From Genome to Phenome: Opportunities and Challenges of Molecular Imaging

OVERVIEW OF MOLECULAR IMAGING MODALITIES

Molecular imaging, encompassing both PET and SPECT, provides a noninvasive means to visualize and quantify biochemical phenomena, such as receptor expression, enzyme activity, and cell migration in living organisms (Table 1). PET imaging is highly sensitive and offers comprehensive spatial coverage of the entire organism, allowing for a detailed map of key molecular processes and functional parameters that influence disease progression and outcomes. For instance, in Alzheimer disease (AD), PET imaging of amyloid-β and tau protein accumulation provides critical insights into the spatial distribution and progression of abnormal protein accumulation, offering valuable information for early diagnosis and precise intervention. Building on disease-specific applications, molecular imaging enables scientists to identify disease phenotypes associated with diagnosis, treatment, and monitoring, thereby providing objective evidence to guide personalized treatment.

Other molecular imaging technologies have evolved significantly, enhancing their capabilities to support human phenomics and precision medicine (Table 1). MRI is primarily used to generate high-resolution anatomic images of soft tissue. MRI focuses on analyzing the chemical composition and metabolic state of tissue by measuring the concentration of specific compounds (e.g., amino acids, fatty acids), thereby elucidating disease-related metabolic changes. Optical imaging plays a vital role in tumor detection and monitoring through the use of bioluminescence, fluorescence, and near-infrared imaging. Near-infrared imaging is particularly beneficial in intraoperative settings because of its high tissue penetration and minimal scatter, thereby enhancing tumor delineation during surgery (7). Ultrasound imaging offers real-time, portable, nonradioactive options for anatomic and flow-based assessments. Its applications are expanding into the microscopic domains, with innovations including microbubbles for targeted drug delivery and photoacoustic imaging for unique cancer signatures, further broadening its utility in biomedical research and clinical practice (8).

However, each of these imaging methods has limitations, including low resolution for PET and SPECT, lower sensitivity to molecular changes for MRI, limited depth penetration for optical imaging, and limited soft-tissue contrast for ultrasound (Table 1). It is difficult to meet the requirements of sensitivity, specificity, and targeting simultaneously. Multiple strategies have been proposed and applied to address this concern. In recent years, increasing multimodality probes have emerged, overcoming the limitations of single-mode imaging and widening the potential applications of molecular imaging. The incorporation of multimodal imaging into integrated multimodal platforms, such as molecular imaging, MRI, and optical imaging, could enhance the evaluation precision of treatment outcomes by synergizing functional, metabolic, and anatomic data. The integration and interpretation of multimodal imaging have created a greater demand for and dependency on image-analysis algorithms. To address this need, complex artificial intelligence (AI) technologies have been developed, leading to the emergence of various deep-learning algorithms, exponential growth of computing power, and abundance of big-data resources.

These imaging technologies have become increasingly important in bridging the gap between microscopic and macroscopic scales (Table 2), serving as critical translational tools from genome to phenome and widening the application of precision medicine.

ROLE OF MOLECULAR IMAGING IN PRECISION MEDICINE

As precision medicine continues to evolve, the application of molecular imaging has expanded, encompassing a wider range of clinical and research settings. This trend underscores the critical importance of tailoring medical interventions to individual patient profiles, thereby enhancing treatment efficacy and efficiency across the health care spectrum. Consequently, molecular imaging is an indispensable tool for exploring precision medicine, impacting the integration processes of measurement, computation, control, and construction (Fig. 2).

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FIGURE 2.

New paradigm of precision medicine. Molecular imaging transforms the traditional medical paradigm by bridging molecular-level insights with clinical manifestations. Empowered by artificial intelligence and phenome-driven parameters, this paradigm facilitates personalized clinical management and advances the implementation of precision medicine.

INTEGRATION OF MOLECULAR IMAGING WITH PHENOMICS DATA

With the emergence of the Human Phenome Project, the application of molecular imaging has further expanded, enabling precise qualitative assessment, localization, and quantitative characterization of phenotypes from micro and macro levels. The combination of molecular imaging with various omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and single-cell transcriptomics of white blood cells (i.e., assessments of innate and adaptive immunities and how they change over time), is expected to revolutionize disease diagnosis and treatment.

Imaging genomics integrates advanced image processing with genomic data to uncover the associations between image phenotypes and genomic information. The use of noninvasive molecular imaging modalities bridges the structural and functional characteristics of tissues with genomic alterations, aiding in the development of imaging biomarkers for predicting risk and clinical outcomes (14). Building on this foundation, imaging transcriptomics extends the analysis to the functional layer of gene expression, linking imaging phenotypes with RNA profiles to explore the dynamic molecular changes underlying disease progression (15). These approaches hold significant potential for exploring the molecular mechanisms of various diseases. This integration of imaging with genomic and transcriptomic data significantly advances the mapping of the human phenome, laying the groundwork for a new era of individualized medicine.

