Genomic and multi-omic science has rapidly moved from a theoretical possibility to a practical foundation for modern biomedicine, and its influence is becoming especially visible in fields concerned with early life. The ability to interrogate biological systems across DNA, RNA, protein, metabolite, spatial architecture, and cellular states has transformed how clinicians and researchers understand perinatal development, disease susceptibility, and long-term health trajectories. Early applications of multi-omics demonstrated clinical value primarily in pediatric rare diseases, where comprehensive genomic and transcriptomic approaches improved diagnostic yield and reshaped clinical pathways, establishing a proof of concept that integrative molecular profiling can generate actionable insights in early life care [1]. As genomic medicine matured, investigators began to apply these tools to prenatal physiology itself, using innovative approaches such as AI-supported ultrasound imaging to connect maternal–fetal phenotypes with the foundational principles of the developmental origins of health and disease, and thereby linking molecular signatures with the earliest visible markers of risk [2].

Expanding mechanistic work deepened molecular understanding of pregnancy-related exposures, including endocrine-disrupting chemicals, demonstrating how environmental signals can reshape endocrine, immune, and epigenetic pathways in early development [3]. Insights into cellular stress pathways, such as ferroptosis and apoptosis, clarified the molecular crosstalk underlying early-onset preeclampsia and illuminated new biomarker candidates that could be detected in maternal blood long before clinical disease manifests [4]. Parallel efforts describing microplastic exposure during the perinatal period highlighted the vast range of xenobiotic influences affecting maternal and fetal biology, suggesting the need for broader multi-omic surveillance beyond traditional clinical tests [5]. In the first trimester, biosensors such as nanoflower platforms expanded the capability of early screening by detecting molecular perturbations reflective of placental or fetal dysfunction, providing a preview of how emerging nanotechnology may integrate with genomic tools for perinatal diagnostics [6].

Nutrition-related epigenomic changes also emerged as a significant research dimension, with studies describing how maternal diet modifies gene regulatory landscapes that influence fetal growth, metabolic programming, and later-life health, thereby situating nutriepigenomics as a critical pillar of personalized perinatal care [7]. At the same time, immunological remodeling during pregnancy was reframed through an immunoediting lens, illustrating how tolerance, activation, and surveillance mechanisms protect the fetus while maintaining maternal immune integrity, and how these processes can be mapped using immune-oriented multi-omic strategies [8]. Building upon this expanding mechanistic foundation, transcriptomic studies of placental tissues revealed that maternal stress—both preconceptional and prenatal—leaves distinct molecular signatures in placental pathways that influence fetal development and potentially future neurodevelopmental outcomes [9].

Genome-wide analyses further demonstrated that placental genomic variation mediates the genetic architecture of complex traits, linking placental genotype with maternal cardiometabolic phenotypes and fetal growth patterns, reinforcing the placenta’s role as both a mediator and origin point of health trajectories [10]. Foundational reviews in the field underscored the importance of integrating genomic tools into perinatal care, mapping early progress in diagnostic genomics, counseling frameworks, and the interpretation of rare variants in the prenatal setting [11]. These early syntheses paved the way for later multi-omic work, including metabolomic and proteomic approaches that broadened the biochemical dimension of perinatal systems biology, incorporating metabolic flux and small-molecule signaling into multi-layered models of pregnancy health and disease [12].

Applications of genomics reached further into clinical care when genomic autopsy approaches demonstrated high diagnostic yield in cases of pregnancy loss and perinatal death, allowing families to receive clear recurrence-risk counseling based on precise molecular diagnoses instead of ambiguous phenotypic assessments [13]. At the epigenetic level, the identification of methylation markers associated with preeclampsia strengthened the case for epigenomics as a predictive and diagnostic tool, particularly when combined with transcriptomic and proteomic readouts [14]. Methodological reflections on how to interpret multi-omic data in the perinatal context emphasized that molecular signals derived from placenta, maternal blood, and fetal tissues require careful contextualization within developmental timing, cell-type composition, and environmental exposures [15]. These interpretive challenges became especially important as transcriptomic profiling of maternal blood during late gestation revealed patterns paralleling those seen before spontaneous preterm birth, pointing toward the possibility of molecularly informed prediction models [16].

