and labeled TX103 cells assessed by flow cytometry.\u003c/p\u003e\n\u003cp\u003e(C) Labeling efficiency of TX103 exposed to 50 or 100 μg/mL ICG-NP compared with unlabeled TX103, assessed by flow cytometry.\u003c/p\u003e\n\u003cp\u003e(D) Iron uptake per TX103 measured by inductively coupled plasma optical emission spectrometry (ICP-OES) under 100 μg/mL ICG-NP + 1× HPC for 20 h versus 50 μg/mL ICG-NP + 1× HPC for 24 h.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9126411/v1/1472a33495ee4de97a9353d5.jpg"},{"id":105282235,"identity":"e465df12-322f-4f95-8314-9275fc033f95","added_by":"auto","created_at":"2026-03-24 10:27:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":371179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiological characteristics of ICG-NP-labeled CAR-T cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Representative flow cytometry plots showing CCR7 and CD45RA expression in unlabeled and ICG-NP–labeled TX103 cells. Quadrants correspond to naïve/TSCM (CCR7⁺CD45RA⁺), central memory (TCM, CCR7⁺CD45RA⁻), effector memory (TEM, CCR7⁻CD45RA⁻), and terminal effector (TEMRA, CCR7⁻CD45RA⁺) subsets.\u003c/p\u003e\n\u003cp\u003e(B) Representative flow cytometry plots of CD69 and 4-1BB expression in unlabeled and labeled TX103 cells.\u003c/p\u003e\n\u003cp\u003e(C–E) Representative histograms of exhaustion markers PD-1 (C), LAG-3 (D), and TIM-3 (E); dashed lines indicate gating thresholds.\u003c/p\u003e\n\u003cp\u003e(F) Representative histograms of CXCR3 expression; dashed lines indicate gating thresholds\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9126411/v1/e5e424cd5d4440acbfb78501.jpg"},{"id":105282150,"identity":"0c54ab76-7f3c-41ea-8163-702935d455fc","added_by":"auto","created_at":"2026-03-24 10:27:26","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":324640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn vitro dual-modality imaging of TX103.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) NIR-II imaging of labeled and unlabeled TX103 across graded cell numbers (performed in triplicate). Mean fluorescence intensity was quantified in ImageJ. Statistical comparisons between labeled and unlabeled TX103 at each cell number were performed using the Mann–Whitney U test and are indicated above the corresponding data points. The limit of detection is indicated by a red dotted line.\u003c/p\u003e\n\u003cp\u003e(B, C) MRI of labeled and unlabeled TX103 at different cell numbers. T2 relaxation time changes (B) and signal-to-noise ratio (SNR) (C) of cell pellets (white arrows; yellow dotted outline) were quantified. One-way ANOVA with Tukey’s post hoc test was used for comparisons between labeled TX103 groups and the unlabeled control\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9126411/v1/ac10d22c9135995703277b0b.jpg"},{"id":105282209,"identity":"cdb67e80-9080-4fb1-962a-318721faa9a4","added_by":"auto","created_at":"2026-03-24 10:27:33","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":726905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn vivo dual-modality imaging of CAR-T cells in the TX103 treated glioma murine model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic of the in vivo CAR-T cell tracking workflow in orthotopic U87MG glioma models.\u003c/p\u003e\n\u003cp\u003e(B, C) Representative coronal T2-weighted MR images (B) and whole-body NIR-II images (C) acquired at different time points after administration in the orthotopic glioma model. ICG-NP–labeled TX103 or untargeted-T cells were administered intracerebroventricularly (ICV) or intravenously (i.v.). Each group initially included nine mice. Baseline scans were obtained before CAR-T administration. Tumor regions are outlined with yellow dotted lines; labeled TX103-associated signals are indicated by red arrows; the ventricular region is indicated by white arrows.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9126411/v1/c3252a9d5cbcf6134ef57556.jpg"},{"id":105282233,"identity":"6028801a-1f5a-4b04-8de6-8b4a80f3bb3e","added_by":"auto","created_at":"2026-03-24 10:27:36","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1605266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathological validation and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e evaluation of CAR-T cell–associated biodistribution.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Immunohistochemical staining of human CD3⁺ T cells in brain, spleen, liver, kidney, heart, and lung harvested on day 7 after treatment. A human-specific CD3 monoclonal antibody was used to detect infiltrating human T cells.\u003cbr\u003e\n(B) Prussian blue staining of the same organs on day 7 after treatment, showing iron deposition associated with ICG-NP–labeled cells.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9126411/v1/c8d5c00f4a3e810f8d82a8a1.jpg"},{"id":108809091,"identity":"17ec0859-0bc5-4d9d-b451-383d3028d80d","added_by":"auto","created_at":"2026-05-08 15:49:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4174917,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9126411/v1/b7db3e40-093d-4f45-936b-0f8047846921.pdf"},{"id":105282253,"identity":"8e990ed8-037a-494f-9b9a-cb2c9bbdfdd8","added_by":"auto","created_at":"2026-03-24 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10:27:36","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":22166382,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract: \u003c/strong\u003eSchematic of the ICG–iron nanoparticle (ICG-NP) labeling workflow for B7-H3–targeting CAR-T cells (TX103) and dual-modality in vivo tracking using MRI (tumor-associated localization) and NIR-II fluorescence imaging (whole-body biodistribution) in an orthotopic glioma model, with pathological validation.\u003c/p\u003e","description":"","filename":"GA.tif","url":"https://assets-eu.researchsquare.com/files/rs-9126411/v1/f699be858ec8314cc313ef26.