AI-Enhanced Morphological Phenotyping in Humanized Mouse Models: A Transformative Approach to Infectious Disease Research

Abstract

Humanized mouse models offer human-specific platforms for investigating complex host–pathogen interactions, addressing shortcomings of conventional preclinical models that often fail to replicate human immune responses accurately. This integrative review examines the intersection of advanced morphological phenotyping and artificial intelligence (AI) to enhance predictive capacity and translational relevance in infectious disease research. A structured literature search was conducted across PubMed, Scopus, and Web of Science (2010–2025), applying defined inclusion and exclusion criteria. Evidence synthesis highlights imaging modalities, AI-driven phenotyping, and standardization strategies, supported by comparative analyses and quality considerations. Persistent challenges include variability in engraftment, lack of harmonized scoring systems, and ethical governance. We propose recommendations for standardized protocols, risk-of-bias mitigation, and collaborative training frameworks to accelerate adoption of these technologies in translational medicine.

1. Introduction

Infectious diseases continue to pose a dreadful threat to global health, responsible for approximately 8.7 million deaths annually and contributing significantly to morbidity and socioeconomic burden, particularly in low- and middle-income countries [1,2]. Despite notable advances in antimicrobial therapies and vaccines, the global impact of diseases such as tuberculosis, malaria, and hepatitis, as well as emerging viral infections like Zika and SARS-CoV-2, underscores persistent gaps in prevention, treatment, and our fundamental understanding of host–pathogen interactions [3,4]. Compounding these challenges is the accelerating threat of antimicrobial resistance (AMR), projected to cause 10 million deaths annually by 2050 if unmitigated [5].

A critical limitation in addressing these challenges lies in the inadequacy of conventional animal models in replicating the complexity of human immune responses. Murine models, while cost-effective and genetically tractable, often lack translational relevance due to species-specific differences in immune signaling, receptor expression, and pathogen tropism [6,7,8,9].

For example, human immunodeficiency virus (HIV-1) and hepatitis C virus (HCV) demonstrate narrow host specificity and require human-specific co-factors for replication that murine cells do not naturally express [10]. Moreover, ethical constraints restrict access to infected human tissues, limiting mechanistic studies during early infection phases [11]. These issues necessitate model systems that more faithfully emulate human biology while maintaining experimental control.

Humanized mouse models (hu-mice) have emerged as powerful tools to bridge this translational gap. Recent advances in morphological phenotyping and AI-driven imaging present an opportunity to transform these models into predictive tools for preclinical research. Unlike prior narrative reviews, this work applies a structured search and selection strategy to provide a rigorous, integrative synthesis of the current evidence on humanized mouse models combined with phenotyping and AI-driven analytics for infectious disease applications. By engrafting immunodeficient mice with human hematopoietic stem cells, fetal tissues, or organ-specific cells, these models facilitate the in vivo study of human–pathogen interactions in a physiologically relevant context [12,13]. Advanced platforms such as BLT (bone marrow–liver–thymus) mice and liver-humanized FRG mice have enabled high-fidelity modeling of diseases like HIV, HCV, and malaria, supporting detailed analysis of viral dynamics, immune responses, and therapeutic efficacy [14,15,16]. Improvements in genome engineering, engraftment techniques, and cytokine support have further enhanced the functionality and stability of these systems. As it is shown in Figure 1, comparative features of traditional and humanized mouse models highlight the differences in physiological and immunological parameters.

The clinical impact of hu-mice has been substantial. They were instrumental in the preclinical validation of direct-acting antivirals (DAAs) for HCV and broadly neutralizing antibodies for HIV, both of which later advanced to successful human trials [17,18]. Foundational studies in the late 1980s and early 2000s first demonstrated the feasibility of engrafting human immune cells into immunodeficient mice [19,20,21,22], establishing the platform upon which current humanized mouse systems are built.

In tuberculosis and malaria research, hu-mice support granuloma formation and liver-stage parasite development, enabling mechanistic insights into pathogen persistence and host resistance [23,24]. Nonetheless, limitations persist, including variability in human cell engraftment, incomplete immune reconstitution, and the need for standardized assessment protocols [25,26].

A particularly promising avenue for improving the utility of humanized models lies in integrating advanced morphological phenotyping. This approach, characterizing disease-induced structural changes at the tissue, cellular, and subcellular levels, offers high-resolution insights into host responses and pathogenesis. When combined with emerging imaging modalities such as multiphoton microscopy and artificial intelligence (AI)-driven analysis, morphological phenotyping enables quantitative tracking of infection dynamics, immune cell behavior, and therapeutic responses in situ [24,27]. These technologies also support high-throughput, automated detection of morphological biomarkers that may inform disease staging and precision treatment. The progressive development of these models over time is summarized in Figure 2.

This review critically examines the transformative role of morphological phenotyping in hu-mice as applied to infectious disease research. We explore cutting-edge imaging technologies, AI-enabled analysis, and standardized phenotyping strategies, illustrated through key case studies spanning viral, bacterial, and parasitic infections. We further discuss ongoing technical and ethical challenges and outline strategic recommendations to advance this emerging interdisciplinary field. By bridging the gap between mechanistic insight and clinical relevance, these integrated approaches offer a robust framework for accelerating translational research and improving global health outcomes.

2. Methodology: Literature Search and Selection

To ensure transparency and enhance the rigor of this review, a structured literature search was performed across PubMed, Scopus, and Web of Science, covering the period from January 2010 to July 2025. The search strategy utilized combinations of relevant keywords, including humanized mouse models, morphological phenotyping, AI-driven image analysis, infectious diseases, and translational predictivity. Boolean operators were applied to refine the results (e.g., “humanized mice AND AI imaging” OR “morphological phenotyping AND infectious disease”).

2.1. Inclusion Criteria

• Peer-reviewed original research or review articles focused on humanized mouse models in the context of infectious diseases.
• Studies incorporating morphological phenotyping or advanced imaging techniques.
• Publications integrating AI-based or machine learning-driven image analysis approaches.
• Articles published in English.

2.2. Exclusion Criteria

• Studies exclusively related to non-infectious diseases.
• Research limited to in vitro or organoid-only models without in vivo validation.
• Abstract-only conference proceedings without full text availability.

2.3. Screening and Selection

The initial search yielded 1462 articles. Following title and abstract screening, 312 studies underwent full text review. After applying inclusion and exclusion criteria, 187 publications were retained for final synthesis. While this review adopted elements from the PRISMA guidelines for transparency, the objective was conceptual integration rather than meta-analytic pooling.

2.4. Risk of Bias and Quality Considerations

Due to the heterogeneity of methodologies, no formal meta-analysis was conducted. Reported quantitative parameters (e.g., engraftment success rates, coefficient of variation [CV] ranges, and fidelity scores) were extracted from individual studies and interpreted contextually. Quality appraisal emphasized study design robustness, completeness of morphological reporting, and reproducibility measures. Limitations concerning variability and inter-laboratory differences are acknowledged in the challenges and future directions section.

Note: Numerical values presented in the tables and text are derived from primary sources and should not be interpreted as standardized benchmarks.

3. Advanced Morphological Phenotyping in Humanized Mouse Models

Building on the structured literature selection approach described in Section 2, evidence synthesized from the included studies indicates that the successful application of humanized mouse models in infectious disease research relies on precise and reproducible assessment of morphological changes across multiple biological scales. This section integrates findings from the reviewed literature to examine the evolving methodological landscape, encompassing tissue-level architectural alterations, immune cell dynamics, and subcellular processes. The convergence of traditional histopathological evaluation, which remains the cornerstone of phenotypic analysis, with advanced imaging platforms has significantly enhanced our ability to visualize and quantify disease progression in human-relevant systems, reinforcing the translational value of these models.

3.1. Assessment of Human Immune System Reconstitution

Morphological phenotyping serves as a critical quantitative framework for assessing this process, functioning as both a quality control measure and a predictor of downstream utility in infectious disease research [34]. The architectural complexity of the human immune system from primary lymphoid organ development to peripheral tissue organization necessitates sophisticated analytical strategies capable of capturing structural integrity and functional competence.