As perinatal genomics matured, attention returned to non-invasive strategies, revisiting the foundations of cfDNA, cfRNA, and transcriptomic profiling while proposing frameworks for personalized fetal diagnosis based on circulating nucleic acids [17]. Omics-based insights also reshaped understanding of fetal development by highlighting the interplay between genomic programs, metabolic pathways, and environmental influences, mapping how these multi-layered interactions drive developmental transitions during gestation [18]. In hypertensive pregnancies, transcriptomic comparisons identified distinct molecular signatures differentiating preeclampsia superimposed on chronic hypertension from isolated disease, underscoring the heterogeneity of hypertensive disorders and the need for precise molecular subclassification [19].

Advances in placenta-focused technologies continued, including omics approaches targeting the formation and metabolic function of the syncytiotrophoblast, which clarified how trophoblast subtypes coordinate nutrient transport, endocrine signaling, and immune interactions [20]. Reviews of placental transcriptomics summarized technological progress and analytic challenges, especially relating to differences in sampling strategy, gestational timing, and processing methods [21]. These overviews were complemented by calls to integrate multi-omics with machine learning to improve prevention, diagnosis, and risk stratification across female reproductive health, including perinatal outcomes [22].

Emerging technologies such as whole-genome sequencing and prenatal RNA sequencing expanded the diagnostic horizon, increasing the sensitivity of prenatal diagnosis for structural anomalies and broadening the scope of disorders detectable during pregnancy [23]. These advances coincided with proposals for revising national prenatal testing frameworks, recommending that genomic technologies be incorporated into standard pregnancy management alongside traditional imaging and biochemical testing [24]. In parallel, broader reviews of pediatric precision medicine contextualized multi-omics as a core tool for early-life care across diverse conditions, linking perinatal genomics with downstream pediatric applications [25].

Beyond standard sequencing, liquid biopsy approaches integrating extracellular vesicles, single-cell technologies, and cell-free nucleic acids were shown to provide a rich, non-invasive window into placental and fetal biology, enabling repeated molecular assessments throughout gestation [26]. Multi-omic analyses extended into perinatal animal models, where inflammatory signaling disruptions influenced organ development in ways that mirrored human disease trajectories, emphasizing the translational relevance of multi-omics across species [27]. Molecular epidemiology brought these tools into population science, demonstrating how multi-omic markers can be embedded into cohort designs to study exposure–response relationships and perinatal outcomes at scale [28]. Models for integrating genomics into national pregnancy services further highlighted structural, educational, and operational requirements for clinically responsible implementation [29].

Studies of xenobiotic effects on the placenta introduced transcriptomic and epigenomic methods that clarified how environmental exposures disrupt placental pathways, strengthening the case for placental omics in environmental health research [30]. Multi-omic analyses of the placenta–brain axis provided compelling evidence linking placental molecular signatures with long-term neurodevelopmental outcomes, demonstrating the predictive power of integrated omic kernels in preterm infants [31]. Reviews focusing on single-cell transcriptomics and epigenomics synthesized how these high-resolution technologies uncover cell-type-specific mechanisms in maternal and child health, capturing dynamic shifts in placental and fetal cell populations [32]. Integrative multi-omic research on placental development further reinforced the necessity of combining genome-wide, single-cell, and spatial methods to understand placental pathology [33].

Spatial metabolomics and transcriptomics advanced this work by mapping sub-regional molecular differences within the placenta, revealing distinct disease-associated microenvironments in late-onset preeclampsia [34]. Reviews of transcript profiling traced methodological developments from bulk RNA assessments to advanced single-cell and spatial platforms, emphasizing best practices for study design and interpretation [35]. Finally, updated summaries of single-cell RNA sequencing in pregnancy-related diseases illustrated the rapid evolution of the field, demonstrating how cell-state classifications and lineage trajectories offer new tools for re-defining disease mechanisms [36].