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual-modality imaging enables longitudinal biodistribution profiling of intracerebroventricular CAR-T therapy in orthotopic glioma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioblastoma (GBM) is the most common type of primary malignant brain tumor and is notorious for its limited response to current therapeutic options[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite aggressive standard-of-care therapies, the median survival for GBM patients is around 15 months[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a promising treatment strategy, with encouraging preclinical and early-phase clinical outcomes reported in recent years, particularly with locoregional delivery approaches[\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, durable benefit is achieved in only a subset of patients, highlighting persistent barriers to therapeutic optimization[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This heterogeneity reflects multiple biological and treatment-related determinants, among which insufficient tumor trafficking and systemic off-target exposure of transferred cells are increasingly recognized as key variables that influence both therapeutic interpretation and risk assessment in cellular immunotherapy[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, a detailed understanding of \u003cem\u003ein vivo\u003c/em\u003e CAR-T cell trafficking and biodistribution following local administration is critical for rational development and optimization of CAR-T therapy in GBM[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA major challenge for CAR-T therapy in GBM is the restrictive blood\u0026ndash;brain barrier, which can limit the access of intravenously infused cells to intracranial tumors[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Accordingly, locoregional delivery approaches have been increasingly adopted in translational studies and clinical trials to enhance intracranial tumor exposure[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, local administration does not necessarily confine transferred cells to the central nervous system. CAR-T cells delivered into the cerebrospinal fluid may redistribute via cerebrospinal fluid drainage and lymphatic outflow pathways and subsequently access peripheral compartments[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Despite its importance, the systemic exposure profile and organ-level biodistribution kinetics following intracranial CAR-T delivery remain incompletely characterized, complicating interpretation of therapeutic heterogeneity and preclinical evaluation of potential off-target organ exposure[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, a practical, non-invasive framework that links intracranial localization with longitudinal whole-body biodistribution and kinetics is needed to support route-informed optimization of CAR-T strategies for GBM[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiple imaging approaches have been explored to characterize post-infusion migration, distribution, and persistence of CAR-T cells[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. MRI and Magnetic Particle Imaging (MPI) have been used to track CAR-T cells labeled with iron-based agents and to assess tumor-associated localization[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nuclear imaging modalities such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) were utilized to image and quantify CAR-T cells labeled with nuclide probes or reporter genes[\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, applying these strategies to intracranial or locoregional delivery settings remains less established and is often constrained by modality-specific trade-offs in sensitivity, spatial resolution, imaging depth, and practical implementation[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Consequently, a framework that links intracranial localization, whole-body biodistribution, and distribution kinetics remains needed to support route-informed interpretation and optimization of CAR-T studies in glioma models[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, second near-infrared window (NIR-II) fluorescence imaging provides a practical complementary modality for sensitive, real-time whole-body biodistribution assessment[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this study, we establish a dual-modality cell-labeling and imaging workflow using a biologically compatible indocyanine green\u0026ndash;conjugated iron oxide nanoparticle (ICG-NP) label to track a clinically manufactured B7-H3\u0026ndash;targeting CAR-T product (TX103) in an orthotopic glioma model. We couple intracranial anatomical localization (MRI) with longitudinal systemic exposure profiling (NIR-II) using a biologically compatible labeling strategy validated by orthogonal pathology. Using this framework, we characterize route-dependent differences in tumor-associated localization and organ-level biodistribution following intracerebroventricular and intravenous administration, providing a practical approach for preclinical biodistribution profiling in intracranial tumor immunotherapy settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSynthesis and characterization analysis of ICG-NP\u003c/h2\u003e \u003cp\u003eICG-NP was synthesized by conjugating ICG-NHS to Fe₃O₄-PEG-NH₂ nanoparticles. Briefly, 15 mg Fe₃O₄-PEG-NH₂ (1 mg/mL in 15 mL) was concentrated using a 100 kDa ultrafiltration tube (3000 g, 15 min), washed once with 0.1 M citrate buffer (CB), and resuspended in 14 mL CB. ICG-NHS (5 mg/mL in methanol) was added, the volume was adjusted to 15 mL with CB, and the mixture was incubated overnight at room temperature with continuous rotation. The reaction mixture was then purified by ultrafiltration (4500 rpm, 15 min) and washed three times with purified water, followed by resuspension to 15 mL. The core size of Fe₃O₄-PEG-NH₂ was measured by TEM (HT-7800, Hitachi, Japan). Hydrodynamic diameter (DLS) and zeta potential of Fe₃O₄-PEG-NH₂ and ICG-NP were measured using a Zetasizer Lab (Malvern Instruments, Malvern, UK). Excitation/absorption and emission spectra were assessed using a lifetime and steady-state spectrometer (FLS980, Edinburgh Instruments, UK).