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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Immunol.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Immunology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Immunol.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1664-3224</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fimmu.2026.1767691</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Mini Review</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Neutrophil and macrophage zonation in liver disease: from spatiotemporal dynamics to advanced computational analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Kim</surname><given-names>Hyeree</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/3272245/overview"/>
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<contrib contrib-type="author">
<name><surname>Lachmann</surname><given-names>Nico</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
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<aff id="aff1"><label>1</label><institution>Department of Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School</institution>, <city>Hannover</city>,&#xa0;<country country="de">Germany</country></aff>
<aff id="aff2"><label>2</label><institution>RESIST, Cluster of Excellence (EXC 2155), Hannover Medical School</institution>, <city>Hannover</city>,&#xa0;<country country="de">Germany</country></aff>
<aff id="aff3"><label>3</label><institution>Biomedical Research in End Stage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL)</institution>, <city>Hannover</city>,&#xa0;<country country="de">Germany</country></aff>
<aff id="aff4"><label>4</label><institution>Center of Translational Regenerative Medicine, Hannover Medical School</institution>, <city>Hannover</city>,&#xa0;<country country="de">Germany</country></aff>
<aff id="aff5"><label>5</label><institution>Fraunhofer Institute for Toxicology and Experimental Medicine ITEM</institution>, <city>Hannover</city>,&#xa0;<country country="de">Germany</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Hyeree Kim, <email xlink:href="mailto:kim.hyeree@mh-hannover.de">kim.hyeree@mh-hannover.de</email>; <email xlink:href="mailto:unohrkim@gmail.com">unohrkim@gmail.com</email></corresp>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-24">
<day>24</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>17</volume>
<elocation-id>1767691</elocation-id>
<history>
<date date-type="received">
<day>15</day>
<month>12</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>09</day>
<month>02</month>
<year>2026</year>
</date>
<date date-type="rev-recd">
<day>04</day>
<month>02</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Kim and Lachmann.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Kim and Lachmann</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-24">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<p>Liver disease progression is profoundly shaped by the spatial and temporal dynamics of innate immune cells, particularly neutrophils and macrophages. Recent advances in single-cell and spatial omics, intravital imaging, and multiplexed histology have revealed how these cells exhibit distinct zonation patterns along the portal-central axis and undergo dynamic reprogramming in response to injury, infection, and metabolic stress. Neutrophils preferentially accumulate in necrotic or pericentral zones, whereas macrophage subsets adopt diverse zonal identities and display remarkable plasticity, collectively orchestrating inflammation and tissue repair. In this review, we consolidate current knowledge on neutrophil and macrophage zonation in liver disease, emphasizing their roles in shaping pathophysiology and clinical outcomes. We also briefly outline how emerging technologies are refining our understanding of immune microanatomy and may pave the way for precision hepatology.</p>
</abstract>
<kwd-group>
<kwd>AI-enhanced analysis</kwd>
<kwd>liver zonation</kwd>
<kwd>macrophage</kwd>
<kwd>multi-omics</kwd>
<kwd>neutrophil</kwd>
<kwd>single cell transcriptomics</kwd>
<kwd>single nuclei transcriptomics</kwd>
<kwd>spatial transcriptomics</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany&#x2019;s Excellence Strategy - EXC 2155 - project number 390874280. (NL); the REBIRTH Research Center for Translational Regenerative Medicine &#x2018;F&#xf6;rderung aus Mitteln des Nieders&#xe4;chsischen Vorab&#x2019; (Grant ZN3340) (NL); the European Research Council (ERC) under the European Union (EU)&#x2019;s Horizon 2020 research and innovation program (Grant agreement 852178); and the EU (Grant agreement 101100859 and 101158172) (NL). The project was additionally supported by zukunft.niedersachsen (Federal State of Lower Saxony), R2N. Micro-Replace-Systems. Additional funding was provided by the German Center of Lung Research (DZL) and the Federal Ministry of Research, Technology and Space (BMFTR, SMARTibone project). This work was supported by the Fraunhofer Internal Programs under Grant No. Attract 40-01696.</funding-statement>
</funding-group>
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<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Inflammation</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>The liver is not only a central hub of systemic metabolism and xenobiotic detoxification but also a uniquely structured immune organ that continuously interfaces with gut-derived antigens, metabolites, and circulating immune cells (<xref ref-type="bibr" rid="B1">1</xref>). Owing to its direct vascular connection with the intestine through the portal vein, the liver is exposed to a constant influx of nutrients, microbial products, and inflammatory signals (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B3">3</xref>). This anatomical arrangement underlies lobular zonation and the spatial organization of immune cells across the liver lobule.</p>
<p>The hepatic lobule, organized along the portal-central axis, contains finely graded microenvironmental transitions in oxygen tension, metabolic activity (<xref ref-type="bibr" rid="B4">4</xref>), nutrient availability, and hormone exposure. These gradients, together with zone-specific stromal and endothelial cues (<xref ref-type="bibr" rid="B5">5</xref>), give rise to spatially specialized transcriptional and metabolic programs in both parenchymal and non-parenchymal cells, thereby establishing the foundation for immune zonation (<xref ref-type="bibr" rid="B6">6</xref>&#x2013;<xref ref-type="bibr" rid="B11">11</xref>).</p>
