Intravital lung microscopy unveils T cell dynamics in mouse tuberculosis lesions

Intravital lung microscopy unveils T cell dynamics in mouse tuberculosis lesions | 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 Intravital lung microscopy unveils T cell dynamics in mouse tuberculosis lesions Léa Fromont, Aizat Iman Abdul Hamid, Elisabeth Bellard, Zachary P. Howard, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8259909/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Protective immunity to Mycobacterium tuberculosis ( Mtb ) depends on the ability of T cells to access and engage infected cells within lung lesions, yet these spatiotemporal interactions remain poorly defined. Direct intravital imaging of Mtb -infected lungs has historically been limited by biosafety and technical constraints, preventing real-time visualization of immune dynamics in situ . Here, we present LiveLung-TB, a biosafety level 3–compatible lung intravital imaging platform that enables high-resolution imaging of immune cell behavior in Mtb -infected lungs. Using this approach, we demonstrate that although most CD4⁺ T cells infiltrate the infected parenchyma, only a small fraction forms transient (~11%) or stable (~5%) contacts with infected macrophages. Strikingly, the majority remain immobilized or display intermittent migration within uninfected, collagen-rich regions or at the periphery of macrophage clusters, independently of antigen recognition. These findings uncover previously unrecognized physical and microenvironmental barriers that restrict T cell motility and limit productive effector engagement. LiveLung-TB thus provides a powerful framework to elucidate and ultimately overcome tissue-imposed constraints on immune efficacy in tuberculosis and other pulmonary infections. Immunology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Mycobacterium tuberculosis ( Mtb ), the causative agent of tuberculosis (TB), remains the leading cause of death from a single infectious agent worldwide ( Global Tuberculosis Report 2025 2025). Despite decades of research, our understanding of the mechanisms that limit bacterial clearance in the lungs, and our ability to overcome them with improved vaccines or therapeutics, remains incomplete, and antibiotic-resistant strains continue to emerge. T cell–mediated immunity, especially via CD4⁺ lymphocytes, is central to host defence against Mtb , yet protective efficacy is often incomplete, allowing long-term survival of the pathogen (Jasenosky et al. 2015 ; Flynn et Chan 2022; Lefrançais et al. 2025 ). While T cell activation and differentiation in lymphoid organs are well characterized, the mechanisms that govern their migration, positioning, and interactions with infected cells in the lung remain poorly understood. Effective immunity requires that effector T cells reach infected sites and establish stable contacts with Mtb -infected macrophages (Srivastava et Ernst 2013), yet histological analyses reveal a striking spatial segregation: infected macrophages are concentrated in granuloma cores, whereas T cells localize predominantly at the periphery (Cohen et al. 2022 ; Kauffman et al. 2018 ; Gideon et al. 2022 ; McCaffrey et al. 2022 ). The dynamic processes underlying this spatial segregation in the lung have remained entirely uncharacterized, largely because imaging a living, ventilated lung under BSL-3 containment has, until now, been technically impossible. Intravital microscopy has revolutionized our understanding of T cell behavior in tumors and other infectious settings by enabling real-time visualization of cell recruitment, migration, and interactions within intact tissues (Hor et Germain 2022; Pittet et al. 2018 ; Sumen et al. 2004 ; Boulch et al. 2019 ). Yet this approach has never been applied to Mtb -infected lungs, where continuous respiratory motion, the need to preserve physiological function, and stringent biosafety constraints have prevented high-resolution imaging (Looney et Headley 2020). In hepatic BCG granulomas, intravital microscopy has revealed that T cells are rapidly recruited to and retained within granuloma structures, and that their migration is constrained by a macrophage-defined boundary (Egen et al. 2008 ). Within these structures, dynamic tracking of antigen-specific T cells showed that they remain highly motile and rarely arrest on antigen-presenting cells (Egen et al. 2011 ), suggesting that effective cognate recognition may be limited. In pulmonary TB, real-time dynamics of T cell–macrophage interactions have not been directly observed, leaving open questions about whether peripheral accumulation reflects insufficient antigen presentation, microenvironmental constraints, or both. Overcoming these obstacles is essential to uncover the mechanisms that constrain T cell access to infected macrophages and to understand why T cell responses often fail to clear Mtb (Lefrançais et al. 2025 ). Here, we developed a BSL-3–compatible intravital lung microscopy platform that enables high-resolution, real-time imaging of immune–pathogen interactions in Mtb -infected lungs. Using this platform, we generate the first in vivo , real-time dynamic map of T lymphocytes within TB lesions, uncovering intermittent migration patterns and a high frequency of arrests occurring away from infected cells. We further show how vascular architecture, extracellular matrix organization, and macrophage cluster collectively shape CD4⁺ T cell positioning, motility, and engagement with infected macrophages. These findings provide new mechanistic insight into why T cells accumulate at a distance from infected cells and offer a framework for understanding immune evasion by Mtb in the lung. Results LiveLung-TB: a BSL-3 intravital microscopy platform for real-time imaging of TB lesions To visualize immune dynamics in Mtb –infected lungs in real time, we developed LiveLung-TB, a biosafety level 3 compatible intravital lung microscopy system. The platform integrates a dedicated surgical setup (Fig. 1 a-b), a TB lesion–adapted thoracic window (Fig. 1 c), and procedures within a BSL-3 facility, equipped with a two-photon microscope (Barlerin et al. 2017 ) ( Extended Data Fig. 1 ). Mice are prepared under a biosafety hood, transferred securely to the microscope isolator, and mechanically ventilated throughout imaging, enabling high-resolution, long-term intravital visualization under full containment ( Extended Data Fig. 1 ). The system features a custom surgical plate for safe transport and manipulation of anesthetized animals (Fig. 1 a) and an adapted imaging window that stabilizes the left lung under gentle suction for high-resolution visualization of subpleural TB lesions. The TB lesion–specific thoracic window was derived from previously described intercostal lung IVM windows (Headley et al. 2016 ; Looney et al. 2011 ), with key modifications including a steeper angle to optimize objective displacement and a flattened base to ensure stable placement over visible TB lesions (Fig. 1 c, 1 d). We combined this imaging platform with optimized reporter systems to visualize key cellular components (Fig. 2 a-b). Fluorescent Mtb bacilli expressing mTurquoise enabled visualization of individual bacteria and small clusters (Fig. 2 c-d). In parallel, infected macrophages were imaged in Siglec1-Cre × mTmG mice, in which macrophage membranes fluoresce green (GFP) and lung structural cells fluoresce red (mTomato) (Fig. 2 c-d). Lesions were readily identifiable, characterized by vascular remodeling, collapsed alveoli, macrophage aggregates, clusters of infected macrophages harboring mTurquoise-labeled bacteria (Fig. 2 d and Extended Data Fig. 2 ), and multinucleated giant cells ( Extended Data Fig. 3 a). In contrast, healthy lungs displayed preserved alveolar architecture, with large alveolar spaces, thin alveolar walls lined by capillaries, and sparse macrophages ( Extended Data Fig. 3 b). To quantify lesion size and heterogeneity accessible by lung intravital microscopy, we measured 53 subpleural lesions using 2D maximum intensity projections. Lesions were defined by macrophage clustering, intracellular bacteria, and clear boundaries formed by local tissue remodeling ( Extended Data Fig. 2 ). Lesion size were heterogenous, equivalent diameters ranging from 179 to 1022 µm (mean 516 µm, SD 203 µm) ( Extended Data Fig. 3 c). 3D-cleared whole-lobe lung imaging combined with lesion segmentation (Fig. 3 a–b) revealed marked lesion heterogeneity, with diameter ranging from 2 to 2,114 µm (mean 646 µm, SD 453), and confirmed that 3D mosaic acquired with the thoracic window (volume of 1134 × 756 × 100 µm) can cover most of lesions in xy and approximately half in z (Fig. 3 c–d). Segmentation revealed complex, often mushroom-shaped lesion architecture connected to airways, predominantly expanding in the XY plane, making subpleural lesions readily accessible for live imaging (Fig. 3 b-d). Together, these results establish LiveLung-TB as an efficient and adapted technique for studying immune–pathogen interactions within intact Mtb -infected lungs in real time. Vascular remodeling and perfusion in TB lesions Vascular remodeling is a hallmark of chronic inflammatory lesions, including those caused by Mtb . In TB granulomas, persistent inflammation and hypoxic stress trigger angiogenic responses, leading to dense, tortuous, and disorganized vascular networks (Datta et al. 2015 ; Oehlers et al. 2015 ; Wells et al. 2021 ). Despite this increased vessel density, the functionality of the granuloma vasculature and the extent of tissue oxygenation remain unclear. While some studies describe hypoxic or necrotic cores, others suggest that subsets of granulomas maintain limited perfusion sufficient to sustain immune activity. To directly assess vascular architecture and function in vivo , we performed intravital microscopy of Mtb -infected lungs, using mTomato labeling of stromal membranes to visualize vessels and surrounding parenchyma (Fig. 2 d, Extended Data Fig. 2 ). This analysis revealed pronounced vascular remodeling within lesions, with marked increases in vessel density and tortuosity. Perfusion and permeability assays using fluorescent dextran and Evans Blue ( Extended Data Fig. 4 a,b and Supplementary Video 1 ) showed that, although granulomas remain perfused, they exhibit extensive vascular leakage into alveolar spaces compared with adjacent healthy regions. Collectively, these findings indicate that TB lesions contain a highly remodeled and permeable vasculature, potentially influencing immune cell trafficking and the local inflammatory environment. CD4⁺ T cells infiltrate TB lesions and preferentially localize around infected macrophage clusters To characterize CD4⁺ T cell dynamics in Mtb -infected lungs, we performed adoptive transfer of Th1-polarized, Deep Red–labeled Mtb -antigen specific (C7, recognizing the ESAT-6 antigen) CD4⁺ T cells, isolated from the spleens of donor mice, into chronically infected recipients. Between 5×10⁶ and 5×10⁷ cells were transferred intravenously, and their migration was tracked 24 hours later by lung intravital microscopy (LIVM) (Fig. 4 a and Supplementary Video 2 ). A custom tracking pipeline was developed to quantify T cell motility, including track length, displacement, velocity, straightness, and behavioral parameters such as localization, directionality, and cell–cell interactions ( Extended Data Fig. 5 a). We tracked 911 CD4⁺ T cells for 30 to 60 minutes in three independently infected mice. We examined CD4⁺ T cells spatial distribution relative to granulomatous lesions and clusters of infected macrophages, as previously defined ( Extended Data Fig. 2 ). Mapping of T cell tracks revealed that 88.8 ± 5.8% of transferred CD4⁺ T cells infiltrated TB lesions and were predominantly concentrated around clusters of infected macrophages (Fig. 4 b-d), highlighting a targeted localization of CD4⁺ T cells within granulomatous microenvironments. Lung intravital imaging reveals distinct CD4⁺ T cell migratory states and predominant parenchymal arrest during M. tuberculosis infection We further analysed their migratory behaviour, revealing three distinct migratory profiles in Mtb -infected lungs: rapidly mobile intravascular cells, mobile extravascular cells migrating within the parenchyma, and arrested extravascular cells (Fig. 5 a-c, Supplementary Video 3 ). These migratory profiles were confirmed by unsupervised clustering of discriminative motility parameters ( Extended Data Fig. 5 b) and mean square displacement (MSD) analysis ( Extended Data Fig. 5 c-d). Intravascular cells, representing 8.8 ± 2.5% of all tracks (Fig. 5 d), displayed the highest average and maximum velocities, greater displacements, and more linear directed trajectories, reflected by elevated straightness and α coefficients (Fig. 5 f-i, Extended Data Fig. 5 d), but were tracked over shorter time intervals ( Extended Data Fig. 5 b ) . The majority of C7 T cells (91.2 ± 2.5%) were extravascular (Fig. 5 d) and could be subdivided into two main subsets: arrested cells (57.5 ± 10.1%) and mobile cells (42.5 ± 10.1%) (Fig. 5 e). Extravascular T cells exhibited intermittent motility, alternating between brief movements and transient pauses, as reflected by their instantaneous velocity and arrest coefficient, the latter defined as the percentage of time moving below 2 µm/min (Fig. 5 j-m). Together, these data delineate three distinct CD4⁺ T cell migratory behaviors within Mtb -infected lungs, reflecting the spatial and kinetic diversity of T cell behavior in granulomatous tissue. While T cell intermittent migration has been previously observed in pulmonary environments (Mrass et al. 2017 ), the large fraction of extravascular T cells exhibiting sustained arrest was unexpected. This contrasts with hepatic granulomas induced by Mycobacterium bovis BCG, where mycobacteria-specific T cells remain predominantly motile and form few stable contacts with antigen-presenting cells (Egen et al. 2011 ), suggesting that the pulmonary microenvironment imposes unique constraints on T cell dynamics during TB. CD4⁺ T cells show only rare and transient interactions with infected macrophages To explore the basis of the prolonged arrest and intermittent migration of CD4⁺ T cells in TB lesions, we examined their interactions with infected macrophages ( Supplementary Video 4 ). While stable contacts with antigen-presenting cells are typically driven by TCR-mediated antigen recognition, only a minority of CD4⁺ T cells engaged directly with infected macrophages, (16.9 ± 10.5% of tracked cells) (Fig. 6 a). Among these, only 36.6 ± 10.7% underwent stable arrest (Fig. 6 b, 6 c), corresponding to just 5.4 ± 1.4% of the total extravascular T cell population (Fig. 6 d). The remaining mobile CD4⁺ T cells (63.4%) made only transient contacts with infected cells (Fig. 6 b, 6 c), characterized by short-lived arrests (7.4 ± 8.5 min; range 1.8–31.0 min) (Fig. 6 e-h). These arrest durations are consistent with initial scanning interactions, exploratory and low-affinity, and therefore insufficient for activation (Celli et al. 2007 ), and were comparable to the brief pauses observed during intermittent migration of cells that did not contact infected cells (Fig. 6 f-h). These observations indicate that CD4⁺ T cell encounters with infected macrophages are rare, and when they do occur, they are predominantly brief, low-affinity interactions. CD4⁺ T cell arrest in Mtb granulomas occurs independently of antigen recognition Given that interactions with infected cells were rare and unstable, we next sought to identify the mechanisms underlying the predominant parenchymal arrest of CD4⁺ T cells (Fig. 5 e). To directly test whether antigen recognition influences T cell arrest within pulmonary granulomas, we compared the migratory behavior of Mtb -specific (C7, recognizing the ESAT-6 antigen) and non-specific (OT-II, recognizing the OVA peptide) CD4⁺ T cells using lung intravital microscopy (Fig. 7 a, Supplementary Video 5 ). Analysis of motility parameters, including the proportion of arrested cells, arrest coefficient, average speed, and displacement, revealed no significant differences between C7 and OT-II T cells (Fig. 7 b-e). These results indicate that T cell arrest and migratory behavior within Mtb -infected lungs are not dependent on antigen recognition, suggesting that factors other than TCR engagement, such as the local microenvironment, cytokine gradients, or extracellular matrix organization, may predominantly regulate T cell dynamics within granulomas. CD4⁺ T cell interactions with collagen fibers and macrophage clusters in TB lesions To understand why CD4⁺ T cell encounters with infected macrophages are rare, we analyzed their spatial distribution relative to infected cells and examined their interactions with surrounding cellular and structural elements. Most CD4⁺ T cells were positioned 20–40 µm away from infected macrophages, and, unexpectedly, arrested cells were located even farther from infected cells than mobile ones (32 ± 7 µm vs. 23 ± 7 µm) (Fig. 8 a-b). Interaction analysis revealed three main elements associated with T cell arrest at a distance from infected macrophages: uninfected macrophages ( Supplementary Video 6) , collagen fibers ( Supplementary Video 7) , and other non-identified structures, with no difference in their distance to infected cells (Fig. 8 b–c). A substantial proportion of CD4⁺ T cells (32.1 ± 5.3%) interacted with uninfected macrophages (Fig. 8 e, Supplementary Video 6 ), typically at the periphery of macrophage clusters ( Extended Fig. 6 a), whereas bacteria accumulated in the center. Among T cells contacting uninfected macrophages, 60.1 ± 6.6% were arrested (Fig. 8 f–h). Second harmonic generation (SHG) imaging showed extensive collagen deposition encasing macrophage aggregates within TB lesions ( Extended Fig. 6 b). Intravital microscopy revealed that 39.1 ± 4.9% of CD4⁺ T cells were closely associated with collagen fibers, either remaining arrested (54.5 ± 10.2%) or migrating along these structures (Fig. 8 i–l, Supplementary Video 7 ), indicating that collagen-rich regions provide both structural scaffolds and guidance cues for T cell movement. Similar ECM-guided migration has been documented in inflamed dermis, brain, and tumors, where fiber orientation and density channel T cell trajectories and constrain access to target cells. Importantly, CD4⁺ mobile T cells did not show preferential or prolonged arrest on uninfected macrophages, collagen fibers, or other non-identified structures ( Extended Fig. 6 e–g), and their distance from infected cells as well as their intermittent migration pattern were independent of the interacting partner (Fig. 8 c). Directional analysis further demonstrated that 70% of mobile extravascular T cells migrated along the periphery of lesions rather than toward infected macrophages (Fig. 8 d). Together, these findings indicate that collagen fibers and the organization of macrophage clusters strongly shape CD4⁺ T cell motility and positioning, thereby limiting productive contacts with infected cells and potentially contributing to the long-term survival of Mtb within granulomatous tissue. Discussion In this study, we present LiveLung-TB, the first platform enabling real-time visualization of T cell migration within Mtb -infected lung lesions. This technique provides a detailed view of CD4⁺ T cell dynamics, revealing unique migration behaviors. Our results reveal that lymphocytes rarely establish stable contacts with infected macrophages and preferentially localize to the periphery of macrophage clusters, where they arrest or migrate along non infected macrophages and collagen fibers. By combining multiphoton intravital imaging with fluorescent labeling, we directly observed how tissue architecture, vascular remodeling, cell aggregates and ECM composition shape immune cell behavior in situ . Granulomas frequently exhibit poorly perfused and structurally altered vasculature (Wells et al. 2021 ; Datta et al. 2015 ; Oehlers et al. 2015 ), which limits T cell extravasation and restricts their access to lesion cores. Such vascular remodeling and perfusion defects likely represent major drivers of immune cell exclusion, as similarly reported in tumors and other chronic inflammatory settings where physical access can be as limiting as effector function. Lung intravital imaging is essential to dissect these dynamics, and our observations in the C57BL/6 mouse model reveal that