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell lines\u003c/h3\u003e\n\u003cp\u003eThe U87MG-Luc human glioma cell line was kindly provided by Fuzhou Tcelltech Biological Science and Technology Inc. (Fuzhou, China). Cell line identity was confirmed by short tandem repeat (STR) profiling (21 loci), showing a 100% match to U-87MG/U-87MG-Luc2 profiles in the Cellosaurus database and no evidence of cross-contamination. Cells were cultured in DMEM supplemented with 10% fetal bovine serum (FBS) and 1% penicillin\u0026ndash;streptomycin at 37\u0026deg;C in a humidified 5% CO₂ incubator. Culture media and supplements were purchased from Gibco (Carlsbad, CA, USA) unless otherwise stated.\u003c/p\u003e\n\u003ch3\u003eManufacturing of B7-H3-targeting CAR-T cells\u003c/h3\u003e\n\u003cp\u003eHuman PBMCs were purchased from Shanghai Saily Biotechnology Co., Ltd. CAR design and construction were described previously[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Briefly, TX103 was generated using a humanized scFv derived from the 7E12 monoclonal antibody linked to intracellular 4-1BB and CD3-ζ domains. TX103 and untargeted-T cells (PBMC without CAR) were cultured in RPMI-1640 supplemented with 10% FBS, 1% L-glutamine, IL-2 (300 IU/mL; MedChem Express, Monmouth Junction, NJ, USA), and 1% penicillin\u0026ndash;streptomycin at 37\u0026deg;C in 5% CO₂.\u003c/p\u003e\n\u003ch3\u003eLabeling CAR-T and untargeted-T cells with ICG-NP and heparin-protamine complex\u003c/h3\u003e\n\u003cp\u003eTX103 and untargeted-T cells were co-incubated with ICG-NP and heparin\u0026ndash;protamine complex (HPC; 1\u0026times; = 2 IU/mL heparin\u0026thinsp;+\u0026thinsp;60 \u0026micro;g/mL protamine). Labeling conditions were screened across incubation times (0, 4, 8, 16, 20, and 24 h), ICG-NP concentrations (0, 50, 100, and 200 \u0026micro;g/mL), and HPC dilutions (0\u0026times;\u0026ndash;4\u0026times;) based on viability and labeling efficiency. Viability and cell number were assessed by trypan blue staining using an automatic cell counter (C-100, RWD Life Science, USA). Labeling efficiency was evaluated by ICP-OES and flow cytometry.\u003c/p\u003e\n\u003ch3\u003eFlow Cytometry\u003c/h3\u003e\n\u003cp\u003eFlow cytometry was performed to assess the impact of ICG-NP labeling on CAR-T cell phenotype (CD45RA, CCR7), activation (CD69, 4-1BB), exhaustion (TIM3, PD1, LAG3), and the homing receptor CXCR3. Transduction efficiency of CAR-T cells was analyzed using PE-conjugated b7h3-mouse igG protein[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Cells were resuspended in FACS buffer (PBS supplemented with 1% FBS and 1 \u0026micro;M EDTA) and stained with fluorochrome-conjugated monoclonal antibodies or corresponding isotype control for 15 minutes at 4\u0026deg;C in the dark (detailed information was provided in Supplementary Table\u0026nbsp;1). After two washes, cells were resuspended in 200 \u0026micro;L of FACS buffer and analyzed on a Beckman CytoFLEX S flow cytometer (Beckman Coulter, Miami, FL). Examples for the gating strategy used for flow cytometry analysis are given in Supplementary Fig.\u0026nbsp;1G. Data analysis was conducted using FlowJo (v10.8.1, BD Biosciences).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell fluorescence staining\u003c/h2\u003e \u003cp\u003eCell membrane staining was performed using the PKH26 Red Fluorescence Cell Linker Kit (Beijing Fluorescence Biotechnology, China). A total of 2 \u0026times; 10⁷ TX103 CAR-T cells, both labeled and unlabeled, were treated with the PKH26 kit and DAPI. Multiplex fluorescence images were then captured using the Thunder Imager 3D Assay (Leica, Wetzlar, Germany).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional assays\u003c/h3\u003e\n\u003cp\u003eThe functional integrity of ICG-NP-labeled and unlabeled TX103 was evaluated using an interferon-γ release assay and a tumor cell killing assay. U87MG-Luc cells were seeded in 96-well plates and cocultured with either labeled or unlabeled TX103 for 16 hours at effector-to-target ratios of 5:1, 1:1, and 0.1:1. IFN-γ levels in culture supernatant were quantified using the Sensitivity Flex Set CBA (Human IFN-γ Flex Set 558269, BD, USA). For cytotoxicity assessments, tumor cells were stained with CFSE (Invitrogen, Carlsbad, C1157, CA, USA). Following co-culturing for 12\u0026ndash;16 hours, cells were harvested and counterstained with DAPI. Finally, flow cytometry was performed to measure the percentage of non-viable tumor cells, identified as CFSE\u003csup\u003e+\u003c/sup\u003eDAPI\u003csup\u003e+\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eWestern blotting for B7-H3 expression\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eWestern blotting for B7-H3 expression\u003c/div\u003e \u003cp\u003eU87MG and additional glioma cell lines were lysed in RIPA buffer supplemented with protease inhibitors. Equal amounts of protein were separated by SDS\u0026ndash;PAGE, transferred onto PVDF membranes, and probed with anti\u0026ndash;B7-H3 antibody (CST, 14058; 1:3,000). The following secondary antibody was used: anti\u0026ndash;rabbit IgG secondary antibody and HRP (Abcam, ab6721; 1:5,000). The specific protein bands were visualized via enhanced chemiluminescence reagents (NCM Biotech, P10300) on an Amersham Imager 600 (GE).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnimal experiment\u003c/h2\u003e \u003cp\u003eSPF NOG mice (male/female, 6\u0026ndash;8 weeks; Charles River Laboratories; n\u0026thinsp;=\u0026thinsp;27; severe immunodeficiency) were housed under SPF barrier conditions (IVC; 5 mice/cage) and monitored daily. All procedures were approved by the Animal Care and Use Committee of our institute (Ethical Number: IA21-2302-420303). The health of the mice was monitored daily, with particular attention to neurological signs, weight, and environmental conditions, including access to food and water.\u003c/p\u003e \u003cp\u003eU87MG-Luc cells (1\u0026times;10⁵ in 5 \u0026micro;L PBS) were stereotactically injected into the right basal ganglia (2 mm lateral to bregma, 3.5 mm below dura) under 3% isoflurane anesthesia using a 25 \u0026micro;L Hamilton syringe with fine-step stereotactic control. Ten days post-inoculation, mice were randomized into three groups (n\u0026thinsp;=\u0026thinsp;9/group): (i) ICV-labeled TX103, (ii) ICV-labeled untargeted-T cells, and (iii) i.v.