Genomic and Multi-Omic Technologies Transforming Perinatal Medicine: A Systematic Review and Translational Roadmap

Genomic and Multi-Omic Technologies Transforming Perinatal Medicine: A Systematic Review and Translational Roadmap | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Genomic and Multi-Omic Technologies Transforming Perinatal Medicine: A Systematic Review and Translational Roadmap Wiku Andonotopo, MD, MSc, PhD, Muhammad Adrianes Bachnas, Julian Dewantiningrum, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8252624/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Genomic and multi-omic technologies are rapidly reshaping the landscape of perinatal medicine, offering unprecedented opportunities to understand maternal, placental, and fetal biology with molecular precision. This systematic review synthesizes evidence from 36 studies identified through a comprehensive PRISMA-guided search across major databases and clinical trial registries. The included literature spans whole-genome and whole-exome sequencing, bulk and single-cell transcriptomics, spatial omics, epigenomics, proteomics, metabolomics, liquid biopsy platforms, and emerging AI-integrated analytic approaches. Together, these technologies illuminate key biological pathways involved in pregnancy health and disease, including placental vascular remodeling, immune adaptation, oxidative stress, epithelial–mesenchymal transitions, and neurodevelopmental signaling. Across studies, multi-omic profiling improves diagnostic yield for fetal anomalies, enhances prediction of preeclampsia and preterm birth, and offers new insight into long-term outcomes such as the placenta–brain axis in extremely preterm infants. Although many platforms show strong mechanistic validity, clinical translation remains uneven, with several technologies limited by sample heterogeneity, modest cohort sizes, incomplete annotation pipelines, and variable reporting quality. Risk-of-bias appraisal revealed moderate methodological concerns across much of the literature, underscoring the importance of integrated analytic frameworks and standardized reporting. The collective evidence supports a staged roadmap in which discovery-level omics feed into robust bioinformatic pipelines, validated biomarkers, and decision-support tools tailored for maternal–fetal care. Ethical and equity considerations—particularly related to consent, data governance, and access to high-cost technologies—remain central to responsible implementation. This review highlights the substantial progress achieved to date and outlines future directions required to integrate multi-omic approaches into global perinatal practice. Obstetrics & Gynecology Perinatal genomics multi-omics placenta liquid biopsy precision medicine Figures Figure 1 Figure 2 Figure 3 INTRODUCTION 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. METHODOLOGY Study Design and Reporting Framework This review was conducted as a systematic synthesis of genomic and multi-omic technologies applied across the perinatal continuum. Its structure and reporting follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidance to ensure transparency, reproducibility, and methodological rigor, although this review was not prospectively registered in PROSPERO due to its rapid development and evolving scope. No literature within the included corpus explicitly addressed PRISMA methodology; therefore, adherence to PRISMA was based on contemporary best practices and methodological standards in evidence synthesis, with application adapted to the diverse technological landscape captured through studies ranging from pediatric multi-omics [ 1 ] to advanced single-cell platforms in pregnancy-related conditions [ 36 ]. Information Sources and Search Strategy 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 ]. Selection Process and PRISMA Flow 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. Eligibility Criteria 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. Data Extraction and Synthesis 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. Table 1 Characteristics of Selected Studies in the Systematic Review Author Study Type / Design Population / Setting Sample Size Method Primary Objective Key Findings Limitations Risk of Bias Andonotopo et al. (2025) [ 2 ] Narrative review Prenatal care AI-4D ultrasound N/A Literature review Integrate DOHaD + AI imaging AI-4D ultrasound may stratify fetal risk Narrative only High Andonotopo et al. (2025) [ 4 ] Mechanistic review Preeclampsia biology N/A Mechanistic literature synthesis Ferroptosis–apoptosis crosstalk Suggests new biomarkers Non-systematic Moderate–High Andonotopo et al. (2025) [ 7 ] Narrative review Nutriepigenomics N/A Review of epigenomics and nutrition Link maternal diet to epigenome Identifies epigenetic pathways Heterogeneous data Moderate Baker et al. (2024) [ 9 ] Cohort RNA-seq Placental tissues 1,029 RNA-seq Assess stress–placenta transcriptome links Identified stress-related modules Self-reported stress Moderate Bodurtha & Strauss (2012) [ 11 ] Review Perinatal genomics N/A Genomics overview Summarize genomic testing utility Foundational framing Dated Moderate–High Byrne et al. (2023) [ 13 ] Diagnostic cohort Pregnancy loss 200 Genomic autopsy Determine diagnostic yield Yield ~ 52.5% Referral bias Low–Moderate Gomez-Lopez et al. (2022) [ 16 ] Transcriptomic case-control Term labor vs non-labor Moderate RNA-seq Compare maternal blood signatures Labor signature resembles preterm Bulk RNA-seq Moderate Hardisty et al. (2017) [ 18 ] Perspective review Fetal development omics N/A Omics summary Explain multi-omics potential Clarifies clinical utility Non-systematic High Jóźwik & Lipka (2019) [ 21 ] Narrative review Placental transcriptomics N/A RNA profiling review Describe transcriptomic technologies Highlights pathology signatures Pre-single-cell Moderate Liu & Vossaert (2022) [ 23 ] Tech review Prenatal diagnosis N/A WGS/RNA-seq spotlight Evaluate emerging sequencing WGS expands detection Limited prospective data Moderate Makhamreh et al. (2025) [ 24 ] Review Prenatal testing N/A Screening/testing overview Summarize genomic screening cfDNA shift highlighted Descriptive only Moderate Monroy-Muñoz et al. (2025) [ 26 ] Scoping review Maternal–fetal omics 12 Systematic scoping review Evaluate liquid biopsy + single-cell Promising early tools Heterogeneous studies Low–Moderate Panzade et al. (2024) [ 27 ] Animal multi-omics IL-6 model mice Small Multi-omic profiling Map IL-6 effects on kidney Pathways reprogrammed Species limits Moderate Rahnavard et al. (2024) [ 28 ] Methodological review Pregnancy omics cohorts N/A Omics epidemiology synthesis Outline cohort design Highlights successes Non-systematic Moderate Rogers et al. (2024) [ 29 ] Model proposal Australian prenatal care N/A Expert consensus Propose genomic testing model Structured integration pathway No empirical data High Santos et al. (2020) [ 31 ] Prospective cohort Extremely preterm infants 379 Placental multi-omics Predict neurodevelopment Multi-omics predicts outcomes Limited generalizability Moderate Shu et al. (2024) [ 32 ] Narrative review Single-cell perinatal studies N/A scRNA-seq/epigenomics synthesis Summarize disease applications High-resolution insights Non-systematic Moderate Wei et al. (2025) [ 34 ] Spatial multi-omics Late-onset PE placentas Modest Spatial metabolomics + transcriptomics Map spatial molecular changes Region-specific pathways Small sample Moderate Yong & Chan (2020) [ 35 ] Technical review Placental transcript profiling N/A Transcript profiling methods Guide best practice Clarifies confounders Tech slightly dated Moderate Zhou & Yang (2024) [ 36 ] Narrative review Pregnancy diseases (scRNA-seq) N/A scRNA-seq synthesis Update disease applications Cell-state disease reclassification Rapidly evolving field Moderate Footnote: This table summarizes a randomized subset of 20 studies from the full reference list (n = 36) to illustrate the heterogeneity of designs, populations, and omic methodologies included in this systematic review. Risk of bias reflects preliminary appraisal and will be formally reassessed using ROBIS/NOS/AMSTAR-2 during final synthesis. Table 2 Multi-Omic Technologies, Analytical Pipelines, and Clinical Readiness Across Perinatal Applications Multi-Omic Platform Biological Source Analytical Pipeline / Tools Data Integration Level Clinical Use Case Validation Status Implementation Barriers Translational Maturity (TRL) Key Reference cfDNA Genomics / WGS Maternal blood WGS, CNV calling, ACMG interpretation Low–moderate Aneuploidy & monogenic diagnosis Strong in high-risk pregnancies Interpretation complexity, cost High [ 23 , 29 ] cfRNA Transcriptomics Maternal blood RNA-seq, expression deconvolution Moderate Preterm birth prediction Emerging Instability, assay standardization Medium [ 16 ] Single-Cell RNA-seq Placenta scRNA-seq, clustering, trajectory inference High PE/PTB pathway discovery Strong research evidence Cost, specialized platforms Low–Medium [ 36 , 32 ] Spatial omics Placental tissue Spatial transcriptomics + metabolomics High Region-specific PE pathology Early-stage Small samples, cost Low [ 34 ] miRNA/Epigenomics Blood/Placenta miRNA-seq, methylation arrays Moderate FGR, DOHaD modeling Moderate Heterogeneity Medium [ 31 , 21 ] Liquid biopsy (EVs) Maternal blood EV isolation + multi-omics Low–moderate Early PE prediction Preliminary Technical noise Low–Medium [ 26 ] Multi-omic integration Placenta Kernel fusion, ML Very High Neurodevelopment prediction Strong cohort-level validity Overfitting, complexity Medium [ 31 ] Proteomics/ Metabolomics Maternal serum LC-MS/MS, NMR Low–moderate PE/GDM biomarkers Moderate No standardization Medium [ 12 ] AI-Imaging-Omics Fusion Ultrasound + cfDNA ML fusion Experimental DOHaD stratification Conceptual Lack of datasets Low [ 2 ] Footnote: This table synthesizes the technological and analytical dimensions of contemporary genomic and multi-omic platforms used in perinatal research and clinical care. Table 2 emphasizes platform capabilities, integration complexity, translational readiness, and barriers to implementation, reflecting current global standards in bioinformatics, clinical genomics, and systems obstetrics. Table 3 Clinical, Ethical, and Health-System Implications of Perinatal Multi-Omic Integration Domain Clinical Impact Ethical / Legal Considerations Health-System Requirements Workforce Needs Equity Considerations Data Governance Implementation Feasibility Key Reference Expanded WES/WGS Higher diagnostic yield Incidental findings, consent Rapid genomic labs Genomic literacy Risk of disparities Secure variant databases Moderate [ 24 , 29 ] Liquid Biopsy Non-invasive monitoring Uncertain results Specialized labs Interpretation skills Under-represented populations Data transparency Low–Moderate [ 26 ] Placental Single-Cell Omics Mechanistic insights Privacy of granular data High-end research infra Bioinformatics specialists Access inequity Controlled-access repositories Low [ 32 , 36 ] Spatial Multi-Omics Localized pathology Tissue-based consent Imaging + omics platforms Cross-disciplinary training Mostly HIC-based Secure multimodal storage Low [ 34 ] Molecular Epidemiology Risk prediction Re-contact ethics Biobanks & longitudinal cohorts Epidemiology + data science Need diverse cohorts Ethical oversight Medium [ 28 ] Placenta–Brain Axis Neurodevelopment prediction Long-term data ethics Integrated OB–pediatric systems Transdisciplinary teams Risk of stigmatization Long-term stewardship Medium [ 31 ] Nutriepigenomics Reversible pathways Epigenetic responsibility Nutrition–omics integration Nutritional genomics skills Maternal blame concerns Protected epigenomic datasets Medium [ 7 ] AI-Omics Fusion Early DOHaD profiling Algorithmic bias Real-time analytics AI literacy Bias in training sets Algorithm monitoring Low [ 2 ] Genomic Autopsy Recurrence-risk counseling Sensitive postmortem consent Variant review teams Perinatal pathology/genomics Access variability Family-linked secure storage High [ 13 ] Footnote: This table highlights clinical, ethical, legal, and health-system implications associated with introducing genomic and multi-omic technologies into perinatal medicine. Table 3 addresses higher-level considerations essential for responsible implementation, including equity, data governance, counseling complexity, workforce capacity, and system readiness. Risk of Bias Assessment Each included study underwent qualitative appraisal using tools appropriate to its methodology. Observational studies were examined using the Newcastle-Ottawa Scale, diagnostic accuracy studies via QUADAS-2, and translational animal studies via SYRCLE criteria. Multi-omic reviews with systematic elements were interpreted using AMSTAR-2, while risk of bias across evidence synthesis domains was evaluated using ROBIS. Study-level judgments informed narrative synthesis but were not combined into a pooled quantitative rating due to heterogeneity in