lymphocytes do infiltrate TB lesions, although the extensive vascular remodeling and increased leakiness may shape their migratory behavior within lesions. We find that only a minority of lymphocytes engage infected macrophages, with ~ 11% forming transient contacts and only ~ 5% establishing stable interactions. These low frequencies point to two sequential limitations: restricted physical access to infected cells, and suboptimal activation even when contact occurs. Stable T cell–macrophage interactions depend on antigen-specific TCR engagement (Moreau et al. 2015 ). Yet, consistent with observations in BCG-induced hepatic granulomas (Egen et al. 2011 ), most CD4⁺ T cells that reach infected macrophages remain highly mobile and rarely form sustained contacts, suggesting low-affinity antigen recognition or insufficient signaling. Notably, in both human lung biopsies and macaque infection models, Mtb -specific CD4⁺ T cells also infrequently contact infected macrophages despite evidence of activation (Kauffman et al. 2018 ), underscoring the combined impact of anatomical constraints and functional limitations. Strikingly, the majority of parenchymal lymphocytes remain immobilized or display intermittent migration at a distance of 10–20 µm from infected macrophages, typically along collagen fibers or at the periphery of macrophage aggregates, and this behavior occurs independently of antigen recognition. Our observation that T cells migrate within the boundaries of macrophage clusters is reminiscent of T cell behavior in BCG-induced granulomas, where their movement is also constrained by macrophage-defined borders (Egen et al. 2008 ). Granulomas and macrophage cluster are encased in dense fibrotic networks composed of collagen, fibronectin, laminin (Kauffman et al. 2018 ; Sawyer et al. 2023 ) and other ECM components, and ECM architecture is known to strongly influence T cell motility. Our observation that CD4⁺ T cells frequently arrest and migrate along collagen fibers mirrors ECM-guided migration described in lymph nodes, lung tissue, tumors, and other inflamed environments, where fiber density and orientation shape T cell trajectories (Overstreet et al. 2013 ; Salmon et al. 2012 ; Wilson et al. 2009 ; You et al. 2021 ; Mrass et al. 2017 ). Stromal networks, such as fibroblastic reticular cells (FRC), also play a central role in directing lymphocyte migration in lymph nodes (Bajénoff et al. 2006 ) and peripheral tissues, including lung tumors (Onder et al. 2025 ), where T cells move along CCL19-expressing FRC-defined tracks. These parallels raise the possibility that similar stromal or ECM-derived guidance cues operate within lung TB granulomas, simultaneously directing T cell movement and restricting their access to infected targets. Surprisingly, OT-II cells (non– Mtb specific) exhibit similar migratory behaviors, indicating the involvement of antigen-independent mechanisms that remain to be identified. T cell positioning can be shaped by recent activation or memory status independently of cognate antigen recognition (Topham et al. 2001 ). Integrins such as VLA-1 (α1β1), which binds collagen and mediates the retention of protective memory T cells in non-lymphoid tissues including the lung (Ray et al. 2004 ), exemplify how the ECM not only imposes physical constraints but may actively organize T cell retention and spatial distribution within granulomas. Additional adhesion pathways (LFA-1; CD2/LFA-3), chemokine gradients, glycosaminoglycans, cytokines and stromal inhibitory signals can similarly promote transient arrest or reduced motility (Gunzer et al. 2000 ; Kerdidani et al. 2022 ). Extracellular ATP sensed by P2RX7 on CD4⁺ T cells enhances their tissue accumulation by inducing CXCR3 expression (Santiago-Carvalho et al. 2023 ), and potent chemokine cues can even override MHC–peptide-driven positioning (Bromley et al. 2000 ). Moreover, homeostatic cytokines help maintain tissue-resident T cell populations despite dynamic desensitization to antigenic stimulation (Boldajipour et al. 2016 ). Thus, LiveLung-TB will uniquely enable dissection of these physical and molecular constraints in real time, providing a platform to evaluate how chemokines, adhesion molecules, and antigen availability influence T cell positioning and effector function. Limitations include the use of C57BL/6 mice, which are relatively resistant to Mtb and do not form fully structured granulomas. Imaging more susceptible models, such as SP140-deficient mice (Ji et al. 2021 ), will be interesting improving translational relevance. Adoptively transferred T cells were analyzed shortly after transfer (24 h); longer residence times may influence motility and functional adaptation (Boldajipour et al. 2016 ; You et al. 2021 ). Longitudinal imaging could clarify how activation or exhaustion states affect migration and arrest. The molecular mechanisms mediating T cell–ECM and T cell–macrophage interactions remain to be defined; identifying the relevant receptors and integrins, and perturbing them, will clarify whether ECM serves primarily as a scaffold or barrier and whether modulating these interactions can improve access to infected macrophages. In conclusion, LiveLung-TB provides a dynamic and mechanistic view of immune cell behavior in Mtb -infected lungs. If physical and molecular barriers limit access to infected macrophages, vaccines or immunotherapies that increase T cell numbers or activation may be insufficient. Strategies such as vascular normalization, ECM remodeling, or chemokine modulation could enhance bacterial clearance. This platform offers a roadmap to dissect the spatial and temporal determinants of T cell access to infected macrophages and to test interventions aimed at enhancing immunity and improving outcomes in TB. Material et Methods Mice Male and female mice aged 6-12 weeks were used throughout the study. C7TCR-Tg Nur77-GFP mice were kindly provided by Dr. Ernst Joel (UCSF, USA) and. Siglec1-mTmG were obtained by crossing Siglec1-cre with Rosa26-mTmG mice (IPBS, France). OVA-specific CD4 + TCR Tg (OTII; Charles River, France) mouse were purchased. All mice were bred at the Institute of Pharmacology and Structural Biology (IPBS) UMR 5089 (agreement F31555005) and procedures complied with French regulations and were approved by the Ministry of Higher Education and Research (APAFIS agreements 47848 and 34716). Bacteria Fluorescent derivatives of the wild-type M. tuberculosis Erdman strain were constructed by transformation of this strain with pGMCS-P1-mTurquoise. Plasmids pGMCS-P1-mTurquoise are integrative vectors in mycobacteria that confer resistance to streptomycin and constitutively express mTurquoise fluorescent reporter. They were constructed by Gateway cloning, as described previously (Ariyachaokun et al. 2020). M. tuberculosis Erdman and its fluorescent mTurquoise transformants were cultured in suspension in Middlebrook 7H9 medium (BD) supplemented with 10% albumin-dextrose-catalase (ADC, BD) and 0.05% Tyloxapol (Sigma). For infection, exponentially growing cultures were centrifuged at 2,301 × g , resuspended in phosphate-buffered saline (PBS; Gibco), and de-aggregated by vortexing with glass beads. Remaining clumps were removed by a low-speed spin (120 × g ). Bacterial concentration was estimated by measuring the optical density at 600 nm (OD₆₀₀), and the suspensions were adjusted in PBS for in vivo infection. Mouse infection Mice were exposed to M. tuberculosis Erdman in an inhalation tower (Buxco Inhalation Exposure System, DSI) calibrated to deliver 100 colony-forming units (CFUs) to the lungs per mouse. CD4 lymphocyte isolation and polarization and adoptive transfer Mice were anesthetized with isoflurane and euthanized by cervical dislocation. Naïve CD4 + T cells were magnetically isolated from spleen cell suspensions of C7TCR-Tg mouse or OTII mice using anti-CD4 microbeads (L3T4) (Miltenyi Biotec) according to the manufacturer’s instructions. CD4 + T cells were cultured in the presence of Th1 skewing cytokines: mouse IL-12p70 (10 ng/ml), mouse IL-2 (20 ng/ml), CD3 (5µg/ml) and CD28 (1µg/ml). Cells were cultured at 37°C with 5% CO2. On days 3 and 5 of culture, cells were split 1∶3 with fresh media containing IL-12p70 and IL-2. Cells were washed with PBS and counted on day 7 of culture before use for in vivo assays. On day 7 of culture, polarized Th1 CD4 lymphocytes were counted and labeled before injection. They were either labeled with CellTracker™ Deep Red or Orange (Invitrogen™) according to the manufacturer’s instructions. Cells were counted and adjusted at 1x10 7 in PBS. The mice were placed in an induction chamber and briefly anesthetized by isoflurane, 100–150 µL of the cell suspension were injected into the retro-orbital sinus using a U100 insulin syringe (BD micro-fine 0.3 mL, 30 gauge needle), 24 hours before lung intravital microscopy. TB lesion adapted thoracic window production and assembly The TB lesion–adapted thoracic window was modified in Blender, based on previously described intercostal lung IVM windows (Headley et al. 2016). Modifications included an increased angle to optimize objective xy movement and a flat base to facilitate stable positioning over TB lesions. Windows were 3D-printed in high-detail stainless steel (Sculpteo). An 8-mm round coverslip (Cat# 64-0701, Thomas Scientific) was inserted and sealed overnight using nail lacquer applied along the outer edge of the coverslip. Lung intravital surgery platform construction and assembly The platform was engineered to maintain an anesthetized, mechanically ventilated mouse throughout three consecutive phases: (i) mouse tracheotomy, mechanical ventilation, and surgical placement of the lung optical window within a microbiological safety cabinet (MSC) using a dedicated mechanical holder; (ii) transfer of the ventilated mouse from the MSC to the intravital microscopy isolator; and (iii) stable immobilization and accurate positioning of the lung beneath the microscope objective throughout image acquisition, along with connection to gas anesthesia and controlled lung aspiration onto the imaging window. The base structure consists of a PVC plate fitted with carrying handles. One section accommodates the mouse and includes an integrated support to position and secure the optical window. The mechanical ventilator and its battery are mounted on the opposite end of the platform, while ventilator tubing and pathogen-protection filters are organized via custom 3D-printed polylactic acid (PLA) holders. Additional accessories, including racks for Eppendorf tubes and holders for surgical instruments, were also 3D-printed. Multiphoton lung intravital imaging in BSL3 Multiphoton lung intravital microscopy was performed using a BSL-3 isolated microscope (Barlerin et al., 2017). Surgical preparation was adapted from established lung IVM protocols (Headley et al., 2016; Looney et al., 2011) and carried out inside a biosafety cabinet using the dedicated surgery platform. Mice were anesthetized with ketamine/xylazine (105/3 mg/kg, i.p.) and received s.c. injections of buprenorphine (0.1 mg/kg) and Ringer Lactate (10 mL/kg). After shaving the left flank, mice were positioned supine on a heating pad mounted on the inclined (45°) surgical platform. A tracheotomy was performed and a small tracheal cannula connected to a MiniVent ventilator (Harvard Apparatus) was inserted and secured. Ventilation parameters were set to a tidal volume of 10 μL compressed air (21% O₂) per gram of body weight, a respiratory rate of 130–140 breaths per minute, and a PEEP of 2–3 cm H₂O. Mice were then placed in right lateral decubitus, and a small skin and fascia incision was made in the left flank to expose the rib cage. Two ribs were transected at each extremity and removed to create an opening adapted to the size and position of visible TB lesions, which were apparent through the intercostal muscle at this infection stage. The surgical platform was transferred from the biosafety cabinet to the microscope isolator using a sealed transfer isolator, ensuring continuous containment. Inside the microscope isolator, temperature was maintained at 30°C and anesthesia was continued with 1% isoflurane. A modified lung window was positioned over a visible lesion and lowered using optical posts and a 90° angle clamp (Thorlabs) to just above the lung surface. Gentle suction (20–25 mmHg; Dexter Medical, 0–250 mbar) was applied to stabilize the lung against the coverslip (Fig. 1d). In Vivo vascular staining In some experiment, Dextran, Dextran FITC 40,000 MW (250 μg, D1844 Invitrogen) or Evans Blue (100 μL of 1% wt/vol solution in PBS) were injected i.v. before imaging to visualize vasculature and its permeability. Lung intravital acquisition Imaging was performed on an upright TriMScope II multiphoton microscope (LaVision Biotec/Miltenyi Biotec) equipped with a 20×/1.0 NA water-immersion objective and four NDD detectors, and a Chameleon Ultra II Ti:Sapphire femtosecond laser was the excitation source (Coherent Inc.) tuned to 870 nm (Deep Red–labeled CD4⁺ T cells only) or 855 nm (Deep Red + CTO). Emission signals from collagen/mTurquoise, GFP, Tomato/CTO, and Deep Red were collected in four separate channels using 450/50, 525/50, 595/40, and LP650 filters, respectively. Four-color image stacks were acquired at 1.4 frames/s for 405 × 405 µm fields at 512 × 512 pixels, or at for 393 × 393 µm fields at 1,024 × 1,024 pixels. Image analysis and lymphocyte tracking Three-dimensional time-lapse sequences (30 min to 2 h) were acquired to analyze T cell dynamics and processed using ImageJ (NIH) and Imaris 9.3.1 (Bitplane). Large xyzt mosaic movies were assembled with the stitching plugin in Fiji/ImageJ (https://imagej.net/plugins/image-stitching), and lesion characteristics, including number, diameter, and surface area, were measured directly from the movies. For lymphocyte tracking, z-stacks were reduced using maximum intensity projection, and individual T cell trajectories (~300 cells per video) were manually tracked using the TrackMate plugin in Fiji/ImageJ (https://imagej.net/plugins/trackmate), as variable cell shape, contrast, and intensity precluded reliable automatic tracking. Three videos were analysed, each from a different infected mouse, tracking a total of 911 CD4⁺ T cells, with an average of 300 cells per video (169, 282, and 460). Trajectory data were analyzed using in-house Python scripts to generate quantitative parameters, graphs, and tracking maps. Cluster analysis of cellular behaviors was performed using the UMAP Python package, which reduces dimensionality and identifies distinct functional groups. This approach provides a comprehensive quantitative mapping of T cell behaviors during M. tuberculosis infection. Behaviour parameters definition For each tracked T cell, the following parameters were analysed: Track metrics: track length (the total length of displacement within the track), displacement (the distance between the first and last lymphocyte positions), straightness (the dimensionless index ranges from 0 to 1 and quantifies the linearity of a track). Speed metrics: instantaneous speed, average and maximal average speed. Pausing behavior: arrest coefficient, defined as the percentage of time a cell’s instantaneous speed was <2 µm/min; number of arrests per track, representing how many times the cell slowed below 2 µm/min; and mean arrest duration, calculated as the average length of these pauses. Vascular localization: intravascular (IV) versus extravascular (EV) localization was determined using lung vessel visualization (mTomato) Distance to infected macrophage was calculated from lymphocyte membrane to membrane of closest infected macrophage Mean Square Displacement (MSD) quantifies the average area covered by a lymphocyte as it moves through space over time Alpha (α) coefficient describes the scaling behavior of MSD over time and is obtained by fitting MSD(t) to a power-law model: an α value of α ≈ 1 indicates random (diffusive) motion, α 1 indicates superdiffusive or directed motion. All trajectory maps, tracking visualizations, and parameter calculations were generated using in-house Python scripts. Dimensionality reduction and clustering of cellular behaviors using UMAP allowed identification of distinct functional groups within the dataset. Lung tissue clearing and 3D multiphoton microscopy Mtb –infected lungs were fixed overnight at 4 °C in periodate–lysine–paraformaldehyde (PLP) buffer [0.05 M phosphate buffer, 0.1 M L-lysine (Sigma-Aldrich), 2 mg/mL NaIO₄ (ThermoFisher Scientific), 4% paraformaldehyde; pH 7.4]. Dehydration, delipidation, and clarification of lungs were performed at 37°C under slight agitation (100 rpm) before multiphoton microscopy as previously described (Jing et al. 2018). Briefly, lungs were delipidated by making successive baths of tert-butanol + 3% Quadrol (30%, 50%, and 70% for 2, 4, and 4 h respectively). Following this step, a dehydration step was carried out by incubation in 70% tert-butanol-30% PEG for 24 h. Finally, for clarification, incubation in a 75% benzyl-benzoate–25% PEG solution for 12 h was performed. All products were purchased from Sigma-Aldrich. Cleared lungs were maintained between a slide and a cover-slide in a 2.5 mm thick imaging chamber (CoverWell; Thermo Fischer Scientific) filled with ethylcinnamate (Sigma). Multiphoton microscopy for 3D cleared lung was performed using a Leica Dive upright multiphoton microscope equipped with a 25x/1.0 objective and a Ti-Sapphire femtosecond laser, Chameleon-Ultra II (Coherent Inc.). Second harmonic generation of collagen/bacteries and Siglec1-mGFP emission signals were detected at 920 nm thanks to the respective bandpass filters: blue (430-485 nm), and green (512–551 nm), and mTomato signal was detected at 870 nm thank to 580-623 nm band pass filter. Images were analyzed using Imaris 7.6.1 software (Bitplane). Three-dimensional segmentation and analysis of TB lesion in cleared lungs The segmentations were performed using Fiji/ImageJ plugins and tools. Granulomas, blood vessels, and bronchi were segmented from channel C2. Granulomas were segmented using the Labkit plugin. Pre-processing of granulomas involved applying a median filter and despeckling. Post-processing was then applied to remove irrelevant, isolated ROIs. The blood vessels (identified by their red blood cell content) and bronchi were manually segmented using the polygon and magic wand tools, respectively. Statistical analysis Sample sizes ( n ), statistical tests, and significance thresholds are indicated in the figure legends. Analyses were performed in Prism 10.0 (GraphPad). Depending on the experiment, one-way ANOVA or unpaired two-tailed Student’s t -test was used as specified in the legends. Significance thresholds were p < 0.05 (* ), p < 0.005 ( ** ), p < 0.0005 (*** ), and p < 0.0001 (****). Declarations Authorship Contributions L.F. conducted experiments, analysed data and drafted the manuscript. A.-I.A.-H. performed experiments and analyses. E.B. performed lung clearing and segmentation analyses. Z.P.H contributed to generation and use of C7-Nur77GFP cells. S.Mo. contributed to the generation of Siglec1-mTmG mice and funding of lung intravital imaging set up in BSL3. J.-P.G. provided financial support and scientific guidance. E.M. provided financial support and supervised L.F. throughout the project. D.H, O.N. and J.D.E. secured major funding and provided regular, in-depth scientific guidance, contributing to experimental design and key expertise in immunity and mouse models of tuberculosis. S.Ma. designed and built the surgical platform and lung window, coded all in-house Python scripts for cell tracking and behaviour analysis, and performed image and movie analyses. E.L. supervised the project, designed, performed and analysed experiments, and wrote the manuscript. All authors critically revised the manuscript. Acknowledgments We acknowledge the cytometry (Genotoul TRI-IPBS) and animal (Genotoul Anexplo-IPBS) facilities, particularly Flavie Moreau, Malory Blasco and Celine Berrone for technical support. TRI-IPBS is member of the national infrastructure France-BioImaging (https://ror.org/01y7vt929) supported by the French National Research Agency (ANR-24-INBS-0005 FBI BIOGEN); Anexplo-IPBS is member of the national infrastructure Celphedia (https://ror.org/00v2cdz24). This work was supported by Fonds de Recherche en Santé Respiratoire - Fondation du Souffle (Grant #198942 to E.L), ANRS (PhD fellowship to L.F), University of Toulouse (Tremplin 2022 #0003970), National Institute of Health NIH (R21 to EL, D.H, ON, and JE), and Horizon Europe TBVAC-Horizon (Grant #101080309 to D.H, O.N and E.L). Figures were created with Biorender.com. References Ariyachaokun, Kanchiyaphat, Anna D. Grabowska, Claude Gutierrez, et Olivier Neyrolles. 