-labeled TX103. For i.v. administration, 5\u0026times;10⁶ labeled TX103 cells in 100 \u0026micro;L PBS were injected via tail vein. For ICV administration, 5\u0026times;10⁶ labeled TX103 or untargeted-T cells were injected into the left lateral ventricle (AP\u0026thinsp;\u0026minus;\u0026thinsp;0.22 mm, ML 1 mm, DV 2.3\u0026ndash;2.8 mm). Tumor engraftment was confirmed by BLI using an IVIS Spectrum In Vivo Imaging System (Caliper Life Sciences, Waltham, MA) on day 7 post-inoculation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMagnetic resonance imaging (MRI)\u003c/h2\u003e \u003cp\u003eMRI was performed using a 7.0T small-animal scanner (PharmaScan 70/16, Bruker, Germany) with a T2-weighted sequence for \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e imaging. Mice underwent baseline MRI on day 9 post-inoculation and follow-up scans on days 1, 4, and 7 after CAR-T administration (TR/TE\u0026thinsp;=\u0026thinsp;2236/35 ms; FOV\u0026thinsp;=\u0026thinsp;22\u0026times;22 mm; matrix\u0026thinsp;=\u0026thinsp;256\u0026times;256; slice thickness\u0026thinsp;=\u0026thinsp;0.5 mm; averages\u0026thinsp;=\u0026thinsp;5; voxel size\u0026thinsp;\u0026asymp;\u0026thinsp;0.0037 mm\u0026sup3;). \u003cem\u003eIn vitro\u003c/em\u003e cell pellets were scanned using TR/TE\u0026thinsp;=\u0026thinsp;2500/33 ms; FOV\u0026thinsp;=\u0026thinsp;63\u0026times;63 mm; matrix\u0026thinsp;=\u0026thinsp;256\u0026times;256; slice thickness\u0026thinsp;=\u0026thinsp;0.35 mm; averages\u0026thinsp;=\u0026thinsp;3; voxel size\u0026thinsp;\u0026asymp;\u0026thinsp;0.0212 mm\u0026sup3;. SNR was calculated as the mean ROI signal divided by the SD of background noise. Tumor volume was segmented using 3D Slicer v4.11.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNIR-II fluorescence imaging\u003c/h2\u003e \u003cp\u003eNIR-II imaging was conducted to monitor the distribution of ICG-NP-labeled TX103 \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. Regarding \u003cem\u003ein vitro\u003c/em\u003e NIR-II imaging of TX103, labeled and unlabeled TX103 from each gradient were centrifuged to the bottom of Eppendorf tubes. For \u003cem\u003ein vivo\u003c/em\u003e NIR-II imaging, mice were anesthetized and placed on the imaging platform following skin preparation. A high-resolution NIR-II camera (Cheetah 640, Xenics, Belgium) captured fluorescence images of the front and back of the mice. The exposure time was set to 1000 ms, and a high-pass filter (FEL1000, Thorlabs, USA) was used to detect the NIR-II signals. Continuous light at 792 nm served as the excitation light, with a power density of 100 mW/cm\u0026sup2; at the imaging site. Imaging parameters were kept constant throughout all experiments. After each imaging session, brains and vital organs (heart, liver, spleen, lungs, and kidneys) from mice in each group were harvested. Before formalin fixation, NIR-II images of the samples were collected to validate the \u003cem\u003ein vivo\u003c/em\u003e results. The average gray value of the NIR-II images was measured using ImageJ (1.8.0_112, National Institutes of Health, USA). Data were log10-transformed to normalize the distribution and facilitate linear regression analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of NIR-II exposure metrics\u003c/h2\u003e \u003cp\u003eFor longitudinal biodistribution profiling, regions of interest (ROIs) were manually delineated in ImageJ on all images acquired under identical imaging settings. Organ-level NIR-II exposure was quantified as the mean fluorescence intensity (mean gray value) within each organ ROI. For statistical analyses, organ-level intensities were log10-transformed and reported as log10-transformed organ-level NIR-II exposure [log10(I)]. To describe intracranial relative exposure over time, a brain-to\u0026ndash;whole-body exposure fraction (%) was calculated as:\u003c/p\u003e \u003cp\u003eBrain-to\u0026ndash;whole-body exposure fraction (%)\u0026thinsp;=\u0026thinsp;100 \u0026times; I_brain / ΣI_organs,\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eI_brain\u003c/em\u003e denotes the mean gray value within the brain ROI and \u003cem\u003eΣI_organs\u003c/em\u003e denotes the sum of mean gray values across all delineated organ ROIs from the same mouse at the same time point.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTissue section pathology and imaging\u003c/h2\u003e \u003cp\u003eTissues were fixed in formalin, paraffin-embedded, and sectioned at 4 \u0026micro;m for IHC and H\u0026amp;E staining. For IHC, sections underwent antigen retrieval and blocking, followed by incubation with anti-CD3 antibody (rabbit, 1:100, abcam ab243874) as previously described, and secondary antibody (1:500, ZB-2301). After DAB development and hematoxylin counterstaining, slides were scanned using Pannoramic MIDI (3DHISTECH). CD3⁺ T-cell density (cells/mm\u0026sup2;) was quantified in QuPath v0.6.0 using automated positive cell detection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePrussian blue staining\u003c/h2\u003e \u003cp\u003ePrussian blue staining was used to detect iron deposition. After dewaxing and hydration, sections were stained with freshly prepared Prussian blue working solution (equal parts Reagents A and B, DJ0001, LEAGENE Biotechnology) for 15\u0026ndash;30 min, rinsed with distilled water, and counterstained with Nuclear Fast Red for 5\u0026ndash;10 min. Slides were dehydrated using alcohol, cleared in xylene, mounted with Neutral Balsam, and scanned using the Pannoramic MIDI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics 24. In vitro assays were conducted in triplicate and are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Data normality was assessed by the Shapiro\u0026ndash;Wilk test and homogeneity of variance by Levene\u0026rsquo;s test. Two-group comparisons were performed using an unpaired Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test, as appropriate. Multi-group comparisons used one-way ANOVA with Tukey\u0026rsquo;s post hoc test or Kruskal\u0026ndash;Wallis with Dunn\u0026rsquo;s test (Bonferroni correction). Linear regression was used to assess associations between fluorescence intensity and cell