Bridging Organ‐on‐a‐Chip and Omics: A Multi‐Dimensional Frontier in Biomedical Research

ABSTRACT

Organ‐on‐a‐Chip (OOC) technology offers a powerful platform for replicating human tissue‐specific microenvironments, thereby narrowing the translational gap between conventional biomedical models and actual human physiology. Concurrently, omics technologies deliver comprehensive molecular‐level insights into biological systems. This review highlights the transformative potential of integrating OOC platforms with high‐throughput omics methodologies. We systematically examine the classification, structural configurations, and engineering principles underlying OOC systems, alongside the defining attributes of key omics domains—genomics, transcriptomics, proteomics, and metabolomics. The convergence of dynamic OOC models with advanced omics technologies enables high‐resolution, multi‐dimensional analyses across numerous biomedical applications, including drug metabolism, disease mechanisms, environmental toxicity assessments, and host‐microbiome interactions. This interdisciplinary integration is driving a paradigm shift in precision and translational medicine. However, several challenges remain to be addressed, such as the development of whole‐organ mimetics, adaptation of sample collection techniques, and real‐time artificial intelligence‐based integration of biosensor data with multi‐omics datasets. Addressing these hurdles will be vital for unlocking the full potential of this technological synergy in biomedical science.

The integration of Organ‐on‐a‐Chip and omics opens new avenues, facilitating multi‐dimensional exploration of key issues in biomedical research.

1. Introduction

Traditional biomedical research has long depended on animal models and in vitro cell culture systems to investigate disease mechanisms and evaluate pharmacological effects. While invaluable, these approaches are increasingly recognized for their limitations. Two‐dimensional (2D) culture models fail to accurately accurately simulate the physiological manifestations of living tissues/organs, intra‐organ interactions and microenvironmental factors. Additionally, species differences often render animal experiments unable to replicate human results (Q. Wu et al. ²⁰²⁰) while also presenting high costs and ethical concerns. and ethical concerns. These shortcomings have intensified the search for more physiologically relevant human‐based models. Emerging as a promising alternative is Organ‐on‐a‐Chip (OOC) technology, which represents an innovative microphysiological system that combines microfluidics with advanced cell culture techniques to simulate the structure and function of human tissues in vitro. These miniature, bioengineered devices recreate key physiological and mechanical aspects of organ systems, providing a more accurate representation of human biology (Low et al. 2020).

Although the application value and academic influence of OOC technology in biomedical engineering have grow increasingly prominent, it still has limitations in its ability to systematically analyze molecular‐level events (such as gene expression regulation, post‐translational modification of proteins, etc.). This hinders the full elaboration of disease molecular mechanisms or the molecular basis of drug actions, thereby rendering the cross‐integration of OOC and omics technologies a highly impactful research frontier. Early integrations utilized mass spectrometry (MS), renowned for its sensitivity and resolution, to develop Chip‐MS platforms capable of analyzing cellular secretions such as metabolites and proteins (Mao et al. 2018). However, these approaches were initially limited to single molecular level. Recent advancements in single‐cell sequencing, and spatial omics technologies have transformed OOC from a platform for one‐dimensional detection into a versatile system for multi‐dimensional analysis. State‐of‐the‐art omics methodologies—including single‐cell sequencing, spatial transcriptomics, and multi‐omics—facilitate holistic characterization of cellular and molecular dynamics within OOC platforms. Single‐cell sequencing allows for the profiling of individual cells, revealing the heterogeneity within cell populations that was previously masked in bulk analysis (Woo and Eyun 2025). Spatial transcriptomics, on the other hand, provides information on the spatial localization of gene expression, enabling the study of how cells interact and function in their native tissue microenvironment (Lim et al. 2025). The combination of these omics techniques with OOC opens novel avenues for understanding the complex biological processes at a more detailed and comprehensive level.

In recent years, research on OOC technology has expanded dramatically, integrating multiple previously disparate technologies (Tabatabaei Rezaei et al. 2023). This has spurred the publication of a series of review articles focused on the integration of OOC with cutting‐edge techniques—such as sensor technology and 3D bioprinting (Clarke et al. 2021; Liao et al. ²⁰²⁵; Y. Wu et al. ²⁰²⁵). However, systematic investigations into the synergistic application of OOC and Omics technologies remain relatively scarce. In this review, we first provide a detailed overview of the fundamental principles, classification schemes, and engineering design strategies of OOC systems, as well as the key methodologies underpinning diverse omics. Subsequently, we further explore how their integration propels multidimensional investigations into drug metabolism mechanisms, refines diagnostic‐therapeutic strategies for complex diseases, delineates dynamic host‐microbial interplay, and enables real‐time monitoring of environmental biomarkers (Figure 1). Finally, a critical and in‐depth discussion of the challenges and potential solutions related to the integrated innovation of these two technologies is provided. This convergence of disciplines not only advances the frontiers of life science research but also injects new momentum into precision medicine and translational medicine.

Figure 1.

Schematic diagram of the synergistic applications of Organ‐on‐a‐Chip and omics technology.