<p>The conceptual origins of liver zonation date back to the 1950s and 1960s, when Rappaport introduced the acinus model to explain heterogeneous physiological conditions within the lobule (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B13">13</xref>). He proposed that portal venous inflow generates progressive gradients in oxygen, substrates, and metabolites as blood flows toward the central vein, leading to the tripartite division of periportal (zone 1), midzonal (zone 2), and pericentral (zone 3) regions. This paradigm first recognized hepatocyte functional heterogeneity despite uniform histological appearance.</p>
<p>Subsequent biochemical and histochemical studies from the 1970s to the 1990s substantiated this framework by demonstrating that key metabolic pathways display discrete spatial compartmentalization (<xref ref-type="bibr" rid="B14">14</xref>&#x2013;<xref ref-type="bibr" rid="B17">17</xref>). Gluconeogenesis, ureagenesis, and &#x3b2;-oxidation are enriched in periportal hepatocytes, whereas glycolysis, lipogenesis, and cytochrome P450-mediated xenobiotic metabolism predominate in pericentral cells. These observations established zonation as a fundamental organizing principle of hepatic physiology, underpinning functions ranging from glucose homeostasis to lipid and ammonia metabolism.</p>
<p>The mechanistic basis of zonation was further elucidated in the 2000s. Wnt/&#x3b2;-catenin signaling emerged as a central determinant of pericentral identity (<xref ref-type="bibr" rid="B18">18</xref>), with central-vein-associated endothelial and stromal cells producing Wnt ligands (e.g. Wnt2, Wnt9b) (<xref ref-type="bibr" rid="B19">19</xref>&#x2013;<xref ref-type="bibr" rid="B22">22</xref>) and potentiators such as R-spondin3 (<xref ref-type="bibr" rid="B23">23</xref>, <xref ref-type="bibr" rid="B24">24</xref>). These signals sustain the transcriptional program of zone 3 hepatocytes, whereas lower Wnt activity in periportal regions permits the expression of zone 1-specific metabolic pathways (<xref ref-type="bibr" rid="B18">18</xref>). Thus, zonation is now understood as a dynamically regulated transcriptional system maintained by spatially restricted niche signals, rather than a passive outcome of hemodynamic gradients alone.</p>
<p>Recent advances in single-cell (<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B25">25</xref>&#x2013;<xref ref-type="bibr" rid="B28">28</xref>) and spatial transcriptomics (<xref ref-type="bibr" rid="B7">7</xref>, <xref ref-type="bibr" rid="B29">29</xref>&#x2013;<xref ref-type="bibr" rid="B32">32</xref>), together with intravital microscopy (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B34">34</xref>), have expanded this view by demonstrating that zonation extends beyond classical metabolic domains. Immune zonation is tightly shaped by the spatial gradients of antigen exposure generated along the portal-central axis. As blood flows from the portal triads toward the central vein, hepatocytes and non-parenchymal cells adopt distinct spatial states and establish zone-specific patterns of innate immune surveillance (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B35">35</xref>). Within this spatially graded microenvironment, resident immune sentinels such as Kupffer cells (KCs) and liver sinusoidal endothelial cells (LSECs) balance tolerance to continuous gut-derived antigen stimulation with rapid innate immune activation upon detection of pathogenic bacteria or danger-associated molecular patterns (DAMPs) (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B36">36</xref>, <xref ref-type="bibr" rid="B37">37</xref>). These zonated differences in antigen exposure, pattern recognition receptor (PRR) expression, phagocytic capacity, and cytokine signaling along the portal-central axis give rise to regionally specialized immune microenvironments within the hepatic lobule (<xref ref-type="bibr" rid="B7">7</xref>, <xref ref-type="bibr" rid="B30">30</xref>).</p>
<p>Extending this concept beyond metabolic specialization, the modern understanding of liver zonation is now viewed as an integrated spatial biology framework in which microenvironmental gradients and niche-specific signaling collectively determine the zonated pattern. This spatial organization underlies key aspects of physiological homeostasis and fundamentally shapes the initiation, progression, and resolution of liver diseases. With the emergence of spatial multi-omics technologies, immune zonation is being mapped with high resolution (<xref ref-type="bibr" rid="B32">32</xref>), providing new insights into how microanatomical context orchestrates hepatic immune function.</p>
<p>Zonation-based organization of immune cells is essential for hepatic homeostasis, while its perturbation in disease leads to an imbalance in immune cell positioning and function (<xref ref-type="bibr" rid="B32">32</xref>). KCs, positioned within the sinusoidal lumen, are particularly sensitive to the metabolic and immunological gradients along the portal-central axis and execute innate immune surveillance in response to blood-borne stimuli (<xref ref-type="bibr" rid="B38">38</xref>).</p>
<p>High-resolution spatial systems have revealed that hepatic immune cell organization is more finely partitioned and dynamically regulated than previously appreciated. Neutrophils preferentially accumulate in specific damage zones such as necrotic lesions (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B31">31</xref>, <xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B40">40</xref>) or periportal inflammatory foci, whereas macrophage subsets acquire region-specific identities and functional programs that differentially contribute to regeneration (<xref ref-type="bibr" rid="B7">7</xref>, <xref ref-type="bibr" rid="B41">41</xref>), fibrosis (<xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B42">42</xref>), and inflammation (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B35">35</xref>, <xref ref-type="bibr" rid="B42">42</xref>&#x2013;<xref ref-type="bibr" rid="B47">47</xref>). Furthermore, infection, metabolic stress, and toxic injury induce rapid reprogramming of these immune populations, reshaping communication networks across the lobule. These findings redefine how hepatic innate immunity is spatially coordinated in health and disease.</p>