Imaging proteomics and metabolomics significantly enhance the integrated framework by delving deeper into the proteomic and metabolic layers of cellular function. These fields also correlate observable imaging phenotypes with protein expression and metabolic networks, potentially revealing new disease biomarkers and mechanisms. For example, combining PET scans with proteomic analysis has been crucial in the identification of specific cardiac proteins that influence heart disease, enabling targeted interventions based on individual proteomic features (16). Most importantly, the labeling of radioactive isotopes allows direct visualization of intratissue metabolic products and processes by using different position-labeled compounds with the same chemical structure, and protein expression changes in vivo, providing quantitative data support for precision medicine. The most widely used imaging agent in PET imaging—¹⁸F-FDG—reflects cellular glucose metabolism and is extensively used in diagnosis and monitoring of tumors and neurologic and cardiovascular diseases. Another agent used in PET imaging is radiolabeled fibroblast activation protein inhibitor, which aids in the detection of primary and metastatic tumor foci. The radiolabeling of key pathologic proteins (e.g., α-synuclein) and observation of their distribution and expression allows for early diagnosis and research into the mechanisms of neurodegenerative diseases (17).

Combining phenomic profiling with molecular imaging can further enhance personalized therapeutic strategies and has the potential to transform precision medicine by refining our understanding of disease mechanisms and enabling the development of specific biomarkers that enhance early diagnosis and treatment.

CHALLENGES AND POTENTIAL SOLUTIONS

Current challenges in integrating molecular imaging with phenomics data include data heterogeneity, standardization issues, the segmented accumulation of various genomic and phenomic data in the health care system, lack of interoperability, cost associated with the multiple phenomic approach, limited (or still developing) early effective intervention technologies for many cancers and neurodegenerative disorders, and the need for interdisciplinary collaboration. Potential solutions involve developing standardized protocols, enhancing data-sharing frameworks, and fostering collaboration among scientists, clinicians, and data engineers. Data heterogeneity, arising from the use of different imaging modalities, formats, and analysis techniques, can be mitigated by standardizing data collection and processing protocols (18). Interdisciplinary collaboration leverages the strengths of each discipline to enhance the understanding of disease mechanisms, guide the development of advanced analytic methods, facilitate the application of innovative imaging agents, and ultimately drive the advancement of precision medicine.

Despite significant advances, effectively integrating AI to analyze large-scale phenomics and imaging data remains challenging. Although AI provides powerful tools for data analysis, concerns regarding algorithm transparency, interpretability, and practical implementation in clinical settings persist (19). AI solutions that prioritize understandability and transparency are critical to building trust among clinicians and researchers when making AI-driven decisions. Additionally, it is essential that AI algorithms are adaptable to clinical environments and compliant with regulatory standards for their successful integration into health care applications.

Another challenge in molecular imaging is enhancing the portability and reducing the costs of imaging devices. Developing compact, cost-effective, and efficient imaging technologies is essential for minimizing diagnostic procedure times and reducing economic burdens. Advances that enable portable and user-friendly operations without compromising diagnostic accuracy are critical for the broader adoption of molecular imaging. These innovations can improve accessibility to molecular imaging and broaden its applicability in human phenomics research and diverse health care settings.

IMPACT AND FUTURE PROSPECTS

Longitudinal and comprehensive detection of phenomic data, combined with AI, will lead to revolutionary changes in health care. As more countries and regions join the International Human Phenome Project, under the phenomics standards and data-sharing framework led by the International Human Phenome Consortium, noninvasive and quantitative molecular imaging technology will be a key tool for resolving the core issue of how microscopic phenotypes systematically affect the human body. On the basis of this understanding, it is anticipated that wearable and portable devices equipped with capabilities for molecular imaging will be developed to provide real-time assessment of biologic phenotypic changes (20). Meanwhile, by leveraging robust capabilities of AI in big-data analysis, pattern recognition, and predictive modeling, critical biomarkers and subtle physiologic changes can be detected well before they manifest as overt health issues. This technologic synergy is expected to transform health care, shifting it from a reactive approach focused on treatment to a proactive model that emphasizes prevention.

Furthermore, the combination of molecular imaging-based human phenome profiling and AI-driven drug development is enabling precise and targeted approaches in the creation of new pharmaceuticals, particularly theranostics (21). This strategy can transform traditional diagnosis-treatment paradigms through early diagnosis and immediate, personalized interventions. For instance, consider a patient at risk of developing AD. Through the combination of molecular imaging and theranostics probes, not only can AD be diagnosed at a nascent stage, targeted drug therapy tailored to the individual’s specific disease biomarkers could be initiated during the same visit. Subsequently, patients could wear smart molecular imaging devices to continuously monitor treatment effectiveness and any potential progression. This strategy maximizes health outcomes and quality of life by ensuring that interventions are both timely and aligned with the patient’s evolving health status, holding promise for a profound advancement in proactive medical care.

DISCLOSURE

This study was partially supported by the National Natural Science Foundation of China (82394433, 82361148130, 82030049, 32027802) and the Fundamental Research Funds for the Central Universities (226-2024-00059). No other potential conflict of interest relevant to this article was reported.