2. Classification and Structure of OOC Systems

OOC platforms are microengineered cell culture devices fabricated using techniques derived from microfabrication and microfluidic engineering. These devices typically incorporate features such as perfusable chambers and microchannels that simulate key aspects of tissue architecture, which can effectively simulate the multi‐cellular layer structure, tissue interface, physico‐chemical microenvironment, and vascular perfusion dynamics (Huh et al. 2013). Their compact design and scalability support high‐throughput experimentation while preserving functional fidelity. One of the core advantages of OOC systems lies in their ability to mimic the native tissue microenvironment at the cellular level, thereby improving experimental accuracy and pharmacological relevance. These platforms also offer logistical benefits: reduced costs, shorter experimental cycles, ease of optical observation, and seamless integration with analytical devices. In recognition of its transformative potential, the World Economic Forum named OOC one of the “Top Ten Emerging Technologies” in 2016 (Q. Wu et al. ²⁰²⁰). A major milestone was achieved in August 2022, when the U.S. Food and Drug Administration (FDA) approved the first clinical trial (NCT04658472) based solely on preclinical data derived from OOC experiments. In 2023, the FDA further signaled a paradigm shift by removing the requirement for animal testing prior to initiating human trials—reinforcing the growing acceptance of OOC as a viable alternative for preclinical assessment (Wadman 2023). In April 2025, the FDA went on to announce that new approach methodologies, including OOC technology, will be prioritized for preclinical research within the next 3–5 years (Shrimali et al. 2025; von Keutz ²⁰²⁵; L. Wang et al. ²⁰²⁵). The FDA defines OOC systems as microfluidic devices capable of faithfully reproducing physiological and mechanical cues experienced by cells in vivo. One common way to classify OOC systems is by the organ or tissue they are designed to emulate (L. Jiang et al. ²⁰²²). Representative models include Liver‐on‐a‐Chip, Kidney‐on‐a‐Chip, Brain‐on‐a‐Chip, Cardiac‐on‐a‐Chip, Gut‐on‐a‐Chip, Lung‐on‐a‐Chip, Muscle‐on‐a‐Chip, and Multiorgan‐on‐a‐Chip platforms, as illustrated in Figure 2 (Chen et al. 2021; Eckstrum et al. ²⁰²²; Kim et al. ²⁰¹²; Kızılkurtlu, Polat, Aydın, and Akpek ²⁰¹⁸; Madden et al. ²⁰¹⁵; Maoz et al. ²⁰¹⁸; Mathur et al. ²⁰¹⁵; McAleer et al. ²⁰¹⁹; Slaughter et al. ²⁰²¹; Weber et al. ²⁰¹⁶).

Figure 2.

Some of the OOC that have been developed for corresponding organs of the human body (L. Jiang et al. ²⁰²²).

A functional OOC device typically comprises four essential components: cells, biomaterials, stimuli, and sensors (Figure 3) (Y. Wang et al. ²⁰²³). Cell selection is a critical initial step and is determined by factors such as accessibility, viability, cultivation requirements, and the capacity to form physiologically relevant structures. Common cellular sources include established human cell lines, primary cells harvested from donors, and cells derived from induced pluripotent stem cells (iPSCs). With ongoing advancements in genome editing, gene‐modified stem cells are also being explored for their potential to support patient‐specific, precision medicine applications. Biomaterials used in chip fabrication must support cell viability, enable tissue organization, and sustain physiologically relevant conditions. Ideal materials are biocompatible, gas‐permeable, and optically transparent. While materials such as glass, silicon, and various polymers are used, polydimethylsiloxane (PDMS), which is a silicon‐based elastomer, is among the most widely employed due to its affordability, compatibility with soft lithography, and rapid prototyping capability (X. Wang et al. ²⁰²⁴). However, PDMS also has drawbacks, including high permeability to gases and a propensity to absorb small hydrophobic molecules, which can affect drug bioavailability and cellular response. Therefore, the polymer‐based surfaces of the device also need to be treated or coated to prevent cell adhesion or drug loss (Low et al. 2020). To simulate the physiological environment, OOC systems require appropriate stimulation that mimics in vivo signals, such as mechanical, chemical, and electrical cues. For example, electrical stimulation is used in Heart‐on‐a‐Chip systems to promote synchronized contraction and functional maturation of cardiomyocytes, thereby enhancing their physiological relevance (L. Jiang et al. ²⁰²²). Lastly, sensor integration is essential for monitoring dynamic changes in the microenvironment and evaluating organ‐level functions in real time. Conventional analytical methods such as polymerase chain reaction (PCR), enzyme‐linked immunosorbent assay (ELISA), and MS are commonly applied (Li and Tian ^2018b). Their integrated systems with OOC are increasingly favored because they enable both sensitive detection and dynamic tracking of cellular and micro‐tissue characteristics. These include electrochemical biosensors, embedded microelectrodes, compact microscopy modules, and optical sensors for measuring pH, oxygen concentration, and temperature. Additional engineering considerations include platform modularity, universal medium compatibility, and the potential incorporation of a “bubble eliminator” to prevent airflow obstruction caused by trapped air bubbles within the channel (Low et al. 2020).

Figure 3.

Schematic diagram of the four main components of OOC (Y. Wang et al. ²⁰²³).

3. Omics: A New Era in Life Sciences

The advent of omics technologies has revolutionized the life sciences space, ushering in a new era that transcends traditional approaches limited to the study of single molecules or isolated phenomena. “Omics” refers to a suite of high‐throughput techniques designed to analyze the complete set of biological components at various molecular levels within a system (Lv et al. 2024). These technologies enable comprehensive profiling of large‐scale data and are categorized into distinct fields based on their primary targets of analysis, such as genomics, transcriptomics, proteomics, and metabolomics. When two or more omics platforms are integrated, the approach is termed multi‐omics. This integrative strategy allows for a multidimensional view of biological systems by capturing both complementary and synergistic interactions across molecular layers, thereby offering a more holistic and dynamic understanding of life processes (Picard et al. 2021).

3.1. Genomics

The term “genomics” was introduced by American geneticist Thomas H. Roderick in 1986, marking the foundation of a field that has since evolved alongside molecular biology and genetics. Genomics investigates the architecture, function, and evolutionary history of genomes, as well as explores gene‐editing technologies and how genetic factors contribute to organismal traits. Leveraging high‐throughput sequencing technologies, scientists can systematically decipher DNA sequence organization, unravel the operational mechanisms of functional genes, non‐coding regions, and regulatory networks (Missinato et al. 2023), and elucidate their roles in organismal development, disease pathogenesis, and environmental adaptation (Nikopoulou et al. 2023). In biomedical contexts, genomics is also instrumental in pinpointing genetic variants linked to disease susceptibility, therapeutic response, and prognostic outcomes (Hasin et al. 2017; Kartiganer et al. ²⁰²³). Notably, the integration of genomics and OOC technology is centered on addressing key practical challenges in OOC research, with genomic techniques (e.g., variation analysis, editing algorithms) deeply embedded into the development and application of OOC. A compelling example is the work by Fieni et al. (2024) who identified interleukin‐30 (IL‐30) as a promoter of prostate cancer progression. By employing two OOC platforms to model tumor cell metastasis in the circulatory system, and using CRISPR‐Cas9 to precisely knock out the IL‐30 gene, the study observed suppressed expression of metastasis‐associated genes and upregulation of tumor suppressors. These findings highlight the potential of IL‐30‐targeted gene editing as a precision oncology strategy to impede prostate cancer growth and spread (Fieni et al. 2024). However, compared with other omics technologies, genomics provides a static genetic blueprint that only reflects genetic potential, failing to capture dynamic changes in gene expression, actual functional performance, or the immediate effects of external stimuli and pathological conditions on biological systems (Du et al. 2024).