Histological evaluation, primarily through immunohistochemistry (IHC), remains the cornerstone for verifying human immune system reconstitution [35,36]. The application of lineage-specific markers provides granular insights into immune cell development and spatial organization. For instance, CD4 and CD8 markers enable the distinction between the helper and cytotoxic T cell subsets, while their distribution across thymic and peripheral tissues reflects developmental progression [37,38]. CD68 expression identifies macrophage populations, indicating myeloid cell engraftment, and HLA-DR staining reveals the presence and activation status of antigen-presenting cells essential for adaptive immunity [39]. Spatial localization of these populations within lymphoid organs serves as a key indicator of reconstitution quality. The presence of CD4⁺ and CD8⁺ T cells in the thymic cortical and medullary zones mirrors human thymic organization, while their subsequent distribution into splenic white pulp and lymph node paracortical areas indicates effective peripheral reconstitution [40,41]. Quantitative morphometric analyses such as cell density measurements, spatial clustering indices, and architectural integrity scoring further provide objective metrics for comparing reconstitution efficiency across experimental conditions [42].

Complementing static assessments, non-invasive imaging modalities have enabled dynamic tracking of immune system development. Magnetic resonance imaging (MRI) allows high-resolution visualization of lymphoid organ structure and longitudinal monitoring of immune reconstitution kinetics, reducing inter-animal variability [43]. Optical coherence tomography (OCT), with superior resolution for superficial structures, is particularly valuable for assessing skin-associated lymphoid tissue and barrier immunity [44]. The integration of these modalities with histological techniques provides a multi-modal framework, ensuring that humanized mouse models achieve stringent quality benchmarks before pathogen challenge studies [45].

Although histological and imaging approaches enable detailed assessment of immune reconstitution, the variability in engraftment between laboratories remains high (coefficients of variation 15–40%). Models such as BLT or NSG-SGM3 (NSG mice transgenic for human stem cell factor (SCF), granulocyte-macrophage colony-stimulating factor (GM-CSF), and interleukin-3 (IL-3)) offer robust reconstitution but differ in stability and lifespan, limiting reproducibility for certain infection contexts. This variability underscores the need for harmonized protocols and transparent reporting. Such challenges have been recognized since the earliest engraftment protocols, when pioneering SCID-hu mouse models revealed both the potential and limitations of immune reconstitution [19,20,21,22].

3.2. Real-Time Host–Pathogen Interaction Imaging

The dynamic nature of infectious disease progression demands imaging approaches that capture the temporal and spatial complexity in host–pathogen interactions. Evidence synthesized from the included studies underscores the transformative role of advanced imaging modalities in enabling direct visualization of infection dynamics within humanized tissues [46]. These tools bridge the gap between mechanistic in vitro investigations and clinical observations, elucidating fundamental aspects of pathogenesis that inform therapeutic development.

Intravital microscopy remains a leading modality for real-time visualization of infection events within living tissues [47]. In HIV research, for example, intravital imaging has enabled observation of viral dissemination pathways, immune cell recruitment, and reservoir establishment in lymphoid microenvironments using fluorescently labeled immune cells [48,49]. These findings revealed tissue-specific sanctuary sites inaccessible to immune surveillance, informing HIV cure strategies. A technical comparison of these modalities is presented in Table 2 (Section 5.1).

Multiphoton microscopy (MPM) offers subcellular resolution imaging of deep tissues with minimal phototoxicity, making it ideal for studying respiratory infections such as SARS-CoV-2. Recent studies using MPM have demonstrated epithelial barrier disruption, immune cell recruitment, and inflammatory focus formation in humanized lung tissue changes that recapitulate clinical COVID-19 pathology [50,51]. The ability to quantify these changes longitudinally provides critical insights into disease kinetics and therapeutic response.

Whole-animal imaging techniques, such as bioluminescence and fluorescence imaging, enable systemic tracking of pathogen dissemination and therapeutic biodistribution [52]. For example, in malaria models, bioluminescent Plasmodium parasites have been visualized during liver-stage and blood-stage infection, supporting drug efficacy evaluations [42]. At the ultrastructural level, correlative light and electron microscopy (CLEM) combines dynamic imaging with an electron-level resolution, offering unparalleled insight into viral assembly and host–cell interactions during egress [53,54].

3.3. Quantitative Morphological Readouts for Therapeutic Evaluation

The translation of morphological observations into robust quantitative metrics represents a pivotal advancement for preclinical therapeutic evaluation. The studies reviewed here demonstrate that AI-enhanced image analysis and machine learning algorithms have enabled automated, high-throughput extraction of quantitative features from complex morphological datasets [55]. These approaches reduce observer bias, facilitate large-scale analyses, and improve statistical power in therapeutic efficacy studies [56].

For instance, in HBV-infected humanized liver models, automated analysis of hepatocyte morphology, immune infiltration, and viral antigen distribution provides comprehensive therapeutic response profiles [57]. Machine learning classifiers can detect subtle morphological variations—such as early-stage fibrosis or microvascular remodeling—that may not be apparent through conventional manual evaluation [58].

Standardized morphological scoring systems, particularly in tuberculosis research, have facilitated cross-study comparisons by integrating multiple histopathological features (e.g., granuloma organization, necrosis, bacterial burden) into composite indices [59,60]. Such frameworks enable reproducible evaluation of drug efficacy and correlate strongly with clinical endpoints. Furthermore, longitudinal morphological assessment allows early identification of therapeutic response markers and optimization of dosing strategies [61]. In HIV studies, restoration of lymphoid tissue architecture following combination antiretroviral therapy has been mapped against viral load reduction, providing mechanistic insights into immune recovery [48].

Integration of morphological readouts with functional and virological endpoints creates a multidimensional evaluation platform that improves mechanistic understanding of therapeutic interventions [62]. These strategies are now being incorporated into preclinical development pipelines, accelerating the identification of promising candidates for clinical trials [63].

4. Comparative Performance Analysis Across Infectious Disease Categories

The diversity of infectious pathogens and their distinct tissue tropisms, immune evasion strategies, and pathogenic mechanisms necessitate careful selection and optimization of humanized mouse models for specific research applications. A systematic comparative analysis of model performance across different infectious disease categories reveals critical insights into model strengths, limitations, and optimal applications. This analysis provides essential guidance for researchers seeking to maximize translational relevance while minimizing experimental variability and resource expenditures.

4.1. Pathogen-Specific Model Optimization

The effectiveness of humanized mouse models varies dramatically depending on pathogen characteristics and the specific aspects of human biology required for faithful disease recapitulation. Systematic evaluation of model performance across major infectious disease categories reveals distinct patterns of success and failure that inform rational model selection strategies. Below is

Table 1. Comparative features of advanced humanized mouse models.

Model	Strategy	Engraftment Metric	Key Application	Strengths	Limitations	Reference
BLT	CD34+ HSC + fetal liver/thymus graft in NSG mice	Stable multilineage human engraftment, including mucosal tissue	HIV, mucosal immunity	HLA-restricted T-cell responses; mucosal compartments	Surgical complexity; GVHD potential	[64,65,66,67,68]
NSG- SGM3	NSG mice + HSC + human SCF/GM-CSF/ IL-3 transgenes	~80% human CD45+; robust myeloid differentiation	HIV/Mtb co-infection; myeloid studies	Enhanced myeloid lineage; granulomas observed	Hemophagocytic syndrome (HLH); reduced lifespan	[45,69,70,71]
NSG- QUAD	NSG-SGM3 + CSF1 transgene, HSC engraftment	Balanced innate lineages (~monocytes/DCs by 6 wk)	Innate immunity, cytokine response	Broad myeloid repertoire; early innate maturity	Limited T-cell development at early time points	[72,73,74,75]
THX (NSGW41-E2)	Neonatal NSGW41 + CD34+ CB + estradiol conditioning	Peripheral 89–96% huCD45+, robust GC formation	Vaccine response; antibody maturation	Germinal centers; class-switched antibodies	Neonatal surgery; hormone conditioning protocol	[76,77,78]

Note: BLT, bone marrow–liver–thymus humanized mouse; CB, cord blood; DCs, dendritic cells; GC, germinal center; GVHD, graft-versus-host disease; HIV, human immunodeficiency virus; HLA, human leukocyte antigen; HLH, hemophagocytic lymphohistiocytosis; HSC, hematopoietic stem cell; IL, nterleukin; Mtb, mycobacterium tuberculosis; NSG, NOD.Cg-Prkdc^scid Il2rg^tm1Wjl/SzJ mouse; NSG SGM3, NSG mouse transgenic for human SCF, GM-CSF, and IL-3; NSG QUAD, NSG SGM3 mouse with additional CSF1 transgene; NSGW41, NSG mouse with Kit^W41/W41 mutation allowing irradiation-free human engraftment; SCF, stem cell factor; THX (NSGW41 E2), estradiol-conditioned NSGW41 mouse model supporting enhanced immune reconstitution; T cells, thymus-derived lymphocytes (adaptive immune cells).