<p>This emerging spatial immunobiology has important clinical implications. Immune cell rearrangements within cirrhotic nodules, zone-specific regenerative responses in acute liver injury, and inflammatory remodeling in metabolic disease all correlate with disease severity and clinical outcomes (<xref ref-type="bibr" rid="B48">48</xref>). These insights indicate that spatial features of immune organization &#x2013; such as cell location, state, and interaction patterns &#x2013; may help refine disease stratification and guide the development of more targeted therapeutic approaches.</p>
<p>This review synthesizes current knowledge on the spatial and temporal dynamics of neutrophils and macrophages during liver disease progression, examining how zonated immune regulation contributes to inflammation, tissue injury, fibrosis, regeneration, and host defense. We also highlight how rapidly advancing spatial omics and artificial intelligence (AI)-enhanced analytic frameworks (<xref ref-type="bibr" rid="B40">40</xref>, <xref ref-type="bibr" rid="B49">49</xref>&#x2013;<xref ref-type="bibr" rid="B55">55</xref>) are redefining the microanatomy of hepatic immunity, ultimately supporting the development of precision hepatology grounded in disease mechanisms.</p>
</sec>
<sec id="s2">
<label>2</label>
<title>Neutrophil zonation in liver disease</title>
<sec id="s2_1">
<label>2.1</label>
<title>Spatial distribution of neutrophils in homeostasis and disease</title>
<p>Neutrophils are short-lived cells that are delivered to tissues in periodic waves through circadian release from the bone marrow. In mice, CXCR4-CXCL12 signaling mediates bone marrow retention, whereas CXCR2-CXCL1/2 signaling promotes rhythmic egress (<xref ref-type="bibr" rid="B56">56</xref>) and resting-phase (ZT2) infiltration into the liver, where neutrophil elastase enhances hepatocyte <italic>Bmal1</italic> and <italic>Clock</italic> expression and lipogenesis. In humans, only the correlation of circadian co-oscillation with <italic>ELANE</italic> and <italic>BMAL1</italic> has been observed rather than causality (<xref ref-type="bibr" rid="B57">57</xref>). Consequently, their spatial distribution within the liver is unlikely to reflect stable residence and is plausibly determined by where incoming neutrophils are preferentially captured, retained, or cleared (<xref ref-type="bibr" rid="B58">58</xref>, <xref ref-type="bibr" rid="B59">59</xref>).</p>
<p>The hepatic vasculature further reinforces their spatial patterning. Unlike in most organs, neutrophils in the liver bypass selectin-mediated rolling and instead undergo direct adhesion or mechanical trapping within low-shear sinusoids (<xref ref-type="bibr" rid="B60">60</xref>). This unique entry mechanism is inherently shaped by a sinusoidal structure, which features a discontinuous endothelial lining that facilitates physiological neutrophil capture. As a result, the hepatic zonated architecture not only influences whether neutrophils enter but also where they localize and how long they persist.</p>
<p>In the steady-state liver, neutrophils are relatively enriched in periportal regions, where they coexist with pathogen-sensing Kupffer cells (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B36">36</xref>, <xref ref-type="bibr" rid="B37">37</xref>). During liver injury, neutrophil spatial dynamics are reshaped in an insult-dependent manner (<xref ref-type="bibr" rid="B58">58</xref>). Sterile toxic injury and partial hepatectomy preferentially drive pericentral accumulation, whereas microbial or bile acid-related signals promote periportal activation (<xref ref-type="bibr" rid="B31">31</xref>, <xref ref-type="bibr" rid="B61">61</xref>). Thus, neutrophil zonation in the liver reflects a dynamic balance between wave-like systemic supply and local hepatic cues that regulate recruitment, retention, and egress across physiological and pathological states.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Zonal accumulation in necrotic and pericentral regions</title>
<p>Neutrophil infiltration becomes particularly prominent in liver diseases where injury is spatially restricted. Toxic and metabolic injuries, such as APAP hepatotoxicity (<xref ref-type="bibr" rid="B40">40</xref>, <xref ref-type="bibr" rid="B41">41</xref>), carbon tetrachloride (CCl<sub>4</sub>) exposure (<xref ref-type="bibr" rid="B41">41</xref>), ischemia-reperfusion (I/R) injury (<xref ref-type="bibr" rid="B40">40</xref>), and metabolic dysfunction-associated steatotic liver disease (MASLD/MASH) (<xref ref-type="bibr" rid="B39">39</xref>), preferentially damage the pericentral region, creating hypoxic, reactive oxygen species (ROS)-rich, DAMP-dense microenvironments that strongly attract neutrophils. Live imaging using LysM-eGFP mice has demonstrated that neutrophils crawl along sinusoidal endothelium using &#x3b2;2-integrins and follow hierarchical chemotactic cues, first sensing CXCR2 ligands near necrosis and then migrating toward mitochondrial formyl peptides released by dying hepatocytes (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B62">62</xref>).</p>
<p>As neutrophils reach necrotic pericentral foci, their directed migration transitions to nondirectional patrolling, enabling them to scan the injury bed even when chemokine gradients diminish (<xref ref-type="bibr" rid="B63">63</xref>). This selective neutrophil enrichment amplifies monocyte recruitment signals, setting the stage for the accumulation of inflammatory and reparative macrophages that orchestrate tissue remodeling (<xref ref-type="bibr" rid="B64">64</xref>, <xref ref-type="bibr" rid="B65">65</xref>).</p>
<p>Neutrophil adhesion mechanisms also exhibit zonal and context-dependent variability. During systemic inflammation, CD44-hyaluronan interactions dominate neutrophil arrest in hepatic sinusoids (<xref ref-type="bibr" rid="B66">66</xref>), whereas local chemoattractants shift reliance toward ICAM-1/integrin-mediated adhesion (<xref ref-type="bibr" rid="B63">63</xref>, <xref ref-type="bibr" rid="B67">67</xref>). These dynamic adaptations were validated through endotoxemia models (<xref ref-type="bibr" rid="B66">66</xref>) highlight that neutrophil accumulation in specific zones is not passive trapping but an actively regulated, stimulus-dependent process.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Functional consequences: tissue injury, inflammation and repair</title>