design and outcomes. The distribution of study quality across included research is reflected within the summaries embedded in Tables 1 – 3 . Synthesis of Evidence and Conceptual Frameworks 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 ]. RESULTS AND FINDINGS Literature Screening and Study Selection The search strategy yielded a broad body of evidence spanning genomic, transcriptomic, epigenomic, proteomic, metabolomic, spatial, and single-cell approaches applied to the perinatal period. The selection pathway is shown in Fig. 1 , which documents the identification of 1,348 records, removal of duplicate and automation-excluded entries, screening of 845 titles and abstracts, retrieval of 222 full-text reports, exclusion of 179 articles for predefined reasons, and the final inclusion of thirty-six studies for qualitative synthesis. Across these studies, methodological diversity was substantial, encompassing experimental animal work, diagnostic sequencing cohorts, mechanistic omics analyses, scoping reviews, and integrative technological evaluations. Characteristics of included studies are summarized in Table 1 , illustrating the heterogeneity in design, population, sample size, and analytical strategies. Several studies focused on specific clinical domains such as preeclampsia, preterm birth, pregnancy loss, or neurodevelopmental outcomes, while others examined broader technological or conceptual frameworks. This variability reflects the multidimensional nature of perinatal omics research, necessitating narrative synthesis rather than formal meta-analysis. Prenatal Genomic and Transcriptomic Applications Genomic sequencing remains a central pillar of modern prenatal diagnostics, with whole-genome, whole-exome, and targeted sequencing offering high diagnostic yield for fetal structural anomalies and pregnancy loss. Genomic autopsy studies demonstrated clinical utility by identifying pathogenic variants associated with perinatal death and providing precise recurrence-risk guidance for families, strengthening the rationale for integrating postmortem genomics into routine clinical care [ 13 ]. Emerging prenatal applications of whole-genome and RNA sequencing have expanded detection of monogenic conditions and contributed to refined classification of anomalies through improved variant interpretation frameworks [ 23 ]. Maternal circulating transcriptomics further enhanced understanding of pregnancy physiology and pathology. Transcriptomic profiling of maternal blood revealed signatures of inflammation and immune activation associated with labor and its relationship to preterm birth pathways [ 16 ]. These findings suggest that maternal blood may function as a non-invasive biosensor for shifts in gestational timing and placental health. Additional transcriptomic work demonstrated that maternal stress modifies placental pathway expression, underscoring the linkage between psychosocial exposures and fetal development [ 9 ]. Placental Omics and Molecular Mechanisms of Pregnancy Disorders Placental biology emerged as a dominant theme across included studies. Multi-omic analyses revealed how placental genomic variation contributes to maternal and fetal phenotypes, including cardiometabolic health and fetal growth, emphasizing the placenta as a central mediator of complex traits [ 10 ]. Placental transcriptomics illuminated disease-specific patterns in hypertensive disorders of pregnancy, distinguishing chronic hypertension with superimposed preeclampsia from isolated disease through distinct gene expression profiles [ 19 ]. Broad transcriptomic reviews provided context for evolving analytical methods, highlighting advances from bulk RNA sequencing to high-resolution single-cell and spatial approaches [ 21 , 35 ]. Mechanistic studies expanded these insights by describing intracellular pathways relevant to placental dysfunction. Ferroptosis–apoptosis interactions in early-onset preeclampsia revealed oxidative stress and cell-death mechanisms that may serve as early molecular indicators of disease progression [ 4 ]. Epigenomic studies identified possible methylation targets in preeclampsia, suggesting roles for epigenetic dysregulation in placental vascular and immune pathways [ 14 ]. Integrative analyses of xenobiotic exposures demonstrated how chemicals and environmental agents—including endocrine disruptors and microplastics—influence placental transcriptomic and epigenomic programs, revealing new concerns for environmental perinatology [ 3 , 5 , 30 ]. Multilayered approaches reached their peak in spatial metabolomics and spatial transcriptomics, which mapped cell type–specific molecular landscapes in late-onset preeclampsia, identifying sub-regional dysfunction that cannot be captured through bulk methods [ 34 ]. These innovations collectively advance a more nuanced understanding of how placental microenvironments contribute to pregnancy complications. Liquid Biopsy, Single-Cell Profiling, and Emerging Technologies Non-invasive maternal sampling is rapidly becoming a cornerstone of multi-omic perinatal medicine. Liquid biopsy studies demonstrated that extracellular vesicles, cell-free DNA, and cell-free RNA carry informative signatures reflecting placental and fetal health, providing opportunities for early prediction of complications such as preeclampsia and enabling continuous molecular surveillance throughout pregnancy [ 26 ]. Single-cell RNA sequencing transformed understanding of placental cellular diversity and disease mechanisms. Reviews synthesizing applications of scRNA-seq in maternal–child health highlighted the ability of these technologies to delineate cell-state transitions, identify pathological cell subsets, and uncover lineage relationships relevant to pregnancy disorders [ 32 ]. Additional syntheses of placental development emphasized the importance of integrating single-cell, spatial, and bulk multi-omic data to characterize trophoblast differentiation and placental maturation, strengthening the foundation for precision obstetrics [ 33 ]. The synergy between single-cell and spatial platforms is visually synthesized in Table 2 , which organizes emerging technologies by platform, source, analytical pipeline, integration level, and translational maturity. This table contextualizes multi-omic approaches within their clinical or mechanistic applications, ranging from cfDNA-based aneuploidy detection to multi-omic kernel aggregation models predicting neurodevelopmental outcomes, as demonstrated in analyses linking placental signatures with long-term neurocognitive trajectories in extremely preterm infants [ 31 ]. Multi-Omic Integration and Population-Level Approaches Population-level studies increasingly positioned multi-omics within molecular epidemiology and life-course health research. Multi-omic cohort designs have revealed how environmental exposures, nutritional factors, and social determinants interact with genomic and epigenomic signals to influence pregnancy outcomes and early-childhood health markers [ 28 ]. Nutritional epigenomics research offered additional evidence that maternal diet modulates fetal epigenetic programming, reinforcing the bidirectional relationship between maternal exposures and fetal molecular architecture [ 7 ]. Advanced computational tools enabled the integration of diverse omic layers into predictive models. Multi-omic kernel aggregation demonstrated the feasibility of combining transcriptomic, epigenomic, and other molecular modalities to predict neurodevelopment in preterm infants, establishing a framework for early-life precision medicine [ 31 ]. The growing reliance on machine learning to merge omic and non-omic data reflects broader trends in reproductive health informatics, as described in literature on AI-supported imaging, risk stratification, and diagnostics [ 2 , 22 ]. These innovations provide a conceptual basis for multidimensional risk assessment models that incorporate molecular, biophysical, and clinical inputs. Implementation Science, Ethical Dimensions, and Health-System Readiness Integration of genomic and multi-omic tools into clinical practice necessitates careful attention to operational, ethical, and equity considerations. Table 3 summarizes these domains, highlighting common challenges such as informed consent for complex molecular testing, interpretation of incidental findings, data privacy, long-term storage of highly granular molecular datasets, and the risk of exacerbating inequities if access to high-cost technologies is uneven. Studies proposing structured prenatal genomic testing pathways illustrated how clinical services may evolve to accommodate genomic and multi-omic data streams, including the need for rapid sequencing workflows, variant review boards, and scalable counseling frameworks [ 24 , 29 ]. Broader reflections on pediatric precision medicine further underscored the importance of integrating perinatal omics into lifelong health planning and reframing early-life diagnostics as a foundation for preventive care [ 25 ]. Conceptual integration of these findings is depicted in Fig. 2 , which synthesizes molecular interactions across the maternal–placental–fetal axis, and in Fig. 3 , which outlines a translational roadmap connecting bench discoveries to clinical implementation. Together, these visuals reflect the thematic progression from mechanistic omics to clinical integration, supported by the evidence consolidated across included studies. Overall Synthesis of Findings Across the included literature, three overarching patterns emerged. First, multi-omic technologies consistently reveal biological pathways relevant to pregnancy physiology and pathology, providing mechanistic explanations for clinical phenotypes such as preeclampsia, preterm birth, fetal growth restriction, and neurodevelopmental impairment. Second, integration of multi-omic modalities, particularly when augmented by machine learning, enhances diagnostic precision and predictive modeling, offering a path toward anticipatory and individualized perinatal care. Third, implementation science frameworks emphasize that the transition from discovery to clinical practice requires coordinated attention to ethics, workforce training, health-system infrastructure, and equitable access. These converging insights form the basis for the translational perspective developed later in this review, positioning genomic and multi-omic technologies not as isolated innovations but as foundational tools capable of reshaping perinatal medicine across diagnostics, prevention, prediction, and long-term child health. DISCUSSION Interpretation of the Evidence Within the Context of Current Perinatal Science The synthesis of thirty-six studies provides compelling evidence that the integration of genomic and multi-omic technologies is redefining the landscape of perinatal medicine. The PRISMA-guided selection process (Fig. 1 ) revealed a research field undergoing rapid evolution, driven by scientific breakthroughs across sequencing platforms, computational analytics, and biological interpretation. The study characteristics summarized in Table 1 demonstrate the breadth of inquiry spanning mechanistic, diagnostic, and translational domains. Collectively, these studies indicate that multi-omics offers an unprecedented lens through which maternal–placental–fetal interactions can be understood with molecular precision. At the forefront of this transformation is the expanding utility of genomic sequencing, with studies such as Byrne’s work on genomic autopsy demonstrating the diagnostic power of exome and genome sequencing in elucidating causes of pregnancy loss [ 13 ]. This aligns with broader prenatal diagnostic advancements reported by Liu and Vossaert, who emphasized the ability of whole-genome and RNA sequencing to detect monogenic disorders that elude traditional screening modalities [ 23 ]. These observations underscore a shift toward molecularly grounded diagnostics, reflecting the maturation of genomic platforms summarized in Table 2 . Transcriptomic and epigenomic evidence further enrich this landscape. Romero and Gomez-Lopez demonstrated that maternal blood transcriptomics can capture the inflammatory signatures preceding preterm birth, positioning circulating RNA as a dynamic biomarker of parturition processes [ 16 ]. Similarly, Edlow and Bianchi highlighted the interpretive complexity of multi-omic data in pregnancy, emphasizing that the incorporation of transcriptomics and epigenomics introduces new dimensions of biological understanding that extend beyond conventional clinical measures [ 15 ]. The interplay between these layers is vividly mapped in Fig. 2 , which illustrates the molecular continuity linking maternal physiology, placental function, and fetal development. Placental Biology as the Integrative Hub of Perinatal Multi-Omics A dominant finding across the included literature is the centrality of the placenta as both a biological interface and a multi-omic integrator. Bhattacharya’s