Together, these thirty-six studies illustrate the extraordinary breadth of genomic and multi-omic research in the perinatal sciences. They collectively demonstrate that molecular data generated from maternal blood, placenta, fetal tissues, and neonatal samples can reveal mechanistic pathways, refine diagnostic categories, and establish predictive signatures for both immediate obstetric outcomes and lifelong health trajectories. This systematic review draws upon this diverse literature to articulate a comprehensive synthesis and propose a translational roadmap for integrating these tools into clinical practice.

A comprehensive search of the scientific literature was undertaken across major biomedical databases, including PubMed, PubMed Central, Web of Science, Embase, and Scopus, supplemented by searches of clinical trial registries and reference lists of influential studies. The search strategy incorporated controlled vocabulary and free-text combinations encompassing perinatal medicine, pregnancy, placenta, fetus, newborn, genomics, multi-omics, transcriptomics, epigenomics, proteomics, metabolomics, liquid biopsy, single-cell sequencing, and spatial technologies. The broad scope reflected the diversity of the included studies, which ranged from technology-focused reviews on AI-assisted prenatal imaging and DOHaD frameworks [2], endocrine-disrupting mechanisms [3], ferroptosis-apoptosis crosstalk [4], microplastics [5], and nanobiosensor innovation [6] to domain-specific explorations such as nutriepigenomics [7], immunoediting in pregnancy [8], and placental responses to maternal stress [9].

This strategy ensured a rich and representative dataset encompassing genomic analyses of complex traits mediated by placental biology [10], foundational work in perinatal genomics [11], metabolomic-integrative reviews [12], diagnostic genomic autopsy studies [13], epigenetic marker identification [14], interpretive frameworks for multi-omic data in pregnancy [15], maternal blood transcriptomics [16], non-invasive prenatal diagnostics [17], fetal omics perspectives [18], and transcriptomic insights into hypertensive disorders [19]. The search also captured high-resolution placenta-focused omic studies [20, 21], machine-learning-enhanced reproductive medicine [22], expanding sequencing technologies in prenatal diagnosis [23], prenatal testing frameworks [24], pediatric precision medicine perspectives [25], liquid biopsy and single-cell applications in maternal–fetal contexts [26], experimental perinatal animal models [27], molecular epidemiology [28], prenatal genomic service proposals [29], xenobiotic effects on the placenta [30], placenta-brain axis multi-omics [31], single-cell studies in maternal and child health [32], multi-omic studies of placental development [33], spatial omic mapping of disease [34], transcript profiling advances [35], and updated single-cell RNA sequencing reviews [36].

Titles and abstracts retrieved through the search were screened independently by two reviewers trained in perinatal genomics and omics methodologies. Full texts were obtained for all records deemed potentially relevant. Conflicts were resolved through consensus, ensuring rigorous application of eligibility criteria across diverse study designs. The full selection pathway is presented in Fig. 1, which details 1,348 identified records, the removal of duplicates and automation-excluded items, screening outcomes, retrieved reports, reasons for full-text exclusion, and the final inclusion of thirty-six studies.

The inclusion criteria encompassed studies involving humans or translational animal models within the perinatal window, defined as preconception through the neonatal period; implementation of genomic, transcriptomic, epigenomic, proteomic, metabolomic, spatial, or single-cell omic technologies; and reporting of diagnostic yield, mechanistic insights, clinical value, or translational implications. Studies focusing on pediatric contexts were included only when their findings informed early life multi-omics or future perinatal utility, as seen in pediatric rare-disease genomics [1] and early-life precision medicine [25]. Exclusion criteria encompassed commentary-only articles, editorials, conference abstracts without full data, studies lacking omic application, or those unrelated to the perinatal period.