3.2. Transcriptomics

Transcriptomics focuses on the comprehensive analysis of all RNA transcripts expressed in a cell or organism, enabling the capture of nuances in gene expression levels, transcript diversity, and the regulatory mechanisms governing these processes (Kaur et al. 2023). Notably, transcriptomic profiles in living organisms exhibit dynamic changes driven by tissue specificity, developmental stages, and pathological conditions, thus, transcriptomic approaches possess substantial theoretical value and practical relevance for investigating the molecular mechanisms through which endogenous and exogenous factors regulate gene expression (Lv et al. 2024). Over the last decade, this field has progressed from bulk measurements to single‐cell resolution, greatly enhanced by high‐throughput single‐cell analysis (Shen et al. 2022). These advancements now enable the whole genome expression of a large number of single cells at unprecedented resolution, making it possible to detect rare cell populations, delineate cell lineages, and decode intercellular communication. Such capabilities have proven crucial in dissecting the complexity of gene regulation, signal transduction, and disease mechanisms (Scholz et al. 2024; Shapiro et al. ²⁰¹³). Furthermore, the integration of transcriptomic with OOC enables real‐time capture of gene expression dynamics in specific cells within dynamic culture systems, providing a more physiologically relevant research model for unraveling molecular response mechanisms under drug intervention or pathological stimuli (Donnaloja et al. 2023; Ferrari et al. ²⁰²³; Palikuqi et al. ²⁰²⁰). In a study by Tian et al. a Gut‐on‐a‐Chip model was employed to replicate inflammatory bowel disease (IBD) by exposing intestinal epithelial monolayers to lipopolysaccharide and tumor necrosis factor. Transcriptomic analysis of the epithelium revealed that Bifidobacterium modulated inflammation‐related pathways, upregulated genes associated with tight junction integrity, and promoted mucosal repair, offering novel insights for probiotic therapy and microbiome‐host interactions (J. Liu et al. ²⁰²³).

3.3. Proteomics

Proteomics is the large‐scale study of the entire complement of proteins—referred to as the proteome—within a cell, tissue, or organism. This discipline delves into protein expression dynamics, post‐translational modifications, and molecular interactions, thereby illuminating the functional execution of genetic instructions. Common proteomic methodologies involve protein extraction, enzymatic digestion, affinity purification, and gel‐based or chromatographic separation techniques (Jaremek et al. 2021). Unlike transcriptomics, proteomics targets the actual executors of biological functions, compensating for transcriptomics’ bias toward transcription over function. It directly reflects protein activity and interaction networks. However, compared with metabolomics, proteomics remains at an upstream molecular level, unable to capture ultimate phenotypic traits or sensitive immediate biochemical responses to external stimuli or pathological states (Bunnik and Le Roch ²⁰¹³; Hayes et al. ²⁰²⁴). By decoding the proteome at a specific time point, researchers can uncover critical roles of proteomics across three aspects: first, analyzing protein expression levels under distinct conditions to construct protein expression profiles, which reveal protein dynamics under specific physiological or pathological states; second, in disease research, enabling the identification of disease biomarkers, in‐depth exploration of disease pathogenesis, and development of targeted drugs (Currie and Delles 2017); third, elucidating the key roles of proteins in processes including cell differentiation, metabolic regulation, and signal transduction. For example, Yang et al. integrated OOC platforms with proteomic analyses to identify distinct neutrophil phenotypes correlated with sepsis severity. Their findings shed light on the reasons behind the ineffectiveness of certain sepsis treatments and identified promising therapeutic targets for more personalized interventions in septic patients (Q. Yang et al. ²⁰²⁴).

3.4. Metabolomics

Modern metabolomics integrates high‐throughput detection techniques, such as mass spectrometry coupling (LC‐MS/GC‐MS) and nuclear magnetic resonance (NMR), and combines them with multivariate statistical analysis and metabolic pathway modeling methods. It enables a systematic analysis of small‐molecule metabolites, including amino acids, lipids, carbohydrates, and other intermediates of metabolic pathways (Qiu et al. 2023). Positioned downstream of the genome, transcriptome, and proteome, metabolomics captures the real‐time biochemical responses of organisms to external stimuli, genetic perturbations, or pathological states (Johnson et al. 2016), aiming to reveal an organism's metabolic status, metabolic pathways, and their dynamic changes under physiological and pathological conditions. As a critical bridge linking macroscopic phenotypes to micromolecular mechanisms, this approach enables in‐depth investigation of how metabolites function in disease initiation, progression, and treatment, while offering a vital perspective for deciphering the metabolic regulatory networks of organisms (Su et al. 2011). In a study by Michael et al. a Tumor‐on‐a‐Chip model was developed to mimic the pancreatic ductal adenocarcinoma microenvironment. Using MS‐based untargeted metabolomics, researchers mapped region‐specific metabolic profiles, revealing tumor‐stroma interactions that could refine the predictive accuracy of future OOC model and enhance understanding of critical pathophysiological processes, thereby offering tools for predictive modeling and therapeutic development (Choucha Snouber et al. 2013; Mohan et al. ²⁰²⁴).