2020. « Multi-Stress Induction of the Mycobacterium Tuberculosis MbcTA Bactericidal Toxin-Antitoxin System ». Toxins 12 (5): 329. https://doi.org/10.3390/toxins12050329. Bajénoff, Marc, Jackson G. Egen, Lily Y. Koo, et al. 2006. « Stromal Cell Networks Regulate Lymphocyte Entry, Migration, and Territoriality in Lymph Nodes ». Immunity 25 (6): 989‑1001. https://doi.org/10.1016/j.immuni.2006.10.011. Barlerin, D., G. Bessière, J. Domingues, M. Schuette, C. Feuillet, et A. Peixoto. 2017. « Biosafety Level 3 Setup for Multiphoton Microscopy in Vivo ». Scientific Reports 7 (1): 571. https://doi.org/10.1038/s41598-017-00702-x. Boldajipour, Bijan, Amanda Nelson, et Matthew F. Krummel. 2016. « Tumor-Infiltrating Lymphocytes Are Dynamically Desensitized to Antigen but Are Maintained by Homeostatic Cytokine ». JCI Insight 1 (20): e89289. https://doi.org/10.1172/jci.insight.89289. Boulch, Morgane, Capucine L. Grandjean, Marine Cazaux, et Philippe Bousso. 2019. « Tumor Immunosurveillance and Immunotherapies: A Fresh Look from Intravital Imaging ». Trends in Immunology 40 (11): 1022‑34. https://doi.org/10.1016/j.it.2019.09.002. Bromley, S. K., D. A. Peterson, M. D. Gunn, et M. L. Dustin. 2000. « Cutting Edge: Hierarchy of Chemokine Receptor and TCR Signals Regulating T Cell Migration and Proliferation ». Journal of Immunology (Baltimore, Md.: 1950) 165 (1): 15‑19. https://doi.org/10.4049/jimmunol.165.1.15. Celli, Susanna, Fabrice Lemaître, et Philippe Bousso. 2007. « Real-Time Manipulation of T Cell-Dendritic Cell Interactions in Vivo Reveals the Importance of Prolonged Contacts for CD4+ T Cell Activation ». Immunity 27 (4): 625‑34. https://doi.org/10.1016/j.immuni.2007.08.018. Cohen, Sara B., Benjamin H. Gern, et Kevin B. Urdahl. 2022. « The Tuberculous Granuloma and Preexisting Immunity ». Annual Review of Immunology 40 (avril): 589‑614. https://doi.org/10.1146/annurev-immunol-093019-125148. Datta, Meenal, Laura E. Via, Walid S. Kamoun, et al. 2015. « Anti-Vascular Endothelial Growth Factor Treatment Normalizes Tuberculosis Granuloma Vasculature and Improves Small Molecule Delivery ». Proceedings of the National Academy of Sciences of the United States of America 112 (6): 1827‑32. https://doi.org/10.1073/pnas.1424563112. Egen, Jackson G., Antonio Gigliotti Rothfuchs, Carl G. Feng, Marcus A. Horwitz, Alan Sher, et Ronald N. Germain. 2011. « Intravital Imaging Reveals Limited Antigen Presentation and T Cell Effector Function in Mycobacterial Granulomas ». Immunity 34 (5): 807‑19. https://doi.org/10.1016/j.immuni.2011.03.022. Egen, Jackson G., Antonio Gigliotti Rothfuchs, Carl G. Feng, Nathalie Winter, Alan Sher, et Ronald N. Germain. 2008. « Macrophage and T Cell Dynamics during the Development and Disintegration of Mycobacterial Granulomas ». Immunity 28 (2): 271‑84. https://doi.org/10.1016/j.immuni.2007.12.010. Flynn, JoAnne L., et John Chan. 2022. « Immune Cell Interactions in Tuberculosis ». Cell 185 (25): 4682‑702. https://doi.org/10.1016/j.cell.2022.10.025. Gideon, Hannah P., Travis K. Hughes, Constantine N. Tzouanas, et al. 2022. « Multimodal Profiling of Lung Granulomas in Macaques Reveals Cellular Correlates of Tuberculosis Control ». Immunity 55 (5): 827-846.e10. https://doi.org/10.1016/j.immuni.2022.04.004. Global Tuberculosis Report 2025 . 2025. 1st ed. World Health Organization. Gunzer, M., A. Schäfer, S. Borgmann, et al. 2000. « Antigen Presentation in Extracellular Matrix: Interactions of T Cells with Dendritic Cells Are Dynamic, Short Lived, and Sequential ». Immunity 13 (3): 323‑32. https://doi.org/10.1016/s1074-7613(00)00032-7. Headley, Mark B., Adriaan Bins, Alyssa Nip, et al. 2016. « Visualization of Immediate Immune Responses to Pioneer Metastatic Cells in the Lung ». Nature 531 (7595): 513‑17. https://doi.org/10.1038/nature16985. Hor, Jyh Liang, et Ronald N. Germain. 2022. « Intravital and High-Content Multiplex Imaging of the Immune System ». Trends in Cell Biology 32 (5): 406‑20. https://doi.org/10.1016/j.tcb.2021.11.007. Jasenosky, Luke D., Thomas J. Scriba, Willem A. Hanekom, et Anne E. Goldfeld. 2015. « T Cells and Adaptive Immunity to Mycobacterium Tuberculosis in Humans ». Immunological Reviews 264 (1): 74‑87. https://doi.org/10.1111/imr.12274. Ji, Daisy X., Kristen C. Witt, Dmitri I. Kotov, et al. 2021. « Role of the Transcriptional Regulator SP140 in Resistance to Bacterial Infections via Repression of Type I Interferons ». eLife 10 (juin): e67290. https://doi.org/10.7554/eLife.67290. Jing, Dian, Shiwen Zhang, Wenjing Luo, et al. 2018. « Tissue Clearing of Both Hard and Soft Tissue Organs with the PEGASOS Method ». Cell Research 28 (8): 803‑18. https://doi.org/10.1038/s41422-018-0049-z. Kauffman, K. D., M. A. Sallin, S. Sakai, et al. 2018. « Defective Positioning in Granulomas but Not Lung-Homing Limits CD4 T-Cell Interactions with Mycobacterium Tuberculosis-Infected Macrophages in Rhesus Macaques ». Mucosal Immunology 11 (2): 462‑73. https://doi.org/10.1038/mi.2017.60. Kerdidani, Dimitra, Emmanouil Aerakis, Kleio-Maria Verrou, et al. 2022. « Lung Tumor MHCII Immunity Depends on in Situ Antigen Presentation by Fibroblasts ». The Journal of Experimental Medicine 219 (2): e20210815. https://doi.org/10.1084/jem.20210815. Lefrançais, Emma, Denis Hudrisier, Olivier Neyrolles, Samuel M. Behar, et Joel D. Ernst. 2025. « Finding and Filling the Knowledge Gaps in Mechanisms of T Cell-Mediated TB Immunity to Inform Vaccine Design ». Nature Reviews. Immunology , publication en ligne anticipée, juin 13. https://doi.org/10.1038/s41577-025-01192-z. Looney, Mark R., et Mark B. Headley. 2020. « Live Imaging of the Pulmonary Immune Environment ». Cellular Immunology 350 (avril): 103862. https://doi.org/10.1016/j.cellimm.2018.09.007. Looney, Mark R, Emily E Thornton, Debasish Sen, Wayne J Lamm, Robb W Glenny, et Matthew F Krummel. 2011. « Stabilized Imaging of Immune Surveillance in the Mouse Lung ». Nature Methods 8 (1): 91‑96. https://doi.org/10.1038/nmeth.1543. McCaffrey, Erin F., Michele Donato, Leeat Keren, et al. 2022. « The Immunoregulatory Landscape of Human Tuberculosis Granulomas ». Nature Immunology 23 (2): 318‑29. https://doi.org/10.1038/s41590-021-01121-x. Moreau, Hélène D., Fabrice Lemaître, Kym R. Garrod, Zacarias Garcia, Ana-Maria Lennon-Duménil, et Philippe Bousso. 2015. « Signal Strength Regulates Antigen-Mediated T-Cell Deceleration by Distinct Mechanisms to Promote Local Exploration or Arrest ». Proceedings of the National Academy of Sciences of the United States of America 112 (39): 12151‑56. https://doi.org/10.1073/pnas.1506654112. Mrass, Paulus, Sreenivasa Rao Oruganti, G. Matthew Fricke, et al. 2017. « ROCK Regulates the Intermittent Mode of Interstitial T Cell Migration in Inflamed Lungs ». Nature Communications 8 (1): 1010. https://doi.org/10.1038/s41467-017-01032-2. Oehlers, Stefan H., Mark R. Cronan, Ninecia R. Scott, et al. 2015. « Interception of Host Angiogenic Signalling Limits Mycobacterial Growth ». Nature 517 (7536): 612‑15. https://doi.org/10.1038/nature13967. Onder, Lucas, Chrysa Papadopoulou, Almut Lütge, et al. 2025. « Fibroblastic Reticular Cells Generate Protective Intratumoral T Cell Environments in Lung Cancer ». Cell 188 (2): 430-446.e20. https://doi.org/10.1016/j.cell.2024.10.042. Overstreet, Michael G, Alison Gaylo, Bastian R Angermann, et al. 2013. « Inflammation-Induced Interstitial Migration of Effector CD4+ T Cells Is Dependent on Integrin αV ». Nature Immunology 14 (9): 949‑58. https://doi.org/10.1038/ni.2682. Pittet, Mikael J., Christopher S. Garris, Sean P. Arlauckas, et Ralph Weissleder. 2018. « Recording the Wild Lives of Immune Cells ». Science Immunology 3 (27): eaaq0491. https://doi.org/10.1126/sciimmunol.aaq0491. Ray, Steven J., Suzanne N. Franki, Robert H. Pierce, et al. 2004. « The Collagen Binding Alpha1beta1 Integrin VLA-1 Regulates CD8 T Cell-Mediated Immune Protection against Heterologous Influenza Infection ». Immunity 20 (2): 167‑79. https://doi.org/10.1016/s1074-7613(04)00021-4. Salmon, Hélène, Katarzyna Franciszkiewicz, Diane Damotte, et al. 2012. « Matrix Architecture Defines the Preferential Localization and Migration of T Cells into the Stroma of Human Lung Tumors ». The Journal of Clinical Investigation 122 (3): 899‑910. https://doi.org/10.1172/JCI45817. Santiago-Carvalho, Igor, Gislane Almeida-Santos, Bruna Gois Macedo, et al. 2023. « T Cell-Specific P2RX7 Favors Lung Parenchymal CD4+ T Cell Accumulation in Response to Severe Lung Infections ». Cell Reports 42 (11): 113448. https://doi.org/10.1016/j.celrep.2023.113448. Sawyer, Andrew J., Ellis Patrick, Jarem Edwards, et al. 2023. « Spatial Mapping Reveals Granuloma Diversity and Histopathological Superstructure in Human Tuberculosis ». The Journal of Experimental Medicine 220 (6): e20221392. https://doi.org/10.1084/jem.20221392. Srivastava, Smita, et Joel D. Ernst. 2013. « Cutting Edge: Direct Recognition of Infected Cells by CD4 T Cells Is Required for Control of Intracellular Mycobacterium Tuberculosis in Vivo ». Journal of Immunology (Baltimore, Md.: 1950) 191 (3): 1016‑20. https://doi.org/10.4049/jimmunol.1301236. Sumen, Cenk, Thorsten R. Mempel, Irina B. Mazo, et Ulrich H. von Andrian. 2004. « Intravital Microscopy: Visualizing Immunity in Context ». Immunity 21 (3): 315‑29. https://doi.org/10.1016/j.immuni.2004.08.006. Topham, D. J., M. R. Castrucci, F. S. Wingo, G. T. Belz, et P. C. Doherty. 2001. « The Role of Antigen in the Localization of Naive, Acutely Activated, and Memory CD8(+) T Cells to the Lung during Influenza Pneumonia ». Journal of Immunology (Baltimore, Md.: 1950) 167 (12): 6983‑90. https://doi.org/10.4049/jimmunol.167.12.6983. Wells, Gordon, Joel N. Glasgow, Kievershen Nargan, et al. 2021. « Micro-Computed Tomography Analysis of the Human Tuberculous Lung Reveals Remarkable Heterogeneity in Three-Dimensional Granuloma Morphology ». American Journal of Respiratory and Critical Care Medicine 204 (5): 583‑95. https://doi.org/10.1164/rccm.202101-0032OC. Wilson, Emma H., Tajie H. Harris, Paulus Mrass, et al. 2009. « Behavior of Parasite-Specific Effector CD8+ T Cells in the Brain and Visualization of a Kinesis-Associated System of Reticular Fibers ». Immunity 30 (2): 300‑311. https://doi.org/10.1016/j.immuni.2008.12.013. You, Ran, Jordan Artichoker, Adam Fries, et al. 2021. « Active Surveillance Characterizes Human Intratumoral T Cell Exhaustion ». The Journal of Clinical Investigation 131 (18): e144353. https://doi.org/10.1172/JCI144353. Additional Declarations The authors declare no competing interests. Supplementary Files ExtendedDataFigure1.jpg Extended Data Fig. 1 | Sequential steps from surgical preparation to multiphoton imaging in BSL-3. Workflow illustrating surgical preparation, animal transfer, and two-photon microscopy within a BSL-3 facility (microscope setup adapted from Barlerin et al., Sci. Rep. 2017). ExtendedDataFigure2.jpg Extended Data Fig. 2 | Intravital imaging and characterization of tuberculosis lesions in the lung. Representative 2D maximum intensity projection of lungs from a Siglec1-mTmG mouse post-infection, illustrating vascular remodeling (mTomato, grey), macrophage accumulation (mGFP, green), and bacterial localization (mTurquoise, magenta). Dashed line indicates ilesion area. Scale bar: 100 µm. ExtendedDataFigure3.jpg Extended Data Fig. 3 | Intravital imaging of Siglec1-mTmG mouse lungs showing multinucleated giant cells during TB infection and alveolar macrophages in non-infected tissue. a, Intravital microscopy of a Siglec1-mTmG lung from a Mycobacterium tuberculosis –infected mouse showing formation of giant multinucleated cells. b, Intravital microscopy of a non-infected Siglec1-mTmG lung showing normal alveolar architecture with thin-walled alveoli, regular capillary network (mTomato), and typically no more than a single Siglec1⁺ macrophage (mGFP) per alveolus. c. Frequency distribution of lesion size (equivalent diameter) measured from lesion areas visualized by lung intravital microscopy (n=53) ExtendedDataFigure4.jpg Extended Data Fig. 4 | Intravital imaging of vascular remodeling and leakage in TB-infected lungs. a,bLIVM images of Mtb –infected lung after intravenous injection of FITC dextran, showing vascular remodelling within the lesion area (white dashed line) and increased vessel permeability indicated by green-filled alveoli (red asterisks) compared to normal alveoli (white asterisks). c,d LIVM images after intravenous injection of Evans Blue revealing vascular remodeling in the lesion area (white dashed line). Panels b and d show magnified views of the regions outlined in red in a and c, respectively. Scale bar, 100 µm. ExtendedDataFigure5.jpg Extended Data Fig. 5 | Pipeline for 4D movie reconstruction and macrophage tracking with FIJI, and analysis of motility parameters. a, Workflow for 4D image reconstruction and cell tracking using FIJI, with automatic or manual quantification of motility parameters. b, Heat map showing clustering of distinct behavioural parameters (normalized from min to max) n=282 tracks. c, Mean squared displacement (MSD) n=460 tracks d, α coefficient of mobile extravascular cells (n=146) ExtendedDataFigure6.jpg Extended Data Fig. 6 | CD4⁺ T cell interactions with lung structures and macrophage clusters influence migratory behavior. a, Proportion of T cells interacting with macrophage clusters. b, Representative LIVM image showing collagen fibers (SHG) surrounding macrophage clusters. c,Proportion of T cells interacting with other non-identified structures. d,Percentage of these cells that are arrested or mobile. e-g, CD4⁺ T cell intermittent migration parameters according to interacting structure (infected macrophages, uninfected cells, or collagen fibers or other structures): e, Arrest coefficient (% of time arrested per track), f, number of arrest events, and g, mean duration of each arrest depending on type of interaction (mean ± SD) (n = 16, 35,38 and 37 tracks, respectively, from one mouse). c,d,e,f.Mean data from three independently infected mice (n = 3) SupplementaryVideo1.mp4 Supplementary Video 1 | Vascular remodelling and perfusion in TB lesions Vascular architecture within lung TB lesions following intravenous injection of Evans Blue (red) or FITC–dextran (green). M. tuberculosis is visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). The video shows a 3 × 2 mosaic and a maximum-intensity Z-projection of a 1100 × 700 × 60 µm volume. Time is displayed as mm:ss. Scale bar, 100 µm. SupplementaryVideo2.mp4 Supplementary Video 2 | Infiltration and behaviour of antigen-specific CD4⁺ T cells in TB lesions. Intravital multiphoton microscopy of TB lesions showing adoptively transferred DeepRed C7 TCR-Tg CD4⁺ T cells (red) and their tracks (described in Fig.4). In Siglec1-mTmG mice, lung architecture and capillaries are visualized by mTomato expression (grey), while macrophages express mGFP (green). M. tuberculosis is visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). The video shows a 3 × 2 mosaic and a maximum-intensity Z-projection of a 1100 × 700 × 60 μm volume. Time is shown as mm:ss. Scale bar : 100µm Representative of three independent experiments. SupplementaryVideo3.mp4 Supplementary Video 3 | Distinct motility behaviors of antigen-specific CD4⁺ T cells in TB lesions. Representative examples of three behaviors displayed by adoptively transferred DeepRed C7 TCR-transgenic CD4⁺ T cells (red): mobile intravascular, mobile extravascular, and arrested extravascular, visualized by intravital multiphoton microscopy in lung TB lesions. In Siglec1-mTmG mice, lung architecture and capillaries are visualized by mTomato expression (grey), while macrophages express mGFP (green). M. tuberculosis is visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). Time is displayed as mm:ss. Scale bar, 20 µm. SupplementaryVideo4.mp4 Supplementary Video 4 | Interactions between antigen-specific CD4⁺ T cells and infected macrophages in TB lesions Representative examples of adoptively transferred DeepRed C7 TCR-transgenic CD4⁺ T cells (red) interacting with infected macrophages, visualized by intravital multiphoton microscopy in lung TB lesions. In Siglec1-mTmG mice, lung architecture and capillaries are visualized by mTomato expression (grey), while macrophages express mGFP (green). M. tuberculosis is visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). Time is displayed as mm:ss. Scale bar, 20 µm. SupplementaryVideo5.mp4 Supplementary Video 5 | Comparative tracking of antigen-specific and irrelevant CD4⁺ T cells in TB lesions. Representative intravital multiphoton microscopy of adoptively transferred DeepRed C7 TCR–transgenic (Mtb antigen–specific) CD4⁺ T cells (red) and CellTracker Orange (CTO)–labelled OT-II TCR–transgenic (OVA antigen–specific) CD4⁺ T cells (white), with tracking of individual cells in lung TB lesions. In Siglec1-mTmG mice, lung architecture and capillaries are visualized by mTomato expression (grey). M. tuberculosis is visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). Time is displayed as mm:ss. Scale bar, 50 µm. SupplementaryVideo6.mp4 Supplementary Video 6 | Interactions between antigen-specific CD4⁺ T cells and non-infected macrophages in TB lesions Representative examples of adoptively transferred DeepRed C7 TCR-transgenic CD4⁺ T cells (red) interacting with non-infected macrophages, visualized by intravital multiphoton microscopy in lung TB lesions. In Siglec1-mTmG mice, macrophages express mGFP (green). M. tuberculosis is visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). Time is displayed as mm:ss. Scale bar, 20 µm. SupplementaryVideo7.mp4 Supplementary Video 7 | Interactions between antigen-specific CD4⁺ T cells and collagen fibers in TB lesions Representative examples of adoptively transferred DeepRed C7 TCR-transgenic CD4⁺ T cells (magenta) interacting with extracellular matrix, visualized by intravital multiphoton microscopy in lung TB lesions. In Siglec1-mTmG mice, macrophages express mGFP (yellow). M. tuberculosis is visualized by mTurquoise expression (blue), and the extracellular matrix is detected by second harmonic generation (SHG, blue). Time is displayed as mm:ss. Scale bar, 20 µm. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8259909","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554065142,"identity":"c24cb4ea-1c0b-4bdf-8a5e-4ddbbfdcecc4","order_by":0,"name":"Léa Fromont","email":"","orcid":"","institution":"Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France","correspondingAuthor":false,"prefix":"","firstName":"Léa","middleName":"","lastName":"Fromont","suffix":""},{"id":554065143,"identity":"c7429ed8-7497-4958-8bcd-15c6873242de","order_by":1,"name":"Aizat Iman Abdul Hamid","email":"","orcid":"","institution":"Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France","correspondingAuthor":false,"prefix":"","firstName":"Aizat","middleName":"Iman Abdul","lastName":"Hamid","suffix":""},{"id":554065144,"identity":"8726a7af-d75c-4546-a914-9f95ba432bce","order_by":2,"name":"Elisabeth Bellard","email":"","orcid":"","institution":"Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France","correspondingAuthor":false,"prefix":"","firstName":"Elisabeth","middleName":"","lastName":"Bellard","suffix":""},{"id":554065145,"identity":"6c7826f1-8dd7-4414-8e61-00f35da70ef4","order_by":3,"name":"Zachary P. Howard","email":"","orcid":"","institution":"Division of Experimental Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA","correspondingAuthor":false,"prefix":"","firstName":"Zachary","middleName":"P.","lastName":"Howard","suffix":""},{"id":554065146,"identity":"94e40d67-1ca3-48b3-aea5-ed9c556d089c","order_by":4,"name":"Sarah Monard","email":"","orcid":"","institution":"Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Monard","suffix":""},{"id":554065147,"identity":"358f2108-14e7-4e94-aba4-280456438ee6","order_by":5,"name":"Jean-Philippe Girard","email":"","orcid":"","institution":"Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France","correspondingAuthor":false,"prefix":"","firstName":"Jean-Philippe","middleName":"","lastName":"Girard","suffix":""},{"id":554065148,"identity":"57e31555-9eec-4ea2-bb9d-e49b00baca46","order_by":6,"name":"Etienne Meunier","email":"","orcid":"","institution":"Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France","correspondingAuthor":false,"prefix":"","firstName":"Etienne","middleName":"","lastName":"Meunier","suffix":""},{"id":554065149,"identity":"4e3a4a6e-162e-4abb-a579-1ef689c4c0aa","order_by":7,"name":"Denis Hudrisier","email":"","orcid":"","institution":"Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France","correspondingAuthor":false,"prefix":"","firstName":"Denis","middleName":"","lastName":"Hudrisier","suffix":""},{"id":554065150,"identity":"6f5ceb1d-d854-4a02-a8df-36594c30ad44","order_by":8,"name":"Olivier Neyrolles","email":"","orcid":"","institution":"Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France","correspondingAuthor":false,"prefix":"","firstName":"Olivier","middleName":"","lastName":"Neyrolles","suffix":""},{"id":554065151,"identity":"7170098c-eb31-41eb-a2a1-b78ef7332acb","order_by":9,"name":"Serge Mazères","email":"","orcid":"","institution":"Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France","correspondingAuthor":false,"prefix":"","firstName":"Serge","middleName":"","lastName":"Mazères","suffix":""},{"id":554065152,"identity":"1ac09ceb-ae8e-42eb-9520-f0a52b07cfaa","order_by":10,"name":"Joël D. Ernst","email":"","orcid":"","institution":"Division of Experimental Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.","correspondingAuthor":false,"prefix":"","firstName":"Joël","middleName":"D.","lastName":"Ernst","suffix":""},{"id":554065153,"identity":"79468ccf-1c86-45fa-a8b9-2b81f225eda7","order_by":11,"name":"Emma Lefrançais","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYNCCAgZ+CMOAQY6NgQfIsCGkxYBBsgHKMIZoSSNaCwNDYgMhLfxipxM/MBjYSJi3nz344UdBXXqfdO/hDwwJ93BqkZydu1mCwSBNQuZMXrJkj8Hh3DaZc2kSDAnFuJ10O3cDUMvhOgmGHDMGHoMDuW0SQAbjjwScWuxv527+wWDwX0KC/40Z4x+DunQ2iRxjoMNwazGQzt0GtOWAhATQcGYeA+YEoBYDCXxaJG7nbrNIMEgGanljLC1jcNgQ7JcEPFr4gd6/8aHCDuiwHMOPb/7UycvPBobYBzxawABVWgJDhCCQIE35KBgFo2AUDH8AAHdhSfHo4prpAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0489-9298","institution":"Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France","correspondingAuthor":true,"prefix":"","firstName":"Emma","middleName":"","lastName":"Lefrançais","suffix":""}],"badges":[],"createdAt":"2025-12-02 11:32:30","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8259909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8259909/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97347055,"identity":"4201fe3c-8640-4846-8afe-d9a52434e061","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12867475,"visible":true,"origin":"","legend":"","description":"","filename":"FromontetalResearchSquare02122025.docx","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/7b56e8dd979a72e0da575f72.docx"},{"id":97347054,"identity":"5c258394-6574-42b4-8581-94bf4be2d8e3","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs8259909.json","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/1a9d331b4ad459f40096d5fc.json"},{"id":97369858,"identity":"0b7efa4e-4ea9-4838-9c46-9273460da15b","added_by":"auto","created_at":"2025-12-03 16:25:56","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155847,"visible":true,"origin":"","legend":"","description":"","filename":"rs82599090enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/0b2d47162a48bbd83d184e5b.xml"},{"id":97371255,"identity":"c2409d62-af5a-4715-bf4d-ff98916fabf9","added_by":"auto","created_at":"2025-12-03 16:28:37","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":846460,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/b78d85d4b9fa5ab21a6247f7.jpeg"},{"id":97347064,"identity":"f871bd46-5f36-4c42-ae72-501ecd842a27","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1972735,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/ab19ac368c24aaf09e3b3a82.png"},{"id":97347052,"identity":"2c871abd-00e2-413f-ab79-e5ebc4f32e15","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1006927,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/51561c9718dae5be013506ea.png"},{"id":97347090,"identity":"4b176c9b-16b8-4c60-8001-584e75cbf93d","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1924505,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/dea5656708297efa26f414b3.png"},{"id":97347092,"identity":"17562f26-ed6d-4346-819f-4ba9f5d65c08","added_by":"auto","created_at":"2025-12-03 11:51:38","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":159380,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/4deb3af0ff8f7854f6a92eb1.png"},{"id":97347056,"identity":"adb42b4b-8178-4075-8e81-0bfb306f7e24","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":381513,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/9235c909f358e524871d436a.png"},{"id":97347066,"identity":"a796888d-2e1a-48fb-bfcb-d428ec83cdef","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1297334,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/3fa6c13d68ec4f1d490fe422.jpeg"},{"id":97347100,"identity":"9dd96f3c-baa9-4f4f-b55a-ca11af3b92ac","added_by":"auto","created_at":"2025-12-03 11:51:38","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":769395,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/c7fb47ca2ff23bce336bc1d4.png"},{"id":97371163,"identity":"6d5a065d-0c45-4f14-92fc-45d357bda84c","added_by":"auto","created_at":"2025-12-03 16:28:28","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1197836,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/6c024fdefc1a831b51565087.jpeg"},{"id":97371300,"identity":"f832dd83-228e-4769-a240-4497c47412da","added_by":"auto","created_at":"2025-12-03 16:28:41","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1422576,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/10e2eb0f1d29e5dc4a946bbd.png"},{"id":97371254,"identity":"3734b9b7-89e9-441e-bb2c-ee0bdd1aba98","added_by":"auto","created_at":"2025-12-03 16:28:37","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":789905,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/dec0653aa13de3e913ab8e72.jpeg"},{"id":97347065,"identity":"242e1d22-edc1-4d8b-9eba-87a038221280","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":517751,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/a3035472bcc5e52969787a66.jpeg"},{"id":97347084,"identity":"3e198c49-336c-44de-8b4b-30b538828b1f","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1000234,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/94837d56c61aa7e28846b62a.jpeg"},{"id":97347099,"identity":"b6eb2faf-037a-46bb-afde-e5c4952a93eb","added_by":"auto","created_at":"2025-12-03 11:51:38","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":833731,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/58387429c7446a0e0af329c8.png"},{"id":97371263,"identity":"36fbff72-f128-4fec-8bc4-a490a11756ce","added_by":"auto","created_at":"2025-12-03 16:28:38","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":218912,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/5cb60604f214a8990d51f44f.png"},{"id":97370850,"identity":"9ee4f6cd-7190-4766-b777-e24a0e8ab192","added_by":"auto","created_at":"2025-12-03 16:28:03","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":215534,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/870cfb09378eb17a31160c0c.png"},{"id":97370291,"identity":"f2c04a46-5134-4e42-a33a-f7fc2ff68002","added_by":"auto","created_at":"2025-12-03 16:27:06","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90581,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/75f01d11865f70647d05a615.png"},{"id":97347074,"identity":"9bdca366-1487-4519-8ce4-30dee343c3b7","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":228176,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/b6b3a93ab27dfe9637a72abf.png"},{"id":97347076,"identity":"47218ed9-7d5e-4299-ae66-371bdff0633d","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36327,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/cb840eaacc8677da5845f6b0.png"},{"id":97347079,"identity":"f33bea60-7c51-48ea-ae69-938d5d5ceec0","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64473,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/ecfbb94bb8721da9c8b93c33.png"},{"id":97347080,"identity":"34209c74-1b6a-467c-b0fb-e865cc0e057e","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":313339,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/0edf0f281e192fa46490a692.png"},{"id":97371391,"identity":"f7534fd3-5ba3-4769-81ab-8acfc06d59e4","added_by":"auto","created_at":"2025-12-03 16:28:51","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108226,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/4232011a442f1591632cebdd.png"},{"id":97347088,"identity":"24863ae0-15de-4f63-83dc-949cdd860762","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":228090,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/2bd54030b46d5c07b0353155.png"},{"id":97347095,"identity":"b119d36f-613c-4e8e-8a85-83a1ceb37c4c","added_by":"auto","created_at":"2025-12-03 11:51:38","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157613,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/41a7bc08a8b3e67376e67aca.png"},{"id":97347094,"identity":"5d982af9-5476-4bf9-813d-d38803f6ac60","added_by":"auto","created_at":"2025-12-03 11:51:38","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":208955,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/a5dba211e05c5fce51175934.png"},{"id":97347101,"identity":"122b09d7-3686-4325-b11d-5576d1791343","added_by":"auto","created_at":"2025-12-03 11:51:38","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":133255,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/e90a27532bbc4778e16c9098.png"},{"id":97347097,"identity":"ea036579-9cf1-46a5-8515-9c60a4ed9c5d","added_by":"auto","created_at":"2025-12-03 11:51:38","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":239653,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/67392f7b7ad46d9edd76f775.png"},{"id":97347086,"identity":"a5f0684c-480b-43c8-949e-d4dd57a0fc24","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86662,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/9694df3cadef3bbc2de37f9e.png"},{"id":97347103,"identity":"81c309a4-52a8-4665-ac48-2453ff82f5a0","added_by":"auto","created_at":"2025-12-03 11:51:38","extension":"xml","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152006,"visible":true,"origin":"","legend":"","description":"","filename":"rs82599090structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/2357b88d27f21d202c39852e.xml"},{"id":97347082,"identity":"f574382f-bb26-4a50-8728-27a96be8002c","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"html","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":169383,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/baad0969e40248735f710205.html"},{"id":97347073,"identity":"947ae44b-e221-4a7a-9035-9ae7ac456d8e","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":682670,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntravital microscopy platform for real-time imaging of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMycobacterium tuberculosis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e–infected lungs under BSL-3 conditions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Custom-designed platform allowing lung stabilization and secure transfer of anesthetized, ventilated mice from the biosafety cabinet to the microscope isolator. \u003cstrong\u003eb,\u003c/strong\u003eSurgical setup in the BSL-3 cabinet using the custom platform. \u003cstrong\u003ec,\u003c/strong\u003eModified lung imaging window optimized for visualization of tuberculosis lesions (adapted from Headley \u003cem\u003eet al.\u003c/em\u003e, \u003cem\u003eNature\u003c/em\u003e 2016). \u003cstrong\u003ed,\u003c/strong\u003eSurgical preparation for lung intravital microscopy (LIVM), including tracheotomy for mechanical ventilation, thoracotomy exposing the left lung with visible lesions, and stabilization with a custom suction imaging window.\u003c/p\u003e","description":"","filename":"Figure1jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/1c7ebb6cce8b4c0b200f66b2.jpg"},{"id":97347053,"identity":"80d94aa2-6c8b-420d-9f15-73165ea27511","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1143947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReal time visualization of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eM. tuberculosis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e within infected macrophage clusters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Schematic of macrophage reporter mice. \u003cstrong\u003eb,\u003c/strong\u003e Infection protocol for lung intravital microscopy (LIVM). \u003cstrong\u003ec-d,\u003c/strong\u003e LIVM images showing \u003cem\u003eM. tuberculosis\u003c/em\u003e(magenta) as single bacteria within Siglec1⁺ macrophage clusters (membrane GFP) in infected lungs. \u003cstrong\u003ed,\u003c/strong\u003e Identification and characterization of TB lesions by intravital microscopy.\u003c/p\u003e","description":"","filename":"Figure2jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/91e17eecb5ab7c42148a59c1.jpg"},{"id":97347061,"identity":"1296f25e-1c0e-4519-8ca4-70440e5b8afe","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":905141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3D clearing and characterization of lung TB lesions and intravital imaging field. a,\u003c/strong\u003eWhole-lung lobe clearing and 3D segmentation of lesions. Colors: grey, lung structure (mTomato); green, Siglec1⁺ macrophage membranes (mGFP); magenta, M. tuberculosis (mturquoise), red, segmented lesions \u003cstrong\u003eb,\u003c/strong\u003e 3D reconstruction showing lesions (red), large vessels (blue), and bronchi (green) in infected cleared lung. \u003cstrong\u003ec,\u003c/strong\u003e Representation of intravital imaging field (white rectangle; 3 × 2 tiles, 1,134 × 756 × 100 µm) illustrating that the LIVM observation area covers an entire lesion in X and Y dimensions but not along the full Z-axis. \u003cstrong\u003ed,\u003c/strong\u003e Representation of lesion size heterogeneity, including the equivalent diameter of lesions detected by intravital microscopy or in the cleared whole lobe (in red), as well as the X, Y, Z dimensions (bounding box X,Y,Z, A, B, C) measured in the cleared lobe, compared with the corresponding LIVM observation volume dimension (LiveLung-TB X, Y, Z).\u003c/p\u003e","description":"","filename":"Figure3jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/4b49db2430cec305601da421.jpg"},{"id":97347077,"identity":"c07c8cde-0f43-4e08-956b-632f278b70e1","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1025855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCD4⁺ T cells infiltrate TB lesions and accumulate around clusters of infected macrophages.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Schematic of C7 CD4⁺ T cell adoptive transfer into \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e–infected mice prior to lung intravital microscopy (LIVM). \u003cstrong\u003eb, \u003c/strong\u003e\u0026nbsp;Quantification of CD4⁺ T cell localization, expressed as the percentage of tracks inside versus outside lesions (intralesional vs. extralesional). Mean data from three infected mice (n = 3), representing 169, 282, and 460 total CD4⁺ T cell tracks \u003cstrong\u003ec,\u003c/strong\u003eRepresentative LIVM images showing CD4⁺ T cell distribution relative to TB lesions (white dashed lines) and clusters of infected macrophages (purple dashed circles). Scale bar, 100µm \u003cstrong\u003ed, \u003c/strong\u003eCorresponding spatial maps of tracked CD4⁺ T cells overlaid on lesion and macrophage cluster locations.\u003c/p\u003e","description":"","filename":"Figure4jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/0a5fd2b75137e8ac5c0efdf8.jpg"},{"id":97347057,"identity":"a2972b6f-884c-48ec-8a12-5495baad9f12","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1340067,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntravital imaging reveals CD4⁺ T cell migratory states in Mtb infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea-c,\u003c/strong\u003e Representative LIVM images showing mobile intravascular \u003cstrong\u003ea\u003c/strong\u003e, mobile extravascular \u003cstrong\u003eb\u003c/strong\u003e, and arrested extravascular \u003cstrong\u003ec,\u003c/strong\u003e C7 CD4⁺ T cells. Scale bar, 20 µm. \u003cstrong\u003ed,\u003c/strong\u003eProportion of intravascular and extravascular cells among all tracked cells. \u003cstrong\u003ee,\u003c/strong\u003eProportion of arrested versus mobile cells within the extravascular compartment. \u003cstrong\u003ef-i,\u003c/strong\u003e Quantification of behavioral parameters: average velocity, maximum velocity, displacement, and straightness, for the three identified populations. \u003cstrong\u003ej,\u003c/strong\u003e Arrest coefficient, expressed as the percentage of time each cell exhibited an instant speed \u0026lt;2 µm/min. \u003cstrong\u003ek-m,\u003c/strong\u003eNumber of arrests, mean duration of arrest, and total time arrested per track. \u003cstrong\u003ed–e: \u003c/strong\u003eMean data from three infected mice (n = 3). \u003cstrong\u003ef–i\u003c/strong\u003e and \u003cstrong\u003ej–m\u003c/strong\u003e: Behavioral parameters were extracted from all tracks within a single video and classified as mobile intravascular (Mob IV), mobile extravascular (Mob EV), or arrested extravascular (Arr EV), with 28, 79, and 254 tracks, respectively.\u003c/p\u003e","description":"","filename":"Figure5jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/a421f4bd56782d25466f3af9.jpg"},{"id":97347087,"identity":"7ed437e3-b431-4b79-b248-34fd931a13fa","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":668802,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCD4⁺ T cell contacts with infected macrophages are rare and transient.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea, \u003c/strong\u003eFraction of tracked CD4⁺ T cells establishing contacts with infected macrophages (% of total tracks). \u003cstrong\u003eb,\u003c/strong\u003e Representative intravital microscopy images showing CD4⁺ T cells interacting with \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e–infected macrophages, either arrested or mobile. Scale bar, 20 µm. \u003cstrong\u003ec,\u003c/strong\u003e Proportion of T cells arrested while in contact with infected macrophages (% of contacting cells). \u003cstrong\u003ed,\u003c/strong\u003e Overall proportion of arrested T cells relative to the total tracked population (% of total tracks). \u003cstrong\u003ee,\u003c/strong\u003eArrest coefficient (expressed as the percentage of time a cell remains arrested per track), \u003cstrong\u003ef,\u003c/strong\u003e number of arrest events, and \u003cstrong\u003eg-h, \u003c/strong\u003eMean arrest duration of motile T cells transiently engaging infected macrophages, compared with T cells that do not contact infected cells; shown in \u003cstrong\u003eg\u003c/strong\u003e as a bar graph (mean ± SD) and in \u003cstrong\u003eh\u003c/strong\u003e as a frequency histogram (n = 16 and 73 tracks, respectively, from one mouse) \u003cstrong\u003ea, c-f \u003c/strong\u003eMean data from three infected mice (n = 3), representing 169, 282, and 460 total tracks, respectively.\u003c/p\u003e","description":"","filename":"Figure6jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/3d3264a8f9782c821d4ead07.jpg"},{"id":97370776,"identity":"4f4d13db-6a25-463b-a1f3-012f4837b9da","added_by":"auto","created_at":"2025-12-03 16:27:55","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":512711,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eT cell arrest is not dependent on antigen recognition.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Representative intravital images and migration tracks of C7 (M. tuberculosis-specific) and OT-II (non-specific) CD4⁺ T cells in infected lungs, observed by LIVM. Scale bar, 100µm. comparison of motility parameters between antigen-specific and non-specific T cells. \u003cstrong\u003eb,\u003c/strong\u003ePercentage of arrested cells. \u003cstrong\u003ec,\u003c/strong\u003e Arrest coefficient (percentage of time each cell spent arrested). \u003cstrong\u003ed,\u003c/strong\u003e Average speed \u003cstrong\u003ee,\u003c/strong\u003e Displacement. Data are displayed as violin plots, showing the full distribution of values with kernel density estimation; the median is indicated by a central line and dashed lines mark the quartiles. n= 215 C7 and 179 OTII tracks collected from one mouse, representative of three independent mice.