, which discusses key characteristics, applications, and limitations across commonly used humanized mouse models in infectious disease research.

4.1.1. Critical Appraisal of Humanized Mouse Models

While Table 1 outlines the comparative features of advanced humanized mouse models, a critical evaluation reveals important limitations that affect their reproducibility and predictive validity. BLT mice, although widely considered the gold standard for HIV research, are constrained by surgical complexity and the risk of graft-versus-host disease (GVHD), both of which reduce reproducibility across laboratories [64,65,66,67,68]. The BLT model was originally established by Melkus et al. (2006) [79], who demonstrated its utility in HIV research, though subsequent work highlighted GVHD-associated limitations. The liver-humanized uPA-SCID and FRG models provide robust support for hepatitis C virus replication but rely on immunosuppressive conditioning, which limits adaptive immune responses, thereby constraining their utility for vaccine evaluation [15,77]. NSG-SGM3 mice, which express human cytokine transgenes, improve myeloid lineage reconstitution and granuloma formation but frequently develop hemophagocytic lymphohistiocytosis (HLH), shortening experimental lifespan and complicating longitudinal studies [45,69,70,71]. In contrast, NSG-QUAD mice broaden the innate immune repertoire and accelerate cytokine responses, but exhibit delayed or incomplete T-cell maturation during early engraftment, which restricts their predictive value for adaptive immune-mediated infections [72,73,74,75].

These limitations demonstrate that no single model universally achieves high reproducibility and translational fidelity. Instead, model selection must be aligned with the specific biological question, balancing strengths against inherent weaknesses. For example, BLT mice are well suited for mucosal HIV studies despite GVHD risks, whereas FRG mice excel in hepatotropic infections at the expense of immune system reconstitution. This critical appraisal underscores the importance of tailoring model choice to research objectives and highlights the need for continued refinement and standardization across platforms.

4.1.2. Viral Infections: Comparative Model Performance in HIV and HCV Research

HIV research has benefited from extensive model development, with bone marrow–liver–thymus (BLT) mice and NOD-scid IL2Rγnull (NSG) mice reconstituted with human CD34+ hematopoietic stem cells representing the current gold standards [18]. Quantitative assessment of these models demonstrates robust HIV replication, with viral loads reaching 10⁵–10⁶ copies/mL and CD4⁺ T cell depletion patterns that closely mirror those observed in human infection [80]. Morphological analysis of lymphoid tissues in HIV-infected humanized mice reveals similar patterns of immune activation and tissue destruction as observed in human lymph nodes, including follicular hyperplasia, paracortical expansion, and progressive architectural disruption. The success rate for achieving productive HIV infection exceeds 85% in optimized protocols, with inter-animal variability in viral load typically falling within one log10 unit [17,81,82].

In contrast, HCV research requires specialized liver-humanized models due to the virus’s strict hepatotropism. uPA-SCID and FRG mice engrafted with primary human hepatocytes achieve infection rates of 70–90% when challenged with patient-derived HCV [15], with viral loads comparable to those observed in chronic human infection. Morphological phenotyping reveals development of hepatic steatosis, inflammatory infiltrates, and early fibrotic changes that recapitulate key features of human HCV pathogenesis. However, these models face limitations in immune reconstitution, as the immunosuppressive protocols required for hepatocyte engraftment often compromise adaptive immune responses [83].

4.1.3. Bacterial Infections: Tuberculosis Model Complexity

Tuberculosis research in humanized mice presents unique challenges due to Mycobacterium tuberculosis’s complex interaction with human macrophages and the requirement for appropriate granuloma formation. NSG mice reconstituted with human mononuclear cells demonstrate variable success in tuberculosis modeling, with granuloma formation occurring in only 40–60% of infected animals [26]. Morphological analysis reveals that successful models develop organized granulomas with central caseous necrosis surrounded by epithelioid cells and lymphocytes, closely resembling human tuberculous lesions. However, the lack of organized lymphoid structures often limits the development of protective immune responses, resulting in progressive disease that may not accurately reflect the spectrum of human tuberculosis outcomes [60].

Recent advances using NSG-SGM3 mice, which express the human stem cell factor, GM-CSF, and IL-3, have improved myeloid cell development and granuloma formation rates to 75–80% [81]. Quantitative morphological analysis demonstrates enhanced macrophage differentiation and improved bacterial containment compared to standard NSG models, although complete sterilization remains rare, reflecting the challenges of modeling latent tuberculosis [45].

4.1.4. Parasitic Infections: Malaria Model Achievements and Limitations

Plasmodium falciparum research has achieved remarkable success with liver-humanized models for studying pre-erythrocytic stages of infection. FRG mice repopulated with human hepatocytes support complete liver-stage development, with sporozoite-to-schizont conversion rates of 80–90% and morphological features indistinguishable from human liver-stage infections. Quantitative analysis of infected hepatocytes reveals typical features including parasitophorous vacuole formation, host–cell hypertrophy, and schizont maturation within 6–7 days post-infection. However, modeling blood-stage malaria requires additional humanization with human erythrocytes, significantly increasing model complexity. NOD-scid IL2Rγnull mice engrafted with human red blood cells support P. falciparum growth for 7–14 days, but progressive clearance of human erythrocytes limits long-term infection studies. Morphological examination of infected erythrocytes shows appropriate parasite morphology and developmental stages, but the artificial nature of the erythrocyte environment may not fully recapitulate the complex host–parasite interactions observed in human malaria [84].

4.2. Quantitative Validation Metrics

The establishment of standardized, quantitative metrics for comparing humanized mouse model performance is essential for rational model selection and optimization. These metrics must encompass both technical parameters related to model generation and biological parameters reflecting disease recapitulation fidelity.

4.2.1. Engraftment Efficiency and Stability Metrics

Human cell engraftment levels serve as fundamental indicators of model quality, but simple cell percentages provide insufficient information for comparative analysis. Comprehensive assessment requires evaluation of engraftment kinetics, stability over time, and functional competence. For hematopoietic humanization, optimal models achieve >50% human CD45⁺ cells in peripheral blood by 12–16 weeks post-transplantation, with stable maintenance for >20 weeks. Morphological analysis of lymphoid organs should demonstrate appropriate tissue architecture with distinct T and B cell zones, a germinal center formation capability, and normal cell trafficking patterns [85].

Tissue-specific humanization requires additional metrics tailored to the target organ. Liver humanization success is typically assessed via the albumin replacement index (ARI), with values > 1000 mg/mL indicating sufficient hepatocyte engraftment for most applications. Morphological evaluation should confirm normal hepatocyte morphology, appropriate zonation patterns, and preserved synthetic function as evidenced by human protein production [86].

4.2.2. Disease Recapitulation Fidelity Scores

Quantitative assessment of how faithfully models recapitulate human disease requires development of composite scoring systems that incorporate multiple biological parameters. For viral infections, these include viral replication kinetics, tissue tropism patterns, immune activation markers, and pathological changes. A validated HIV disease fidelity score incorporates viral load dynamics (weighted 25%), CD4⁺ T cell depletion patterns (25%), immune activation markers (25%), and lymphoid tissue pathology (25%), with scores > 80/100 indicating high-fidelity models suitable for therapeutic testing [87,88].

The morphological components of fidelity scores require standardized histopathological assessment protocols. For HIV models, lymphoid tissue scoring evaluates follicular hyperplasia (0–3 scale), paracortical expansion (0–3 scale), architectural disruption (0–3 scale), and inflammatory infiltrate patterns (0–3 scale). The composite morphological score correlates strongly with functional measures of disease progression and provides rapid assessment of model quality.