3.5. Multi‐Omics

In the process of life science research, as human exploration continues to deepen, single‐omics approaches alone are sometime insufficient to fully unravel complex biological mechanisms (Zhu et al. 2024). While such techniques can characterize functions of specific dimensions within biological systems (Lv et al. 2024), they frequently omit critical regulatory information and fail to capture the crosstalk and complementary associations across distinct molecular levels. However, by conducting comprehensive analyses across multiple molecular levels, we can better capture the complementary effects and synergistic interactions among different molecular layers throughout life processes, thereby gaining a more holistic and in‐depth understanding of biological phenomena. Multi‐omics technology, as a research method that integrates two or more omics disciplines, offers an even more comprehensive and systematic perspective on life processes by synthesizing these multiple layers of data (Sun and Hu 2016). When applied to OOC systems, multi‐omics analysis enables dynamic characterization of molecular network responses to perturbations—such as drug exposure or disease simulation—thereby yielding mechanistic insights into complex physiological and pathological mechanisms. Wang et al. (2024) reviewed that by integrating biosensors, multi‐omics, and 3D imaging in microfluidic‐based intestinal barrier chip, intestinal epithelial cell differentiation, tight junction protein expression, and transepithelial electrical resistance (TEER) changes could be tracked in real time to assess barrier integrity. In IBD models, multi‐omics data combined with single‐cell sequencing can identify key genes and protein markers associated with barrier dysfunction, providing multi‐dimensional indicators for drug toxicity assessment (H. Wang et al. ²⁰²⁴).

4. Integrating OOC and Omics: Synergistic Applications and Innovations

To establish a clear roadmap for the practical integration of OOC and omics technologies, this review proposes a systematic integration framework composed of five sequential and interlinked modules: (1) experimental design and OOC model selection; (2) omics modality selection tailored to specific biological questions; (3) microscale sample collection and pretreatment; (4) high‐dimensional omics data acquisition; and (5) multi‐omics data integration, mechanistic interpretation, and translational validation. This structured workflow transforms abstract concepts of “multi‐dimensional biological exploration” into an operable research strategy for investigators.

The convergence of OOC systems with omics technologies represents a groundbreaking advance in life sciences. In traditional research, the inherent complexity of biological systems frequently results in ambiguous connections between observed phenotypes and their underlying molecular mechanisms. However, through the integration of OOC platforms and omics approaches, a far more direct link is established between phenotypic changes and molecular signatures: On the one hand, OOC platforms replicate physiologically relevant tissue microenvironments, thereby producing biologically meaningful samples that enhance the accuracy of omics analyses. On the other hand, omics technologies enable high‐resolution mapping of molecular events within OOC systems, providing a systems‐level understanding of complex biological phenomena (Dervisevic et al. 2019). As compelling evidence, Claudia et al. demonstrated that intestinal epithelial cells cultured on extracellular matrix gels under dynamic flow exhibited significantly upregulated expression of genes governing core intestinal functions—including digestion, nutrient transport, and hormonal regulation—compared with static cultures, and these results were further validated by transcriptomic analysis (Beaurivage et al. 2020). Correspondingly, many studies have confirmed that a gut‐on‐a‐chip system shortens the differentiation period of Caco‐2 cells from 21 days to approximately 1 week (Hosic et al. 2021; W. Shin and Kim ²⁰¹⁸). This bidirectional synergy not only accelerates mechanistic insights but also validates the physiological relevance and biomimetic reliability of OOC models (Beaurivage et al. 2020; Middelkamp et al. ²⁰²¹).

4.1. Drug Development

4.1.1. Mechanistic Studies of Drug Metabolism

Drug metabolism is a multifaceted physiological process that plays a pivotal role throughout all phases of drug development. Understanding metabolic pathways allows for rational modification of drug structures to improve pharmacokinetics, bioavailability, and therapeutic efficacy (Z. Zhang and Tang ²⁰¹⁸), while the experiments are also tailored to individual metabolic differences can accelerate drug development timelines. As the body's core organ for metabolism and detoxification, the liver has witnessed the deep integration of Liver‐on‐a‐Chip systems and omics technologies—an integration that has emerged as a key technical approach in hepatic research, with substantial application value for simulating drug metabolism kinetics (J. Jiang et al. ²⁰¹⁹). In 2013, (Legendre et al. 2013). utilized microfluidic biochips to culture rat hepatocytes and, through combined metabolomic and proteomic analyses, discovered enhanced metabolic activity and elevated expression of key enzymes under microfluidic conditions. This provided a more reliable in vitro platform for evaluating drug metabolism and toxicity (Legendre et al. 2013). With the emergence of microfluidics‐based OOC, Mathieu et al. refined this approach by cultivating hepatocyte‐like cells (HLCs) derived from human iPSCs in microfluidic devices that simulated the liver microenvironment. Coupling this system with multi‐omics analysis enabled the characterization of mature hepatic functions and demonstrated the platform's capability to metabolize a broad spectrum of drugs (Danoy et al. 2021). Further supporting these findings, Woojung et al. developed an Intestinal‐on‐a‐Chip model to recapitulate physiological fluid flow and mechanical strain. Using single‐cell RNA sequencing, their study revealed that dynamic 3D culture significantly enhanced the expression of drug metabolism‐related genes in Caco‐2 cells compared with static conditions, validating the improved physiological relevance and reliability of OOC systems for preclinical drug assessment (Figure 4A) (Woojung Shin et al. 2022). Similarly, Messelmani et al. (2023) explored pharmacokinetic interactions by co‐culturing various cell types in OOC microenvironments and exposing them to pharmaceutical agents. Metabolomic profiling revealed significant alterations in drug‐related metabolic pathways, underscoring the potential of the Metabolomics‐on‐a‐Chip strategy to predict individualized drug responses (Messelmani et al. 2023). Collectively, these studies highlight the promise of integrating omics with OOC platforms in advancing pharmaco‐metabolomics and precision drug development.

Figure 4.

(A) Single‐cell transcriptomic characterization of epithelial heterogeneity of CACO‐2 under different culture conditions (Woojung Shin et al. 2022). (B) RAC1 and FOS‐mediated gene expression differentials and signaling pathways (Hiratsuka et al. 2022). (C) The overall structure of the ocular surface and the corresponding CEpOC structure and detection device, LC‐MS (Abdalkader et al. 2021).