<p>Zonated neutrophil accumulation has profound consequences for the progression of liver injury. In pericentral necrotic regions, neutrophils amplify tissue damage by releasing ROS, proteases, and pro-inflammatory cytokines, thereby exacerbating hepatocellular damage (<xref ref-type="bibr" rid="B58">58</xref>, <xref ref-type="bibr" rid="B68">68</xref>, <xref ref-type="bibr" rid="B69">69</xref>). Their rapid recruitment functions as a spatial checkpoint for subsequent immune escalation, as shown in Con-A-induced hepatitis (<xref ref-type="bibr" rid="B70">70</xref>), where early neutrophil activation through L-selectin shedding and ROS production facilitates CD4<sup>+</sup> T-cell infiltration (<xref ref-type="bibr" rid="B60">60</xref>). These observations position neutrophils as zone-specific &#x201c;gatekeepers&#x201d; that translate microanatomical context into coordinated inflammatory and adaptive immune responses.</p>
<p><italic>In vivo</italic> imaging has further revealed that neutrophils produce neutrophil extracellular traps (NETs) in response to endotoxin or bacterial stimuli (<xref ref-type="bibr" rid="B71">71</xref>, <xref ref-type="bibr" rid="B72">72</xref>). NETs, DNA-protein lattices with microbicidal properties (<xref ref-type="bibr" rid="B59">59</xref>), also exhibit potent pro-inflammatory activity in the liver (<xref ref-type="bibr" rid="B73">73</xref>). During I/R injury, DAMPs, such as HMGB1 and IL-33 originating from stressed hepatocytes and LSECs, drive robust neutrophil infiltration and NET formation (<xref ref-type="bibr" rid="B40">40</xref>, <xref ref-type="bibr" rid="B49">49</xref>, <xref ref-type="bibr" rid="B68">68</xref>, <xref ref-type="bibr" rid="B74">74</xref>). Superoxide-mediated activation of TLR4-NOX pathways provides an additional trigger (<xref ref-type="bibr" rid="B75">75</xref>). Interventions that degrade NETs by Dnase or inhibit their formation by PAD4 inhibitors markedly attenuate liver inflammation (<xref ref-type="bibr" rid="B72">72</xref>, <xref ref-type="bibr" rid="B74">74</xref>), underscoring NETosis as a therapeutic target in I/R injury.</p>
<p>Neutrophils also contribute to tissue remodeling and fibrogenesis through zone-dependent mechanisms. In pericentral injury models, ROS and DAMP accumulation strongly induce NETosis. NETs sustain inflammatory signaling and augment monocyte recruitment, and the associated release of histones, proteases, and chemokines activate hepatic stellate cells (HSCs). This environment favors the transition of pericentral HSCs into collagen-producing myofibroblasts, key drivers of progressive fibrosis. Blocking NET formation reduces NASH-HCC progression and mitigates inflammation, implying the pathological relevance of neutrophil-centered zonal networks (<xref ref-type="bibr" rid="B73">73</xref>).</p>
<p>Despite their tissue-damaging potential, neutrophils also contribute to repair. Their removal of cellular debris, containment of microbial translocation, and facilitation of macrophage differentiation collectively promote resolution (<xref ref-type="bibr" rid="B61">61</xref>). However, the outcome of neutrophil action between injury amplification and repair initiation is largely determined by the zone in which they accumulate, as each lobular region imposes distinct metabolic, stromal, and immunological cues that differentially shape neutrophil function. Pericentral niches enriched in hypoxia, ROS, and hepatocyte-derived DAMPs drive cytotoxic programs and NETosis, whereas periportal environments dominated by microbial signals and tolerogenic cytokines favor debris clearance by neutrophils and restrain excessive neutrophil activation. Thus, the functional imprint of neutrophils is inseparable from the hepatic spatial organization, highlighting zonation as a critical determinant of inflammatory dynamics, fibrogenic progression, and regenerative trajectories.</p>
</sec>
</sec>
<sec id="s3">
<label>3</label>
<title>Macrophage zonation and plasticity</title>
<sec id="s3_1">
<label>3.1</label>
<title>Distinct zonal identities of resident and recruited macrophages</title>
<p>KCs are the dominant tissue-resident macrophages of the liver and are positioned primarily within periportal to midzonal sinusoids (<xref ref-type="bibr" rid="B26">26</xref>, <xref ref-type="bibr" rid="B39">39</xref>). Their asymmetric distribution is not developmentally pre-determined but arises from sustained exposure to gut-derived microbial products via MyD88-dependent signaling in LSECs (<xref ref-type="bibr" rid="B3">3</xref>), along with chemokine gradients that retain KCs in zone 1 (<xref ref-type="bibr" rid="B39">39</xref>). Periportal MARCO<sup>+</sup> F4/80<sup>+</sup> CLECF4<sup>+</sup> KCs specialize in clearing bacteria (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B76">76</xref>) entering through the portal vein and maintaining immune tolerance by producing IL-10 and scavenging microbial metabolites (<xref ref-type="bibr" rid="B3">3</xref>). Under pathological conditions, however, KC identity and localization are altered. During steatosis, VSIG4<sup>+</sup> FOLR2<sup>+</sup> CD163<sup>+</sup> CD169<sup>+</sup> KCs are enriched in zone 2 (<xref ref-type="bibr" rid="B39">39</xref>), whereas acute liver injury is characterized by the emergence of MARCO<sup>-</sup> F4/80<sup>+</sup> KCs in zone 3 (<xref ref-type="bibr" rid="B3">3</xref>). In chronic injury, KCs enriched in zone 3 progressively acquire a LAM-like phenotype, characterized by expression of TREM2<sup>+</sup> GPNMB<sup>+</sup> TIM4<sup>+</sup> CLEC4F<sup>+</sup> F4/80<sup>int</sup> CD36<sup>hi</sup> (<xref ref-type="bibr" rid="B41">41</xref>). Although early single-cell studies debated KC heterogeneity (<xref ref-type="bibr" rid="B8">8</xref>, <xref ref-type="bibr" rid="B26">26</xref>), recent spatial profiling has clarified that KCs possess zonally distinct identities shaped by their microanatomical context (<xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B77">77</xref>).</p>
<p>In homeostasis, KCs remain largely immobile and maintain liver immune equilibrium by removing pathogens, apoptotic cells, and toxins, while suppressing unnecessary inflammation (<xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B78">78</xref>). However, their distribution is prone to modulation by physiological stress and disease. Age-related functional decline (<xref ref-type="bibr" rid="B79">79</xref>), chronic inflammation, and metabolic injury result in marked KC depletion, particularly in periportal niches, which subsequently replenished by circulating monocytes (<xref ref-type="bibr" rid="B80">80</xref>). This process is dynamically regulated rather than stochastic, as monocytes trafficking and tissue infiltration follow circadian rhythms coordinated by cell-intrinsic clocks and hepatic cues (<xref ref-type="bibr" rid="B81">81</xref>, <xref ref-type="bibr" rid="B82">82</xref>). The zonated hepatic microenvironment determines whether these incoming monocytes acquire tolerogenic KC-like identity or retain inflammatory programs, particularly in the milieu dominated by pathogen-associated molecular pattern (PAMP) or DAMP signals, pro-inflammatory cytokines, and metabolic stress. These dynamics underscore macrophage identity as a spatially governed state.</p>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Dynamic reprogramming in response to injury and metabolic stress</title>