number \u003cem\u003ein vitro\u003c/em\u003e. To account for within-animal clustering when multiple organs were sampled from the same mouse, a linear mixed-effects model (random-intercept) was applied with Mouse ID as a random effect and NIR-II fluorescence intensity as the dependent variable. Statistical significance was defined as *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCharacterization of ICG-NP\u003c/h2\u003e \u003cp\u003e To enable dual-modality tracking of CAR-T cell\u0026ndash;associated signals \u003cem\u003ein vivo\u003c/em\u003e, we synthesized an indocyanine green\u0026ndash;conjugated iron oxide nanoparticle probe (ICG-NP) and characterized its physicochemical and optical properties. ICG-NP was synthesized by conjugating ICG with PEG-Fe₃O₄ (-NH₂) nanoparticles through an amide reaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The TEM revealed a uniform size distribution of ICG-NP, with a diameter of approximately 20 nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). DLS measurements showed a hydrodynamic size of 69.42 nm for ICG-NP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), and the zeta potential was \u0026minus;\u0026thinsp;36.82 mV (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Spectroscopic analysis confirmed that the probe retained NIR-II fluorescence, with the highest emission at 924 nm, and displayed robust signals within the 900\u0026ndash;1200 nm near-infrared range (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F). These characteristics supported the use of ICG-NP for subsequent CAR-T cell labeling and imaging studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eOptimization of a viable and efficient ICG-NP labeling protocol for TX103\u003c/h2\u003e \u003cp\u003eWe next optimized ICG-NP labeling conditions for TX103 (clinically available B7-H3 CAR-T cell product [NCT05241392]) to maximize uptake while preserving viability, because biological compatibility is essential for interpreting downstream trafficking and biodistribution readouts. Because heparin\u0026ndash;protamine complex (HPC) can enhance iron nanoparticle internalization by T cells [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], we tested HPC concentrations and selected 1\u0026times; HPC based on minimal cytotoxicity (Supplementary Fig.\u0026nbsp;1A). TX103 cells were then co-incubated with different concentrations of ICG-NP plus 1\u0026times; HPC; viability remained comparable to controls at 50 and 100 \u0026micro;g/mL (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Supplementary Fig.\u0026nbsp;1B\u0026ndash;D). CAR expression remained unchanged under either labeling condition, with consistently high CAR positivity across groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB; Supplementary Fig.\u0026nbsp;2A). Labeling efficiency and iron uptake were higher with 100 \u0026micro;g/mL ICG-NP for 20 h than with 50 \u0026micro;g/mL for 24 h (83.1% vs. 78.5%), supported by flow cytometry, ICP-OES, and Prussian blue staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u0026ndash;D; Supplementary Fig.\u0026nbsp;1E\u0026ndash;F). Functionally, labeled TX103 retained cytotoxic activity, with tumor-killing capacity comparable to or slightly higher than controls despite modestly reduced IFN-γ release at higher E/T ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE\u0026ndash;F). Confocal imaging further indicated that ICG-NPs were predominantly localized intracellularly around the perinuclear region rather than on the cell surface, supporting stable labeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Therefore, 100 \u0026micro;g/mL ICG-NP with 1\u0026times; HPC for 20 h was selected for subsequent experiments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eViability of TX103 under different labeling conditions, including varying ICG-NP concentrations in the presence of 1\u0026times; heparin\u0026ndash;protamine complex (HPC), assessed by trypan blue staining after 4, 8, 16, 20, and 24 h of in vitro labeling. Unlabeled TX103 (0 \u0026micro;g/mL) served as control. Statistical comparisons versus the 0 \u0026micro;g/mL control are indicated above the corresponding curves.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCAR expression in unlabeled and labeled TX103 cells assessed by flow cytometry.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e(C) Labeling efficiency of TX103 exposed to 50 or 100 \u0026micro;g/mL ICG-NP compared with unlabeled TX103, assessed by flow cytometry.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e(D) Iron uptake per TX103 measured by inductively coupled plasma optical emission spectrometry (ICP-OES) under 100 \u0026micro;g/mL ICG-NP\u0026thinsp;+\u0026thinsp;1\u0026times; HPC for 20 h versus 50 \u0026micro;g/mL ICG-NP\u0026thinsp;+\u0026thinsp;1\u0026times; HPC for 24 h.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eICG-NP labeling preserves key immunophenotypic readouts of TX103\u003c/h2\u003e \u003cp\u003eTo assess biological compatibility, we evaluated whether ICG-NP labeling altered TX103 immunophenotype, activation/exhaustion marker profiles, or homing receptor expression by flow cytometry. The distribution of na\u0026iuml;ve/TSCM (CCR7⁺CD45RA⁺), central memory (CCR7⁺CD45RA⁻), and terminal effector (CCR7⁻CD45RA⁺) subsets was comparable between unlabeled and labeled TX103 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA; Supplementary Fig.\u0026nbsp;2B), and activation markers (CD69, 4-1BB) were unchanged (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; Supplementary Fig.\u0026nbsp;2C). PD-1 and LAG-3 expression remained low and unaffected by labeling, while TIM-3 was uniformly high across groups (96.9% \u0026plusmn; 1.03%) without concomitant PD-1/LAG-3 upregulation, consistent with an activation-associated rather than exhausted phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u0026ndash;E; Supplementary Fig.\u0026nbsp;2D)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. CXCR3 expression was also preserved (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF; Supplementary Fig.\u0026nbsp;2E). Collectively, these data indicate that ICG-NP labeling does not measurably perturb key immunophenotypic readouts of TX103.