\u003c/p\u003e","description":"","filename":"Figure7jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/ab552eda1825daa8249ef78e.jpg"},{"id":97347067,"identity":"0f0ce562-bd7c-4d2e-a846-cdd2ddd5ed64","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":859451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCD4⁺ T cell migration and arrest are influenced by macrophage clusters and collagen fibers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Distance to the nearest infected cell for mobile and arrested extravascular CD4⁺ T cells. \u003cstrong\u003eb,\u003c/strong\u003eProportion of extravascular T cells interacting with infected macrophages, uninfected macrophage, collagen fibers, or other structures (% of total tracks). \u003cstrong\u003ec,\u003c/strong\u003e Distance to the nearest infected cell for mobile and arrested extravascular T cells, categorized by interaction type (uninfected macrophage, collagen fibers, or other). \u003cstrong\u003ed,\u003c/strong\u003e Directionality of mobile extravascular T cells relative to the nearest infected macrophage, expressed as the percentage of cells moving toward, around, or away from the target. \u003cstrong\u003ee,\u003c/strong\u003eProportion of T cells interacting with uninfected macrophage, and \u003cstrong\u003ef,\u003c/strong\u003epercentage of these cells that are arrested or mobile. \u003cstrong\u003eg,h,\u003c/strong\u003eRepresentative intravital lung images showing a CD4⁺ T cell\u003cstrong\u003e g\u003c/strong\u003e, arrested on or h, migrating around a macrophage cluster. \u003cstrong\u003ei, \u003c/strong\u003eProportion of T cells interacting with collagen fibers and \u003cstrong\u003ej,\u003c/strong\u003e percentage of these cells that are arrested or mobile. Scale bar, 20µm\u003cstrong\u003e k,l,\u003c/strong\u003e Representative intravital lung images showing a CD4⁺ T cell \u003cstrong\u003ek, \u003c/strong\u003earrested on a collagen fiber (second harmonic generation, SHG) or \u003cstrong\u003el, \u003c/strong\u003emigrating along a collagen fiber. Mean data from three infected mice (n = 3) representing 169, 282, and 460 total tracks, respectively\u003c/p\u003e","description":"","filename":"Figure8jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/04b06553301452eea469bab9.jpg"},{"id":97665595,"identity":"adc1e04e-f2d8-4852-9449-939297d635a6","added_by":"auto","created_at":"2025-12-08 09:19:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8644768,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/45b9630a-90a1-4f77-a8f4-6b3df2077e00.pdf"},{"id":97347068,"identity":"ddd28da7-3d3b-4953-b419-b6b0b2364fbd","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":453996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 1 | Sequential steps from surgical preparation to multiphoton imaging in BSL-3\u003c/strong\u003e. Workflow illustrating surgical preparation, animal transfer, and two-photon microscopy within a BSL-3 facility (microscope setup adapted from Barlerin et al., Sci. Rep. 2017).\u003c/p\u003e","description":"","filename":"ExtendedDataFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/8929afedb66f79a83a301922.jpg"},{"id":97371169,"identity":"bbb90f5c-a44e-4bdf-8a8d-cd7118855535","added_by":"auto","created_at":"2025-12-03 16:28:29","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2276148,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 2 | Intravital imaging and characterization of tuberculosis lesions in the lung.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepresentative 2D maximum intensity projection of lungs from a Siglec1-mTmG mouse post-infection, illustrating vascular remodeling (mTomato, grey), macrophage accumulation (mGFP, green), and bacterial localization (mTurquoise, magenta). Dashed line indicates ilesion area. Scale bar: 100 µm.\u003c/p\u003e","description":"","filename":"ExtendedDataFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/a9c4366baafe977a0aa9529c.jpg"},{"id":97347071,"identity":"99ee872f-56ec-4984-a52c-60fb4238f824","added_by":"auto","created_at":"2025-12-03 11:51:36","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":587271,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 3 | Intravital imaging of Siglec1-mTmG mouse lungs showing multinucleated giant cells during TB infection and alveolar macrophages in non-infected tissue.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Intravital microscopy of a Siglec1-mTmG lung from a \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e–infected mouse showing formation of giant multinucleated cells. \u003cstrong\u003eb,\u003c/strong\u003e Intravital microscopy of a non-infected Siglec1-mTmG lung showing normal alveolar architecture with thin-walled alveoli, regular capillary network (mTomato), and typically no more than a single Siglec1⁺ macrophage (mGFP) per alveolus. \u003cstrong\u003ec\u003c/strong\u003e. Frequency distribution of lesion size (equivalent diameter) measured from lesion areas visualized by lung intravital microscopy (n=53)\u003c/p\u003e","description":"","filename":"ExtendedDataFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/55cef41743f1cdedd0233cd5.jpg"},{"id":97347091,"identity":"9206115e-8a4c-4409-bd3d-98989b463d1c","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2331155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 4 | Intravital imaging of vascular remodeling and leakage in TB-infected lungs. a,b\u003c/strong\u003eLIVM images of \u003cem\u003eMtb\u003c/em\u003e –infected lung after intravenous injection of FITC dextran, showing vascular remodelling within the lesion area (white dashed line) and increased vessel permeability indicated by green-filled alveoli (red asterisks) compared to normal alveoli (white asterisks). \u003cstrong\u003ec,d\u003c/strong\u003e LIVM images after intravenous injection of Evans Blue revealing vascular remodeling in the lesion area (white dashed line). Panels \u003cstrong\u003eb\u003c/strong\u003e and \u003cstrong\u003ed\u003c/strong\u003e show magnified views of the regions outlined in red in \u003cstrong\u003ea\u003c/strong\u003e and \u003cstrong\u003ec\u003c/strong\u003e, respectively. Scale bar, 100 µm.\u003c/p\u003e","description":"","filename":"ExtendedDataFigure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/21b46675f4abb42c39aa5c16.jpg"},{"id":97370810,"identity":"9a412a37-e409-4f13-810b-98c42ff14e73","added_by":"auto","created_at":"2025-12-03 16:27:58","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":692583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 5 | Pipeline for 4D movie reconstruction and macrophage tracking with FIJI, and analysis of motility parameters. a\u003c/strong\u003e, Workflow for 4D image reconstruction and cell tracking using FIJI, with automatic or manual quantification of motility parameters. \u003cstrong\u003eb,\u003c/strong\u003e Heat map showing clustering of distinct behavioural parameters (normalized from min to max) n=282 tracks. \u003cstrong\u003ec,\u003c/strong\u003e Mean squared displacement (MSD) n=460 tracks \u003cstrong\u003ed, \u003c/strong\u003eα coefficient of mobile extravascular cells (n=146)\u003c/p\u003e","description":"","filename":"ExtendedDataFigure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/6fc3d888699d13bcc36c05f6.jpg"},{"id":97370203,"identity":"5360dcd0-a9e1-4586-963b-96d2873c1723","added_by":"auto","created_at":"2025-12-03 16:26:53","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":523995,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 6 | CD4⁺ T cell interactions with lung structures and macrophage clusters influence migratory behavior. a,\u003c/strong\u003e Proportion of T cells interacting with macrophage clusters. \u003cstrong\u003eb,\u003c/strong\u003e Representative LIVM image showing collagen fibers (SHG) surrounding macrophage clusters. \u003cstrong\u003ec,\u003c/strong\u003eProportion of T cells interacting with other non-identified structures. \u003cstrong\u003ed,\u003c/strong\u003ePercentage of these cells that are arrested or mobile. \u003cstrong\u003ee-g,\u003c/strong\u003e CD4⁺ T cell intermittent migration parameters according to interacting structure (infected macrophages, uninfected cells, or collagen fibers or other structures): \u003cstrong\u003ee, \u003c/strong\u003eArrest coefficient (% of time arrested per track), \u003cstrong\u003ef, \u003c/strong\u003enumber of arrest events, and \u003cstrong\u003eg,\u003c/strong\u003e mean duration of each arrest depending on type of interaction (mean ± SD) (n = 16, 35,38 and 37 tracks, respectively, from one mouse). \u003cstrong\u003ec,d,e,f.\u003c/strong\u003eMean data from three independently infected mice (n = 3)\u003c/p\u003e","description":"","filename":"ExtendedDataFigure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/b61547eefcddb9395b3894f4.jpg"},{"id":97370775,"identity":"afd9443d-8b04-479f-9bd8-82e7d96d3149","added_by":"auto","created_at":"2025-12-03 16:27:55","extension":"mp4","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":22728399,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Video 1 | Vascular remodelling and perfusion in TB lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVascular architecture within lung TB lesions following intravenous injection of Evans Blue (red) or FITC–dextran (green). \u003cem\u003eM. tuberculosis\u003c/em\u003e is visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). The video shows a 3 × 2 mosaic and a maximum-intensity Z-projection of a 1100 × 700 × 60 µm volume. Time is displayed as mm:ss. Scale bar, 100 µm.\u003c/p\u003e","description":"","filename":"SupplementaryVideo1.mp4","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/ef0b197a3ca70be0c51db66f.mp4"},{"id":97347098,"identity":"c676fd81-021a-473f-99af-9a14837427f7","added_by":"auto","created_at":"2025-12-03 11:51:38","extension":"mp4","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":77735094,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Video 2 | Infiltration and behaviour of antigen-specific CD4⁺ T cells in TB lesions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntravital multiphoton microscopy of TB lesions showing adoptively transferred DeepRed C7 TCR-Tg CD4⁺ T cells (red) and their tracks (described in Fig.4). In Siglec1-mTmG mice, lung architecture and capillaries are visualized by mTomato expression (grey), while macrophages express mGFP (green). \u003cem\u003eM. tuberculosis\u003c/em\u003eis visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). The video shows a 3 × 2 mosaic and a maximum-intensity Z-projection of a 1100 × 700 × 60 μm volume. Time is shown as mm:ss. Scale bar : 100µm Representative of three independent experiments.\u003c/p\u003e","description":"","filename":"SupplementaryVideo2.mp4","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/49df3f14b595dd6788663e5b.mp4"},{"id":97347105,"identity":"6f9a2195-2e2d-473d-ad40-6d20de89642c","added_by":"auto","created_at":"2025-12-03 11:51:38","extension":"mp4","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":43719042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Video 3 | Distinct motility behaviors of antigen-specific CD4⁺ T cells in TB lesions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepresentative examples of three behaviors displayed by adoptively transferred DeepRed C7 TCR-transgenic CD4⁺ T cells (red): mobile intravascular, mobile extravascular, and arrested extravascular, visualized by intravital multiphoton microscopy in lung TB lesions. In Siglec1-mTmG mice, lung architecture and capillaries are visualized by mTomato expression (grey), while macrophages express mGFP (green). \u003cem\u003eM. tuberculosis\u003c/em\u003e is visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). Time is displayed as mm:ss. Scale bar, 20 µm.\u003c/p\u003e","description":"","filename":"SupplementaryVideo3.mp4","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/244498d120d3eb0edcac4d5d.mp4"},{"id":97347081,"identity":"692fadb2-45c5-4ade-b1b2-2be5455d9d00","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"mp4","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":40963377,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Video 4 | Interactions between antigen-specific CD4⁺ T cells and infected macrophages in TB lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepresentative examples of adoptively transferred DeepRed C7 TCR-transgenic CD4⁺ T cells (red) interacting with infected macrophages, visualized by intravital multiphoton microscopy in lung TB lesions. In Siglec1-mTmG mice, lung architecture and capillaries are visualized by mTomato expression (grey), while macrophages express mGFP (green). \u003cem\u003eM. tuberculosis\u003c/em\u003e is visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). Time is displayed as mm:ss. Scale bar, 20 µm.\u003c/p\u003e","description":"","filename":"SupplementaryVideo4.mp4","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/f965e844de408aca70b21d5d.mp4"},{"id":97347085,"identity":"4d51caad-d410-4eeb-99bc-e71cf42e4c22","added_by":"auto","created_at":"2025-12-03 11:51:37","extension":"mp4","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":35312657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Video 5 | Comparative tracking of antigen-specific and irrelevant CD4⁺ T cells in TB lesions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepresentative intravital multiphoton microscopy of adoptively transferred DeepRed C7 TCR–transgenic (Mtb antigen–specific) CD4⁺ T cells (red) and CellTracker Orange (CTO)–labelled OT-II TCR–transgenic (OVA antigen–specific) CD4⁺ T cells (white), with tracking of individual cells in lung TB lesions. In Siglec1-mTmG mice, lung architecture and capillaries are visualized by mTomato expression (grey). \u003cem\u003eM. tuberculosis\u003c/em\u003e is visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). Time is displayed as mm:ss. Scale bar, 50 µm.\u003c/p\u003e","description":"","filename":"SupplementaryVideo5.mp4","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/6a6f98b5a985cafe73db4488.mp4"},{"id":97371293,"identity":"eebf5513-d4a0-4476-a22b-7c945e8e5035","added_by":"auto","created_at":"2025-12-03 16:28:41","extension":"mp4","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":61279323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Video 6 | Interactions between antigen-specific CD4⁺ T cells and non-infected macrophages in TB lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepresentative examples of adoptively transferred DeepRed C7 TCR-transgenic CD4⁺ T cells (red) interacting with non-infected macrophages, visualized by intravital multiphoton microscopy in lung TB lesions. In Siglec1-mTmG mice, macrophages express mGFP (green). \u003cem\u003eM. tuberculosis\u003c/em\u003e is visualized by mTurquoise expression (magenta), and the extracellular matrix is detected by second harmonic generation (SHG, magenta). Time is displayed as mm:ss. Scale bar, 20 µm.\u003c/p\u003e","description":"","filename":"SupplementaryVideo6.mp4","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/f8c2d7846bed0d63ebd43ab5.mp4"},{"id":97369933,"identity":"ad669c51-e767-420a-aff4-ae606c0c1e54","added_by":"auto","created_at":"2025-12-03 16:26:06","extension":"mp4","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":66476360,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Video 7 |\u003c/strong\u003e \u003cstrong\u003eInteractions between antigen-specific CD4⁺ T cells and collagen fibers in TB lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRepresentative examples of adoptively transferred DeepRed C7 TCR-transgenic CD4⁺ T cells (magenta) interacting with extracellular matrix, visualized by intravital multiphoton microscopy in lung TB lesions. In Siglec1-mTmG mice, macrophages express mGFP (yellow). \u003cem\u003eM. tuberculosis\u003c/em\u003e is visualized by mTurquoise expression (blue), and the extracellular matrix is detected by second harmonic generation (SHG, blue). Time is displayed as mm:ss. Scale bar, 20 µm.\u003c/p\u003e","description":"","filename":"SupplementaryVideo7.mp4","url":"https://assets-eu.researchsquare.com/files/rs-8259909/v1/bd34eaa67f5004692dc0005f.mp4"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntravital lung microscopy unveils T cell dynamics in mouse tuberculosis lesions\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (\u003cem\u003eMtb\u003c/em\u003e), the causative agent of tuberculosis (TB), remains the leading cause of death from a single infectious agent worldwide (\u003cem\u003eGlobal Tuberculosis Report 2025\u003c/em\u003e 2025). Despite decades of research, our understanding of the mechanisms that limit bacterial clearance in the lungs, and our ability to overcome them with improved vaccines or therapeutics, remains incomplete, and antibiotic-resistant strains continue to emerge. T cell\u0026ndash;mediated immunity, especially via CD4⁺ lymphocytes, is central to host defence against \u003cem\u003eMtb\u003c/em\u003e, yet protective efficacy is often incomplete, allowing long-term survival of the pathogen (Jasenosky et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Flynn et Chan 2022; Lefran\u0026ccedil;ais et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While T cell activation and differentiation in lymphoid organs are well characterized, the mechanisms that govern their migration, positioning, and interactions with infected cells in the lung remain poorly understood. Effective immunity requires that effector T cells reach infected sites and establish stable contacts with \u003cem\u003eMtb\u003c/em\u003e-infected macrophages (Srivastava et Ernst 2013), yet histological analyses reveal a striking spatial segregation: infected macrophages are concentrated in granuloma cores, whereas T cells localize predominantly at the periphery (Cohen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kauffman et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gideon et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; McCaffrey et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The dynamic processes underlying this spatial segregation in the lung have remained entirely uncharacterized, largely because imaging a living, ventilated lung under BSL-3 containment has, until now, been technically impossible. Intravital microscopy has revolutionized our understanding of T cell behavior in tumors and other infectious settings by enabling real-time visualization of cell recruitment, migration, and interactions within intact tissues (Hor et Germain 2022; Pittet et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sumen et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Boulch et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Yet this approach has never been applied to \u003cem\u003eMtb\u003c/em\u003e-infected lungs, where continuous respiratory motion, the need to preserve physiological function, and stringent biosafety constraints have prevented high-resolution imaging (Looney et Headley 2020). In hepatic BCG granulomas, intravital microscopy has revealed that T cells are rapidly recruited to and retained within granuloma structures, and that their migration is constrained by a macrophage-defined boundary (Egen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Within these structures, dynamic tracking of antigen-specific T cells showed that they remain highly motile and rarely arrest on antigen-presenting cells (Egen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), suggesting that effective cognate recognition may be limited. In pulmonary TB, real-time dynamics of T cell\u0026ndash;macrophage interactions have not been directly observed, leaving open questions about whether peripheral accumulation reflects insufficient antigen presentation, microenvironmental constraints, or both. Overcoming these obstacles is essential to uncover the mechanisms that constrain T cell access to infected macrophages and to understand why T cell responses often fail to clear \u003cem\u003eMtb\u003c/em\u003e (Lefran\u0026ccedil;ais et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Here, we developed a BSL-3\u0026ndash;compatible intravital lung microscopy platform that enables high-resolution, real-time imaging of immune\u0026ndash;pathogen interactions in \u003cem\u003eMtb\u003c/em\u003e-infected lungs. Using this platform, we generate the first \u003cem\u003ein vivo\u003c/em\u003e, real-time dynamic map of T lymphocytes within TB lesions, uncovering intermittent migration patterns and a high frequency of arrests occurring away from infected cells. We further show how vascular architecture, extracellular matrix organization, and macrophage cluster collectively shape CD4⁺ T cell positioning, motility, and engagement with infected macrophages. These findings provide new mechanistic insight into why T cells accumulate at a distance from infected cells and offer a framework for understanding immune evasion by \u003cem\u003eMtb\u003c/em\u003e in the lung.