4.1.2. Drug Target Discovery

The traditional paradigm of drug development through target‐based drug discovery emphasizes the identification of specific biological molecules (such as proteins, enzymes, or receptors) that are implicated in disease processes. These targets serve as pivotal control points in disease onset and progression and can be directly modulated by therapeutic agents designed to bind selectively to them. OOC platforms, when integrated with omics technologies, offer a powerful strategy to uncover surrogate clinical endpoints. These surrogate markers, derived from comprehensive molecular profiling, can serve as robust indicators of therapeutic efficacy in human patients and may be widely adopted in clinical research settings (Low et al. 2020). In a notable example, Ken et al. (2022) developed a Kidney‐on‐a‐Chip model capable of mimicking the physical forces associated with urine flow within renal tubules. Through transcriptomic and proteomic analyses, they discovered that mechanical stimuli activated signaling pathways involving the two mechanosensitive molecules FOS and RAC1. Follow‐up validation through gene editing and pharmacological interventions confirmed the potential of these molecules as therapeutic targets (Figure 4B) (Hiratsuka et al. 2022). Building on such methodologies, Valentina and other scholars from multiple countries collaborated to established an Artery‐on‐a‐Chip disease model. They integrated multi‐omics data from diseased tissue samples to identify actionable molecular targets and validated the efficacy of clinically relevant drugs, such as lenvatinib, within the microfluidic system. The synergistic application of OOC and omics technologies is rapidly gaining traction as a cutting‐edge approach in therapeutic target identification, enabling translational insights into disease biology and pharmacological responsiveness (Paloschi et al. 2023).

4.1.3. Drug Screening

Drug screening constitutes a critical phase of early‐stage drug development, involving the evaluation of extensive chemical libraries to identify compounds with high specificity and potency against defined therapeutic targets. This stage often resembles the proverbial search for a needle in a haystack—complex, time‐intensive, and resource‐demanding. Since the seminal work by Huh et al. in ²⁰¹³, who introduced the first Lung‐on‐a‐Chip model at the Wyss Institute, OOC platforms have evolved into sophisticated biomimetic systems capable of recapitulating the physiological and mechanical properties of native tissues. These advancements have significantly enhanced their utility as preclinical drug screening tools (Li and Tian ^2018a), particularly in the development and precision screening of antitumor agents. When effectively integrated with omics technologies, OOC systems can incorporate co‐culture models of patient‐derived primary cells and immune cells across molecular, cellular, and tissue scales. This integration enables the development of personalized efficacy evaluation platforms, thereby enabling accurate prediction of drug sensitivity (Miller et al. 2020; Trujillo‐de Santiago et al. ²⁰¹⁹). For instance, Jie et al. (2017) designed a biomimetic intestine‐liver glioblastoma microfluidic platform to evaluate the therapeutic efficacy of drug combinations for glioblastoma. By employing liquid chromatography‐mass spectrometry (LC‐MS), the platform enabled quantitative assessment of drug metabolism and apoptosis induction, ultimately identifying synergistic drug pairs with heightened anticancer activity (Jie et al. 2017). Similarly, Zhang's research group developed a multi‐organ, three‐dimensional microfluidic system to simulate inter‐organ communication between lung tumors and liver tissue. Using RNA sequencing and proteomic profiling, they investigated the impact of oxygen gradients on on lung cancer‐induced liver metastatic signaling (Zheng et al. 2021). The results illuminated key pathways involved in hypoxia‐driven metastasis, affirming the system's value for identifying hypoxia‐responsive therapeutic agents. Notably, while single‐organ chips provide superior signal‐to‐noise ratios for resolving organ‐intrinsic molecular events, the interrogation of inter‐organ metabolic crosstalk, as exemplified by tumor‐liver communication, necessitates the deployment of multi‐organ chip architectures. These multi‐organ systems uniquely capture the systemic biochemical relays that govern distal organ reprogramming, providing a more comprehensive understanding of complex biological interactions.

4.1.4. Drug Toxicity Evaluation

Toxicological assessment is an essential component of new drug development, providing critical insights into a candidate compound's safety, pharmacodynamics, and adverse effect profile prior to clinical trials. OOC models are increasingly being leveraged to simulate human‐specific responses to drug exposure under controlled conditions, enabling more predictive and mechanistic toxicity studies. Ben et al. employed an OOC model of the human neurovascular unit (NVU) to dissect the contributions of individual cell types to drug responses. Quantitative proteomic profiling was applied to characterize methamphetamine‐induced neurotoxicity and barrier dysfunction. In situ microscale sampling and proteomic analysis revealed marked alterations in proteins associated with barrier integrity, oxidative stress, and mitochondrial function, supporting the reliability and translational potential of this strategy for mechanism‐focused human‐relevant toxicity evaluation (Maoz et al. 2018). In a separate study, Danoy et al. (2021) engineered a Liver‐on‐a‐Chip platform featuring spatially resolved oxygen gradients. Targeted transcriptomic analysis of distinct microenvironments within the chip revealed differential expression of detoxification and xenobiotic metabolism pathways, highlighting the importance of microenvironmental control in toxicity modeling (Danoy et al. 2020). Additionally, Rodi et al. pioneered the integration of a Corneal Epithelium‐on‐a‐Chip (CEpOC) system with untargeted metabolomics to study ocular drug permeability and toxicity. Their experiments demonstrated active secretion of oxidative stress biomarkers such as glutathione and uric acid across the corneal epithelium, mediated by specific transporters. By combining extracellular metabolite profiling with transporter gene expression analysis, they developed a dual‐functional platform capable of supporting both pharmacokinetic/pharmacodynamic (PK/PD) modeling and toxicological biomarker discovery (Figure 4C) (Abdalkader et al. 2021). Moreover, research teams led by Tyler and Erin both utilized multi‐omics Liver‐on‐a‐Chip platforms to investigate the toxicodynamics of VX, which is a potent nerve agent. Their work revealed that VX exposure induces mitochondrial dysfunction, oxidative stress, and inflammatory responses at the cellular level. These findings not only corroborate known toxicological mechanisms but also underscore the ability of omics‐integrated OOC models to serve as refined human surrogates in toxicology research (Gallagher et al. 2023; Garrett et al. ²⁰²³).