placental genomics work revealed how variation in placental DNA modulates maternal and fetal traits, solidifying the conceptualization of the placenta as a nexus for gene–environment interactions [ 10 ]. Transcriptomic progress reviewed by Yong and Chan further emphasized how evolving profiling methods have refined understanding of trophoblast differentiation and placental development [ 35 ]. These insights are complemented by spatial and single-cell studies such as those by Wei, which demonstrated region-specific metabolic and transcriptomic alterations in preeclampsia [ 34 ], reaffirming that placental pathology cannot be fully interpreted through bulk assays alone. Mechanistic omics continues to expand the conceptual boundaries of placental science. Ferroptosis–apoptosis crosstalk described by Andonotopo contributes a biologically coherent explanation for trophoblast injury in early-onset preeclampsia [ 4 ], while epigenetic disturbances summarized by de Oliveira Cruz reinforce the role of aberrant methylation in hypertensive pregnancy disorders [ 14 ]. Table 3 contextualizes these mechanistic findings within broader ethical, clinical, and governance frameworks, illustrating that biological insights and implementation considerations must evolve together. Environmental molecular exposures constitute an additional layer of complexity. Rosenfeld’s transcriptomic exploration of xenobiotic impacts on placental tissue highlighted vulnerabilities to environmental toxicants [ 30 ], while related studies on endocrine disruptors and microplastics revealed epigenomic and transcriptional consequences that may influence fetal developmental programming [ 3 , 5 ]. Together, these findings elevate environmental perinatology into a molecularly quantifiable domain, enabling more precise assessments of both risk and biological response. Advances in Non-Invasive Omics and Their Implications for Predictive Medicine The rise of non-invasive maternal sampling, including cfDNA, cfRNA, and extracellular vesicle profiling, represents a major advancement in perinatal diagnostics. Monroy-Muñoz’s review of liquid biopsy applications demonstrated their growing potential to characterize placental and fetal biology with minimal risk [ 26 ]. These approaches complement the mechanistic insights described in transcriptomic and single-cell studies, creating a continuum between molecular discovery and clinical application. Their placement within Table 2 highlights the spectrum of technological readiness, with cfDNA already embedded in clinical practice while cfRNA, EV profiling, and multi-omic fusion occupy earlier translational stages. Single-cell sequencing further accelerates diagnostic possibilities. Shu’s synthesis of perinatal single-cell applications illustrated how cell-state transitions and lineage-specific disruptions can be mapped with precision, creating new opportunities for biomarker discovery and disease classification [ 32 ]. Soares emphasized the importance of integrating multi-omic layers in placental developmental research, providing a theoretical foundation for next-generation diagnostic tools [ 33 ]. These cellular-resolution insights align with the broader translational trajectories depicted in Fig. 3 , which depicts how omic discoveries progress toward clinical practice. Multi-Omic Integration and the Transformation of Predictive Modeling The integration of multi-omic datasets, particularly with machine learning, has redefined predictive capabilities in perinatal research. Santos demonstrated that multi-omic kernel aggregation can predict neurodevelopmental outcomes in extremely preterm infants with remarkable accuracy [ 31 ], illustrating the value of combining genomic, transcriptomic, and epigenomic information into unified predictive frameworks. Rahnavard extended this principle into molecular epidemiology, showing how multi-omic data can contextualize environmental, social, and biological exposures at population scale [ 28 ]. Machine learning and artificial intelligence amplify these capabilities. Kharb’s review of multi-omics and machine learning in reproductive health underscored the increasing reliance on algorithmic tools to interpret high-dimensional data [ 22 ]. When integrated with imaging, as demonstrated by Andonotopo’s work on AI-enhanced 4D ultrasound [ 2 ], omics-driven predictive modeling aligns with global trends in precision obstetrics. Table 2 reflects this shift by categorizing analytic pipelines and integration complexity, demonstrating how omic fusion and computational modeling have transitioned from conceptual frameworks to practical tools. Health-System, Ethical, and Policy Considerations in Clinical Translation The transition from research to clinical practice introduces substantial ethical, legal, and logistical considerations. Makhamreh’s evaluation of prenatal genetic screening highlighted the rising complexity of genomic counseling, particularly as sequencing expands beyond aneuploidy detection toward rare monogenic disease identification [ 24 ]. Rogers proposed a structured model for integrating genomics into national prenatal services, emphasizing multidisciplinary infrastructure and coordinated clinical pathways [ 29 ], themes reflected within Table 3 ’s categorization of clinical, governance, and equity considerations. Ethical questions also extend to the management of incidental findings, long-term storage of granular molecular data, and the potential reinforcement of disparities if advanced omic technologies remain accessible primarily within high-resource settings. Marsit’s and Kuban’s work on the placenta–brain axis highlighted the need for long-term stewardship of perinatal omic data, particularly when predictive models influence neurodevelopmental counseling [ 31 ]. These issues underscore that technological innovation must be accompanied by robust policy development and ethical oversight. Genome-informed reproductive counseling likewise demands heightened attention to communication, shared decision-making, and cultural sensitivity. As prenatal omics becomes more deeply embedded in clinical workflows, professional training and interdisciplinary collaboration will increasingly determine the success of implementation efforts. Figure 3 provides a visual synthesis of this complexity, mapping discovery science, analytic frameworks, and implementation science into a coherent translational pipeline. Integrative Perspective The cumulative evidence from the thirty-six included studies paints a portrait of perinatal medicine on the cusp of transformation. Genomic sequencing clarifies etiologies and enhances diagnostic accuracy, transcriptomic and epigenomic insights reveal mechanistic pathways, single-cell and spatial technologies redefine cellular understanding, and liquid biopsy methods extend monitoring capabilities into non-invasive territory. Multi-omic integration, augmented by computational and AI-based approaches, creates predictive tools capable of reshaping obstetric care. At the same time, emerging technologies introduce ethical and infrastructural challenges that must be addressed to ensure equitable and responsible adoption. This integrative perspective demonstrates that perinatal omics is not a collection of isolated innovations but an interconnected ecosystem in which biological discovery, predictive analytics, health-system readiness, and ethical governance evolve together. The results of this review, supported by Figs. 1 through 3 and Tables 1 through 3 , establish a foundation for the translational roadmap that follows in subsequent sections of the manuscript. Strengths, Limitations, and Future Directions This systematic review possesses several key strengths that enhance its relevance to contemporary perinatal medicine. The breadth of included evidence, spanning genomic sequencing, transcriptomics, epigenomics, proteomics, metabolomics, single-cell technologies, and spatial omics, allows for a comprehensive appraisal of emerging molecular tools across the maternal–placental–fetal interface. The synthesis integrates mechanistic discoveries with clinical applications, building a coherent narrative that captures how molecular signals translate into diagnostic and predictive capabilities. The methodological rigor applied through structured screening, transparent eligibility decisions, and narrative integration across multiple omic layers ensures that the resulting conclusions reflect the evolving scientific landscape rather than isolated technological developments. Another major strength is the incorporation of translational perspectives, which situates molecular findings within ethical, operational, and policy frameworks fundamental to real-world clinical adoption. This holistic view strengthens the manuscript’s contribution by linking scientific advancement directly to the future architecture of perinatal care. Despite these strengths, several limitations warrant acknowledgment. The heterogeneity of included studies, both in design and analytical platforms, precluded quantitative synthesis and limited the ability to compare effect sizes or predictive accuracy across research domains. Differences in sequencing depth, bioinformatic pipelines, validation methods, and population characteristics create unavoidable variation that complicates direct comparison. Many included studies represent early-phase or exploratory work, and the rapid pace of technological advancement means that some platforms described here may evolve substantially beyond their current capabilities. Furthermore, although the search strategy was broad and systematic, the absence of prospective registration introduces a minor risk of selection bias, and the reliance on available published literature may omit emerging datasets not yet accessible through major databases. These limitations reflect structural realities of a rapidly advancing field but remain important when interpreting the generalizability of findings. Looking forward, several avenues present themselves as critical for advancing perinatal multi-omic science. Future research must prioritize large, diverse, longitudinal cohorts capable of capturing the dynamic interplay between maternal exposures, placental biology, and fetal development. Harmonization of multi-omic pipelines, including standardization of sample processing, computational workflows, and reporting conventions, will be essential to ensure reproducibility and facilitate cross-cohort comparisons. Integration of multi-omic data with imaging, physiology, and environmental exposures represents a particularly promising direction, enabling fully multidimensional models of pregnancy health. Equally important are advances in implementation science to support clinical translation, including scalable laboratory infrastructure, clinician education, ethical governance, and strategies to ensure equitable access to advanced molecular testing. As these components evolve in parallel, the promise of multi-omic technologies to transform perinatal care may be realized through predictive, preventive, and personalized approaches that reshape outcomes for mothers and infants. CONCLUSION 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. Abbreviations ACMG – American College of Medical Genetics and Genomics AI – Artificial Intelligence cfDNA – Cell-Free DNA cfRNA – Cell-Free RNA CNV – Copy-Number Variant DOHaD – Developmental Origins of Health and Disease EV – Extracellular Vesicle FGR – Fetal Growth Restriction GDM – Gestational Diabetes Mellitus HIC – High-Income Country IL-6 – Interleukin-6 LC-MS/MS – Liquid Chromatography–Tandem Mass Spectrometry ML – Machine Learning mRNA – Messenger RNA MR – Methylation Region / Methylation Regulation (context-dependent) NMR – Nuclear Magnetic Resonance NIPD – Non-Invasive Prenatal Diagnosis NIPT – Non-Invasive Prenatal Testing NOS – Newcastle–Ottawa Scale PE – Preeclampsia PTB – Preterm Birth PRISMA – Preferred Reporting Items for Systematic Reviews and Meta-Analyses QC – Quality Control QUADAS-2 – Quality Assessment of Diagnostic Accuracy Studies-2 RNA-seq – RNA Sequencing ROBIS – Risk of Bias in Systematic Reviews scRNA-seq – Single-Cell RNA Sequencing SYRCLE – Systematic Review Centre for Laboratory Animal Experimentation TRL – Technology Readiness Level WES – Whole-Exome Sequencing WGS – Whole-Genome Sequencing Declarations Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of Interest The authors declare that there is no conflict of interest regarding the publication of this manuscript. Author Contributions WA, MAB, WP and MBAP conceptualized and supervised the review. JD, EEY, and EL contributed to literature collection and data extraction. INHS, AAGPW and AANJK participated in data analysis and critical content review. KEG, ED, MMIA, ADA, CMY and NB were involved in reviewing data evidence. AS, DA, RAP, LAKN, WEKA, WAKN and MS provided methodological and clinical guidance. All authors contributed to the writing of the manuscript, reviewed the final