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eLiveLung-TB: a BSL-3 intravital microscopy platform for real-time imaging of TB lesions\u003c/h2\u003e\u003cp\u003eTo visualize immune dynamics in \u003cem\u003eMtb\u003c/em\u003e\u0026ndash;infected lungs in real time, we developed LiveLung-TB, a biosafety level 3 compatible intravital lung microscopy system. The platform integrates a dedicated surgical setup (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-b), a TB lesion\u0026ndash;adapted thoracic window (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), and procedures within a BSL-3 facility, equipped with a two-photon microscope (Barlerin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Mice are prepared under a biosafety hood, transferred securely to the microscope isolator, and mechanically ventilated throughout imaging, enabling high-resolution, long-term intravital visualization under full containment (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The system features a custom surgical plate for safe transport and manipulation of anesthetized animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) and an adapted imaging window that stabilizes the left lung under gentle suction for high-resolution visualization of subpleural TB lesions. The TB lesion\u0026ndash;specific thoracic window was derived from previously described intercostal lung IVM windows (Headley et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Looney et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with key modifications including a steeper angle to optimize objective displacement and a flattened base to ensure stable placement over visible TB lesions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). We combined this imaging platform with optimized reporter systems to visualize key cellular components (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b). Fluorescent \u003cem\u003eMtb\u003c/em\u003e bacilli expressing mTurquoise enabled visualization of individual bacteria and small clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-d). In parallel, infected macrophages were imaged in Siglec1-Cre \u0026times; mTmG mice, in which macrophage membranes fluoresce green (GFP) and lung structural cells fluoresce red (mTomato) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-d). Lesions were readily identifiable, characterized by vascular remodeling, collapsed alveoli, macrophage aggregates, clusters of infected macrophages harboring mTurquoise-labeled bacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ed \u003cb\u003eand Extended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and multinucleated giant cells (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In contrast, healthy lungs displayed preserved alveolar architecture, with large alveolar spaces, thin alveolar walls lined by capillaries, and sparse macrophages (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). To quantify lesion size and heterogeneity accessible by lung intravital microscopy, we measured 53 subpleural lesions using 2D maximum intensity projections. Lesions were defined by macrophage clustering, intracellular bacteria, and clear boundaries formed by local tissue remodeling (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Lesion size were heterogenous, equivalent diameters ranging from 179 to 1022 \u0026micro;m (mean 516 \u0026micro;m, SD 203 \u0026micro;m) (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). 3D-cleared whole-lobe lung imaging combined with lesion segmentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u0026ndash;b) revealed marked lesion heterogeneity, with diameter ranging from 2 to 2,114 \u0026micro;m (mean 646 \u0026micro;m, SD 453), and confirmed that 3D mosaic acquired with the thoracic window (volume of 1134 \u0026times; 756 \u0026times; 100 \u0026micro;m) can cover most of lesions in xy and approximately half in z (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ec\u0026ndash;d). Segmentation revealed complex, often mushroom-shaped lesion architecture connected to airways, predominantly expanding in the XY plane, making subpleural lesions readily accessible for live imaging (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-d). Together, these results establish LiveLung-TB as an efficient and adapted technique for studying immune\u0026ndash;pathogen interactions within intact \u003cem\u003eMtb\u003c/em\u003e-infected lungs in real time.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVascular remodeling and perfusion in TB lesions\u003c/h3\u003e\n\u003cp\u003eVascular remodeling is a hallmark of chronic inflammatory lesions, including those caused by \u003cem\u003eMtb\u003c/em\u003e. In TB granulomas, persistent inflammation and hypoxic stress trigger angiogenic responses, leading to dense, tortuous, and disorganized vascular networks (Datta et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Oehlers et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wells et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite this increased vessel density, the functionality of the granuloma vasculature and the extent of tissue oxygenation remain unclear. While some studies describe hypoxic or necrotic cores, others suggest that subsets of granulomas maintain limited perfusion sufficient to sustain immune activity. To directly assess vascular architecture and function \u003cem\u003ein vivo\u003c/em\u003e, we performed intravital microscopy of \u003cem\u003eMtb\u003c/em\u003e-infected lungs, using mTomato labeling of stromal membranes to visualize vessels and surrounding parenchyma (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This analysis revealed pronounced vascular remodeling within lesions, with marked increases in vessel density and tortuosity. Perfusion and permeability assays using fluorescent dextran and Evans Blue (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ea,b and \u003cb\u003eSupplementary Video 1\u003c/b\u003e) showed that, although granulomas remain perfused, they exhibit extensive vascular leakage into alveolar spaces compared with adjacent healthy regions. Collectively, these findings indicate that TB lesions contain a highly remodeled and permeable vasculature, potentially influencing immune cell trafficking and the local inflammatory environment.\u003c/p\u003e\n\u003ch3\u003eCD4⁺ T cells infiltrate TB lesions and preferentially localize around infected macrophage clusters\u003c/h3\u003e\n\u003cp\u003eTo characterize CD4⁺ T cell dynamics in \u003cem\u003eMtb\u003c/em\u003e-infected lungs, we performed adoptive transfer of Th1-polarized, Deep Red\u0026ndash;labeled \u003cem\u003eMtb\u003c/em\u003e-antigen specific (C7, recognizing the ESAT-6 antigen) CD4⁺ T cells, isolated from the spleens of donor mice, into chronically infected recipients. Between 5\u0026times;10⁶ and 5\u0026times;10⁷ cells were transferred intravenously, and their migration was tracked 24 hours later by lung intravital microscopy (LIVM) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cb\u003eSupplementary Video 2\u003c/b\u003e). A custom tracking pipeline was developed to quantify T cell motility, including track length, displacement, velocity, straightness, and behavioral parameters such as localization, directionality, and cell\u0026ndash;cell interactions (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). We tracked 911 CD4⁺ T cells for 30 to 60 minutes in three independently infected mice. We examined CD4⁺ T cells spatial distribution relative to granulomatous lesions and clusters of infected macrophages, as previously defined (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Mapping of T cell tracks revealed that 88.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8% of transferred CD4⁺ T cells infiltrated TB lesions and were predominantly concentrated around clusters of infected macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-d), highlighting a targeted localization of CD4⁺ T cells within granulomatous microenvironments.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLung intravital imaging reveals distinct CD4⁺ T cell migratory states and predominant parenchymal arrest during\u003c/b\u003e \u003cb\u003eM. tuberculosis\u003c/b\u003e \u003cb\u003einfection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe further analysed their migratory behaviour, revealing three distinct migratory profiles in \u003cem\u003eMtb\u003c/em\u003e-infected lungs: rapidly mobile intravascular cells, mobile extravascular cells migrating within the parenchyma, and arrested extravascular cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-c, \u003cb\u003eSupplementary Video 3\u003c/b\u003e). These migratory profiles were confirmed by unsupervised clustering of discriminative motility parameters (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) and mean square displacement (MSD) analysis (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-d). Intravascular cells, representing 8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5% of all tracks (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), displayed the highest average and maximum velocities, greater displacements, and more linear directed trajectories, reflected by elevated straightness and α coefficients (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003ef-i, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), but were tracked over shorter time intervals (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. The majority of C7 T cells (91.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5%) were extravascular (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003ed) and could be subdivided into two main subsets: arrested cells (57.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1%) and mobile cells (42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). Extravascular T cells exhibited intermittent motility, alternating between brief movements and transient pauses, as reflected by their instantaneous velocity and arrest coefficient, the latter defined as the percentage of time moving below 2 \u0026micro;m/min (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003ej-m). Together, these data delineate three distinct CD4⁺ T cell migratory behaviors within \u003cem\u003eMtb\u003c/em\u003e-infected lungs, reflecting the spatial and kinetic diversity of T cell behavior in granulomatous tissue. While T cell intermittent migration has been previously observed in pulmonary environments (Mrass et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the large fraction of extravascular T cells exhibiting sustained arrest was unexpected. This contrasts with hepatic granulomas induced by \u003cem\u003eMycobacterium bovis\u003c/em\u003e BCG, where mycobacteria-specific T cells remain predominantly motile and form few stable contacts with antigen-presenting cells (Egen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), suggesting that the pulmonary microenvironment imposes unique constraints on T cell dynamics during TB.\u003c/p\u003e\n\u003ch3\u003eCD4⁺ T cells show only rare and transient interactions with infected macrophages\u003c/h3\u003e\n\u003cp\u003eTo explore the basis of the prolonged arrest and intermittent migration of CD4⁺ T cells in TB lesions, we examined their interactions with infected macrophages (\u003cb\u003eSupplementary Video 4\u003c/b\u003e). While stable contacts with antigen-presenting cells are typically driven by TCR-mediated antigen recognition, only a minority of CD4⁺ T cells engaged directly with infected macrophages, (16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5% of tracked cells) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Among these, only 36.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7% underwent stable arrest (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), corresponding to just 5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4% of the total extravascular T cell population (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). The remaining mobile CD4⁺ T cells (63.4%) made only transient contacts with infected cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), characterized by short-lived arrests (7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5 min; range 1.8\u0026ndash;31.0 min) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003ee-h). These arrest durations are consistent with initial scanning interactions, exploratory and low-affinity, and therefore insufficient for activation (Celli et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and were comparable to the brief pauses observed during intermittent migration of cells that did not contact infected cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003ef-h). These observations indicate that CD4⁺ T cell encounters with infected macrophages are rare, and when they do occur, they are predominantly brief, low-affinity interactions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCD4⁺ T cell arrest in\u003c/b\u003e \u003cb\u003eMtb\u003c/b\u003e \u003cb\u003egranulomas occurs independently of antigen recognition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven that interactions with infected cells were rare and unstable, we next sought to identify the mechanisms underlying the predominant parenchymal arrest of CD4⁺ T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). To directly test whether antigen recognition influences T cell arrest within pulmonary granulomas, we compared the migratory behavior of \u003cem\u003eMtb\u003c/em\u003e-specific (C7, recognizing the ESAT-6 antigen) and non-specific (OT-II, recognizing the OVA peptide) CD4⁺ T cells using lung intravital microscopy (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, \u003cb\u003eSupplementary Video 5\u003c/b\u003e). Analysis of motility parameters, including the proportion of arrested cells, arrest coefficient, average speed, and displacement, revealed no significant differences between C7 and OT-II T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e7\u003c/span\u003eb-e). These results indicate that T cell arrest and migratory behavior within \u003cem\u003eMtb\u003c/em\u003e-infected lungs are not dependent on antigen recognition, suggesting that factors other than TCR engagement, such as the local microenvironment, cytokine gradients, or extracellular matrix organization, may predominantly regulate T cell dynamics within granulomas.\u003c/p\u003e\n\u003ch3\u003eCD4⁺ T cell interactions with collagen fibers and macrophage clusters in TB lesions\u003c/h3\u003e\n\u003cp\u003eTo understand why CD4⁺ T cell encounters with infected macrophages are rare, we analyzed their spatial distribution relative to infected cells and examined their interactions with surrounding cellular and structural elements. Most CD4⁺ T cells were positioned 20\u0026ndash;40 \u0026micro;m away from infected macrophages, and, unexpectedly, arrested cells were located even farther from infected cells than mobile ones (32\u0026thinsp;\u0026plusmn;\u0026thinsp;7 \u0026micro;m vs. 23\u0026thinsp;\u0026plusmn;\u0026thinsp;7 \u0026micro;m) (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-b). Interaction analysis revealed three main elements associated with T cell arrest at a distance from infected macrophages: uninfected macrophages (\u003cb\u003eSupplementary Video 6)\u003c/b\u003e, collagen fibers (\u003cb\u003eSupplementary Video 7)\u003c/b\u003e, and other non-identified structures, with no difference in their distance to infected cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e8\u003c/span\u003eb\u0026ndash;c). A substantial proportion of CD4⁺ T cells (32.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3%) interacted with uninfected macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e8\u003c/span\u003ee, \u003cb\u003eSupplementary Video 6\u003c/b\u003e), typically at the periphery of macrophage clusters (\u003cb\u003eExtended\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), whereas bacteria accumulated in the center. Among T cells contacting uninfected macrophages, 60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6% were arrested (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e8\u003c/span\u003ef\u0026ndash;h). Second harmonic generation (SHG) imaging showed extensive collagen deposition encasing macrophage aggregates within TB lesions (\u003cb\u003eExtended\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Intravital microscopy revealed that 39.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9% of CD4⁺ T cells were closely associated with collagen fibers, either remaining arrested (54.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2%) or migrating along these structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e8\u003c/span\u003ei\u0026ndash;l, \u003cb\u003eSupplementary Video 7\u003c/b\u003e), indicating that collagen-rich regions provide both structural scaffolds and guidance cues for T cell movement. Similar ECM-guided migration has been documented in inflamed dermis, brain, and tumors, where fiber orientation and density channel T cell trajectories and constrain access to target cells. Importantly, CD4⁺ mobile T cells did not show preferential or prolonged arrest on uninfected macrophages, collagen fibers, or other non-identified structures (\u003cb\u003eExtended\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003ee\u0026ndash;g), and their distance from infected cells as well as their intermittent migration pattern were independent of the interacting partner (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). Directional analysis further demonstrated that 70% of mobile extravascular T cells migrated along the periphery of lesions rather than toward infected macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e8\u003c/span\u003ed). Together, these findings indicate that collagen fibers and the organization of macrophage clusters strongly shape CD4⁺ T cell motility and positioning, thereby limiting productive contacts with infected cells and potentially contributing to the long-term survival of \u003cem\u003eMtb\u003c/em\u003e within granulomatous tissue.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we present LiveLung-TB, the first platform enabling real-time visualization of T cell migration within \u003cem\u003eMtb\u003c/em\u003e-infected lung lesions. This technique provides a detailed view of CD4⁺ T cell dynamics, revealing unique migration behaviors. Our results reveal that lymphocytes rarely establish stable contacts with infected macrophages and preferentially localize to the periphery of macrophage clusters, where they arrest or migrate along non infected macrophages and collagen fibers. By combining multiphoton intravital imaging with fluorescent labeling, we directly observed how tissue architecture, vascular remodeling, cell aggregates and ECM composition shape immune cell behavior \u003cem\u003ein situ\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eGranulomas frequently exhibit poorly perfused and structurally altered vasculature (Wells et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Datta et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Oehlers et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which limits T cell extravasation and restricts their access to lesion cores. Such vascular remodeling and perfusion defects likely represent major drivers of immune cell exclusion, as similarly reported in tumors and other chronic inflammatory settings where physical access can be as limiting as effector function. Lung intravital imaging is essential to dissect these dynamics, and our observations in the C57BL/6 mouse model reveal that lymphocytes do infiltrate TB lesions, although the extensive vascular remodeling and increased leakiness may shape their migratory behavior within lesions.