4.2.3. Reproducibility and Inter-Laboratory Variability

Standardization of model generation and assessment protocols is crucial for ensuring reproducible results across different research groups. Analysis of inter-laboratory variability in humanized mouse model performance reveals significant differences in engraftment success rates (coefficient of variation 15–40%) and disease outcomes (CV 20–50%), highlighting the need for improved standardization [16,17]. Several studies, including those cited in [89], have reported inter-observer agreement exceeding 90% for major histopathological features when standardized staining protocols and digital automation are applied. While these findings demonstrate the potential for enhanced reproducibility, most originate from oncology and toxicologic pathology rather than infectious disease-specific contexts. In the infectious disease studies reviewed here, reproducibility metrics were less consistently reported, though trends suggest that implementing similar standardization strategies, such as harmonized staining workflows and automated image analysis, can substantially reduce variability. Future work should focus on validating these approaches within pathogen-challenged humanized models to establish infectious disease-specific benchmarks.

Quality control measures, including regular assessment of donor cell characteristics, standardized injection procedures, and systematic monitoring protocols, can reduce inter-experimental variability to <20% for most endpoints [69]. The implementation of centralized training programs and proficiency testing has further improved consistency across research groups.

4.3. Translational Efficacy: Model to Clinic

The ultimate validation of humanized mouse models lies in their ability to predict clinical outcomes and guide therapeutic development decisions. Systematic analysis of translation success rates across different infectious disease categories provides crucial insights into model utility and limitations.

4.3.1. Antiviral Therapy Translation Success

Humanized mouse models have become indispensable tools in HIV therapeutic development, offering enhanced predictive value for clinical efficacy compared to traditional in vitro systems and non-human primate models. These models effectively recapitulate human immune responses, thereby improving the translational relevance of preclinical findings [90,91]. Notably, morphological assessments, such as reductions in lymphoid tissue inflammation and restoration of normal architecture, have been associated with improved clinical outcomes, underscoring the utility of these models in evaluating therapeutic interventions. Such histopathological evaluations provide insights into the efficacy of treatments and the progression of HIV-related pathologies [92].

The success of broadly neutralizing antibodies exemplifies effective translation, where humanized mouse studies have correctly predicted clinical efficacy for antibodies that subsequently entered human trials [93]. Morphological analysis in these studies revealed antibody-mediated reduction in viral antigen distribution and restoration of normal follicular dendritic cell networks, changes that correlated with viral load reductions in clinical trials [94].

4.3.2. Challenges in Bacterial and Parasitic Disease Translation

Translation success rates for bacterial infections show greater variability, reflecting the complexity of modeling host–pathogen interactions that depend heavily on intact immune architecture [95]. Tuberculosis therapeutic development shows a higher translation success rate, with failures often attributed to inadequate modeling of latent infection and immune-mediated bacterial clearance mechanisms [96]. Morphological analysis reveals that models showing organized granuloma formation and bacterial containment are more predictive of clinical success than those with progressive, uncontrolled infection [97].

Malaria therapeutic translation faces unique challenges due to its complex life cycle and multiple intervention opportunities [98]. Pre-erythrocytic stage interventions show higher translation success than blood-stage therapies, which likely reflect the more straightforward nature of liver-stage modeling compared to the complex immune interactions during blood-stage infection [99,100].

4.3.3. Factors Influencing Translation Success

Systematic analyses of translational bottlenecks have identified several key factors that influence preclinical model predictivity. Models that incorporate human-like tissue architecture and functional immune cell organization are shown to offer greater translational relevance compared to models with incomplete or simplified systems [101]. The inclusion of morphological endpoints such as tissue remodeling, cellular infiltration, and spatial immune organization provides critical context for interpreting therapeutic effects beyond basic pathogen load measurements. These integrative assessments are increasingly recognized as essential for improving the predictive value of preclinical studies and enhancing their alignment with clinical outcomes [101].

Host genetic diversity also influences translation success, with models incorporating multiple HLA types showing better predictive value than those with limited genetic diversity. This finding emphasizes the importance of considering human population diversity in model design and interpretation of results [102].

The integration of multiple humanized model types in therapeutic evaluation by combining tissue-specific and immune system humanization approaches provides the highest predictive value for clinical outcomes. This comprehensive approach, while resource-intensive, maximizes the likelihood of successful clinical translation and reduces the risk of late-stage therapeutic failures. As illustrated in Figure 3, advanced imaging and AI-driven analysis converge to enhance morphological phenotyping in humanized mouse models.

5. AI-Driven Morphological Phenotyping in Infectious Disease Research

The integration of imaging modalities (e.g., multiphoton microscopy, PET) with AI has enabled quantifiable metrics such as granuloma compactness, hepatocyte size variability, and immune cell infiltration density. These parameters provide direct correlates of clinical outcomes such as fibrosis staging, pathogen clearance, and immune restoration. This technological convergence addresses a critical gap in translational medicine by enabling researchers to capture the dynamic, three-dimensional landscape of infectious disease progression with remarkable precision and temporal resolution.

5.1. Next-Generation Imaging Technologies

Modern imaging modalities have revolutionized our ability to visualize and quantify morphological changes in humanized mouse models during infectious disease progression. Bioluminescence imaging (BLI) stands at the forefront of this revolution, offering non-invasive, real-time monitoring capabilities that allow researchers to track pathogen distribution and tissue tropism throughout the entire course of infection. This technology has proven particularly transformative in studying neglected tropical diseases, for example, where traditional endpoint analyses fail to capture the dynamic nature of host–pathogen interactions in African trypanosomiasis and Chagas disease models [106].

Table 2. Advanced phenotyping and imaging tools.

Technique	Spatial/Temporal Resolution	Throughput	AI Integration	Primary Use Case	Reference
Intravital Microscopy	~500 nm spatial; real-time temporal	Low (single animal)	CNN-based cell tracking	Visualizing host–pathogen interactions in vivo	[70,72,107,108]
Multiphoton Microscopy	~200 nm; minutes per frame	Moderate	ROI segmentation for viral spread	Deep-tissue viral infection imaging	[109,110,111,112]
Correlative Light-Electron Microscopy	Light: ~200 nm; EM: ~1 nm (fixed time)	Very low	Deep-learning morphology analysis	Ultrastructural viral assembly and budding	[113,114,115]
Bioluminescence Imaging	~1 mm; hourly captures	High (whole animal)	ML regression for signal quantification	Temporal tracking of systemic infections	[116,117,118]
PET Imaging	~1–2 mm; minutes per scan	Moderate	Metabolic signature clustering	Profiling inflammation or metabolic hotspots	[119,120,121]

Note: AI, artificial intelligence; CNN, convolutional neural network; EM, electron microscopy; ML, machine learning; PET, positron emission tomography; ROI, region of interest.

shows the summary of key characteristics, throughput capabilities, AI integration approaches, and primary applications across commonly used imaging modalities for studying host–pathogen interactions in humanized mouse models.

The power of BLI extends beyond simple pathogen tracking, enabling quantitative assessment of tissue-specific immune responses and morphological alterations in humanized immune organs. When combined with fluorescence microscopy, researchers can now visualize cellular infiltration patterns, tissue architecture disruption, and organ-specific pathological changes with subcellular resolution [122]. This dual-imaging approach has revealed previously hidden morphological signatures of immune activation in humanized lymphoid tissues, providing critical insights into how human immune responses differ from conventional mouse models.

Advanced positron emission tomography (PET) imaging further enhances morphological phenotyping by enabling metabolic profiling of infected tissues in living humanized mice. This technique has uncovered distinct metabolic signatures associated with tissue remodeling and inflammatory responses, revealing how infectious agents alter cellular metabolism and tissue homeostasis [123]. The integration of multiple imaging modalities creates a comprehensive morphological atlas that captures both structural and functional changes throughout disease progression.

Fluorescent reporter systems have refined tissue tropism analysis by enabling researchers to distinguish between different cell populations and track their morphological responses to infection. These systems reveal how pathogens exploit specific cellular niches and how humanized immune cells respond morphologically to infectious challenges [124]. Despite technical challenges such as light scattering in deep tissues, recent advances in multiphoton microscopy and light-sheet imaging have overcome these limitations, enabling detailed morphological analysis throughout entire organ systems [125].

5.2. AI-Powered Phenotyping: Precision and Scalability

Artificial intelligence has transformed morphological phenotyping from a largely subjective endeavor into a quantitative, reproducible science. Machine learning algorithms now process vast datasets of morphological information with unprecedented speed and accuracy, identifying subtle phenotypic changes that would escape human observation. This technological leap has particular significance in humanized mouse models, where morphological differences between human and mouse tissues require sophisticated analytical approaches to interpret correctly.