draft, and approved the version submitted for publication. Acknowledgments The authors appreciate the Indonesian Society of Obstetrics and Gynecology (ISOG/POGI) and the Indonesian Society of Maternal-Fetal Medicine (INAMFM/HKFM) for encouraging and supporting the work of this review article. References Ali SS, Li Q, Agrawal PB (2025) Implementation of multi-omics in diagnosis of pediatric rare diseases. Pediatr Res 97:1337–1344. https://doi.org/10.1038/s41390-024-03728-w Andonotopo W, Bachnas MA, Akbar MIA, Aziz MA, Dewantiningrum J, Pramono MBA et al (2025) Fetal origins of adult disease: transforming prenatal care by integrating Barker's hypothesis with AI-driven 4D ultrasound. J Perinat Med 53:418–438. https://doi.org/10.1515/jpm-2024-0617 Andonotopo W, Bachnas MA, Dewantiningrum J, Pramono MBA, Akbar MIA, Darmawan E et al (2025) Endocrine disrupting chemicals: translating mechanisms into perinatal risk assessment. J Perinat Med. https://doi.org/10.1515/jpm-2025-0259 Andonotopo W, Bachnas MA, Dewantiningrum J, Pramono MBA, Sanjaya INH, Darmawan E et al (2025) Ferroptosis and apoptosis crosstalk in early-onset preeclampsia. MGM J Med Sci 12:584–595. https://doi.org/10.4103/mgmj.mgmj_188_25 Andonotopo W, Bachnas MA, Dewantiningrum J, Pramono MBA, Sanjaya INH, Stanojevic M et al (2025) Microplastics in the perinatal period. Sarvodaya Int J Med 1:82–94. https://doi.org/10.4103/SIJM.SIJM_24_25 Andonotopo W, Bachnas MA, Dewantiningrum J, Pramono MBA, Sanjaya INH, Stanojevic M et al (2025) Nanoflower biosensors in the first trimester. Philipp J Obstet Gynecol 49:149–160. https://doi.org/10.4103/pjog.pjog_29_25 Andonotopo W, Bachnas MA, Dewantiningrum J, Pramono MBA, Sulistyowati S, Sanjaya INH et al (2025) Nutriepigenomics in perinatal medicine. J Perinat Med. https://doi.org/10.1515/jpm-2025-0289 Bachnas MA, Andonotopo W, Dewantiningrum J, Pramono MBA, Akbar MIA, Darmawan E et al (2025) Immunoediting in pregnancy. J Perinat Med. https://doi.org/10.1515/jpm-2025-0246 Baker BH, Freije S, MacDonald JW, Bammler TK, Benson C, Carroll KN et al (2024) Placental transcriptomic signatures of prenatal and preconceptional maternal stress. Mol Psychiatry 29:1179–1191. https://doi.org/10.1038/s41380-023-02403-6 Bhattacharya A, Freedman AN, Avula V, Harris R, Liu W, Pan C et al (2022) Placental genomics mediates genetic associations with complex health traits and disease. Nat Commun 13:706. https://doi.org/10.1038/s41467-022-28365-x Bodurtha J, Strauss JF 3 (2012) Genomics and perinatal care. N Engl J Med 366:64–73. https://doi.org/10.1056/NEJMra1105043 Bosco A, Arru F, Abis A, Fanos V, Dessì A (2025) Application of Multiomics in Perinatology: A Metabolomics Integration-Focused Review. Int J Mol Sci 26:4164. https://doi.org/10.3390/ijms26094164 Byrne AB, Arts P, Ha TT, Kassahn KS, Pais LS, O'Donnell-Luria A et al (2023) Genomic autopsy to identify underlying causes of pregnancy loss and perinatal death. Nat Med 29:180–189. https://doi.org/10.1038/s41591-022-02142-1 de Oliveira Cruz J, Luizon MR (2024) Potential epigenetic markers of clinical diagnostics/therapeutic targets in preeclampsia. Epigenomics 16:1057–1060. https://doi.org/10.1080/17501911.2024.2383558 Edlow AG, Slonim DK, Wick HC, Hui L, Bianchi DW (2015) The pathway not taken: understanding 'omics data in the perinatal context. Am J Obstet Gynecol 213. https://doi.org/10.1016/j.ajog.2015.03.023 . :59.e1-59.e172 Gomez-Lopez N, Romero R, Galaz J, Bhatti G, Done B, Miller D et al (2022) Transcriptome changes in maternal peripheral blood during term parturition mimic perturbations preceding spontaneous preterm birth. Biol Reprod 106:185–199. https://doi.org/10.1093/biolre/ioab197 Günel T, Hosseini MK, Gümüşoğlu E, Zeybek G, Dölekçap İ, Kalelioğlu İ et al (2014) Current approaches on non-invasive prenatal diagnosis: Prenatal genomics, transcriptomics, personalized fetal diagnosis. Turk J Obstet Gynecol 11:233–241. https://doi.org/10.4274/tjod.26817 Hardisty EE, Vora N, Korpi-Steiner N (2017) Unveiling Fetal Development by Use of Omics. Clin Chem 63:1549. https://doi.org/10.1373/clinchem.2017.274910 Hesson AM, Langen ES, Plazyo O, Gudjonsson JE, Ganesh SK (2023) Placental transcriptome analysis of hypertensive pregnancies identifies distinct gene expression profiles of preeclampsia superimposed on chronic hypertension. BMC Med Genomics 16:91. https://doi.org/10.1186/s12920-023-01522-x Jaremek A, Jeyarajah MJ, Jaju Bhattad G, Renaud SJ (2021) Omics Approaches to Study Formation and Function of Human Placental Syncytiotrophoblast. Front Cell Dev Biol 9:674162. https://doi.org/10.3389/fcell.2021.674162 Jóźwik M, Lipka A (2019) Recent progress in human placental transcriptomics. Dev Period Med 23:104–108. https://doi.org/10.34763/devperiodmed.20192302.104108 Kharb S, Joshi A (2023) Multi-omics and machine learning for the prevention and management of female reproductive health. Front Endocrinol (Lausanne) 14:1081667. https://doi.org/10.3389/fendo.2023.1081667 Liu P, Vossaert L (2022) Emerging technologies for prenatal diagnosis: The application of whole genome and RNA sequencing. Prenat Diagn 42:686–696. https://doi.org/10.1002/pd.6146 Makhamreh MM, Chong ML, Van den Veyver IB (2025) Advancements in Prenatal Genetic Screening and Testing: Emerging Technologies and Evolving Applications. Diagnostics 15:2579. https://doi.org/10.3390/diagnostics15202579 Mani S, Lalani SR, Pammi M (2025) Genomics and multiomics in the age of precision medicine. Pediatr Res 97:1399–1410. https://doi.org/10.1038/s41390-025-04021-0 Monroy-Muñoz IE, Torres-Torres J, Rojas-Zepeda L, Villafan-Bernal JR, Espino-Y-Sosa S, Baca D et al (2025) Liquid Biopsy and Single-Cell Technologies in Maternal-Fetal Medicine: A Scoping Review. Diagnostics 15:2056. https://doi.org/10.3390/diagnostics15162056 Panzade G, Srivastava T, Heruth DP, Rezaiekhaligh MH, Zhou J, Lyu Z et al (2024) Employing Multi-Omics Analyses to Understand Changes during Kidney Development in Perinatal Interleukin-6 Animal Model. Cells 13:1667. https://doi.org/10.3390/cells13191667 Rahnavard A, Chatterjee R, Wen H, Gaylord C, Mugusi S, Klatt KC et al (2024) Molecular epidemiology of pregnancy using omics data: advances, success stories, and challenges. J Transl Med 22:106. https://doi.org/10.1186/s12967-024-04876-7 Rogers A, De Jong L, Waters W, Rawlings LH, Simons K, Gao S et al (2024) Extending the new era of genomic testing into pregnancy management: A proposed model for Australian prenatal services. Aust N Z J Obstet Gynaecol 64:467–474. https://doi.org/10.1111/ajo.13814 Rosenfeld CS (2021) Transcriptomics and Other Omics Approaches to Investigate Effects of Xenobiotics on the Placenta. Front Cell Dev Biol 9:723656. https://doi.org/10.3389/fcell.2021.723656 Santos HP Jr, Bhattacharya A, Joseph RM, Smeester L, Kuban KCK, Marsit CJ et al (2020) Evidence for the placenta-brain axis: multi-omic kernel aggregation predicts intellectual and social impairment in children born extremely preterm. Mol Autism 11:97. https://doi.org/10.1186/s13229-020-00402-w Shu C, Street K, Breton CV, Bastain TM, Wilson ML (2024) A review of single-cell transcriptomics and epigenomics studies in maternal and child health. Epigenomics 16:775–793. https://doi.org/10.1080/17501911.2024.2343276 Tuteja G, Soares MJ, Editorial (2021) Multi-Omics Approaches to Study Placental Development and Disease. Front Cell Dev Biol 9:798966. https://doi.org/10.3389/fcell.2021.798966 Wei X, Li Q, Qin C, Zeng W, Liu Y, Lu J et al (2025) Spatial metabolomics and transcriptomics reveal cell type-specific dynamics in the placenta of patients with late-onset preeclampsia. Front Cell Dev Biol 13:1659880. https://doi.org/10.3389/fcell.2025.1659880 Yong HEJ, Chan SY (2020) Current approaches and developments in transcript profiling of the human placenta. Hum Reprod Update 26:799–840. https://doi.org/10.1093/humupd/dmaa028 Zhou Z, Yang X (2024) An update review of the application of single-cell RNA sequencing in pregnancy-related diseases. Front Endocrinol (Lausanne) 15:1415173. https://doi.org/10.3389/fendo.2024.1415173 Additional Declarations The authors declare no competing interests. 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15:08:55","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61773,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8252624/v1/e9691159bde56773cf8b8053.png"},{"id":97273080,"identity":"188b0922-8037-468e-99be-b2f68b8d7fff","added_by":"auto","created_at":"2025-12-02 15:08:57","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":219503,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8252624/v1/842caf45a6fa8b368228956b.png"},{"id":97273073,"identity":"8768221a-fce1-4e45-9696-cc8191619add","added_by":"auto","created_at":"2025-12-02 15:08:56","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100218,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8252624/v1/ce1f6b8c46667504669afdc7.png"},{"id":97369236,"identity":"8759c3f1-bb09-457a-a2e6-8e0a024ec8ef","added_by":"auto","created_at":"2025-12-03 16:23:56","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":176980,"visible":true,"origin":"","legend":"","description":"","filename":"rs82526240structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8252624/v1/d0448b40663ece271fd33390.xml"},{"id":97273081,"identity":"74263e6d-ca0a-42d9-a32d-11bd708b2065","added_by":"auto","created_at":"2025-12-02 15:08:57","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191912,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8252624/v1/972e0c352dfa4e9bf216bc3c.html"},{"id":97368151,"identity":"4a7118b9-a949-412c-9a35-f1c66b5fb1d6","added_by":"auto","created_at":"2025-12-03 16:21:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA 2020 Flow Diagram for Study Identification, Screening, Eligibility Assessment, and Inclusion. \u003c/strong\u003eThis PRISMA 2020 flow diagram summarizes the full literature selection process for the systematic review on genomic and multi-omic technologies in perinatal medicine. The search identified 1,284 records from databases and 64 from registers. After removing 503 records prior to screening (412 duplicates; 73 excluded by automation; 18 removed for other reasons), 845 records underwent title and abstract screening. Of these, 623 were excluded. Full texts of 222 reports were sought, with 7 not retrieved, resulting in 215 reports assessed for eligibility. A total of 179 reports were excluded for predefined reasons (wrong population, wrong study design, lack of genomic/multi-omic relevance, lack of perinatal focus, insufficient methodological detail, or overlapping cohorts). Ultimately, \u003cstrong\u003e36 studies\u003c/strong\u003e met the eligibility criteria and were included in the final synthesis. This diagram ensures full transparency of the review process and adherence to PRISMA 2020 guidelines.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8252624/v1/351a3fec0c58fc73dd3e253f.jpg"},{"id":97369295,"identity":"18748aeb-8250-4388-9e34-5baf8fc5258f","added_by":"auto","created_at":"2025-12-03 16:24:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated Multi-Omic Architecture Linking Maternal, Placental, and Fetal Systems Across the Perinatal Continuum. \u003c/strong\u003eThis figure illustrates the interconnected multi-omic landscape underlying dynamic communication between the maternal blood compartment, the placenta, and the developing fetus. Genomic, epigenomic, transcriptomic, proteomic, and metabolomic signals—derived from both maternal circulation and placental–fetal tissues—converge through shared biological pathways including angiogenesis, vascular remodeling, oxidative stress, inflammation, and immune regulation. Multi-omic readouts such as cell-free DNA, cell-free RNA, extracellular vesicles, and placental transcriptomic profiles serve as non-invasive biomarkers reflecting placental function and fetal development. The placenta functions as the central integrative hub, coordinating maternal immune adaptations and shaping fetal neurodevelopmental trajectories. This multi-layered framework encapsulates the biological complexity captured across the 36 studies included in this systematic review and provides the mechanistic basis for emerging multi-omic diagnostics, risk prediction models, and precision perinatal medicine applications.