<p>Acute and chronic liver injury deplete resident KCs, allowing monocytes to migrate toward the vacant KC niches, where they differentiate into monocyte-derived macrophages (MoMFs) (<xref ref-type="bibr" rid="B46">46</xref>). In fibrosis-prone regions, particularly pericentral zone 3, these MoMFs accumulate in proximity to HSCs (<xref ref-type="bibr" rid="B42">42</xref>), where they contribute to the early phases of fibrogenesis and inflammation. Spatial omics data support this pattern, revealing macrophage-HSC niches forming specifically around injury-associated fibrotic bands (<xref ref-type="bibr" rid="B42">42</xref>).</p>
<p>Metabolic and immune perturbations further diversify the reprogramming trajectories of MoMFs (<xref ref-type="bibr" rid="B35">35</xref>, <xref ref-type="bibr" rid="B44">44</xref>). In MASLD/MASH (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B41">41</xref>, <xref ref-type="bibr" rid="B43">43</xref>, <xref ref-type="bibr" rid="B45">45</xref>, <xref ref-type="bibr" rid="B46">46</xref>) and cholestatic disorders [e.g. primary biliary cirrhosis (<xref ref-type="bibr" rid="B83">83</xref>, <xref ref-type="bibr" rid="B84">84</xref>) and primary sclerosing cholangitis (<xref ref-type="bibr" rid="B83">83</xref>)], MoMFs accumulate in distinct zones depending on the dominant insult. Some MoMFs localize periportally, contributing to ductular reaction (<xref ref-type="bibr" rid="B83">83</xref>), while others cluster near steatotic or pericentral regions in response to lipid overload or hepatocyte stress (<xref ref-type="bibr" rid="B39">39</xref>). These findings highlight that macrophage zonation is not static but reflects the dynamic mapping of macrophage subsets to metabolic, immunological, and stromal cues present in specific hepatic regions.</p>
<p>Furthermore, depending on gradients of oxygen, metabolites, cytokines, and DAMPs, MoMFs may adopt inflammatory phenotypes, lipid-associated macrophage (LAM) states, or transition toward KC-like identity (<xref ref-type="bibr" rid="B41">41</xref>, <xref ref-type="bibr" rid="B43">43</xref>&#x2013;<xref ref-type="bibr" rid="B46">46</xref>). Cholangiocyte-derived signals can recruit MoMFs to periportal areas during ductular reaction after 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) injury (<xref ref-type="bibr" rid="B85">85</xref>, <xref ref-type="bibr" rid="B86">86</xref>), whereas steatotic hepatocytes drive MoMF accumulation in pericentral or panzonal patterns in MASLD/MASH (<xref ref-type="bibr" rid="B41">41</xref>, <xref ref-type="bibr" rid="B43">43</xref>, <xref ref-type="bibr" rid="B45">45</xref>, <xref ref-type="bibr" rid="B46">46</xref>). Thus, macrophage plasticity reflects not only their ontogeny but also the zonated biochemical landscapes into which they are recruited.</p>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Zonal macrophage functions in fibrosis, regeneration, and infection</title>
<p>As fibrosis progresses, MoMFs diversify into specialized subsets, including LAMs and scar-associated macrophages (SAMs), characterized by TREM2, CD9, and osteopontin (<xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B41">41</xref>, <xref ref-type="bibr" rid="B43">43</xref>, <xref ref-type="bibr" rid="B46">46</xref>, <xref ref-type="bibr" rid="B47">47</xref>). These populations preferentially localize to collagen-rich, inflamed, or ECM-remodeling regions (<xref ref-type="bibr" rid="B28">28</xref>). Spatial transcriptomics further demonstrated that TREM2<sup>+</sup> macrophages form structured fibrotic niches (<xref ref-type="bibr" rid="B8">8</xref>, <xref ref-type="bibr" rid="B41">41</xref>, <xref ref-type="bibr" rid="B46">46</xref>, <xref ref-type="bibr" rid="B84">84</xref>, <xref ref-type="bibr" rid="B87">87</xref>), and circulating soluble TREM2 correlates with MASH severity (<xref ref-type="bibr" rid="B87">87</xref>), highlighting their combined fibrogenic and metabolic roles.</p>
<p>In advanced fibrosis and cirrhosis, resident TIMD4<sup>+</sup>/MARCO<sup>+</sup> KCs progressively diminish (<xref ref-type="bibr" rid="B32">32</xref>), whereas SAMs expressing TREM2 and CE9 accumulate along pericentral and bridging fibrotic septa (<xref ref-type="bibr" rid="B28">28</xref>). These SAMs directly interact with HSCs through TGF-&#x3b2;, PDGF, and CCL/MMP signaling, promoting myofibroblast activation and extracellular matrix deposition (<xref ref-type="bibr" rid="B88">88</xref>). Their spatial co-localization with HSCs within pericentral fibrotic niches provides a mechanistic link between zonated macrophage recruitment, HSC pathogenic transformation, and progressive collagen production.</p>
<p>Macrophage plasticity also influences regeneration. Following KC depletion, MoMFs repopulate hepatocellular interfaces and support repair through phagocytosis (<xref ref-type="bibr" rid="B45">45</xref>), efferocytosis (<xref ref-type="bibr" rid="B41">41</xref>), and secretion of pro-regenerative mediators (<xref ref-type="bibr" rid="B89">89</xref>). In fibrotic liver, MoMFs can fuse to form multinucleated macrophage syncytia, a process that restores KC-like functions by enhancing phagocytic capacity. This fusion-mediated reprogramming is critical for regeneration, as failure to form syncytia leads to impaired debris clearance, persistent inflammation, and defective restoration of macrophage zonation (<xref ref-type="bibr" rid="B34">34</xref>).</p>
<p>Within the fibrotic niche microenvironment, the relocation of HSCs around remodeled and enlarged vessels appears to provide instructive cues that facilitate the acquisition of KC-like features by syncytia, enabling them to capture more particles than individual KCs in their native sinusoidal setting. These findings suggest that KC-like identity can be rebuilt <italic>de novo</italic> from recruited monocytes under appropriate spatial cues (<xref ref-type="bibr" rid="B34">34</xref>).</p>