Automated image analysis platforms exemplify this transformation, with systems like HRMAn employing deep learning algorithms to quantify cellular morphology, tissue architecture, and immune cell infiltration patterns with remarkable precision [126]. These platforms eliminate inter-observer variability while standardizing morphological assessments across different research groups and experimental conditions. The consistency achieved through AI-driven analysis has enabled large-scale collaborative studies that were previously impossible due to subjective interpretation differences.

Convolutional neural networks (CNNs) have proven particularly powerful in microbial identification and morphological classification within complex tissue environments. These systems can rapidly differentiate between bacterial, viral, and fungal pathogens based on morphological signatures in infected tissues, enabling real-time pathogen identification during disease progression studies [127]. This capability has proven invaluable in mixed infection models, where multiple pathogens simultaneously challenge humanized immune systems.

The predictive power of AI extends beyond morphological analysis to therapeutic outcome prediction. Machine learning models trained on morphological data from humanized mice can now predict antiviral drug efficacy based on early morphological changes in target tissues. This predictive capability accelerates drug discovery by identifying promising candidates before extensive animal testing, reducing both research timelines and animal usage [128].

Pattern recognition algorithms have revolutionized biomarker discovery by identifying morphological signatures associated with disease progression and treatment response. These AI-driven approaches uncover hidden relationships between cellular morphology and immune function, revealing new therapeutic targets and diagnostic markers [105]. The ability to process longitudinal morphological data enables researchers to map disease trajectories and identify critical transition points where therapeutic intervention might be most effective.

Concrete applications of AI in infectious disease research illustrate its transformative potential. For example, convolutional neural networks (CNNs) have been applied to intravital microscopy datasets in HIV-infected humanized mice, enabling automated detection of viral spread and CD4⁺ T-cell depletion with accuracy exceeding manual scoring [129]. In liver-humanized HBV models, machine learning classifiers have identified early-stage fibrosis and microvascular remodeling, whereas they were invisible to conventional histopathology, providing sensitive readouts of therapeutic efficacy [130]. Similarly, AI-enhanced Raman spectroscopy has achieved species-level bacterial identification within minutes, supporting real-time analysis of complex co-infection models [131]. Predictive modeling has also shown promise: algorithms trained on morphological features from HIV-infected mice successfully forecasted antiviral drug efficacy, reducing preclinical study timelines and prioritizing candidate therapies for clinical translation [132]. Collectively, these examples demonstrate that AI is no longer a theoretical tool but is already improving pathogen detection, disease staging, and therapeutic evaluation in humanized models.

5.3. Integrated Imaging–AI Approaches

The synergy between advanced imaging and artificial intelligence has unlocked a new mechanistic understanding of infectious disease pathogenesis in humanized models. This integrated approach reveals how pathogens manipulate host–cell morphology and tissue architecture to establish infection and evade immune responses. Real-time visualization of host–pathogen interactions has exposed previously unknown morphological adaptations that occur during infection establishment and progression.

Digital pathology platforms powered by AI have automated the analysis of complex morphological changes, including immune cell infiltration patterns, tissue remodeling processes, and inflammatory responses. These systems provide quantitative measurements of morphological parameters that correlate with disease severity and treatment efficacy [133]. The standardization achieved through AI analysis has enabled researchers to compare morphological responses across different infection models and treatment protocols with unprecedented precision.

This is especially valuable in studies where subtle morphological changes are key to understanding disease progression. AI-driven predictive modeling and pattern recognition systems have been used to identify immune-oncology biomarkers. These biomarkers are critical for patient stratification and optimizing therapeutic interventions. Tools analyze large datasets to uncover hidden patterns in immune responses, paving the way for more personalized treatments for infectious diseases [105]. Moreover, convolutional neural networks (CNNs) are highly accurate in microbial identification and classification. They enable rapid and precise differentiation of bacteria, viruses, and fungi in complex tissue samples [127].

Mass spectrometry imaging combined with AI-driven analysis has created new opportunities to correlate morphological changes with molecular alterations in infected tissues. This approach reveals how infectious agents alter cellular metabolism and protein expression patterns, linking morphological phenotypes to underlying molecular mechanisms [134]. The integration of morphological and molecular data provides a comprehensive understanding of how infections disrupt tissue homeostasis and immune function.

Longitudinal studies benefit enormously from AI-enhanced morphological analysis, particularly in chronic infection models where subtle morphological changes accumulate over extended periods. Machine learning algorithms can detect morphological trends that predict disease progression and treatment response, enabling early intervention strategies [135]. This capability has proven particularly valuable in tuberculosis and HIV models, where morphological changes in lymphoid organs precede clinical symptoms by weeks or months.

The transformative impact of integrated imaging–AI approaches extends to therapeutic development, where morphological biomarkers identified through machine learning guide drug design and dosing strategies. These biomarkers provide early indicators of treatment efficacy, enabling rapid optimization of therapeutic protocols [136]. The precision achieved through AI-enhanced morphological analysis has accelerated the translation of promising therapies from humanized mouse models to human clinical trials.

Despite these remarkable advances, successful implementation of AI-driven morphological phenotyping requires careful consideration of data quality, algorithm training, and validation protocols. The generation of large, well-annotated datasets remains a significant challenge, requiring collaboration between imaging specialists, infectious disease researchers, and computational biologists. However, the transformative potential of these technologies in advancing our understanding of infectious disease mechanisms and accelerating therapeutic development makes these investments essential for the future of translational medicine.

6. Interdisciplinary Integration in Morphological Phenotyping

The complexity of morphological phenotyping in humanized mouse models demands a fundamental shift from traditional single-discipline research approaches to integrated, collaborative frameworks that harness expertise across multiple scientific domains. This interdisciplinary integration has become essential for addressing the multifaceted challenges of infectious disease modeling, where morphological changes reflect intricate interactions between human immune systems, pathogen biology, and tissue microenvironments. The convergence of immunology, computational biology, bioengineering, and clinical medicine has created unprecedented opportunities to decode the morphological signatures of disease progression and therapeutic response.

6.1. Collaborative Innovation: Integrating Expertise for Advanced Model Development

The development of sophisticated humanized mouse models for morphological phenotyping demands seamless collaboration among immunologists, tissue engineers, and computational biologists. Each contributes essential expertise to address the inherent limitations of traditional animal models. Immunologists guide the selection and engraftment of appropriate human immune cell populations, ensuring models recapitulate the architecture and function of the human immune system. Tissue engineers enable the reconstruction of human-like tissue microenvironments, including lymphoid and epithelial structures, within murine hosts [67,137]. Computational biologists apply machine learning and spatial analysis tools to quantify morphological endpoints and to model complex cellular interactions, facilitating rigorous assessment of immune-mediated changes during infection. Together, these complementary skill sets produce biologically valid, analytically robust platforms poised for breakthrough discoveries in infectious disease research.

Bioengineers contribute essential innovations in tissue scaffolding and microenvironment design, creating conditions that support proper morphological development of human tissues within mouse hosts. Their work addresses critical challenges in achieving appropriate tissue architecture and cellular organization, which are fundamental prerequisites for meaningful morphological phenotyping. For instance, highly porous biodegradable scaffolds that mimic the human extracellular matrix have been shown to facilitate engraftment and structural maturation of human immune tissues in murine models, enabling more accurate recapitulation of immune cell localization and tissue function [138]. The integration of bioengineering principles with immunological expertise has led to next-generation humanized models that maintain a tissue morphology more closely resembling human physiology.

Computational biologists and bioinformaticians play an increasingly vital role in processing and interpreting the vast datasets generated by morphological phenotyping studies. Their analytical frameworks enable researchers to identify subtle morphological patterns that correlate with disease progression and treatment response, transforming raw imaging data into actionable biological insights [139]. The development of specialized algorithms for morphological analysis has revolutionized the field by providing quantitative metrics that were previously impossible to obtain through traditional histological approaches.

The synergy between these disciplines has produced humanized models with enhanced immune system reconstitution and improved morphological fidelity to human disease states. Collaborative approaches have improved reproducibility in engraftment efficiency (coefficient of variation reduced to <20%) and enhanced structural fidelity of lymphoid tissues, parameters that are critical predictors of therapeutic efficacy in clinical HIV and TB interventions [140]. The integration of artificial intelligence and machine learning approaches has further amplified these advances, enabling predictive modeling of morphological changes and optimization of experimental designs for maximum translational relevance [141].