4.2. Disease Diagnosis and Treatment

OOC technology has become an indispensable tool for modeling disease pathophysiology, testing therapeutic interventions, and evaluating off‐target effects. When coupled with omics techniques, these platforms allow for in‐depth molecular characterization of gene mutations, transcriptomic signatures, and protein expression patterns in a physiologically relevant microenvironment. This integration is transforming the landscape of disease modeling and therapeutic strategy development. Marder et al. (2024) demonstrated the versatility of their OOC platform by modeling atherosclerosis and validating their findings through proteomic analyses (Marder et al. 2024). Further enhancing physiological relevance, Jacquelyn and colleagues combined OOC with metabolomics and other advanced analytical methods to investigate the inflammatory regulation of the blood‐brain barrier (BBB). Their dual‐chamber NVU chip, in conjunction with cytokine assays and MS, allowed for precise profiling of metabolic changes triggered by inflammatory stimuli (e.g., IL‐1β, TNF‐α, MCP1/2), shedding light on the BBB's dynamic responses during neuroinflammation (Brown et al. 2016). Shi's team employed single‐cell RNA sequencing in tandem with an integrated microfluidic chip to isolate and analyze circulating tumor cells (CTCs) from patient blood samples. The chip‐based system yielded high‐quality single‐cell RNA profiles of both CTCs and white blood cells, facilitating studies on tumor heterogeneity, metastasis, and treatment resistance (Shi et al. 2021). In another study, Xu et al. created a multi‐organ chip model to replicate the metastatic cascade from primary lung tumors to the brain. Proteomic analyses revealed hyperactivation of the glutathione (GSH) metabolic pathway in metastatic brain lesions, offering new targets to overcome therapeutic resistance in brain metastases of lung cancer (Figure 5A) (M. Xu et al. ²⁰²⁰). Pediaditakis et al. (2021) developed a Substantia Nigra brain‐chip to model Parkinson's disease by introducing α‐synuclein (αSyn) fibrils into the system. Their study demonstrated that αSyn aggregation activates inflammatory pathways that compromise the BBB, recapitulating key features of Parkinsonian pathology. These models offer invaluable insights into the spatial and temporal progression of neurodegenerative diseases (Pediaditakis et al. 2021). Other recent applications have employed transcriptomics‐integrated OOC platforms to study inflammatory responses triggered by gas exposure, demonstrating that multi‐omics integration can cover the entire chain of analysis from molecular perturbations to observable phenotypes (Z. Li et al. ²⁰²⁴). As summarized in Table 1, the convergence of omics technologies and OOC models is increasingly recognized as a cornerstone in mechanistic research and precision diagnostics, offering unprecedented resolution and translational potential across diverse biomedical domains.

Figure 5.

(A) Schematics of BM process construction/operation on a multi‐organ microfluidic chip, and proteomics identified hyperactive GSH metabolism in cells (M. Xu et al. ²⁰²⁰). (B) Construction schematic of virus‐infected human gut‐on‐chip and transcriptional analysis of intestinal epithelial/endothelial cells post‐infection (Guo et al. 2021). (C) Metabolome comparison of cocultured different cell lines (HepG2/C3a/SK‐HEP‐1) with/without drug treatment and Venn diagram of specific and common signatures between cell lines (Messelmani et al. 2023).

Table 1.

Representative applications of integrating OOC systems and omics technologies in the context of disease diagnosis and treatment.

Disease model	OOC platform	Omics	Experimental findings	Ref.
Inflammation of the BBB	NVU‐on‐a‐Chip	UPLC‐IM‐MS	Activation of pro‐inflammatory cytokines during initial exposure BBB rebound‐induced upregulation of repair‐promoting cytokine activation	Brown et al. (2016)
EHEC infection	Two‐channel Colon Chip	16S rRNA gene sequencing Metabolomics RNA‐seq	EHEC damage enhancement by 4 known molecules Species‐specific commensal metabolite‐induced alterations in EHEC chemotaxis gene transcription and motility	Tovaglieri et al. (2019)
Metastatic lung cancer	Microvasculature‐on‐a‐Chip	Transcriptome	A549 lung cancer cell paraclone extravasation via contact‐dependent juxtacrine signaling	Schmid et al. (2024)
Lung cancer brain metastasis	Multi‐organ microfluidic chip consisting of two organ chip units (an upstream “lung” and a downstream “brain” unit)	Proteomics	GSH metabolism pathway with the overexpression of various GSH metabolism‐related enzymes Increased expression of the drug resistance‐associated protein aldehyde dehydrogenase in BM	M. Xu et al. (2020)
Uterine dysfunction	Endometrium‐on‐a‐Chip	RNA‐seq Nano‐LC‐MS	Glucose‐ and insulin‐mediated alteration of endometrial extracellular matrix composition	De Bem et al. (2021)
Human synucleinopathies	Brain‐on‐a‐Chip	RNA‐seq	αSyn fiber‐induced gene expression changes in disease‐related biological processes	Pediaditakis et al. (2021)
Abnormal intestinal physiology	Gut‐on‐a‐Chip	scRNA‐seq	Gut‐on‐a‐Chip cultures induce post‐mitotic reprogramming of cancer‐associated genes Reversal of the cancerous transcriptome to a normal pattern	Woojung Shin et al. (2022)
Hydronephrosis	Renal‐on‐a‐Chip	Proteomic Metabolomic	Distinct biomarker expressions at different stages of hydronephrosis	Xiao et al. (2021)
Diabetes	Organ‐on‐a‐Chip	Transcriptome Metabolomic	Upregulation of genes related to hormone metabolism The presence of metabolites involved in hormone regulation and signal transduction	Essaouiba et al. (2022)
Necrotizing enterocolitis	Neonatal Intestine‐on‐a‐Chip	RNA‐seq	Increased expression of several cytokines and inflammatory markers The simulated pro‐inflammatory microenvironment is comparable to that in the human body	Lanik et al. (2023)
Sepsis	Organ‐on‐a‐Chip	Proteomic	Significant differences in expression among the patient groups in neutrophil proteins involved in critical aspects of the septic response	Q. Yang et al. (2024)
Inflammation in cells exposed to cigarette smoke	Lung‐on‐a‐Chip	RNA‐seq	Significant involvement of the MAPK signaling pathway is associated with the inflammatory response	Z. Li et al. (2024)
Cardiovascular disease	Vessels‐on‐a‐Chip	scRNA‐seq LC‐MS	Generally lower cell stress under flow‐culture conditions	Marder et al. (2024)
Gut microbiota‐related diseases	Gut‐on‐a‐Chip	Single‐bacterium omics Spatial metabolomics	Generating a spatial map of gene expression at single‐cell resolution Precise localization of microbial host interactions	Y. Zhang et al. (2024)
Atherosclerosis	Vascular‐on‐a‐Chip	RNA‐seq	More genes were differentially expressed in the direct exposure method than in the indirect exposure method Modeling of atherosclerosis via indirect macrophage exposure to cigarette smoke extract	Ohashi et al. (2024)