\u003c/p\u003e\u003cp\u003eWe find that only a minority of lymphocytes engage infected macrophages, with ~\u0026thinsp;11% forming transient contacts and only\u0026thinsp;~\u0026thinsp;5% establishing stable interactions. These low frequencies point to two sequential limitations: restricted physical access to infected cells, and suboptimal activation even when contact occurs. Stable T cell\u0026ndash;macrophage interactions depend on antigen-specific TCR engagement (Moreau et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Yet, consistent with observations in BCG-induced hepatic granulomas (Egen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), most CD4⁺ T cells that reach infected macrophages remain highly mobile and rarely form sustained contacts, suggesting low-affinity antigen recognition or insufficient signaling. Notably, in both human lung biopsies and macaque infection models, \u003cem\u003eMtb\u003c/em\u003e-specific CD4⁺ T cells also infrequently contact infected macrophages despite evidence of activation (Kauffman et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), underscoring the combined impact of anatomical constraints and functional limitations.\u003c/p\u003e\u003cp\u003eStrikingly, the majority of parenchymal lymphocytes remain immobilized or display intermittent migration at a distance of 10\u0026ndash;20 \u0026micro;m from infected macrophages, typically along collagen fibers or at the periphery of macrophage aggregates, and this behavior occurs independently of antigen recognition. Our observation that T cells migrate within the boundaries of macrophage clusters is reminiscent of T cell behavior in BCG-induced granulomas, where their movement is also constrained by macrophage-defined borders (Egen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Granulomas and macrophage cluster are encased in dense fibrotic networks composed of collagen, fibronectin, laminin (Kauffman et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sawyer et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and other ECM components, and ECM architecture is known to strongly influence T cell motility. Our observation that CD4⁺ T cells frequently arrest and migrate along collagen fibers mirrors ECM-guided migration described in lymph nodes, lung tissue, tumors, and other inflamed environments, where fiber density and orientation shape T cell trajectories (Overstreet et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Salmon et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wilson et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; You et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mrass et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Stromal networks, such as fibroblastic reticular cells (FRC), also play a central role in directing lymphocyte migration in lymph nodes (Baj\u0026eacute;noff et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and peripheral tissues, including lung tumors (Onder et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), where T cells move along CCL19-expressing FRC-defined tracks. These parallels raise the possibility that similar stromal or ECM-derived guidance cues operate within lung TB granulomas, simultaneously directing T cell movement and restricting their access to infected targets.\u003c/p\u003e\u003cp\u003eSurprisingly, OT-II cells (non\u0026ndash;\u003cem\u003eMtb\u003c/em\u003e specific) exhibit similar migratory behaviors, indicating the involvement of antigen-independent mechanisms that remain to be identified. T cell positioning can be shaped by recent activation or memory status independently of cognate antigen recognition (Topham et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Integrins such as VLA-1 (α1β1), which binds collagen and mediates the retention of protective memory T cells in non-lymphoid tissues including the lung (Ray et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), exemplify how the ECM not only imposes physical constraints but may actively organize T cell retention and spatial distribution within granulomas. Additional adhesion pathways (LFA-1; CD2/LFA-3), chemokine gradients, glycosaminoglycans, cytokines and stromal inhibitory signals can similarly promote transient arrest or reduced motility (Gunzer et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kerdidani et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Extracellular ATP sensed by P2RX7 on CD4⁺ T cells enhances their tissue accumulation by inducing CXCR3 expression (Santiago-Carvalho et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and potent chemokine cues can even override MHC\u0026ndash;peptide-driven positioning (Bromley et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Moreover, homeostatic cytokines help maintain tissue-resident T cell populations despite dynamic desensitization to antigenic stimulation (Boldajipour et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Thus, LiveLung-TB will uniquely enable dissection of these physical and molecular constraints in real time, providing a platform to evaluate how chemokines, adhesion molecules, and antigen availability influence T cell positioning and effector function.\u003c/p\u003e\u003cp\u003eLimitations include the use of C57BL/6 mice, which are relatively resistant to \u003cem\u003eMtb\u003c/em\u003e and do not form fully structured granulomas. Imaging more susceptible models, such as SP140-deficient mice (Ji et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), will be interesting improving translational relevance. Adoptively transferred T cells were analyzed shortly after transfer (24 h); longer residence times may influence motility and functional adaptation (Boldajipour et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; You et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Longitudinal imaging could clarify how activation or exhaustion states affect migration and arrest. The molecular mechanisms mediating T cell\u0026ndash;ECM and T cell\u0026ndash;macrophage interactions remain to be defined; identifying the relevant receptors and integrins, and perturbing them, will clarify whether ECM serves primarily as a scaffold or barrier and whether modulating these interactions can improve access to infected macrophages.\u003c/p\u003e\u003cp\u003eIn conclusion, LiveLung-TB provides a dynamic and mechanistic view of immune cell behavior in \u003cem\u003eMtb\u003c/em\u003e-infected lungs. If physical and molecular barriers limit access to infected macrophages, vaccines or immunotherapies that increase T cell numbers or activation may be insufficient. Strategies such as vascular normalization, ECM remodeling, or chemokine modulation could enhance bacterial clearance. This platform offers a roadmap to dissect the spatial and temporal determinants of T cell access to infected macrophages and to test interventions aimed at enhancing immunity and improving outcomes in TB.\u003c/p\u003e"},{"header":"Material et Methods","content":"\u003cp\u003eMice\u003c/p\u003e\n\u003cp\u003eMale and female mice aged 6-12 weeks were used throughout the study. C7TCR-Tg Nur77-GFP mice were kindly provided by Dr. Ernst Joel (UCSF, USA) and. Siglec1-mTmG were obtained by crossing Siglec1-cre with Rosa26-mTmG mice (IPBS, France). OVA-specific CD4\u003csup\u003e+\u003c/sup\u003e TCR Tg (OTII; Charles River, France) mouse were purchased. \u0026nbsp;All mice were bred at the Institute of Pharmacology and Structural Biology (IPBS) UMR 5089 (agreement F31555005) and procedures complied with French regulations and were approved by the Ministry of Higher Education and Research (APAFIS agreements 47848 and 34716).\u003c/p\u003e\n\u003cp\u003eBacteria\u003c/p\u003e\n\u003cp\u003eFluorescent derivatives of the wild-type M. tuberculosis Erdman strain were constructed by transformation of this strain with pGMCS-P1-mTurquoise. Plasmids pGMCS-P1-mTurquoise are integrative vectors in mycobacteria that confer resistance to streptomycin and constitutively express mTurquoise fluorescent reporter. They were constructed by Gateway cloning, as described previously\u0026nbsp;(Ariyachaokun et al. 2020).\u0026nbsp;\u003cem\u003eM. tuberculosis\u003c/em\u003e Erdman and its fluorescent mTurquoise transformants were cultured in suspension in Middlebrook 7H9 medium (BD) supplemented with 10% albumin-dextrose-catalase (ADC, BD) and 0.05% Tyloxapol (Sigma). For infection, exponentially growing cultures were centrifuged at 2,301 \u0026times; \u003cem\u003eg\u003c/em\u003e, resuspended in phosphate-buffered saline (PBS; Gibco), and de-aggregated by vortexing with glass beads. Remaining clumps were removed by a low-speed spin (120 \u0026times; \u003cem\u003eg\u003c/em\u003e). Bacterial concentration was estimated by measuring the optical density at 600 nm (OD₆₀₀), and the suspensions were adjusted in PBS for \u003cem\u003ein vivo\u003c/em\u003e infection.\u003c/p\u003e\n\u003cp\u003eMouse infection\u003c/p\u003e\n\u003cp\u003eMice were exposed to \u003cem\u003eM. tuberculosis\u003c/em\u003e Erdman in an inhalation tower (Buxco Inhalation Exposure System, DSI) calibrated to deliver 100 colony-forming units (CFUs) to the lungs per mouse.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCD4 lymphocyte isolation and polarization and adoptive transfer\u003c/p\u003e\n\u003cp\u003eMice were anesthetized with isoflurane and euthanized by cervical dislocation. Na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells were magnetically isolated from spleen cell suspensions of C7TCR-Tg mouse or OTII mice using anti-CD4 microbeads (L3T4) (Miltenyi Biotec) according to the manufacturer\u0026rsquo;s instructions. CD4\u003csup\u003e+\u003c/sup\u003e T cells were cultured in the presence of Th1 skewing cytokines: mouse IL-12p70 (10 ng/ml), mouse IL-2 (20 ng/ml), CD3 (5\u0026micro;g/ml) and CD28 (1\u0026micro;g/ml). Cells were cultured at 37\u0026deg;C with 5% CO2. On days 3 and 5 of culture, cells were split 1∶3 with fresh media containing IL-12p70 and IL-2. Cells were washed with PBS and counted on day 7 of culture before use for \u003cem\u003ein vivo\u003c/em\u003e assays.\u0026nbsp;On day 7 of culture, polarized Th1 CD4 lymphocytes were counted and labeled before injection. They were either labeled with CellTracker\u0026trade; Deep Red or Orange (Invitrogen\u0026trade;) according to the manufacturer\u0026rsquo;s instructions. Cells were counted and adjusted at 1x10\u003csup\u003e7\u003c/sup\u003e in PBS.\u0026nbsp;The mice were placed in an induction chamber and briefly anesthetized by isoflurane, 100\u0026ndash;150\u0026thinsp;\u0026micro;L of the cell suspension were injected into the retro-orbital sinus using a U100 insulin syringe (BD micro-fine 0.3\u0026thinsp;mL, 30 gauge needle), 24 hours before lung intravital microscopy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTB lesion adapted thoracic window production and assembly\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TB lesion\u0026ndash;adapted thoracic window was modified in Blender, based on previously described intercostal lung IVM windows\u0026nbsp;(Headley et al. 2016). Modifications included an increased angle to optimize objective xy movement and a flat base to facilitate stable positioning over TB lesions. Windows were 3D-printed in high-detail stainless steel (Sculpteo). An 8-mm round coverslip (Cat# 64-0701, Thomas Scientific) was inserted and sealed overnight using nail lacquer applied along the outer edge of the coverslip.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLung intravital surgery platform construction and assembly\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe platform was engineered to maintain an anesthetized, mechanically ventilated mouse throughout three consecutive phases: (i) mouse tracheotomy, mechanical ventilation, and surgical placement of the lung optical window within a microbiological safety cabinet (MSC) using a dedicated mechanical holder; (ii) transfer of the ventilated mouse from the MSC to the intravital microscopy isolator; and (iii) stable immobilization and accurate positioning of the lung beneath the microscope objective throughout image acquisition, along with connection to gas anesthesia and controlled lung aspiration onto the imaging window. The base structure consists of a PVC plate fitted with carrying handles. One section accommodates the mouse and includes an integrated support to position and secure the optical window. The mechanical ventilator and its battery are mounted on the opposite end of the platform, while ventilator tubing and pathogen-protection filters are organized via custom 3D-printed polylactic acid (PLA) holders. Additional accessories, including racks for Eppendorf tubes and holders for surgical instruments, were also 3D-printed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultiphoton lung intravital imaging in BSL3\u003c/p\u003e\n\u003cp\u003eMultiphoton lung intravital microscopy was performed using a BSL-3 isolated microscope (Barlerin et al., 2017). Surgical preparation was adapted from established lung IVM protocols (Headley et al., 2016; Looney et al., 2011) and carried out inside a biosafety cabinet using the dedicated surgery platform. Mice were anesthetized with ketamine/xylazine (105/3 mg/kg, i.p.) and received s.c. injections of buprenorphine (0.1 mg/kg) and Ringer Lactate (10 mL/kg). After shaving the left flank, mice were positioned supine on a heating pad mounted on the inclined (45\u0026deg;) surgical platform. A tracheotomy was performed and a small tracheal cannula connected to a MiniVent ventilator (Harvard Apparatus) was inserted and secured. Ventilation parameters were set to a tidal volume of 10 \u0026mu;L compressed air (21% O₂) per gram of body weight, a respiratory rate of 130\u0026ndash;140 breaths per minute, and a PEEP of 2\u0026ndash;3 cm H₂O. Mice were then placed in right lateral decubitus, and a small skin and fascia incision was made in the left flank to expose the rib cage. Two ribs were transected at each extremity and removed to create an opening adapted to the size and position of visible TB lesions, which were apparent through the intercostal muscle at this infection stage. The surgical platform was transferred from the biosafety cabinet to the microscope isolator using a sealed transfer isolator, ensuring continuous containment. Inside the microscope isolator, temperature was maintained at 30\u0026deg;C and anesthesia was continued with 1% isoflurane. A modified lung window was positioned over a visible lesion and lowered using optical posts and a 90\u0026deg; angle clamp (Thorlabs) to just above the lung surface. Gentle suction (20\u0026ndash;25 mmHg; Dexter Medical, 0\u0026ndash;250 mbar) was applied to stabilize the lung against the coverslip (Fig. 1d).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn Vivo\u003c/em\u003e vascular staining\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn some experiment, Dextran, Dextran FITC 40,000 MW (250 \u0026mu;g, D1844 Invitrogen) or Evans Blue (100 \u0026mu;L of 1% wt/vol solution in PBS) were injected i.v. before imaging to visualize vasculature and its permeability.\u003c/p\u003e\n\u003cp\u003eLung intravital acquisition\u003c/p\u003e\n\u003cp\u003eImaging was performed on an upright TriMScope II multiphoton microscope (LaVision Biotec/Miltenyi Biotec) equipped with a 20\u0026times;/1.0 NA water-immersion objective and four NDD detectors, and a Chameleon Ultra II Ti:Sapphire femtosecond laser was the excitation source (Coherent Inc.) tuned to 870 nm (Deep Red\u0026ndash;labeled CD4⁺ T cells only) or 855 nm (Deep Red + CTO). Emission signals from collagen/mTurquoise, GFP, Tomato/CTO, and Deep Red were collected in four separate channels using 450/50, 525/50, 595/40, and LP650 filters, respectively. Four-color image stacks were acquired at 1.4 frames/s for 405 \u0026times; 405 \u0026micro;m fields at 512 \u0026times; 512 pixels, or at for 393 \u0026times; 393 \u0026micro;m fields at 1,024 \u0026times; 1,024 pixels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage analysis and lymphocyte tracking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree-dimensional time-lapse sequences (30 min to 2 h) were acquired to analyze T cell dynamics and processed using ImageJ (NIH) and Imaris 9.3.1 (Bitplane). Large xyzt mosaic movies were assembled with the stitching plugin in Fiji/ImageJ (https://imagej.net/plugins/image-stitching), and lesion characteristics, including number, diameter, and surface area, were measured directly from the movies. For lymphocyte tracking, z-stacks were reduced using maximum intensity projection, and individual T cell trajectories (~300 cells per video) were manually tracked using the TrackMate plugin in Fiji/ImageJ (https://imagej.net/plugins/trackmate), as variable cell shape, contrast, and intensity precluded reliable automatic tracking. Three videos were analysed, each from a different infected mouse, tracking a total of 911 CD4⁺ T cells, with an average of 300 cells per video (169, 282, and 460). Trajectory data were analyzed using in-house Python scripts to generate quantitative parameters, graphs, and tracking maps. Cluster analysis of cellular behaviors was performed using the UMAP Python package, which reduces dimensionality and identifies distinct functional groups. This approach provides a comprehensive quantitative mapping of T cell behaviors during \u003cem\u003eM. tuberculosis\u003c/em\u003e infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBehaviour parameters definition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each tracked T cell, the following parameters were analysed:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eTrack metrics: track length (the total length of displacement within the track), displacement (the distance between the first and last lymphocyte positions), straightness (the dimensionless index ranges from 0 to 1 and quantifies the linearity of a track).\u003c/li\u003e\n \u003cli\u003eSpeed metrics: instantaneous speed, average and maximal average speed.\u003c/li\u003e\n \u003cli\u003ePausing behavior: arrest coefficient, defined as the percentage of time a cell\u0026rsquo;s instantaneous speed was \u0026lt;2 \u0026micro;m/min; number of arrests per track, representing how many times the cell slowed below 2 \u0026micro;m/min; and mean arrest duration, calculated as the average length of these pauses.\u003c/li\u003e\n \u003cli\u003eVascular localization: intravascular (IV) versus extravascular (EV) localization was determined using lung vessel visualization (mTomato)\u003c/li\u003e\n \u003cli\u003eDistance to infected macrophage was calculated from lymphocyte membrane to membrane of closest infected macrophage\u003c/li\u003e\n \u003cli\u003eMean Square Displacement (MSD) quantifies the average area covered by a lymphocyte as it moves through space over time\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAlpha (\u0026alpha;) coefficient describes the scaling behavior of MSD over time and is obtained by fitting MSD(t) to a power-law model: an \u0026alpha; value of \u0026alpha; \u0026asymp; 1 indicates random (diffusive) motion, \u0026alpha; \u0026lt; 1 indicates subdiffusive or confined movement, \u0026alpha; \u0026gt; 1 indicates superdiffusive or directed motion.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll trajectory maps, tracking visualizations, and parameter calculations were generated using in-house Python scripts. Dimensionality reduction and clustering of cellular behaviors using UMAP allowed identification of distinct functional groups within the dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLung tissue clearing and 3D multiphoton microscopy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMtb\u003c/em\u003e \u0026ndash;infected lungs were fixed overnight at 4 \u0026deg;C in periodate\u0026ndash;lysine\u0026ndash;paraformaldehyde (PLP) buffer [0.05 M phosphate buffer, 0.1 M L-lysine (Sigma-Aldrich), 2 mg/mL NaIO₄ (ThermoFisher Scientific), 4% paraformaldehyde; pH 7.4]. Dehydration, delipidation, and clarification of lungs were performed at 37\u0026deg;C under slight agitation (100 rpm) before multiphoton microscopy as previously described\u0026nbsp;(Jing et al. 2018). Briefly, lungs were delipidated by making successive baths of tert-butanol + 3% Quadrol (30%, 50%, and 70% for 2, 4, and 4 h respectively). Following this step, a dehydration step was carried out by incubation in 70% tert-butanol-30% PEG for 24 h. Finally, for clarification, incubation in a 75% benzyl-benzoate\u0026ndash;25% PEG solution for 12 h was performed. All products were purchased from Sigma-Aldrich. Cleared lungs were maintained between a slide and a cover-slide in a 2.5 mm thick imaging chamber (CoverWell; Thermo Fischer Scientific) filled with ethylcinnamate (Sigma). Multiphoton microscopy for 3D cleared lung was performed using a Leica Dive upright multiphoton microscope equipped with a 25x/1.0 objective and a Ti-Sapphire femtosecond laser, Chameleon-Ultra II (Coherent Inc.). Second harmonic generation of collagen/bacteries and Siglec1-mGFP emission signals were detected at 920 nm thanks to the respective bandpass filters: blue (430-485 nm), and green (512\u0026ndash;551 nm), and mTomato signal was detected at 870 nm thank to 580-623 nm band pass filter. Images were analyzed using Imaris 7.6.1 software (Bitplane).