Cross-disciplinary validation protocols have emerged as a cornerstone of rigorous morphological phenotyping, ensuring that findings from humanized models accurately reflect human disease biology. These protocols incorporate multiple analytical approaches, from traditional histopathology to advanced imaging and molecular profiling, creating comprehensive morphological profiles that withstand scrutiny across different experimental platforms [142]. The establishment of standardized validation frameworks has facilitated reproducibility across different research groups and institutions, accelerating the pace of discovery in infectious disease research.

6.2. System-Based Integration of Morphological and Molecular Data

The integration of morphological phenotyping with multi-omics technologies has created powerful system-based approaches that reveal the molecular underpinnings of tissue-level changes in humanized mouse models. This convergence enables researchers to correlate morphological alterations with specific genomic, transcriptomic, and proteomic signatures, providing mechanistic insights that guide therapeutic development. The ability to simultaneously analyze morphological and molecular phenotypes has transformed our understanding of how infectious agents disrupt tissue homeostasis and immune function.

Genomic profiling combined with morphological analysis has revealed critical host genetic variants that influence tissue susceptibility to infection and morphological response patterns. Studies integrating these approaches in HIV-infected humanized mice have identified specific genetic polymorphisms associated with distinct morphological changes in lymphoid tissues, providing new targets for personalized therapeutic interventions [143]. This integration has proven particularly valuable in understanding individual variations in disease susceptibility and treatment response.

Transcriptomic analyses coupled with morphological phenotyping have illuminated the dynamic gene expression changes that drive tissue remodeling during infection. Real-time monitoring of morphological changes alongside transcriptional profiling has revealed temporal sequences of molecular events that culminate in observable tissue alterations. These integrated studies have identified critical transcriptional switches that precede morphological changes, enabling early intervention strategies before irreversible tissue damage occurs [35].

Proteomic integration with morphological analysis has provided unprecedented insights into the protein networks that orchestrate tissue-level responses to infection. Spatial proteomics combined with morphological imaging has mapped protein expression patterns within specific tissue compartments, revealing how infectious agents manipulate local protein environments to facilitate tissue invasion and immune evasion. These approaches have identified morphology-associated protein signatures that serve as biomarkers for disease progression and treatment efficacy [144].

Single-cell technologies integrated with morphological phenotyping have revolutionized our understanding of cellular heterogeneity within infected tissues. By combining single-cell RNA sequencing with morphological analysis, researchers can now identify distinct cell populations that contribute to specific morphological changes, revealing the cellular basis of tissue-level alterations. This integration has proven particularly powerful in studying neurotropic infections, where morphological changes in neural tissues reflect complex interactions between multiple cell types [145].

System immunology approaches have emerged as a unifying framework for integrating morphological, molecular, and functional data from humanized mouse models. These approaches employ computational modeling to predict how molecular changes translate into morphological alterations, enabling researchers to design targeted interventions that prevent or reverse pathological tissue changes. The predictive power of these integrated systems has accelerated the identification of therapeutic targets and the optimization of treatment protocols.

6.3. Translational Frameworks: Discovery to Application

The ultimate goal of interdisciplinary morphological phenotyping in humanized mouse models is the acceleration of translational medicine, where mechanistic discoveries drive the development of clinically relevant therapeutic interventions. This translational framework requires close collaboration between basic researchers, clinical investigators, and regulatory scientists to ensure that morphological insights from humanized models translate effectively to human patients. The integration of clinical perspectives with basic research has refined the selection of morphological endpoints that predict therapeutic efficacy in human trials.

Clinician-scientist partnerships have been instrumental in validating morphological biomarkers identified in humanized mouse models through correlation with clinical outcomes in human patients. These collaborations have established morphological phenotyping protocols that bridge preclinical and clinical research, ensuring that the findings from humanized models provide actionable insights for patient care [146]. The development of translational morphological biomarkers has streamlined the drug development process by providing early indicators of therapeutic efficacy.

Regulatory science integration has ensured that morphological phenotyping approaches in humanized models meet the standards required for regulatory approval of new therapeutics. Collaboration with regulatory agencies has established standardized protocols for morphological assessment that support regulatory submissions, accelerating the path from preclinical discovery to clinical application [11]. These partnerships have created clear pathways for translating morphological findings into regulation-compliant evidence for therapeutic development.

The integration of patient-derived materials with humanized mouse models has created personalized disease modeling platforms that enhance the clinical relevance of morphological phenotyping. By incorporating patient-specific immune cells or tissue samples into humanized models, researchers can study how individual genetic backgrounds influence morphological responses to infection and treatment. This personalized approach has revealed patient-specific morphological signatures that guide individualized therapeutic strategies [139].

Collaborative research consortiums have emerged as powerful vehicles for translating morphological discoveries into clinical applications. These consortiums bring together academic researchers, pharmaceutical companies, regulatory agencies, and patient advocacy groups to ensure that morphological phenotyping research addresses real-world clinical needs. The establishment of standardized data-sharing platforms has accelerated the translation of morphological biomarkers from humanized models to clinical practice [147].

The development of companion diagnostic platforms based on morphological biomarkers has created new opportunities for precision medicine in infectious diseases. These platforms integrate morphological analysis with molecular diagnostics to provide comprehensive patient assessment tools that guide treatment selection and monitoring. The translation of morphological phenotyping approaches from humanized models to clinical diagnostics represents a major advancement in personalized infectious disease medicine [148].

Global health initiatives have leveraged interdisciplinary morphological phenotyping to address infectious diseases that disproportionately affect resource-limited settings. International collaborations have adapted morphological phenotyping approaches for use in diverse clinical environments, ensuring that advances in humanized mouse models benefit patients worldwide. These initiatives have demonstrated the global impact of interdisciplinary research in advancing infectious disease treatment and prevention strategies [149].

The success of interdisciplinary approaches in morphological phenotyping has established new paradigms for infectious disease research that prioritize collaboration, integration, and translation. These frameworks have demonstrated that the complexity of infectious disease pathogenesis requires multifaceted research approaches that draw on diverse scientific expertise. The continued evolution of these interdisciplinary frameworks promises to accelerate the development of more effective diagnostics, therapeutics, and preventive interventions for infectious diseases that threaten global health.

7. Ethical Frameworks and Standardization

The rapid advancement of morphological phenotyping in humanized mouse models has created unprecedented opportunities for infectious disease research while simultaneously raising complex ethical questions that demand careful consideration and proactive governance. As these models become increasingly sophisticated in their ability to recapitulate human disease pathology, the scientific community faces the critical challenge of balancing research innovation with ethical responsibility. The integration of human tissues and cells into animal models for morphological analysis creates unique ethical considerations that extend beyond traditional animal research paradigms, requiring comprehensive frameworks that address both the scientific potential and moral implications of these powerful research tools.

7.1. Ethics in Human–Animal Chimeric Research

The development of humanized mouse models for morphological phenotyping introduces distinctive ethical challenges that arise from the creation of human–animal chimeric organisms, where the integration of human cells and tissues may confer enhanced cognitive or sensory capabilities that alter the moral status of these research subjects. These ethical considerations become particularly complex when human neural tissues are incorporated into models designed to study neurotropic pathogens, as the resulting chimeric organisms may possess enhanced neurological functions that approach human-like characteristics. The scientific community has recognized that traditional animal welfare frameworks may be insufficient to address these emerging ethical challenges, necessitating the development of specialized ethical guidelines that account for the unique properties of humanized research models.

The sourcing and utilization of human tissues for morphological phenotyping research raises fundamental questions about informed consent, tissue ownership, and the ethical boundaries of human material use in research contexts. Researchers must navigate complex consent processes that adequately inform donors about the potential uses of their tissues in creating chimeric organisms, while ensuring that consent procedures respect donor autonomy and cultural sensitivities regarding human tissue use [150]. The long-term storage and potential commercialization of research outcomes derived from human tissues create additional ethical considerations that require transparent policies and fair benefit-sharing arrangements.