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHigh\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e \u003cb\u003edetection capacity of the ICG-NP-labeled CAR-T Cells by MRI and NIR-II Imaging\u003c/b\u003e\u003c/p\u003e \u003cp\u003e We then defined the \u003cem\u003ein vitro\u003c/em\u003e detectability and quantitative range of labeled TX103 by NIR-II imaging and MRI using cell pellets with graded cell numbers. Compared with unlabeled TX103, labeled TX103 produced stronger signals in both NIR-II imaging and MRI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;B; Supplementary Fig.\u0026nbsp;3A). NIR-II imaging also exhibited a linear correlation with cell numbers from 1\u0026times;10\u003csup\u003e3\u003c/sup\u003e to 2\u0026times;10\u003csup\u003e5\u003c/sup\u003e [R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.973, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], and its \u003cem\u003ein vitro\u003c/em\u003e detection threshold was as low as 1000 cells [p\u0026thinsp;=\u0026thinsp;0.008, 95% CI\u0026thinsp;=\u0026thinsp;43.767\u0026ndash;161.85] (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Under the current \u003cem\u003ein vitro\u003c/em\u003e conditions, MRI detection required higher labeled cell numbers to achieve reliable signal changes. The minimum cell number required for reliable MRI detection was 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e labeled TX103, which significantly reduced T2 relaxation time (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) and increased SNR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Together, these data indicate that NIR-II imaging provides a sensitive, semi-quantitative readout across a broad range, while MRI offers complementary anatomical context for subsequent \u003cem\u003ein vivo\u003c/em\u003e analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMRI-based assessment of tumor-associated localization of cell-associated signals in an orthotopic glioma model\u003c/h2\u003e \u003cp\u003eAt day 7 post-inoculation, bioluminescence imaging confirmed successful tumor engraftment in all mice (Supplementary Fig.\u0026nbsp;4A), and B7-H3 expression in U87MG cells was confirmed by Western blotting (Supplementary Fig.\u0026nbsp;4B). Baseline MRI and NIR-II scans were acquired on day 9, followed by infusion of ICG-NP\u0026ndash;labeled TX103 or untargeted-T cells via intracerebroventricular (ICV) or intravenous (i.v.) routes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;B; Supplementary Fig.\u0026nbsp;4C). The i.v.-delivered TX103 and ICV-delivered untargeted-T cells served as negative controls due to their minimal expected tumor-associated localization[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. MRI showed a tumor-associated signal pattern in the ICV-TX103 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), supported by human CD3 immunohistochemistry and Prussian blue staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;B). In contrast, MRI and pathology showed no or minimal tumor-associated signals in the i.v.-TX103 and ICV-untargeted-T cells groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;B). The ICV-TX103 group also exhibited smaller tumor volumes and better body-weight preservation within the study window (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC\u0026ndash;D). Together, these findings are consistent with enhanced tumor-associated localization after ICV delivery[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and indicate that ICG-NP labeling enables MRI-based assessment of intracranial localization \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vivo\u003c/b\u003e \u003cb\u003etracing of the distribution of ICG-NP-labeled CAR-T Cells using NIR-II imaging\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNIR-II imaging did not resolve distinct intratumoral CAR-T cell localization between the ICV-delivered TX103 and untargeted-T cells groups on day 1 or day 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), likely due to its limited resolution in brain structure. Nevertheless, given its strength in whole-body imaging[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and broad detection range (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), NIR-II imaging was further applied to analyze the \u003cem\u003ein vivo\u003c/em\u003e biodistribution of CAR-T cells. Consistent with MRI findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), NIR-II imaging showed higher brain-associated exposure after ICV-administered TX103 than after intravenous delivery (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u0026ndash;E), suggesting greater intracranial exposure under the ICV route within the imaging window. In addition, ICV-delivered TX103 exhibited a slower decline in brain-associated fluorescence over time compared with ICV-delivered untargeted-T cells, with the brain-to\u0026ndash;whole-body exposure fraction (%) decreasing from 49.6% to 36% over 7 days, whereas the corresponding ratio in the untargeted group declined from 51% to 11% (Supplementary Fig.\u0026nbsp;4F). These findings support tumor-associated retention of CAR-T cells.\u003c/p\u003e \u003cp\u003eBeyond the central nervous system, the liver and spleen were identified as two major extra-CNS organs exhibiting marked fluorescence accumulation regardless of the administration route (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u0026ndash;D). These distribution patterns were corroborated by CD3 immunohistochemistry and Prussian blue staining, supporting substantial T-cell infiltration and iron deposition in both organs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;B). In the spleen, abundant T-cell presence across all groups likely reflects physiological homing to secondary lymphoid organs[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], whereas in the liver, intravenous administration resulted in stronger fluorescence intensity and iron deposition compared with ICV delivery (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), consistent with first-pass hepatic distribution and Kupffer cell uptake[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To account for within-animal clustering across sampled organs, a linear mixed-effects model with Mouse ID as a random intercept showed a significant positive association between log10-transformed organ-level NIR-II exposure [log10(I)] and CD3⁺ T-cell density (β\u0026thinsp;=\u0026thinsp;0.000614, 95% CI 0.000458\u0026ndash;0.000771; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). In contrast, minimal fluorescence signal and negligible CD3⁺ T-cell infiltration were observed in the heart, lungs, and intestine (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, Supplementary Fig.