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThree-dimensional segmentation and analysis of TB lesion in cleared lungs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe segmentations were performed using Fiji/ImageJ plugins and tools. Granulomas, blood vessels, and bronchi were segmented from channel C2. Granulomas were segmented using the Labkit plugin. Pre-processing of granulomas involved applying a median filter and despeckling. Post-processing was then applied to remove irrelevant, isolated ROIs. The blood vessels (identified by their red blood cell content) and bronchi were manually segmented using the polygon and magic wand tools, respectively.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eSample sizes (\u003cem\u003en\u003c/em\u003e), statistical tests, and significance thresholds are indicated in the figure legends. Analyses were performed in Prism 10.0 (GraphPad). Depending on the experiment, one-way ANOVA or unpaired two-tailed Student\u0026rsquo;s\u0026nbsp;\u003cem\u003et\u003c/em\u003e-test was used as specified in the legends. Significance thresholds were\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (*\u003cem\u003e), p \u0026lt; 0.005 (\u003cstrong\u003e**\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e),\u0026nbsp;\u003c/strong\u003e\u003cem\u003ep\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u0026lt; 0.0005 (***\u003c/strong\u003e), and\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 (****).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.F. conducted experiments, analysed data and drafted the manuscript. A.-I.A.-H. performed experiments and analyses. E.B. performed lung clearing and segmentation analyses. Z.P.H contributed to generation and use of C7-Nur77GFP cells. S.Mo. contributed to the generation of Siglec1-mTmG mice and funding of lung intravital imaging set up in BSL3. J.-P.G. provided financial support and scientific guidance. E.M. provided financial support and supervised L.F. throughout the project. D.H, O.N. and J.D.E. secured major funding and provided regular, in-depth scientific guidance, contributing to experimental design and key expertise in immunity and mouse models of tuberculosis. S.Ma. designed and built the surgical platform and lung window, coded all in-house Python scripts for cell tracking and behaviour analysis, and performed image and movie analyses. E.L. supervised the project, designed, performed and analysed experiments, and wrote the manuscript. All authors critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the cytometry (Genotoul TRI-IPBS) and animal (Genotoul Anexplo-IPBS) facilities, particularly Flavie Moreau, Malory Blasco and Celine Berrone for technical support. TRI-IPBS is member of the national infrastructure France-BioImaging (https://ror.org/01y7vt929) supported by the French National Research Agency (ANR-24-INBS-0005 FBI BIOGEN); Anexplo-IPBS is member of the national infrastructure Celphedia (https://ror.org/00v2cdz24). This work was supported by Fonds de Recherche en Santé Respiratoire - Fondation du Souffle (Grant #198942 to E.L), ANRS (PhD fellowship to L.F), University of Toulouse (Tremplin 2022 #0003970), National Institute of Health NIH (R21 to EL, D.H, ON, and JE), and Horizon Europe TBVAC-Horizon (Grant #101080309 to D.H, O.N and E.L). Figures were created with Biorender.com.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAriyachaokun, Kanchiyaphat, Anna D. Grabowska, Claude Gutierrez, et Olivier Neyrolles. 2020. \u0026laquo; Multi-Stress Induction of the Mycobacterium Tuberculosis MbcTA Bactericidal Toxin-Antitoxin System \u0026raquo;. \u003cem\u003eToxins\u003c/em\u003e 12 (5): 329. https://doi.org/10.3390/toxins12050329.\u003c/li\u003e\n\u003cli\u003eBaj\u0026eacute;noff, Marc, Jackson G. Egen, Lily Y. Koo, et al. 2006. \u0026laquo; Stromal Cell Networks Regulate Lymphocyte Entry, Migration, and Territoriality in Lymph Nodes \u0026raquo;. \u003cem\u003eImmunity\u003c/em\u003e 25 (6): 989‑1001. https://doi.org/10.1016/j.immuni.2006.10.011.\u003c/li\u003e\n\u003cli\u003eBarlerin, D., G. Bessi\u0026egrave;re, J. Domingues, M. Schuette, C. Feuillet, et A. Peixoto. 2017. \u0026laquo; Biosafety Level 3 Setup for Multiphoton Microscopy in Vivo \u0026raquo;. \u003cem\u003eScientific Reports\u003c/em\u003e 7 (1): 571. https://doi.org/10.1038/s41598-017-00702-x.\u003c/li\u003e\n\u003cli\u003eBoldajipour, Bijan, Amanda Nelson, et Matthew F. Krummel. 2016. \u0026laquo; Tumor-Infiltrating Lymphocytes Are Dynamically Desensitized to Antigen but Are Maintained by Homeostatic Cytokine \u0026raquo;. \u003cem\u003eJCI Insight\u003c/em\u003e 1 (20): e89289. https://doi.org/10.1172/jci.insight.89289.\u003c/li\u003e\n\u003cli\u003eBoulch, Morgane, Capucine L. Grandjean, Marine Cazaux, et Philippe Bousso. 2019. \u0026laquo; Tumor Immunosurveillance and Immunotherapies: A Fresh Look from Intravital Imaging \u0026raquo;. \u003cem\u003eTrends in Immunology\u003c/em\u003e 40 (11): 1022‑34. https://doi.org/10.1016/j.it.2019.09.002.\u003c/li\u003e\n\u003cli\u003eBromley, S. K., D. A. Peterson, M. D. Gunn, et M. L. Dustin. 2000. \u0026laquo; Cutting Edge: Hierarchy of Chemokine Receptor and TCR Signals Regulating T Cell Migration and Proliferation \u0026raquo;. \u003cem\u003eJournal of Immunology (Baltimore, Md.: 1950)\u003c/em\u003e 165 (1): 15‑19. https://doi.org/10.4049/jimmunol.165.1.15.\u003c/li\u003e\n\u003cli\u003eCelli, Susanna, Fabrice Lema\u0026icirc;tre, et Philippe Bousso. 2007. \u0026laquo; Real-Time Manipulation of T Cell-Dendritic Cell Interactions in Vivo Reveals the Importance of Prolonged Contacts for CD4+ T Cell Activation \u0026raquo;. \u003cem\u003eImmunity\u003c/em\u003e 27 (4): 625‑34. https://doi.org/10.1016/j.immuni.2007.08.018.\u003c/li\u003e\n\u003cli\u003eCohen, Sara B., Benjamin H. Gern, et Kevin B. Urdahl. 2022. \u0026laquo; The Tuberculous Granuloma and Preexisting Immunity \u0026raquo;. \u003cem\u003eAnnual Review of Immunology\u003c/em\u003e 40 (avril): 589‑614. https://doi.org/10.1146/annurev-immunol-093019-125148.\u003c/li\u003e\n\u003cli\u003eDatta, Meenal, Laura E. Via, Walid S. Kamoun, et al. 2015. \u0026laquo; Anti-Vascular Endothelial Growth Factor Treatment Normalizes Tuberculosis Granuloma Vasculature and Improves Small Molecule Delivery \u0026raquo;. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e 112 (6): 1827‑32. https://doi.org/10.1073/pnas.1424563112.\u003c/li\u003e\n\u003cli\u003eEgen, Jackson G., Antonio Gigliotti Rothfuchs, Carl G. Feng, Marcus A. Horwitz, Alan Sher, et Ronald N. Germain. 2011. \u0026laquo; Intravital Imaging Reveals Limited Antigen Presentation and T Cell Effector Function in Mycobacterial Granulomas \u0026raquo;. \u003cem\u003eImmunity\u003c/em\u003e 34 (5): 807‑19. https://doi.org/10.1016/j.immuni.2011.03.022.\u003c/li\u003e\n\u003cli\u003eEgen, Jackson G., Antonio Gigliotti Rothfuchs, Carl G. Feng, Nathalie Winter, Alan Sher, et Ronald N. Germain. 2008. \u0026laquo; Macrophage and T Cell Dynamics during the Development and Disintegration of Mycobacterial Granulomas \u0026raquo;. \u003cem\u003eImmunity\u003c/em\u003e 28 (2): 271‑84. https://doi.org/10.1016/j.immuni.2007.12.010.\u003c/li\u003e\n\u003cli\u003eFlynn, JoAnne L., et John Chan. 2022. \u0026laquo; Immune Cell Interactions in Tuberculosis \u0026raquo;. \u003cem\u003eCell\u003c/em\u003e 185 (25): 4682‑702. https://doi.org/10.1016/j.cell.2022.10.025.\u003c/li\u003e\n\u003cli\u003eGideon, Hannah P., Travis K. Hughes, Constantine N. Tzouanas, et al. 2022. \u0026laquo; Multimodal Profiling of Lung Granulomas in Macaques Reveals Cellular Correlates of Tuberculosis Control \u0026raquo;. \u003cem\u003eImmunity\u003c/em\u003e 55 (5): 827-846.e10. https://doi.org/10.1016/j.immuni.2022.04.004.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eGlobal Tuberculosis Report 2025\u003c/em\u003e. 2025. 1st ed. World Health Organization.\u003c/li\u003e\n\u003cli\u003eGunzer, M., A. Sch\u0026auml;fer, S. Borgmann, et al. 2000. \u0026laquo; Antigen Presentation in Extracellular Matrix: Interactions of T Cells with Dendritic Cells Are Dynamic, Short Lived, and Sequential \u0026raquo;. \u003cem\u003eImmunity\u003c/em\u003e 13 (3): 323‑32. https://doi.org/10.1016/s1074-7613(00)00032-7.\u003c/li\u003e\n\u003cli\u003eHeadley, Mark B., Adriaan Bins, Alyssa Nip, et al. 2016. \u0026laquo; Visualization of Immediate Immune Responses to Pioneer Metastatic Cells in the Lung \u0026raquo;. \u003cem\u003eNature\u003c/em\u003e 531 (7595): 513‑17. https://doi.org/10.1038/nature16985.\u003c/li\u003e\n\u003cli\u003eHor, Jyh Liang, et Ronald N. Germain. 2022. \u0026laquo; Intravital and High-Content Multiplex Imaging of the Immune System \u0026raquo;. \u003cem\u003eTrends in Cell Biology\u003c/em\u003e 32 (5): 406‑20. https://doi.org/10.1016/j.tcb.2021.11.007.\u003c/li\u003e\n\u003cli\u003eJasenosky, Luke D., Thomas J. Scriba, Willem A. Hanekom, et Anne E. Goldfeld. 2015. \u0026laquo; T Cells and Adaptive Immunity to Mycobacterium Tuberculosis in Humans \u0026raquo;. \u003cem\u003eImmunological Reviews\u003c/em\u003e 264 (1): 74‑87. https://doi.org/10.1111/imr.12274.\u003c/li\u003e\n\u003cli\u003eJi, Daisy X., Kristen C. Witt, Dmitri I. Kotov, et al. 2021. \u0026laquo; Role of the Transcriptional Regulator SP140 in Resistance to Bacterial Infections via Repression of Type I Interferons \u0026raquo;. \u003cem\u003eeLife\u003c/em\u003e 10 (juin): e67290. https://doi.org/10.7554/eLife.67290.\u003c/li\u003e\n\u003cli\u003eJing, Dian, Shiwen Zhang, Wenjing Luo, et al. 2018. \u0026laquo; Tissue Clearing of Both Hard and Soft Tissue Organs with the PEGASOS Method \u0026raquo;. \u003cem\u003eCell Research\u003c/em\u003e 28 (8): 803‑18. https://doi.org/10.1038/s41422-018-0049-z.\u003c/li\u003e\n\u003cli\u003eKauffman, K. D., M. A. Sallin, S. Sakai, et al. 2018. \u0026laquo; Defective Positioning in Granulomas but Not Lung-Homing Limits CD4 T-Cell Interactions with Mycobacterium Tuberculosis-Infected Macrophages in Rhesus Macaques \u0026raquo;. \u003cem\u003eMucosal Immunology\u003c/em\u003e 11 (2): 462‑73. https://doi.org/10.1038/mi.2017.60.\u003c/li\u003e\n\u003cli\u003eKerdidani, Dimitra, Emmanouil Aerakis, Kleio-Maria Verrou, et al. 2022. \u0026laquo; Lung Tumor MHCII Immunity Depends on in Situ Antigen Presentation by Fibroblasts \u0026raquo;. \u003cem\u003eThe Journal of Experimental Medicine\u003c/em\u003e 219 (2): e20210815. https://doi.org/10.1084/jem.20210815.\u003c/li\u003e\n\u003cli\u003eLefran\u0026ccedil;ais, Emma, Denis Hudrisier, Olivier Neyrolles, Samuel M. Behar, et Joel D. Ernst. 2025. \u0026laquo; Finding and Filling the Knowledge Gaps in Mechanisms of T Cell-Mediated TB Immunity to Inform Vaccine Design \u0026raquo;. \u003cem\u003eNature Reviews. Immunology\u003c/em\u003e, publication en ligne anticip\u0026eacute;e, juin 13. https://doi.org/10.1038/s41577-025-01192-z.\u003c/li\u003e\n\u003cli\u003eLooney, Mark R., et Mark B. Headley. 2020. \u0026laquo; Live Imaging of the Pulmonary Immune Environment \u0026raquo;. \u003cem\u003eCellular Immunology\u003c/em\u003e 350 (avril): 103862. https://doi.org/10.1016/j.cellimm.2018.09.007.\u003c/li\u003e\n\u003cli\u003eLooney, Mark R, Emily E Thornton, Debasish Sen, Wayne J Lamm, Robb W Glenny, et Matthew F Krummel. 2011. \u0026laquo; Stabilized Imaging of Immune Surveillance in the Mouse Lung \u0026raquo;. \u003cem\u003eNature Methods\u003c/em\u003e 8 (1): 91‑96. https://doi.org/10.1038/nmeth.1543.\u003c/li\u003e\n\u003cli\u003eMcCaffrey, Erin F., Michele Donato, Leeat Keren, et al. 2022. \u0026laquo; The Immunoregulatory Landscape of Human Tuberculosis Granulomas \u0026raquo;. \u003cem\u003eNature Immunology\u003c/em\u003e 23 (2): 318‑29. https://doi.org/10.1038/s41590-021-01121-x.\u003c/li\u003e\n\u003cli\u003eMoreau, H\u0026eacute;l\u0026egrave;ne D., Fabrice Lema\u0026icirc;tre, Kym R. Garrod, Zacarias Garcia, Ana-Maria Lennon-Dum\u0026eacute;nil, et Philippe Bousso. 2015. \u0026laquo; Signal Strength Regulates Antigen-Mediated T-Cell Deceleration by Distinct Mechanisms to Promote Local Exploration or Arrest \u0026raquo;. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e 112 (39): 12151‑56. https://doi.org/10.1073/pnas.1506654112.\u003c/li\u003e\n\u003cli\u003eMrass, Paulus, Sreenivasa Rao Oruganti, G. Matthew Fricke, et al. 2017. \u0026laquo; ROCK Regulates the Intermittent Mode of Interstitial T Cell Migration in Inflamed Lungs \u0026raquo;. \u003cem\u003eNature Communications\u003c/em\u003e 8 (1): 1010. https://doi.org/10.1038/s41467-017-01032-2.\u003c/li\u003e\n\u003cli\u003eOehlers, Stefan H., Mark R. Cronan, Ninecia R. Scott, et al. 2015. \u0026laquo; Interception of Host Angiogenic Signalling Limits Mycobacterial Growth \u0026raquo;. \u003cem\u003eNature\u003c/em\u003e 517 (7536): 612‑15. https://doi.org/10.1038/nature13967.\u003c/li\u003e\n\u003cli\u003eOnder, Lucas, Chrysa Papadopoulou, Almut L\u0026uuml;tge, et al. 2025. \u0026laquo; Fibroblastic Reticular Cells Generate Protective Intratumoral T Cell Environments in Lung Cancer \u0026raquo;. \u003cem\u003eCell\u003c/em\u003e 188 (2): 430-446.e20. https://doi.org/10.1016/j.cell.2024.10.042.\u003c/li\u003e\n\u003cli\u003eOverstreet, Michael G, Alison Gaylo, Bastian R Angermann, et al. 2013. \u0026laquo; Inflammation-Induced Interstitial Migration of Effector CD4+ T Cells Is Dependent on Integrin \u0026alpha;V \u0026raquo;. \u003cem\u003eNature Immunology\u003c/em\u003e 14 (9): 949‑58. https://doi.org/10.1038/ni.2682.\u003c/li\u003e\n\u003cli\u003ePittet, Mikael J., Christopher S. Garris, Sean P. Arlauckas, et Ralph Weissleder. 2018. \u0026laquo; Recording the Wild Lives of Immune Cells \u0026raquo;. \u003cem\u003eScience Immunology\u003c/em\u003e 3 (27): eaaq0491. https://doi.org/10.1126/sciimmunol.aaq0491.\u003c/li\u003e\n\u003cli\u003eRay, Steven J., Suzanne N. Franki, Robert H. Pierce, et al. 2004. \u0026laquo; The Collagen Binding Alpha1beta1 Integrin VLA-1 Regulates CD8 T Cell-Mediated Immune Protection against Heterologous Influenza Infection \u0026raquo;. \u003cem\u003eImmunity\u003c/em\u003e 20 (2): 167‑79. https://doi.org/10.1016/s1074-7613(04)00021-4.\u003c/li\u003e\n\u003cli\u003eSalmon, H\u0026eacute;l\u0026egrave;ne, Katarzyna Franciszkiewicz, Diane Damotte, et al. 2012. \u0026laquo; Matrix Architecture Defines the Preferential Localization and Migration of T Cells into the Stroma of Human Lung Tumors \u0026raquo;. \u003cem\u003eThe Journal of Clinical Investigation\u003c/em\u003e 122 (3): 899‑910. https://doi.org/10.1172/JCI45817.\u003c/li\u003e\n\u003cli\u003eSantiago-Carvalho, Igor, Gislane Almeida-Santos, Bruna Gois Macedo, et al. 2023. \u0026laquo; T Cell-Specific P2RX7 Favors Lung Parenchymal CD4+ T Cell Accumulation in Response to Severe Lung Infections \u0026raquo;. \u003cem\u003eCell Reports\u003c/em\u003e 42 (11): 113448. https://doi.org/10.1016/j.celrep.2023.113448.\u003c/li\u003e\n\u003cli\u003eSawyer, Andrew J., Ellis Patrick, Jarem Edwards, et al. 2023. \u0026laquo; Spatial Mapping Reveals Granuloma Diversity and Histopathological Superstructure in Human Tuberculosis \u0026raquo;. \u003cem\u003eThe Journal of Experimental Medicine\u003c/em\u003e 220 (6): e20221392. https://doi.org/10.1084/jem.20221392.\u003c/li\u003e\n\u003cli\u003eSrivastava, Smita, et Joel D. Ernst. 2013. \u0026laquo; Cutting Edge: Direct Recognition of Infected Cells by CD4 T Cells Is Required for Control of Intracellular Mycobacterium Tuberculosis in Vivo \u0026raquo;. \u003cem\u003eJournal of Immunology (Baltimore, Md.: 1950)\u003c/em\u003e 191 (3): 1016‑20. https://doi.org/10.4049/jimmunol.1301236.\u003c/li\u003e\n\u003cli\u003eSumen, Cenk, Thorsten R. Mempel, Irina B. Mazo, et Ulrich H. von Andrian. 2004. \u0026laquo; Intravital Microscopy: Visualizing Immunity in Context \u0026raquo;. \u003cem\u003eImmunity\u003c/em\u003e 21 (3): 315‑29. https://doi.org/10.1016/j.immuni.2004.08.006.\u003c/li\u003e\n\u003cli\u003eTopham, D. J., M. R. Castrucci, F. S. Wingo, G. T. Belz, et P. C. Doherty. 2001. \u0026laquo; The Role of Antigen in the Localization of Naive, Acutely Activated, and Memory CD8(+) T Cells to the Lung during Influenza Pneumonia \u0026raquo;. \u003cem\u003eJournal of Immunology (Baltimore, Md.: 1950)\u003c/em\u003e 167 (12): 6983‑90. https://doi.org/10.4049/jimmunol.167.12.6983.\u003c/li\u003e\n\u003cli\u003eWells, Gordon, Joel N. Glasgow, Kievershen Nargan, et al. 2021. \u0026laquo; Micro-Computed Tomography Analysis of the Human Tuberculous Lung Reveals Remarkable Heterogeneity in Three-Dimensional Granuloma Morphology \u0026raquo;. \u003cem\u003eAmerican Journal of Respiratory and Critical Care Medicine\u003c/em\u003e 204 (5): 583‑95. https://doi.org/10.1164/rccm.202101-0032OC.\u003c/li\u003e\n\u003cli\u003eWilson, Emma H., Tajie H. Harris, Paulus Mrass, et al. 2009. \u0026laquo; Behavior of Parasite-Specific Effector CD8+ T Cells in the Brain and Visualization of a Kinesis-Associated System of Reticular Fibers \u0026raquo;. \u003cem\u003eImmunity\u003c/em\u003e 30 (2): 300‑311. https://doi.org/10.1016/j.immuni.2008.12.013.\u003c/li\u003e\n\u003cli\u003eYou, Ran, Jordan Artichoker, Adam Fries, et al. 2021. \u0026laquo; Active Surveillance Characterizes Human Intratumoral T Cell Exhaustion \u0026raquo;. \u003cem\u003eThe Journal of Clinical Investigation\u003c/em\u003e 131 (18): e144353. https://doi.org/10.1172/JCI144353.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Institut de Pharmacologie et de Biologie Structurale CNRS Université de Toulouse","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8259909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8259909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProtective immunity to \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (\u003cem\u003eMtb\u003c/em\u003e) depends on the ability of T cells to access and engage infected cells within lung lesions, yet these spatiotemporal interactions remain poorly defined. Direct intravital imaging of \u003cem\u003eMtb\u003c/em\u003e-infected lungs has historically been limited by biosafety and technical constraints, preventing real-time visualization of immune dynamics \u003cem\u003ein situ\u003c/em\u003e. Here, we present LiveLung-TB, a biosafety level 3–compatible lung intravital imaging platform that enables high-resolution imaging of immune cell behavior in \u003cem\u003eMtb\u003c/em\u003e-infected lungs. Using this approach, we demonstrate that although most CD4⁺ T cells infiltrate the infected parenchyma, only a small fraction forms transient (~11%) or stable (~5%) contacts with infected macrophages. Strikingly, the majority remain immobilized or display intermittent migration within uninfected, collagen-rich regions or at the periphery of macrophage clusters, independently of antigen recognition. These findings uncover previously unrecognized physical and microenvironmental barriers that restrict T cell motility and limit productive effector engagement. LiveLung-TB thus provides a powerful framework to elucidate and ultimately overcome tissue-imposed constraints on immune efficacy in tuberculosis and other pulmonary infections.\u003c/p\u003e","manuscriptTitle":"Intravital lung microscopy unveils T cell dynamics in mouse tuberculosis lesions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 11:51:30","doi":"10.21203/rs.3.rs-8259909/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2589988e-42aa-4ed5-b666-12198e5de5ee","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58959420,"name":"Immunology"}],"tags":[],"updatedAt":"2025-12-03T11:51:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 11:51:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8259909","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8259909","identity":"rs-8259909","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}