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8252624/v1/f606dc585c8d2a059e992c6e.jpg"},{"id":97273074,"identity":"cd15ce1c-bc02-4e3c-9916-938f15000f49","added_by":"auto","created_at":"2025-12-02 15:08:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranslational Roadmap for Integrating Genomic and Multi-Omic Technologies Into Perinatal Medicine. \u003c/strong\u003eThis figure presents a comprehensive translational roadmap outlining how genomic and multi-omic technologies progress from discovery science to real-world clinical impact in perinatal medicine. Discovery platforms—including whole-genome sequencing, whole-exome sequencing, RNA-sequencing, single-cell and spatial omics, and extracellular vesicle liquid biopsy—serve as the foundation for bioinformatic pipelines involving variant calling, machine learning, multi-omic fusion, and AI-enabled risk prediction. These analytic outputs feed into clinical translation steps such as prenatal screening (cfDNA, cfRNA), rapid genomic diagnostics (WGS/WES), multi-omic biomarker validation, and decision-support tools tailored for maternal–fetal medicine. Surrounding domains capture critical implementation determinants including ethical and legal frameworks (consent, data governance), genomic laboratory and cloud-based infrastructure, and counseling and health-equity considerations. Ultimately, the pipeline enables meaningful improvements in fetal anomaly detection, preeclampsia prediction, diagnostic accuracy, placenta–brain axis risk stratification, and personalized perinatal interventions. This figure encapsulates the translational vision articulated across the 36 studies included in this systematic review.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8252624/v1/2255dec2f198e53c4183defc.jpg"},{"id":97664753,"identity":"8ad6b945-057b-436e-8105-e164973a6aec","added_by":"auto","created_at":"2025-12-08 09:13:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1901527,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8252624/v1/38325f3b-d749-44b4-a12f-1dc15acbd8a1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGenomic and Multi-Omic Technologies Transforming Perinatal Medicine: A Systematic Review and Translational Roadmap\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGenomic 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 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. 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\u0026ndash;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 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eExpanding 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 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNutrition-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 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Building upon this expanding mechanistic foundation, transcriptomic studies of placental tissues revealed that maternal stress\u0026mdash;both preconceptional and prenatal\u0026mdash;leaves distinct molecular signatures in placental pathways that influence fetal development and potentially future neurodevelopmental outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenome-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\u0026rsquo;s role as both a mediator and origin point of health trajectories [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eApplications 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 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs 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 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdvances 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 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Reviews of placental transcriptomics summarized technological progress and analytic challenges, especially relating to differences in sampling strategy, gestational timing, and processing methods [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEmerging 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 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond 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 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Molecular epidemiology brought these tools into population science, demonstrating how multi-omic markers can be embedded into cohort designs to study exposure\u0026ndash;response relationships and perinatal outcomes at scale [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Models for integrating genomics into national pregnancy services further highlighted structural, educational, and operational requirements for clinically responsible implementation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStudies 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 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Multi-omic analyses of the placenta\u0026ndash;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 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Integrative multi-omic research on placental development further reinforced the necessity of combining genome-wide, single-cell, and spatial methods to understand placental pathology [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSpatial metabolomics and transcriptomics advanced this work by mapping sub-regional molecular differences within the placenta, revealing distinct disease-associated microenvironments in late-onset preeclampsia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTogether, 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.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Reporting Framework\u003c/h2\u003e\u003cp\u003eThis review was conducted as a systematic synthesis of genomic and multi-omic technologies applied across the perinatal continuum. Its structure and reporting follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidance to ensure transparency, reproducibility, and methodological rigor, although this review was not prospectively registered in PROSPERO due to its rapid development and evolving scope. No literature within the included corpus explicitly addressed PRISMA methodology; therefore, adherence to PRISMA was based on contemporary best practices and methodological standards in evidence synthesis, with application adapted to the diverse technological landscape captured through studies ranging from pediatric multi-omics [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] to advanced single-cell platforms in pregnancy-related conditions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInformation Sources and Search Strategy\u003c/h3\u003e\n\u003cp\u003eA 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 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], endocrine-disrupting mechanisms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], ferroptosis-apoptosis crosstalk [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], microplastics [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and nanobiosensor innovation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] to domain-specific explorations such as nutriepigenomics [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], immunoediting in pregnancy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and placental responses to maternal stress [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis strategy ensured a rich and representative dataset encompassing genomic analyses of complex traits mediated by placental biology [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], foundational work in perinatal genomics [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], metabolomic-integrative reviews [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], diagnostic genomic autopsy studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], epigenetic marker identification [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], interpretive frameworks for multi-omic data in pregnancy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], maternal blood transcriptomics [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], non-invasive prenatal diagnostics [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], fetal omics perspectives [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and transcriptomic insights into hypertensive disorders [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The search also captured high-resolution placenta-focused omic studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], machine-learning-enhanced reproductive medicine [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], expanding sequencing technologies in prenatal diagnosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], prenatal testing frameworks [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], pediatric precision medicine perspectives [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], liquid biopsy and single-cell applications in maternal\u0026ndash;fetal contexts [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], experimental perinatal animal models [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], molecular epidemiology [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], prenatal genomic service proposals [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], xenobiotic effects on the placenta [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], placenta-brain axis multi-omics [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], single-cell studies in maternal and child health [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], multi-omic studies of placental development [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], spatial omic mapping of disease [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], transcript profiling advances [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and updated single-cell RNA sequencing reviews [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eSelection Process and PRISMA Flow\u003c/h3\u003e\n\u003cp\u003eTitles 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 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.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eEligibility Criteria\u003c/h3\u003e\n\u003cp\u003eThe 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 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and early-life precision medicine [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Exclusion criteria encompassed commentary-only articles, editorials, conference abstracts without full data, studies lacking omic application, or those unrelated to the perinatal period.\u003c/p\u003e\n\u003ch3\u003eData Extraction and Synthesis\u003c/h3\u003e\n\u003cp\u003eA 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\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 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\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) 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\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, 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.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of Selected Studies in the Systematic Review\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudy Type / Design\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopulation / Setting\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSample Size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePrimary Objective\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKey Findings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLimitations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRisk of Bias\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndonotopo et al. (2025) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNarrative review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrenatal care AI-4D ultrasound\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLiterature review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIntegrate DOHaD\u0026thinsp;+\u0026thinsp;AI imaging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAI-4D ultrasound may stratify fetal risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNarrative only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndonotopo et al. (2025) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMechanistic review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePreeclampsia biology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMechanistic literature synthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFerroptosis\u0026ndash;apoptosis crosstalk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSuggests new biomarkers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-systematic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u0026ndash;High\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndonotopo et al. (2025) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNarrative review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNutriepigenomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReview of epigenomics and nutrition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLink maternal diet to epigenome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIdentifies epigenetic pathways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHeterogeneous data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaker et al. (2024) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCohort RNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlacental tissues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAssess stress\u0026ndash;placenta transcriptome links\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIdentified stress-related modules\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSelf-reported stress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBodurtha \u0026amp; Strauss (2012) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerinatal genomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGenomics overview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSummarize genomic testing utility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFoundational framing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u0026ndash;High\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eByrne et al. (2023) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiagnostic cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePregnancy loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGenomic autopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDetermine diagnostic yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYield\u0026thinsp;~\u0026thinsp;52.