Regulatory compliance in humanized mouse research involves navigating multiple overlapping jurisdictions and ethical frameworks that may conflict or provide insufficient guidance for emerging research applications. The complexity of these regulatory landscapes requires researchers to engage proactively with institutional review boards, animal care committees, and regulatory agencies to ensure comprehensive ethical oversight [151]. The development of standardized ethical review processes specifically designed for humanized model research has become essential for maintaining research integrity while enabling scientific progress.

The potential for enhanced animal welfare considerations in humanized models necessitates the implementation of refined monitoring protocols that assess not only traditional measures of animal distress but also novel indicators of cognitive or sensory enhancement that may require specialized care approaches. These enhanced welfare protocols must be integrated with morphological phenotyping procedures to ensure that research objectives can be achieved without compromising animal well-being [152]. The development of humane endpoints that account for the unique characteristics of humanized models represents a critical advancement in ethical research practices.

International collaboration in humanized mouse research creates additional ethical challenges related to varying regulatory standards and cultural attitudes toward human–animal chimeric research across different countries and institutions. The establishment of internationally harmonized ethical frameworks that respect cultural diversity while maintaining rigorous research standards has become increasingly important as research collaborations expand globally [153]. These frameworks must address questions of research justice and ensure that the benefits of humanized model research are distributed equitably across different populations and research settings.

7.2. Standardization of Assessment Protocols

The standardization of morphological phenotyping protocols in humanized mouse models has emerged as a critical requirement for ensuring the reproducibility, reliability, and translational relevance of infectious disease research. The complexity of morphological assessment in chimeric organisms, where human and mouse tissues interact in novel ways, demands standardized approaches that can accommodate biological variability while maintaining analytical precision. The development of these standardized protocols requires careful consideration of technical variables, experimental design principles, and quality control measures that collectively ensure the validity of morphological findings across different research settings.

The implementation of standardized morphological assessment protocols has demonstrated a remarkable impact on research quality, with studies showing significant reductions in inter-laboratory variability and improved reproducibility of experimental outcomes. These standardization efforts have proven particularly valuable in multicenter research initiatives, where consistent morphological endpoints enable meaningful data aggregation and meta-analysis approaches that would be impossible with methods lacking standardization [154]. The establishment of reference standards for morphological assessment has created benchmarks that facilitate quality control and enable researchers to validate their experimental approaches against established criteria.

The Minimal Information for Standardization of Humanized Mice (MISHUM) initiative represents a landmark achievement in research standardization, providing comprehensive guidelines that ensure critical experimental parameters are consistently documented and reported across studies. This standardization framework has proven essential for morphological phenotyping research, where subtle variations in experimental conditions can significantly impact tissue development and morphological outcomes [155]. The adoption of MISHUM guidelines has facilitated improved data sharing and collaborative research by ensuring that morphological findings can be meaningfully compared across different studies and research groups.

Technological advances in morphological assessment have created new opportunities for standardization through the implementation of automated analysis platforms and machine learning algorithms that minimize human bias and enhance reproducibility. These technological solutions have proven particularly valuable in morphological phenotyping, where subjective interpretation of tissue characteristics can introduce significant variability in research outcomes [156]. The integration of artificial intelligence and machine learning approaches has enabled the development of standardized morphological assessment protocols that can be consistently applied across different research settings and experimental conditions.

Quality assurance programs specifically designed for morphological phenotyping in humanized models have established comprehensive validation procedures that ensure research findings meet regulatory standards for translational applications. These quality assurance frameworks incorporate multiple levels of validation, from technical replication to biological validation, creating robust systems that support the transition from preclinical research to clinical applications [157]. The implementation of these quality control measures significantly enhanced the credibility of morphological phenotyping research and improved the acceptance of humanized model findings by regulatory agencies.

The establishment of standardized data management and sharing protocols has created new opportunities for collaborative research and meta-analysis approaches that leverage morphological phenotyping data from multiple studies and research groups. These data standardization efforts have proven essential for identifying morphological biomarkers and developing predictive models that can guide therapeutic development and clinical decision-making [158]. The development of standardized data formats and sharing platforms has accelerated the pace of discovery in morphological phenotyping research while ensuring that research findings can be efficiently translated into clinical applications.

7.3. Global Regulatory Frameworks

The globalization of morphological phenotyping research in humanized mouse models has necessitated the development of comprehensive regulatory frameworks that can accommodate diverse research environments while maintaining rigorous standards for scientific quality and ethical conduct. These regulatory frameworks must address the unique characteristics of humanized model research, including the complex interactions between human and animal tissues, the potential for enhanced animal welfare considerations, and the specific requirements for translational research applications. The harmonization of these regulatory approaches across different countries and institutions has become essential for facilitating international collaboration and ensuring that research findings can be effectively translated into clinical benefits.

Regulatory agencies worldwide have recognized the unique value of humanized mouse models for morphological phenotyping in infectious disease research, leading to the development of specialized guidance documents and approval pathways that acknowledge the distinctive characteristics of these research tools. The U.S. Food and Drug Administration and other regulatory bodies have established frameworks that recognize humanized models as valuable preclinical research platforms while implementing appropriate oversight mechanisms to ensure research quality and safety [159]. These regulatory recognition efforts have created clear pathways for translating morphological phenotyping findings from humanized models to regulatory submissions for therapeutic development.

International harmonization efforts have focused on establishing common standards for morphological assessment protocols, data reporting requirements, and quality control measures that can be consistently applied across different regulatory jurisdictions. These harmonization initiatives have proven particularly important for morphological phenotyping research, where variations in assessment protocols can significantly impact research outcomes and regulatory acceptance [160]. The development of internationally recognized standards has facilitated global collaboration and enabled research findings to be more easily translated across different regulatory environments.

The implementation of risk-based regulatory approaches has enabled more efficient oversight of morphological phenotyping research while maintaining appropriate safety and quality standards. These approaches recognize that different types of humanized model research may require different levels of regulatory oversight, with morphological phenotyping studies that pose minimal risks receiving streamlined approval processes [161]. The development of these risk-based frameworks has accelerated the pace of research while ensuring that appropriate safeguards remain in place for more complex or potentially risky research applications.

Regulatory science initiatives have emerged as critical components of the global governance framework, providing the scientific foundation for evidence-based regulatory decision-making in humanized model research. These initiatives have focused on developing standardized methods for assessing the translational relevance of morphological phenotyping findings and establishing criteria for evaluating the quality and reliability of humanized model research [162]. The integration of regulatory science principles into morphological phenotyping research has enhanced the credibility of research findings and improved their acceptance by regulatory agencies.

The evolution of global governance frameworks has increasingly emphasized the importance of stakeholder engagement and public participation in the development of policies governing humanized model research. These participatory approaches have proven essential for ensuring that regulatory frameworks reflect societal values and address public concerns about the ethical implications of human–animal chimeric research [163]. The integration of public input into regulatory decision-making has enhanced the legitimacy of governance frameworks and improved public acceptance of morphological phenotyping research.

Adaptive regulatory frameworks have emerged as innovative approaches to governance that can evolve with advancing scientific knowledge and changing research needs. These frameworks recognize that the rapid pace of innovation in morphological phenotyping research requires regulatory approaches that can adapt to new scientific developments while maintaining appropriate oversight and quality standards [164]. The implementation of adaptive regulatory mechanisms has enabled more responsive governance that can keep pace with scientific innovation while ensuring that ethical and safety considerations remain paramount in research oversight.

Recent real-world debates on chimera and human–animal model research provide critical context for the ethical use of humanized mouse models. The International Society for Stem Cell Research (ISSCR) guidelines emphasize proportionate oversight, recommending tiered review for experiments involving significant human–animal chimerism to balance scientific benefit with ethical safeguards. Similarly, the U.S. National Institutes of Health (NIH) has periodically restricted federal funding for certain types of chimera research, reflecting ongoing societal concerns about human–animal boundary-crossing. In Europe, the European Union (EU) applies their precautionary principle more stringently, with some member states imposing moratoria on specific forms of chimera studies. These positions underscore that regulatory and ethical standards are not uniform, and evolving policy landscapes may directly influence how humanized mouse studies are designed, reviewed, and funded. Connecting humanized models to these broader ethical debates highlights the need for proactive engagement with policymakers and ethicists to ensure that advances in infectious disease research proceed responsibly and with public trust.