\u0026nbsp;4E), with no evidence of iron deposition or pathological abnormalities (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;B, Supplementary Fig.\u0026nbsp;5). Collectively, these results demonstrate route-dependent organ-level exposure patterns and support this workflow for longitudinal biodistribution profiling in orthotopic glioma models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCAR-T therapy has shown promise for glioblastoma (GBM), including early clinical studies of locoregional administration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, heterogeneous responses and safety considerations underscore the need for practical approaches to characterize \u003cem\u003ein vivo\u003c/em\u003e trafficking and organ-level exposure of transferred cells, which are important for preclinical interpretation and route-informed risk assessment[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this context, an effective cell-labeling strategy should provide imaging-visible readouts while preserving key T-cell phenotypic and functional attributes. Here, we developed an indocyanine green\u0026ndash;iron nanoparticle probe (ICG-NP) and established a dual-modality tracking workflow integrating NIR-II fluorescence imaging with MRI to enable non-invasive assessment of CAR-T cell distribution in orthotopic glioma models.\u003c/p\u003e \u003cp\u003eIron-based agents (including USPIOs[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], SPIONs[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and ferumoxytol[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]) have been widely used to support MRI cell tracking[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and ferumoxytol-labeled CAR-T cells have enabled multimodal imaging in solid tumor models[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In parallel, advances in NIR-II fluorescence imaging have expanded the feasibility of repeated, whole-body biodistribution readouts over time[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Building on these concepts, our data support a complementary framework in which MRI provides anatomical context for intracranial localization and tumor-associated signal evaluation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), whereas NIR-II enables longitudinal, whole-body profiling of distribution patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The positive association between NIR-II signal intensity and CD3⁺ T-cell density across analyzed organs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE), supported by multi-organ pathological analyses, further supports the utility of this workflow for semi-quantitative organ-level biodistribution profiling \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIntracranial delivery strategies, including intracerebroventricular (ICV) administration, are increasingly adopted in glioma CAR-T studies to enhance intracranial exposure in the setting of blood\u0026ndash;brain barrier constraints[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Importantly, intracranial delivery does not necessarily imply confinement to the central nervous system; rather, it can reshape exposure compartments and yield distinct organ-level exposure profiles over time[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In our orthotopic glioma model, longitudinal NIR-II imaging revealed route-dependent peripheral exposure profiles, with prominent liver and spleen signals and differential hepatic signal intensity across routes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u0026ndash;E). These whole-body readouts were supported by CD3 immunohistochemistry and Prussian blue staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;B), indicating that systemic exposure should be explicitly considered when interpreting intracranial CAR-T studies. Together, these observations highlight the value of whole-body biodistribution profiling for route-informed evaluation of CAR-T exposure in GBM models.\u003c/p\u003e \u003cp\u003eNotably, we observed differences in clearance kinetics between labeled CAR-T and T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) and detectable cell-associated signals in cervical lymph nodes after ICV administration (Supplementary Fig.\u0026nbsp;4D). This pattern is consistent with reports that macromolecules and immune cells can drain from the central nervous system to deep cervical lymph nodes via meningeal lymphatic pathways[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These observations suggest that locally administered cells can be associated with peripheral exposure, potentially through cerebrospinal fluid drainage and systemic recirculation, and provide context for interpreting systemic readouts following intracranial delivery. Such information may aid the design and interpretation of preclinical CAR-T studies, particularly in settings where inefficient trafficking and off-target exposure remain important translational considerations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we establish a biocompatible dual-modality imaging workflow for longitudinal, organ-level biodistribution profiling of CAR-T cell\u0026ndash;associated signals in an orthotopic glioma model. By coupling MRI-based intracranial anatomical localization with NIR-II whole-body readouts and anchoring imaging signals with multi-organ pathology, this framework enables route-informed interpretation of peripheral exposure following intracranial CAR-T delivery.