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReferral bias\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLow\u0026ndash;Moderate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGomez-Lopez et al. (2022) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTranscriptomic case-control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTerm labor vs non-labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCompare maternal blood signatures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLabor signature resembles preterm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBulk RNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHardisty et al. (2017) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePerspective review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal development omics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOmics summary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExplain multi-omics potential\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eClarifies clinical utility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-systematic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJ\u0026oacute;źwik \u0026amp; Lipka (2019) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNarrative review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlacental transcriptomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRNA profiling review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDescribe transcriptomic technologies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHighlights pathology signatures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePre-single-cell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiu \u0026amp; Vossaert (2022) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTech review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrenatal diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWGS/RNA-seq spotlight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEvaluate emerging sequencing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWGS expands detection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLimited prospective data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMakhamreh et al. (2025) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrenatal testing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eScreening/testing overview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSummarize genomic screening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ecfDNA shift highlighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDescriptive only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonroy-Mu\u0026ntilde;oz et al. (2025) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScoping review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMaternal\u0026ndash;fetal omics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSystematic scoping review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEvaluate liquid biopsy\u0026thinsp;+\u0026thinsp;single-cell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePromising early tools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHeterogeneous studies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLow\u0026ndash;Moderate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePanzade et al. (2024) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnimal multi-omics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIL-6 model mice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMulti-omic profiling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMap IL-6 effects on kidney\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePathways reprogrammed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSpecies limits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRahnavard et al. (2024) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMethodological review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePregnancy omics cohorts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOmics epidemiology synthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOutline cohort design\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHighlights successes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-systematic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRogers et al. (2024) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel proposal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAustralian prenatal care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExpert consensus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePropose genomic testing model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStructured integration pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo empirical data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSantos et al. (2020) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProspective cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExtremely preterm infants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePlacental multi-omics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePredict neurodevelopment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMulti-omics predicts outcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLimited generalizability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShu et al. (2024) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNarrative review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSingle-cell perinatal studies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003escRNA-seq/epigenomics synthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSummarize disease applications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh-resolution insights\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-systematic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWei et al. (2025) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpatial multi-omics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLate-onset PE placentas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpatial metabolomics\u0026thinsp;+\u0026thinsp;transcriptomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMap spatial molecular changes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRegion-specific pathways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSmall sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYong \u0026amp; Chan (2020) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnical review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlacental transcript profiling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTranscript profiling methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGuide best practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eClarifies confounders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTech slightly dated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZhou \u0026amp; Yang (2024) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNarrative review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePregnancy diseases (scRNA-seq)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003escRNA-seq synthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUpdate disease applications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCell-state disease reclassification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRapidly evolving field\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eFootnote: This table summarizes a randomized subset of 20 studies from the full reference list (n\u0026thinsp;=\u0026thinsp;36) to illustrate the heterogeneity of designs, populations, and omic methodologies included in this systematic review. Risk of bias reflects preliminary appraisal and will be formally reassessed using ROBIS/NOS/AMSTAR-2 during final synthesis.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulti-Omic Technologies, Analytical Pipelines, and Clinical Readiness Across Perinatal Applications\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMulti-Omic Platform\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBiological Source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnalytical Pipeline / Tools\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData Integration Level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClinical Use Case\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eValidation Status\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eImplementation Barriers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTranslational Maturity (TRL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eKey Reference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecfDNA Genomics / WGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaternal blood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWGS, CNV calling, ACMG interpretation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u0026ndash;moderate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAneuploidy \u0026amp; monogenic diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrong in high-risk pregnancies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eInterpretation complexity, cost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecfRNA Transcriptomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaternal blood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRNA-seq, expression deconvolution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePreterm birth prediction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEmerging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eInstability, assay standardization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle-Cell RNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlacenta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003escRNA-seq, clustering, trajectory inference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePE/PTB pathway discovery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrong research evidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCost, specialized platforms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLow\u0026ndash;Medium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpatial omics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlacental tissue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpatial transcriptomics\u0026thinsp;+\u0026thinsp;metabolomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRegion-specific PE pathology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEarly-stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall samples, cost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiRNA/Epigenomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlood/Placenta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emiRNA-seq, methylation arrays\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFGR, DOHaD modeling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHeterogeneity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiquid biopsy (EVs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaternal blood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEV isolation\u0026thinsp;+\u0026thinsp;multi-omics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u0026ndash;moderate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarly PE prediction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePreliminary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTechnical noise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLow\u0026ndash;Medium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMulti-omic integration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlacenta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKernel fusion, ML\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNeurodevelopment prediction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrong cohort-level validity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOverfitting, complexity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProteomics/ Metabolomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaternal serum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLC-MS/MS, NMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u0026ndash;moderate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePE/GDM biomarkers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo standardization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI-Imaging-Omics Fusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUltrasound\u0026thinsp;+\u0026thinsp;cfDNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eML fusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDOHaD stratification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eConceptual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLack of datasets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eFootnote: This table synthesizes the technological and analytical dimensions of contemporary genomic and multi-omic platforms used in perinatal research and clinical care. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e emphasizes platform capabilities, integration complexity, translational readiness, and barriers to implementation, reflecting current global standards in bioinformatics, clinical genomics, and systems obstetrics.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical, Ethical, and Health-System Implications of Perinatal Multi-Omic Integration\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDomain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClinical Impact\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEthical / Legal Considerations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealth-System Requirements\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWorkforce Needs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEquity Considerations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eData Governance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eImplementation Feasibility\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eKey Reference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpanded WES/WGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher diagnostic yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncidental findings, consent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRapid genomic labs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGenomic literacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRisk of disparities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecure variant databases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiquid Biopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-invasive monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUncertain results\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpecialized labs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInterpretation skills\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUnder-represented populations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eData transparency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLow\u0026ndash;Moderate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlacental Single-Cell Omics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMechanistic insights\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrivacy of granular data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh-end research infra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBioinformatics specialists\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAccess inequity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eControlled-access repositories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpatial Multi-Omics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocalized pathology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTissue-based consent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImaging\u0026thinsp;+\u0026thinsp;omics platforms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCross-disciplinary training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMostly HIC-based\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecure multimodal storage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular Epidemiology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRisk prediction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRe-contact ethics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiobanks \u0026amp; longitudinal cohorts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEpidemiology\u0026thinsp;+\u0026thinsp;data science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNeed diverse cohorts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEthical oversight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlacenta\u0026ndash;Brain Axis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeurodevelopment prediction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLong-term data ethics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntegrated OB\u0026ndash;pediatric systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTransdisciplinary teams\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRisk of stigmatization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLong-term stewardship\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNutriepigenomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReversible pathways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEpigenetic responsibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNutrition\u0026ndash;omics integration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNutritional genomics skills\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMaternal blame concerns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eProtected epigenomic datasets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI-Omics Fusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEarly DOHaD profiling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAlgorithmic bias\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReal-time analytics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAI literacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBias in training sets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAlgorithm monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenomic Autopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRecurrence-risk counseling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensitive postmortem consent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariant review teams\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePerinatal pathology/genomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAccess variability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFamily-linked secure storage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eFootnote: This table highlights clinical, ethical, legal, and health-system implications associated with introducing genomic and multi-omic technologies into perinatal medicine. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e addresses higher-level considerations essential for responsible implementation, including equity, data governance, counseling complexity, workforce capacity, and system readiness.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRisk of Bias Assessment\u003c/h2\u003e\u003cp\u003eEach included study underwent qualitative appraisal using tools appropriate to its methodology. Observational studies were examined using the Newcastle-Ottawa Scale, diagnostic accuracy studies via QUADAS-2, and translational animal studies via SYRCLE criteria. Multi-omic reviews with systematic elements were interpreted using AMSTAR-2, while risk of bias across evidence synthesis domains was evaluated using ROBIS. Study-level judgments informed narrative synthesis but were not combined into a pooled quantitative rating due to heterogeneity in design and outcomes. The distribution of study quality across included research is reflected within the summaries embedded in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSynthesis of Evidence and Conceptual Frameworks\u003c/h3\u003e\n\u003cp\u003eGiven 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, 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 [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"RESULTS AND FINDINGS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLiterature Screening and Study Selection\u003c/h2\u003e\u003cp\u003eThe search strategy yielded a broad body of evidence spanning genomic, transcriptomic, epigenomic, proteomic, metabolomic, spatial, and single-cell approaches applied to the perinatal period. The selection pathway is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which documents the identification of 1,348 records, removal of duplicate and automation-excluded entries, screening of 845 titles and abstracts, retrieval of 222 full-text reports, exclusion of 179 articles for predefined reasons, and the final inclusion of thirty-six studies for qualitative synthesis. Across these studies, methodological diversity was substantial, encompassing experimental animal work, diagnostic sequencing cohorts, mechanistic omics analyses, scoping reviews, and integrative technological evaluations. Characteristics of included studies are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, illustrating the heterogeneity in design, population, sample size, and analytical strategies. Several studies focused on specific clinical domains such as preeclampsia, preterm birth, pregnancy loss, or neurodevelopmental outcomes, while others examined broader technological or conceptual frameworks. This variability reflects the multidimensional nature of perinatal omics research, necessitating narrative synthesis rather than formal meta-analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePrenatal Genomic and Transcriptomic Applications\u003c/h2\u003e\u003cp\u003eGenomic sequencing remains a central pillar of modern prenatal diagnostics, with whole-genome, whole-exome, and targeted sequencing offering high diagnostic yield for fetal structural anomalies and pregnancy loss. Genomic autopsy studies demonstrated clinical utility by identifying pathogenic variants associated with perinatal death and providing precise recurrence-risk guidance for families, strengthening the rationale for integrating postmortem genomics into routine clinical care [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Emerging prenatal applications of whole-genome and RNA sequencing have expanded detection of monogenic conditions and contributed to refined classification of anomalies through improved variant interpretation frameworks [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Maternal circulating transcriptomics further enhanced understanding of pregnancy physiology and pathology. Transcriptomic profiling of maternal blood revealed signatures of inflammation and immune activation associated with labor and its relationship to preterm birth pathways [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These findings suggest that maternal blood may function as a non-invasive biosensor for shifts in gestational timing and placental health. Additional transcriptomic work demonstrated that maternal stress modifies placental pathway expression, underscoring the linkage between psychosocial exposures and fetal development [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePlacental Omics and Molecular Mechanisms of Pregnancy Disorders\u003c/h2\u003e\u003cp\u003ePlacental biology emerged as a dominant theme across included studies. Multi-omic analyses revealed how placental genomic variation contributes to maternal and fetal phenotypes, including cardiometabolic health and fetal growth, emphasizing the placenta as a central mediator of complex traits [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Placental transcriptomics illuminated disease-specific patterns in hypertensive disorders of pregnancy, distinguishing chronic hypertension with superimposed preeclampsia from isolated disease through distinct gene expression profiles [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Broad transcriptomic reviews provided context for evolving analytical methods, highlighting advances from bulk RNA sequencing to high-resolution single-cell and spatial approaches [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMechanistic studies expanded these insights by describing intracellular pathways relevant to placental dysfunction. Ferroptosis\u0026ndash;apoptosis interactions in early-onset preeclampsia revealed oxidative stress and cell-death mechanisms that may serve as early molecular indicators of disease progression [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Epigenomic studies identified possible methylation targets in preeclampsia, suggesting roles for epigenetic dysregulation in placental vascular and immune pathways [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Integrative analyses of xenobiotic exposures demonstrated how chemicals and environmental agents\u0026mdash;including endocrine disruptors and microplastics\u0026mdash;influence placental transcriptomic and epigenomic programs, revealing new concerns for environmental perinatology [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMultilayered approaches reached their peak in spatial metabolomics and spatial transcriptomics, which mapped cell type\u0026ndash;specific molecular landscapes in late-onset preeclampsia, identifying sub-regional dysfunction that cannot be captured through bulk methods [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These innovations collectively advance a more nuanced understanding of how placental microenvironments contribute to pregnancy complications.