<p>Zonation also governs macrophage responses to infection. Periportal MARCO<sup>+</sup> KCs restrict bacterial dissemination by capturing gut-derived microbes (<xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B76">76</xref>), whereas pericentral macrophages become more activated in viral infections or in metabolic stress conditions (<xref ref-type="bibr" rid="B35">35</xref>, <xref ref-type="bibr" rid="B44">44</xref>, <xref ref-type="bibr" rid="B83">83</xref>). In both contexts, macrophages act as spatially anchored regulators linking local hepatocyte damage, neutrophil recruitment, and adaptive immune activation.</p>
<p>Altogether, these zonal macrophage circuits underscore the liver&#x2019;s microanatomy as a spatially compartmentalized immune organ in which macrophage identity, function, and plasticity are continuously redefined by the architecture and metabolic gradients of the lobule.</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>Building on emerging evidence of intrahepatic neutrophils and macrophages heterogeneity (<xref ref-type="table" rid="T1"><bold>Tables&#xa0;1</bold></xref>, <xref ref-type="table" rid="T2"><bold>2</bold></xref>), recent computational advances have profoundly enhanced the spatial and functional resolution at which immune zonation can be interrogated (<xref ref-type="bibr" rid="B32">32</xref>). Advanced computational analysis applied to intravital microscopy and high-dimensional histology (<xref ref-type="bibr" rid="B90">90</xref>) now enables automated tracking of neutrophil dynamics within the sinusoidal network, allowing precise investigation of how neutrophils interact with microvasculature under homeostatic, necrotic, and inflammatory conditions (<xref ref-type="bibr" rid="B91">91</xref>). These approaches reveal subtle zone-specific differences in crawling velocity, adhesion patterns, and sinusoidal trapping-features that were previously not feasible to measure manually. Physics-informed deep learning applied to light-field intravital microscopy has further enabled long-term, high-resolution imaging of Ly6G<sup>+</sup> neutrophils and F4/80<sup>+</sup> KCs within intact vascular niches, revealing their interactions in I/R and toxic injury models (<xref ref-type="bibr" rid="B40">40</xref>, <xref ref-type="bibr" rid="B55">55</xref>, <xref ref-type="bibr" rid="B92">92</xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Human intrahepatic neutrophil and macrophage heterogeneity across physiological and pathological conditions.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Species</th>
<th valign="middle" align="left">Conditions</th>
<th valign="middle" align="left">Key features of intrahepatic neutrophils and macrophages</th>
<th valign="middle" align="left">Technology</th>
<th valign="middle" align="left">Year</th>
<th valign="middle" align="left">References</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Human</td>
<td valign="middle" align="left">Homeostasis</td>
<td valign="middle" align="left">CD68<sup>+</sup> MARCO<sup>+</sup> tolerogenic, non-inflammatory KC concentrated in PP</td>
<td valign="middle" align="left">scRNA-seq, snRNA-seq (total 6,000 cells)</td>
<td valign="middle" align="left">2018</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B26">26</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Human</td>
<td valign="middle" align="left">Homeostasis</td>
<td valign="middle" align="left">Non-inflammatory M&#x3a6; expressing <italic>CD86</italic>, <italic>LYZ</italic>, <italic>MARCO</italic>, <italic>CD163</italic> in PV<break/>Inflammatory M&#x3a6; expressing <italic>CD86</italic>, <italic>LYZ</italic>, <italic>MARCO</italic>, <italic>CD163</italic> in CV</td>
<td valign="middle" align="left">scRNA-seq (29,432 cells), snRNA-seq (43,863 cells), Visium</td>
<td valign="middle" align="left">2022</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B29">29</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Human</td>
<td valign="middle" align="left">Homeostasis</td>
<td valign="middle" align="left">VSIG4<sup>+</sup> FOLR2<sup>+</sup> CD163<sup>+</sup> CD169<sup>+</sup> KC expressing <italic>CD5L</italic> in mid zone<break/>CD68<sup>+</sup> VSIG4<sup>-</sup> M&#x3a6; in the liver capsule, in close proximity to CV, PV, BDs</td>
<td valign="middle" align="left">Visium, molecular cartography, scCITE-seq, snRNA-seq (8,000-10,000 cells), MICS</td>
<td valign="middle" align="left">2022</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B39">39</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Human</td>
<td valign="middle" align="left">Homeostasis</td>
<td valign="middle" align="left">MARCO<sup>-</sup> CD5L<sup>-</sup> CD68<sup>lo</sup> non-inflammatory M&#x3a6; expressing <italic>CD74</italic> in PP and also dispersed through the lobules<break/>KC expressing <italic>CD74</italic>, <italic>CD5L</italic>, <italic>MARCO</italic> in scattered more diffusely through the lobules<break/>Enriched MARCO<sup>+</sup> CD5L<sup>+</sup> CD68<sup>+</sup> VSIG4<sup>+</sup> KC in PP</td>
<td valign="middle" align="left">MERFISH, snRNA-seq (~310,000 cells)</td>
<td valign="middle" align="left">2025</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B32">32</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Human</td>
<td valign="middle" align="left">Fibrosis</td>
<td valign="middle" align="left">Spatially recruited TREM2<sup>+</sup> CD9<sup>+</sup> MNDA<sup>+</sup> SAMs in collagen-positive scar regions</td>
<td valign="middle" align="left">scRNA-seq, smFISH (total 100,000 cells)</td>
<td valign="middle" align="left">2019</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B28">28</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Human</td>
<td valign="middle" align="left">Fibrosis</td>
<td valign="middle" align="left">Enriched CD9<sup>+</sup> TREM2<sup>+</sup> SPP1<sup>+</sup> GPNMB<sup>+</sup> FABP5<sup>+</sup> CD63<sup>+</sup> SAMs at scarring edges, clustered with <italic>MMP9</italic>-expressing neutrophils</td>
<td valign="middle" align="left">scRNA-seq, CycIF</td>
<td valign="middle" align="left">2023</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B47">47</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Human</td>
<td valign="middle" align="left">NAFLD/NASH<break/>AH<break/>PSC<break/>PBC</td>
<td valign="middle" align="left">IBA1<sup>+</sup> CD16<sup>lo</sup> CD163<sup>lo</sup> M&#x3a6; in the vicinity of BD</td>
<td valign="middle" align="left">InSituPlex Ultivue</td>