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eNo potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Natural Science Foundation of China (NSFC) (81930048, 92059207, 92359301), CAS Youth Interdisciplinary Team (JCTD-2021-08), and Beijing Postdoctoral Research Foundation.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eChunzhao Li: Investigation; Data curation; Formal analysis; Validation; Visualization; Writing-original draft. Na Xian: Resources; Investigation; Data curation; Formal analysis.Rui Feng: Resources; Investigation; Data curation; Formal analysis. Peng Zhang: Investigation; Methodology. Xiaobin Zhao: Data curation; Formal analysis. Nan Ji: Conceptualization; Supervision; Project administration; Writing - review \u0026amp; editing. Zhenhua Hu: Conceptualization; Supervision; Writing - review \u0026amp; editing. Yang Zhang: Conceptualization; Supervision; Writing-review \u0026amp; editing. Gangxiong Huang: Conceptualization; Supervision.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank AiMi Academic Services (www.aimieditor.com) for English language editing and review services.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOstrom QT, Price M, Neff C, Cioffi G, Waite KA, Kruchko C, Barnholtz-Sloan JS (2023) CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2016\u0026ndash;2020. 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Light Sci Appl 13:96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41377-024-01450-0\u003c/span\u003e\u003cspan address=\"10.1038/s41377-024-01450-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Glioblastoma, CAR-T, NIR-II, MRI, cell tracking","lastPublishedDoi":"10.21203/rs.3.rs-9126411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9126411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLocoregional CAR-T delivery is increasingly explored for glioblastoma to improve intracranial tumor exposure; however, organ-level biodistribution kinetics after intracranial administration remain poorly quantified in vivo, limiting route-informed optimization and preclinical risk assessment. Here, we report a dual-modality cell labeling and tracking strategy based on indocyanine green\u0026ndash;conjugated iron nanoparticles (ICG-NPs) for \u003cem\u003ein vivo\u003c/em\u003e assessment of B7-H3-targeting CAR-T cell (TX103) biodistribution using second near-infrared window (NIR-II) fluorescence imaging and magnetic resonance imaging (MRI). Using a heparin\u0026ndash;protamine-assisted protocol, TX103 cells were labeled with high efficiency (83.1%) without detectable changes in viability, CAR expression, immunophenotype (including activation/exhaustion marker profile and CXCR3 expression), or cytotoxic function. \u003cem\u003eIn vitro\u003c/em\u003e imaging demonstrated a linear correlation between NIR-II fluorescence intensity and labeled cell numbers (R\u0026sup2; = 0.973, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while MRI provided complementary anatomical context at higher cell densities. In an orthotopic glioma mouse model, longitudinal MRI and NIR-II imaging captured route-dependent differences in tumor-associated localization and whole-body biodistribution following intracerebroventricular and intravenous administration. Furthermore, NIR-II signal intensity correlated with CD3⁺ T-cell density across organs (R\u0026sup2; = 0.552, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supported by multi-organ pathological validation. Collectively, we establish a biocompatible, materials-enabled dual-modality workflow that links intracranial anatomical localization with longitudinal whole-body biodistribution readouts for preclinical CAR-T tracking in solid tumor models.\u003c/p\u003e","manuscriptTitle":"Dual-modality imaging enables longitudinal biodistribution profiling of intracerebroventricular CAR-T therapy in orthotopic glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 10:26:03","doi":"10.21203/rs.3.rs-9126411/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-25T08:37:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T16:46:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T16:12:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212276001735872609490243870714156436802","date":"2026-03-20T09:38:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101215662794397536251652613439898517566","date":"2026-03-19T15:09:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257216095746902341593386906434653015310","date":"2026-03-19T14:52:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233971105674626450324154199436121859881","date":"2026-03-19T13:25:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329957793510536209688631116132522898706","date":"2026-03-18T16:31:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272959738298870632123080667585714613846","date":"2026-03-18T15:28:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33479842841180903266648965657180686140","date":"2026-03-18T13:02:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11742505518583763086059677763748031728","date":"2026-03-18T10:53:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T09:31:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T11:59:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T11:59:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Immunology, Immunotherapy","date":"2026-03-15T05:27:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2676acd5-f728-468f-b241-babe8a14a52c","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T15:06:02+00:00","versionOfRecord":{"articleIdentity":"rs-9126411","link":"https://doi.org/10.1007/s00262-026-04403-1","journal":{"identity":"cancer-immunology-immunotherapy","isVorOnly":false,"title":"Cancer Immunology, Immunotherapy"},"publishedOn":"2026-05-02 15:57:10","publishedOnDateReadable":"May 2nd, 2026"},"versionCreatedAt":"2026-03-24 10:26:03","video":"","vorDoi":"10.1007/s00262-026-04403-1","vorDoiUrl":"https://doi.org/10.1007/s00262-026-04403-1","workflowStages":[]},"version":"v1","identity":"rs-9126411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9126411","identity":"rs-9126411","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}