Abbreviations: BBB, blood‐brain barrier; EHEC, Enterohemorrhagic Escherichia coli; GSH, glutathione; MAPK, mitogen‐activated protein kinase; NVU, neurovascular units.

4.3. Host‐Microbial Interactions

Advancements in sequencing technologies over the past decade have significantly deepened our understanding of the complex interplay between host organisms and their resident microbial communities. The human gastrointestinal tract harbors a diverse microbiota that plays essential roles in nutrient digestion, metabolic processing of macromolecules, and the synthesis of bioactive compounds, including vitamins, neurotransmitters like serotonin, and a variety of host‐regulating molecules (Cassotta et al. 2020; Kau et al. ²⁰¹¹; J. Wang et al. ²⁰¹⁸). These microbial activities critically influence systemic physiology. Investigating host–microbe dynamics requires robust in vitro platforms that can accurately recapitulate the intestinal microenvironment. OOC systems, coupled with multi‐omics approaches, offer an ideal solution. For instance, Shah et al. (2016) engineered a modular microfluidic device named HuMiX (Human–Microbial Crosstalk), which simulates the human gastrointestinal interface. This chip comprises three interconnected microchannels with individual inlets and outlets designed for cell seeding and eluate collection. The configuration enables high‐resolution multi‐omics analyses, facilitating detailed studies of host gene expression and immune modulation following co‐culture with microbial strains (Shah et al. 2016). Similarly, Tovaglieri et al. (2019) employed OOC technology to replicate epithelial damage in the human colon induced by enterohemorrhagic Escherichia coli (EHEC) infection. Using metabolomic profiling, they uncovered that reduced infection resistance might be partially attributed to microbiome‐derived metabolites, providing insight into population‐specific susceptibility to bacterial pathogens (Tovaglieri et al. 2019). The COVID‐19 pandemic underscored the utility of OOC systems for studying viral pathogenesis beyond the respiratory tract. Clinical findings identified the gastrointestinal system as a secondary target of SARS‐CoV‐2. In response, Guo and colleagues developed an Intestinal‐on‐a‐Chip model replicating key structural and functional features of the intestinal epithelial–vascular endothelial barrier. Transcriptomic analyses following viral infection revealed aberrant RNA and protein metabolism alongside immune activation in both epithelial and endothelial compartments, likely contributing to intestinal barrier dysfunction. This in vitro model proved instrumental in expediting COVID‐19‐related research and therapeutic development (Figure 5B) (Guo et al. 2021). Overall, the integration of OOC platforms with omics technologies enables detailed investigation of host–microbe molecular interactions. This combined approach is illuminating previously inaccessible aspects of gut microbiome function and its links to human health and disease.

4.4. Environmental Monitoring and Other Applications

Environmental pollution remains a pressing global issue, contributing significantly to the burden of disease and public health risks. Accurate and timely assessment of environmental contaminants is, therefore, imperative. However, traditional animal and 2D cell models often fall short due to species‐specific differences and their limited ability to replicate complex human physiological responses. OOC platforms have emerged as powerful alternatives, offering physiologically relevant microenvironments to investigate pollutant toxicity, metabolic processing, and cellular responses in a human‐relevant context (S. Yang et al. ²⁰²¹). Xiao et al. (2021) introduced a novel approach termed “Metabolomics‐on‐a‐Chip,” in which they integrated OOC systems with proton nuclear magnetic resonance (¹H NMR)‐based metabolomic footprinting. This technique enabled the evaluation of toxicity profiles for compounds such as ammonia (NH₃) and dimethyl sulfoxide (DMSO), identifying specific metabolic biomarkers and pathways affected by toxic exposure. Xiao's group developed a Renal‐on‐a‐Chip model equipped with a force‐sensitive resistor to detect mechanical stimuli. Integrating this platform with proteomic and metabolomic analyses enabled comprehensive studies on the pathogenesis of hydronephrosis and provided new insights into the nephrotoxic effects of PM2.5 and endocrine‐disrupting environmental chemicals (Xiao et al. 2021). Beyond the aforementioned areas, the combination of OOC and high‐throughput omics also opens new avenues for studying intercellular communication and tissue‐specific signaling networks (D. Liu et al. ²⁰²⁴). For example, Messelmani et al. employed an OOC‐based liver model to investigate intrahepatic crosstalk. Their study utilized metabolomic profiling to confirm cell‐type specificity and elucidated how liver tissue responds to pharmaceutical compounds at a systems level (Figure 5C) (Messelmani et al. 2023).