<td valign="middle" align="left">2023</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B83">83</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Human</td>
<td valign="middle" align="left">PSC</td>
<td valign="middle" align="left">Enriched TREM2<sup>+</sup> monocyte-like M&#x3a6; within fibrotic niche<break/>MHCII<sup>+</sup> LAM-like M&#x3a6;/KC in PC<break/>Activated M&#x3a6; in PP<break/>VCAM1<sup>+</sup> KC-like cells in both PC and PP (but only increased in PP)<break/>Recruited SAM-like MoMF in the center of fibrotic regions, whereas KC were localized outside of the scar regions</td>
<td valign="middle" align="left">scRNA-seq (107,542 cells in NDD; 47,156 cells in PSC; 18,240 cells in PBC), snRNA-seq (23,000 cells in PSC; 20,202 cells in PBC), Visium, Nanostring GeoMx DSP</td>
<td valign="middle" align="left">2024</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B84">84</xref>)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>AH, alcoholic hepatitis; BD, bile duct; CV, central vein; KC, Kupffer cell; M&#x3a6;, macrophage; MICS, MACSima Imaging Cyclic Staining; MERFISH, multiplexed error robust fluorescent in situ hybridization; MoMF, monocyte-derived macrophage; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; NDD, neurologically deceased healthy donor; PBC, primary biliary cholangitis; PP, periportal; PSC, primary sclerosing cholangitis; PV, portal vein; SAM, scar-associated macrophage; scCITE-seq, single-cell cellular indexing of transcriptomes and epitopes by sequencing; scRNA-seq, single-cell RNA sequencing; smFISH, single molecule fluorescence in situ hybridization; snRNA-seq, single nucleus RNA sequencing.</p></fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Mouse intrahepatic neutrophil and macrophage heterogeneity across physiological and pathological conditions.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Species</th>
<th valign="middle" align="left">Conditions</th>
<th valign="middle" align="left">Key features of intrahepatic neutrophils and macrophages</th>
<th valign="middle" align="left">Technology</th>
<th valign="middle" align="left">Year</th>
<th valign="middle" align="left">References</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">Homeostasis</td>
<td valign="middle" align="left">KC expressing Clec4f in PP</td>
<td valign="middle" align="left">scRNA-seq</td>
<td valign="middle" align="left">2021</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B90">90</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">Homeostasis</td>
<td valign="middle" align="left">KC preferentially localized in PP and mid zones, without a strong zonation pattern</td>
<td valign="middle" align="left">scRNA-seq (16,900 cells), scATAC-seq (9,702 cells), scATAC-seq + scRNA-seq (12,898 cells), MICS, smFISH, AI</td>
<td valign="middle" align="left">2024</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B52">52</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">Homeostasis</td>
<td valign="middle" align="left">LAMs expressing Gpnmb, Spp1 in PP, further defined by more Il1b expression compared with KC<break/>M&#x3a6; expressing Cd207 in CV<break/>M&#x3a6; expressing Ccr2, Chil3 at the PV and CV, resembling transitioning Mo<break/>Enriched M&#x3a6; expressing Gpnmb around the bile ducts (termed as bile-duct LAMs)<break/>KC in PP, whereas sporadically distributed Mo</td>
<td valign="middle" align="left">Visium, molecular cartography, scCITE-seq, snRNA-seq (8,000-10,000 cells), MICS</td>
<td valign="middle" align="left">2022</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B39">39</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left"/>
<td valign="middle" align="left">MASLD/MASH</td>
<td valign="middle" align="left">M&#x3a6;/Mo in mid-CV<break/>LAMs primarily in PC, defined by lower gene expression of Il1b, Tnf, Il10 in WD</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">Fibrosis</td>
<td valign="middle" align="left">Enriched CD9<sup>+</sup> TREM2<sup>+</sup> SPP1<sup>+</sup> GPNMB<sup>+</sup> FABP5<sup>+</sup> CD63<sup>+</sup> SAMs, at scarring edges, clustered with MMP9-expressing neutrophils<break/>Co-localization of Iba1<sup>+</sup> M&#x3a6; and increased S100A8/A9<sup>+</sup> neutrophils in scarring region</td>
<td valign="middle" align="left">scRNA-seq, CycIF</td>
<td valign="middle" align="left">2023</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B47">47</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">Fibrosis</td>
<td valign="middle" align="left">F4/80<sup>+</sup> CLEC4F<sup>+</sup>, CRIg<sup>+</sup>, TIM-4<sup>+</sup> KC in PP<break/>Recruited CX3CR1<sup>+</sup> MoMF in CV, forming syncytia via CD36</td>
<td valign="middle" align="left">IVM</td>
<td valign="middle" align="left">2023</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B34">34</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">Fibrosis<break/>Liver injury<break/>MASH</td>
<td valign="middle" align="left">TREM2<sup>+</sup> GPNMB<sup>+</sup> TIM4<sup>+</sup> CLEC4F<sup>+</sup> F4/80<sup>int</sup> CD36<sup>hi</sup> LAM-like resident KCs in CV, further defined by Mmp12 expression<break/>F4/80<sup>+</sup> TREM2<sup>+</sup> recruited M&#x3a6; in CV</td>
<td valign="middle" align="left">IVM, scRNA-seq, snRNA-seq, Visium</td>
<td valign="middle" align="left">2025</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B41">41</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">MAFLD</td>
<td valign="middle" align="left">CLEC4F<sup>+</sup> TIM4<sup>+</sup> ResKCs, CLEC4F<sup>+</sup> TIM4<sup>-</sup> moKCs, and sporadically distributed CLEC4F<sup>-</sup> M&#x3a6;<break/>CLEC4F<sup>-</sup> M&#x3a6; in close proximity to the large vessels (CV/PV) in fibrotic zones</td>
<td valign="middle" align="left">scRNA-seq (56,407 cells), metabolomics, lipidomics</td>
<td valign="middle" align="left">2020</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B43">43</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">Homeostasis</td>
<td valign="middle" align="left">Evenly distributed KC</td>
<td valign="middle" align="left">scRNA-seq (82,168 cells)</td>
<td valign="middle" align="left">2021</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B10">10</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left"/>
<td valign="middle" align="left">NAFLD</td>
<td valign="middle" align="left">Accumulated KC around the injured regions to form unique CLS</td>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
<td valign="middle" align="left"/>