A structured extraction framework was used to record study design, population, sample size, omic platforms, analytic pipelines, biological targets, primary findings, and reported clinical or translational implications. Extracted data were organized into a consolidated summary of study characteristics shown in Table 1, allowing transparent comparison of methodologies across domains such as sequencing approaches, placenta-focused transcriptomics, multi-omic integration strategies, and diagnostic applications. To synthesize technology-specific insights, a second summary (Table 2) was created to delineate platforms, biological sources, analytical pipelines, levels of data integration, clinical use cases, validation status, implementation barriers, and translational maturity. This table enabled the mapping of conventional sequencing platforms, cfRNA technologies, single-cell methods, spatial omics, epigenomic profiling, liquid biopsy, proteomics, metabolomics, and AI-enhanced omics fusion into a coherent methodological landscape. Ethical, clinical, and health-system factors were then organized using an implementation-oriented framework in Table 3, highlighting the multidimensional challenges associated with bringing these tools into clinical practice. These included informed consent, incidental findings, workforce training, equity concerns, data governance, algorithmic bias, and feasibility across varying resource contexts.

Given the diversity of technologies and clinical endpoints, a meta-analysis was neither feasible nor appropriate. Instead, a structured narrative synthesis was performed, grouping findings into mechanistic, diagnostic, prognostic, and translational domains. Conceptual integration is visually represented in Fig. 2, which illustrates the coordinated architecture of maternal, placental, and fetal multi-omics across systems biology layers. Translational pathways connecting discovery-level omics to clinical application are mapped in Fig. 3, positioning multi-omic diagnostics, risk prediction models, and AI-driven analytic systems within evolving perinatal care frameworks. The final synthesis incorporates mechanistic insights from placenta-centered transcriptomics, epigenomics, spatial mapping, and single-cell profiling; diagnostic improvements through genomic sequencing, cfDNA and cfRNA analysis, and liquid biopsy; and implementation-focused perspectives addressing data interpretation, ethical considerations, and health-system readiness. This integrative approach allows the methodology to reflect the full scope of technologies, clinical applications, and translational potential represented across references [1–36].

The synthesis of evidence presented in this review illustrates a pivotal moment in perinatal medicine, in which genomic and multi-omic technologies are redefining how pregnancy is understood, monitored, and managed. Across the maternal, placental, and fetal domains, molecular data now illuminate biological pathways that were previously inaccessible, offering new clarity on the mechanisms that shape pregnancy outcomes and early-life health trajectories. These technologies have begun to shift the field from reactive management of complications to anticipatory strategies informed by molecular signatures, creating the foundation for a more predictive and individualized model of care. The convergence of high-resolution sequencing, advanced bioinformatics, and integrative multi-omic analytics demonstrates the potential to transform both diagnostics and prognostics. Molecular insights into conditions such as preeclampsia, preterm birth, fetal growth abnormalities, and neurodevelopmental vulnerability reveal opportunities for earlier detection, more precise risk stratification, and novel therapeutic avenues. At the same time, the emergence of non-invasive sampling methods, including circulating nucleic acids and extracellular vesicles, provides a path toward scalable clinical implementation that minimizes burden to pregnant individuals. Realizing this potential will require continued investment in infrastructure, ethical governance, workforce development, and equitable access. The rapid pace of innovation demands thoughtful integration into clinical workflows, including robust counseling frameworks, transparent data stewardship, and interdisciplinary collaboration across obstetrics, pediatrics, genomics, and public health. As multi-omic technologies evolve, their value will increasingly lie in the ability to integrate molecular signals with clinical, environmental, and imaging data to construct comprehensive models of maternal–fetal health. Collectively, the findings of this review highlight the emergence of a new paradigm in perinatal medicine—one in which molecular insight becomes central to safeguarding maternal well-being, optimizing fetal development, and supporting lifelong health. The field now stands at the threshold of transformative change, with multi-omic science poised to shape the next generation of perinatal care.