Several ongoing initiatives illustrate practical pathways for standardization in humanized model research. The Minimum Information Standard for Humanized Mouse Models (MISHUM) provides structured reporting guidelines to enhance reproducibility and transparency across studies. Similarly, the PATHBIO project, supported by the European Commission, offers standardized training modules, databases, and phenotyping protocols for mouse models of human disease. In addition, federated data-sharing frameworks are emerging that allow multi-institutional integration of imaging and phenotyping datasets without requiring centralized storage, thereby preserving data privacy while enabling large-scale analyses. By aligning future recommendations with such initiatives, the field can move from abstract calls for standardization toward concrete practices that accelerate collaboration, reproducibility, and translational impact.

8. Challenges and Future Directions

The field of morphological phenotyping in humanized mouse models stands at a pivotal juncture where remarkable technological advances converge with persistent challenges that must be addressed to fully realize the transformative potential of these research approaches. While the integration of advanced imaging technologies, artificial intelligence, and interdisciplinary collaboration has created unprecedented opportunities for understanding infectious disease pathogenesis, significant barriers continue to limit the widespread adoption and standardization of morphological phenotyping techniques. The resolution of these challenges will determine whether morphological phenotyping can achieve its promise of revolutionizing translational medicine and accelerating the development of life-saving therapeutics for infectious diseases that threaten global health.

8.1. Current Technical and Translational Barriers

The technical complexity of morphological phenotyping in humanized mouse models requires integration of imaging, computational, and immunological expertise, which may not be readily available in all research settings. The lack of standardized protocols across research institutions contributes to variability in outcomes, limiting reproducibility and regulatory acceptance [165]. This challenge is particularly acute in morphological phenotyping, where subtle differences in imaging protocols, tissue processing, and analytical algorithms can significantly affect outcomes.

Specialized technical expertise is another barrier, as morphological phenotyping requires interdisciplinary knowledge spanning immunology, imaging, computational biology, and data science. Many institutions lack personnel with these skill sets, slowing progress and increasing variability [166]. The steep learning curve and time demands associated with advanced imaging and AI-driven analysis may exceed traditional research timelines and funding cycles.

Resource disparities also remain. Access to high-resolution imaging and computational infrastructure is uneven, and acquiring state-of-the-art microscopes, high-performance computing clusters, and specialized software requires substantial capital investment [167]. This creates inequities between well-funded and resource-limited institutions.

Current imaging approaches also face technical limitations. Fixed-sample analyses fail to capture temporal dynamics, while live imaging often lacks the spatial resolution required to detect early or subtle morphological changes [168]. Optimization strategies must balance temporal and spatial detail, which is not always feasible for all applications.

Integration of morphological phenotyping with other high-dimensional datasets introduces computational challenges. Large-scale imaging datasets require specialized algorithms to extract features such as cell clustering density, granuloma compactness indices, and hepatocyte size variability while ensuring analytical rigor [169]. The absence of standardized data formats complicates sharing and collaborative validation.

Validation and reproducibility are further challenged by the complexity of humanized models and observer bias in morphological assessment. Reported engraftment successes show coefficients of variation (CVs) of 15–40% across laboratories, while TB granuloma formation rates vary from 40–60% in NSG mice to 75–80% in NSG-SGM3 mice [170]. These findings underscore the need for objective, quantitative morphological metrics to ensure comparability and reliability.

Despite these advances, important gaps remain where AI has not yet delivered on its full promise. Current algorithms often rely on small, institution-specific datasets, limiting generalizability across different pathogens and model systems. Rare infection phenotypes, such as latent tuberculosis or mixed co-infections, remain under-represented in training datasets, reducing predictive accuracy. Another limitation is the “black-box” nature of deep learning outputs, which hinders biological interpretation and regulatory acceptance. Furthermore, no standardized benchmarks currently exist for validating AI-derived morphological predictions across laboratories, complicating reproducibility and comparison. Resource disparities also persist, as many institutions lack the high-performance computing infrastructure needed for large-scale AI-driven phenotyping. Finally, while predictive models show potential in mice, rigorous cross-validations against human clinical trial outcomes are still scarce, leaving an important translational gap. Addressing these limitations will be essential to ensure that AI can deliver on its promise of improving infectious disease modeling and therapeutic development.

8.2. Training the Next Generation of Researchers

Comprehensive training frameworks are needed to address the gap between the technical complexity of morphological phenotyping and current skill sets among infectious disease researchers. Training must span imaging, computational analysis, and translational methodology, equipping researchers to handle quantitative imaging outputs (e.g., granuloma integrity scores, fibrosis staging indices, lymphoid tissue organization metrics) and link them with prospective clinical endpoints such as treatment success, fibrosis regression, and CD4⁺ T-cell recovery [171].

Interdisciplinary training programs are central to this effort. These combine immunology and infectious disease foundations with hands-on experience in high-resolution imaging, automated analysis algorithms, and statistical modeling tailored to morphological phenotyping [172]. Integration of AI and machine learning training is now essential, as these tools underpin reproducible feature extraction and predictive modeling.

Standardized curricula must address diverse career tracks, from basic researchers to clinicians and scientists, and should include both methodological and regulatory considerations [16]. Partnerships between academia, industry, and regulatory bodies provide exposure to real-world challenges, such as optimizing morphological biomarkers for therapeutic development.

International collaborations and exchange programs can reduce resource disparities by sharing expertise and access to imaging platforms. Virtual training and online resources expand opportunities globally and allow flexible participation [173]. Mentorship networks also provide ongoing support, particularly for early-career researchers, while diversity and inclusion initiatives ensure broad representation within the field [174].

8.3. Emerging Technologies and Translational Relevance

New imaging and computational approaches are advancing morphological phenotyping by producing standardized, quantifiable endpoints that correlate with human trial outcomes. For example, AI-assisted fibrosis quantification in liver-humanized mice predicts a sustained virological response in HCV patients, while automated lymphoid tissue scoring in HIV models mirrors CD4⁺ T-cell recovery [175].

Label-free imaging technologies, such as reflectance-based REM-I platforms, preserve tissue architecture without staining artifacts, enabling accurate assessment of cellular morphology and tissue integrity [176]. AI-driven deep learning algorithms can extract subtle features such as hepatocyte size variability, granuloma compactness, and immune -cell clustering density, which predict treatment outcomes including fibrosis regression and TB control [177].

Spatial omics technologies link morphological alterations with molecular pathways at a single-cell resolution. In humanized mouse models, this enables precise analysis of species-specific immune responses, correlating structural changes with molecular markers of pathogenesis [178].

Live imaging combined with automated analysis allows tracking of morphological changes over time, linking dynamic granuloma maturation indices or hepatocyte injury scores with disease progression [179]. Multi-scale analysis frameworks that integrate cellular, tissue, and whole-animal phenotyping provide system-level maps of infection and therapeutic response.

Personalized approaches are emerging, enabling measurement of individual-specific indices such as granuloma integrity, AI-predicted fibrosis progression, and CD4⁺ T-cell recovery. These correlate with clinical outcomes including TB cure rates, liver fibrosis regression, and durable viral suppression in HIV [180]. Integrating patient-specific genetic or immunological data with morphological readouts strengthens predictive modeling for therapeutic response.

The future of morphological phenotyping will depend on greater integration, automation, and reproducibility. Sustained investment in technology, training, and infrastructure is required to establish morphological phenotyping as a standardized, clinically relevant component of translational infectious disease research.

9. Conclusions

Infectious diseases remain a global health threat, exacerbated by rising antimicrobial resistance and the limited predictive power of traditional animal models. The integration of humanized mouse models with quantitative morphological phenotyping (e.g., granuloma integrity scores, fibrosis staging, lymphoid architectural indices) and AI-driven analysis provides measurable endpoints that align with clinical outcomes such as pathogen load reduction, fibrosis regression, and immune recovery. These models enable high-resolution, real-time analysis of infection processes, while AI enhances data interpretation, scalability, and therapeutic prediction. Despite challenges such as engraftment variability and ethical complexities, the potential for transformative impact is clear. To realize this promise, the field must prioritize standardized protocols, cross-institutional training, and collaborative data ecosystems. As we stand at the intersection of technology and translational medicine, the integration of humanized mouse models with morphological scoring systems (e.g., standardized granuloma grading, lymphoid tissue architecture indices) and AI-enhanced image analysis provides measurable endpoints that can predict clinical efficacy of antivirals, antibiotics, and vaccines, supporting precision infectious disease research and global health solutions.