</tr>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">NASH</td>
<td valign="middle" align="left">SAM expressing TREM2 and CD9 in close proximity to fibrotic region<break/>Enriched Ly6C<sup>hi</sup> CD301b<sup>-</sup> RM in both PV and CV<break/>Enriched Ly6C<sup>lo</sup> CD301b<sup>+</sup> RM in closer proximity to CV</td>
<td valign="middle" align="left">scRNA-seq, snATAC-seq (50,000 cells)</td>
<td valign="middle" align="left">2020</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B44">44</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">Liver injury</td>
<td valign="middle" align="left">Neutrophils preferentially accumulated at damage sites in CV<break/>Marco<sup>+</sup> F4/80<sup>+</sup> expressing Clec4f KC in PV, defined by gene expression of Il10, Il1rn, Tgfb1<break/>Marco<sup>-</sup> F4/80<sup>+</sup> KC in CV</td>
<td valign="middle" align="left">Visium, IVM</td>
<td valign="middle" align="left">2024</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B3">3</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">Liver regeneration</td>
<td valign="middle" align="left">Infiltrated neutrophils (expressing S100a8, S100a9, Cxcr2, F13a1, Fgr) expanding from the midzonal to PC area upon PHx<break/>Enriched Bmp10-Acvrl1 interaction in KC in the midzonal area</td>
<td valign="middle" align="left">Stereo-seq, scRNA-seq (473,290 cells), smFISH, multiplex RNAscope</td>
<td valign="middle" align="left">2024</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B31">31</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Mouse</td>
<td valign="middle" align="left">Obesity-related steatohepatitis</td>
<td valign="middle" align="left">Prominent F4/80<sup>+</sup> M&#x3a6; infiltration in PP</td>
<td valign="middle" align="left">scRNA-seq (~2,500 cells/sample)</td>
<td valign="middle" align="left">2020</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B35">35</xref>)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>AI, artificial intelligence; CLS, crown-like structure; CV, central vein; CycIF, cyclic immunofluorescence; IVM, intravital microscopy; KC, Kupffer cell; LAM, lipid-associated macrophage; M&#x3a6;, macrophage; MAFLD, metabolic dysfunction-associated fatty liver disease; MASH, metabolic dysfunction-associated steatohepatitis; MICS, MACSima Imaging Cyclic Staining; Mo, monocyte; MoMF, monocyte-derived macrophage; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; PHx, partial hepatectomy; PP, periportal; PV, portal vein; SAM, scar-associated macrophage; scCITE-seq, single-cell cellular indexing of transcriptomes and epitopes by sequencing; scRNA-seq, single-cell RNA sequencing; scATAC-seq, single-cell assay for transposase-accessible chromatin using sequencing; smFISH, single molecule fluorescence in situ hybridization; snRNA-seq, single nucleus RNA sequencing; Stereo-seq, spatiotemporal enhanced resolution omics-sequencing; WD, westerndiet.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>Deep learning methods have also transformed the characterization of KC zonation states (<xref ref-type="bibr" rid="B52">52</xref>). By integrating marker expression, morphological signatures, and sinusoidal topology (<xref ref-type="bibr" rid="B50">50</xref>), convolutional neural networks can distinguish periportal tolerogenic KCs, midzonal transitional states, and pericentral stress-responsive populations with high fidelity (<xref ref-type="bibr" rid="B53">53</xref>). When coupled with cell-cell proximity analysis, these models reconstruct the spatial rules governing KC interactions with hepatocytes, LSECs, and infiltrating MoMFs (<xref ref-type="bibr" rid="B32">32</xref>). This analytical framework has revealed that specific KC states preferentially associate with discrete vascular niches, creating spatially distinct immunoregulatory microenvironments. Deep-learning-assisted three-dimensional (3D) reconstruction frameworks extend these capabilities, enabling the mapping of KCs together with hepatocytes, stellate cells, and the sinusoidal network at single-cell resolution (<xref ref-type="bibr" rid="B53">53</xref>).</p>
<p>Beyond image-based mapping, the integration of spatial transcriptomics with AI-enhanced inference tools is redefining how immune communication networks are conceptualized (<xref ref-type="bibr" rid="B54">54</xref>, <xref ref-type="bibr" rid="B90">90</xref>). Deep learning models trained on multi-omic datasets can infer ligand-receptor interactions within zonated niches, disentangling how cytokine, chemokine, and growth factor signaling differ across the portal-central axis. A recent study employing enhancer-resolved spatial multi-omics demonstrated that deep neural networks can reconstruct zonated gene-regulatory networks, revealing enhancer-driven programs that shape immune cell identity more precisely than bulk or single-cell data alone (<xref ref-type="bibr" rid="B52">52</xref>).</p>
<p>Collectively, these technological advances illustrate how advanced spatial biology is fundamentally redefining immune zonation. Rather than static maps of cell localization, the liver is now understood as a dynamically encoded spatial immune system, where neutrophils, KCs, and MoMFs continuously respond to microenvironmental cues and engage in structured circuit-level interactions. This integration of advanced computational tools with spatial multi-omics provides the conceptual and analytical foundation for the emerging era of precision hepatology.</p>
</sec>
</body>
<back>
<sec id="s5" sec-type="author-contributions">
<title>Author contributions</title>
<p>HK: Conceptualization, Data curation, Investigation, Writing &#x2013; original draft, Writing &#x2013; review &amp; editing. NL: Conceptualization, Funding acquisition, Project administration, Supervision, Writing &#x2013; review &amp; editing.</p></sec>
<sec id="s7" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec>
<sec id="s8" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p></sec>
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<fn id="n1" fn-type="custom" custom-type="edited-by">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/607202">Andrea Baragetti</ext-link>, University of Milan, Italy</p></fn>
<fn id="n2" fn-type="custom" custom-type="reviewed-by">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3312387">Ottavia Terenghi</ext-link>, University of Milan, Italy</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3336410">Anna Parolini</ext-link>, University of Milan, Italy</p></fn>
</fn-group>
</back>
</article>