OOC technologies also hold great promise for advancing the study of traditional Chinese medicine (TCM), which emphasizes a holistic and syndrome‐differentiated approach to therapy. The complex nature of TCM, which is characterized by multicomponent formulations that act on multiple targets via diverse pathways, aligns well with the capabilities of OOC platforms. Coupling these systems with integrated omics analysis offers an unprecedented opportunity to dissect the multifaceted mechanisms underlying TCM efficacy. This synergy may serve as a bridge between traditional medical philosophies and modern evidence‐based precision medicine (X. Xu et al. ²⁰²⁴).

5. Conclusions and Prospects

In conclusion, OOC technology represents a transformative advancement that supplements and enhances conventional in vitro and in vivo models. By enabling the construction of cost‐effective, highly controllable, and physiologically relevant organ‐mimetic systems, OOC platforms have revolutionized biomedical research. When integrated with omics technologies, OOC systems gain powerful multidimensional analytical capabilities, allowing deeper insights into complex biological processes. The convergence of OOC and omics has demonstrated substantial value across multiple domains, including drug metabolism, toxicity assessment, target validation, disease modeling, and environmental toxicity evaluation. This integrated approach can drastically reduce drug development timelines, improve clinical trial success rates, and accelerate the overall drug discovery pipeline. Moreover, disease‐specific OOC models enriched with multi‐omics data allow for precise modeling of pathophysiological conditions, advancing the development of personalized diagnostics and therapeutic strategies. The use of OOC systems in environmental toxicology enables real‐time observation and accurate interpretation of toxicant exposure effects, contributing significantly to public health monitoring. The integration of microfluidics, omics, and computational tools has led to the emergence of a new interdisciplinary research paradigm characterized by dynamic organ simulation, high‐throughput molecular profiling, and data integration. This paradigm supports a shift from reductionist studies to comprehensive analyses of molecular networks, offering a robust framework for systems‐level research in the life sciences.

Despite these promising developments, the field remains in its early stages and faces several challenges. Most current OOC models focus on single‐organ systems, limiting their ability to fully replicate the complex structure and function of entire organs. Future research must aim to design more sophisticated multi‐organ chips and develop universal culture media suitable for diverse tissues. Additionally, existing OOC platforms struggle with minute sample acquisition and sensitivity‐compatible detection. Their microscale design leads to far lower cell densities than conventional cultures, which reduces transcript, protein and metabolite yields—often losing low‐abundance molecules and limiting omics data's ability to reflect on‐chip cell molecular states accurately. To address this, future research should integrate advances in single‐cell technologies and ultra‐sensitive analytical methods. For instance, the issue of limited input material can be directly mitigated by advancements in scRNA‐seq, such as Smart‐seq. 3, and highly sensitive MS (e.g., nano LC‐MS/MS) for proteomics, which are designed to analyze minute quantities of biological material (Asadian et al. 2024). Furthermore, strategies for optimizing sample processing are essential, including the direct microchannel injection of reagents to minimize transfer loss and the use of magnetic nanoparticles for the targeted capture of low‐abundance proteins. Integrating chip‐detection systems directly within the OOC platform can further enhance sensitivity and reduce sample handling losses (X. Li et al. ²⁰²⁴). This integration would enable the acquisition of high‐quality molecular data from the constrained microenvironments of OOC, thereby providing a more accurate representation of cellular states under dynamic physiological conditions (X. Xu et al. ²⁰²⁴).

Multi‐omics integration is another major bottleneck. OOC generate diverse data (e.g., sensor measurements, genomics, transcriptomics), and integrating these heterogeneous datasets is critical for biological interpretation—simple concatenation or dimensionality reduction is inadequate due to cross‐modality differences. Key technical barriers include normalization and batch effects, cross‐platform variability, and temporal misalignment. First, different omics platforms and experimental runs introduce technical variations, or “batch effects”, which may confound biological signals; therefore, rigorous normalization strategies are required to make data comparable across batches and modalities. Second, data obtained from distinct analytical platforms suffer from varying data formats, measurement sensitivities, and inherent biases. Third, OOCs often generate continuous, real‐time biosensor outputs alongside sparse, snapshot omics data, making it challenging to align these disparate temporal resolutions (Sabaté Del Río et al. 2023). Tools such as Multi‐Omics Factor Analysis (MOFA+) and iCluster represent promising advancements, MOFA+ extracts shared regulatory factors from dynamic OOC and omics data via multimodal analysis, suiting synchronized “transcriptomic‐metabolic” tracking during OOC differentiation; iCluster uses integrative clustering to link copy number variations, methylation, and expression data in Tumor‐on‐a‐Chip research, enabling accurate identification of drug resistance‐related molecular subtypes (Argelaguet et al. 2018; X. Zhang et al. ²⁰²²).

Furthermore, while AI shows significant potential in analyzing the vast data generated by OOC and identifying drug‐induced cellular changes (Paek et al. 2023), discrepancies between the static datasets from omics platforms and the dynamic, real‐time data from OOC present challenges in terms of format compatibility and computational demands, which limits the dynamic interpretation of complex biological networks (Hawkins et al. 2020). Against this backdrop, the broader adoption and impact of AI in OOC research can be substantially enhanced by prioritizing these key strategic directions. First, the paucity of publicly available, the lack of comprehensive, time‐resolved multi‐organ OOC omics datasets limits machine learning model performance, which can be alleviated using federated learning to securely aggregate multi‐laboratory data while preserving privacy. Second, developing algorithms to integrate real‐time biosensor data with omics profiles and building digital twin systems combining biophysical models and graph neural networks will improve predictions of drug efficacy and toxicity, enabling reliable extrapolation to human physiology. Therefore, the development of online, real‐time, multimodal deep learning algorithms capable of integrating these heterogeneous datasets is urgently needed. Continued exploration in this area will be essential for the next generation of precision medicine.

Conflicts of Interest

The authors declare no conflicts of interest.

Cheng, Z. , Zhang C., Li X., et al. 2026. “Bridging Organ‐on‐a‐Chip and Omics: A Multi‐Dimensional Frontier in Biomedical Research.” Biotechnology and Bioengineering 123: 2474–2490. 10.1002/bit.70280.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.