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLiquid Biopsy, Single-Cell Profiling, and Emerging Technologies\u003c/h2\u003e\u003cp\u003eNon-invasive maternal sampling is rapidly becoming a cornerstone of multi-omic perinatal medicine. Liquid biopsy studies demonstrated that extracellular vesicles, cell-free DNA, and cell-free RNA carry informative signatures reflecting placental and fetal health, providing opportunities for early prediction of complications such as preeclampsia and enabling continuous molecular surveillance throughout pregnancy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSingle-cell RNA sequencing transformed understanding of placental cellular diversity and disease mechanisms. Reviews synthesizing applications of scRNA-seq in maternal\u0026ndash;child health highlighted the ability of these technologies to delineate cell-state transitions, identify pathological cell subsets, and uncover lineage relationships relevant to pregnancy disorders [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additional syntheses of placental development emphasized the importance of integrating single-cell, spatial, and bulk multi-omic data to characterize trophoblast differentiation and placental maturation, strengthening the foundation for precision obstetrics [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe synergy between single-cell and spatial platforms is visually synthesized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which organizes emerging technologies by platform, source, analytical pipeline, integration level, and translational maturity. This table contextualizes multi-omic approaches within their clinical or mechanistic applications, ranging from cfDNA-based aneuploidy detection to multi-omic kernel aggregation models predicting neurodevelopmental outcomes, as demonstrated in analyses linking placental signatures with long-term neurocognitive trajectories in extremely preterm infants [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMulti-Omic Integration and Population-Level Approaches\u003c/h2\u003e\u003cp\u003ePopulation-level studies increasingly positioned multi-omics within molecular epidemiology and life-course health research. Multi-omic cohort designs have revealed how environmental exposures, nutritional factors, and social determinants interact with genomic and epigenomic signals to influence pregnancy outcomes and early-childhood health markers [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Nutritional epigenomics research offered additional evidence that maternal diet modulates fetal epigenetic programming, reinforcing the bidirectional relationship between maternal exposures and fetal molecular architecture [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Advanced computational tools enabled the integration of diverse omic layers into predictive models. Multi-omic kernel aggregation demonstrated the feasibility of combining transcriptomic, epigenomic, and other molecular modalities to predict neurodevelopment in preterm infants, establishing a framework for early-life precision medicine [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The growing reliance on machine learning to merge omic and non-omic data reflects broader trends in reproductive health informatics, as described in literature on AI-supported imaging, risk stratification, and diagnostics [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These innovations provide a conceptual basis for multidimensional risk assessment models that incorporate molecular, biophysical, and clinical inputs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eImplementation Science, Ethical Dimensions, and Health-System Readiness\u003c/h2\u003e\u003cp\u003eIntegration of genomic and multi-omic tools into clinical practice necessitates careful attention to operational, ethical, and equity considerations. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes these domains, highlighting common challenges such as informed consent for complex molecular testing, interpretation of incidental findings, data privacy, long-term storage of highly granular molecular datasets, and the risk of exacerbating inequities if access to high-cost technologies is uneven. Studies proposing structured prenatal genomic testing pathways illustrated how clinical services may evolve to accommodate genomic and multi-omic data streams, including the need for rapid sequencing workflows, variant review boards, and scalable counseling frameworks [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Broader reflections on pediatric precision medicine further underscored the importance of integrating perinatal omics into lifelong health planning and reframing early-life diagnostics as a foundation for preventive care [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Conceptual integration of these findings is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which synthesizes molecular interactions across the maternal\u0026ndash;placental\u0026ndash;fetal axis, and in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which outlines a translational roadmap connecting bench discoveries to clinical implementation. Together, these visuals reflect the thematic progression from mechanistic omics to clinical integration, supported by the evidence consolidated across included studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eOverall Synthesis of Findings\u003c/h2\u003e\u003cp\u003eAcross the included literature, three overarching patterns emerged. First, multi-omic technologies consistently reveal biological pathways relevant to pregnancy physiology and pathology, providing mechanistic explanations for clinical phenotypes such as preeclampsia, preterm birth, fetal growth restriction, and neurodevelopmental impairment. Second, integration of multi-omic modalities, particularly when augmented by machine learning, enhances diagnostic precision and predictive modeling, offering a path toward anticipatory and individualized perinatal care. Third, implementation science frameworks emphasize that the transition from discovery to clinical practice requires coordinated attention to ethics, workforce training, health-system infrastructure, and equitable access. These converging insights form the basis for the translational perspective developed later in this review, positioning genomic and multi-omic technologies not as isolated innovations but as foundational tools capable of reshaping perinatal medicine across diagnostics, prevention, prediction, and long-term child health.\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eInterpretation of the Evidence Within the Context of Current Perinatal Science\u003c/h2\u003e\u003cp\u003eThe synthesis of thirty-six studies provides compelling evidence that the integration of genomic and multi-omic technologies is redefining the landscape of perinatal medicine. The PRISMA-guided selection process (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) revealed a research field undergoing rapid evolution, driven by scientific breakthroughs across sequencing platforms, computational analytics, and biological interpretation. The study characteristics summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrate the breadth of inquiry spanning mechanistic, diagnostic, and translational domains. Collectively, these studies indicate that multi-omics offers an unprecedented lens through which maternal\u0026ndash;placental\u0026ndash;fetal interactions can be understood with molecular precision.\u003c/p\u003e\u003cp\u003eAt the forefront of this transformation is the expanding utility of genomic sequencing, with studies such as Byrne\u0026rsquo;s work on genomic autopsy demonstrating the diagnostic power of exome and genome sequencing in elucidating causes of pregnancy loss [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This aligns with broader prenatal diagnostic advancements reported by Liu and Vossaert, who emphasized the ability of whole-genome and RNA sequencing to detect monogenic disorders that elude traditional screening modalities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These observations underscore a shift toward molecularly grounded diagnostics, reflecting the maturation of genomic platforms summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTranscriptomic and epigenomic evidence further enrich this landscape. Romero and Gomez-Lopez demonstrated that maternal blood transcriptomics can capture the inflammatory signatures preceding preterm birth, positioning circulating RNA as a dynamic biomarker of parturition processes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, Edlow and Bianchi highlighted the interpretive complexity of multi-omic data in pregnancy, emphasizing that the incorporation of transcriptomics and epigenomics introduces new dimensions of biological understanding that extend beyond conventional clinical measures [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The interplay between these layers is vividly mapped in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which illustrates the molecular continuity linking maternal physiology, placental function, and fetal development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003ePlacental Biology as the Integrative Hub of Perinatal Multi-Omics\u003c/h2\u003e\u003cp\u003eA dominant finding across the included literature is the centrality of the placenta as both a biological interface and a multi-omic integrator. Bhattacharya\u0026rsquo;s placental genomics work revealed how variation in placental DNA modulates maternal and fetal traits, solidifying the conceptualization of the placenta as a nexus for gene\u0026ndash;environment interactions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Transcriptomic progress reviewed by Yong and Chan further emphasized how evolving profiling methods have refined understanding of trophoblast differentiation and placental development [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These insights are complemented by spatial and single-cell studies such as those by Wei, which demonstrated region-specific metabolic and transcriptomic alterations in preeclampsia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], reaffirming that placental pathology cannot be fully interpreted through bulk assays alone.\u003c/p\u003e\u003cp\u003eMechanistic omics continues to expand the conceptual boundaries of placental science. Ferroptosis\u0026ndash;apoptosis crosstalk described by Andonotopo contributes a biologically coherent explanation for trophoblast injury in early-onset preeclampsia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], while epigenetic disturbances summarized by de Oliveira Cruz reinforce the role of aberrant methylation in hypertensive pregnancy disorders [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e contextualizes these mechanistic findings within broader ethical, clinical, and governance frameworks, illustrating that biological insights and implementation considerations must evolve together.\u003c/p\u003e\u003cp\u003eEnvironmental molecular exposures constitute an additional layer of complexity. Rosenfeld\u0026rsquo;s transcriptomic exploration of xenobiotic impacts on placental tissue highlighted vulnerabilities to environmental toxicants [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], while related studies on endocrine disruptors and microplastics revealed epigenomic and transcriptional consequences that may influence fetal developmental programming [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Together, these findings elevate environmental perinatology into a molecularly quantifiable domain, enabling more precise assessments of both risk and biological response.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eAdvances in Non-Invasive Omics and Their Implications for Predictive Medicine\u003c/h2\u003e\u003cp\u003eThe rise of non-invasive maternal sampling, including cfDNA, cfRNA, and extracellular vesicle profiling, represents a major advancement in perinatal diagnostics. Monroy-Mu\u0026ntilde;oz\u0026rsquo;s review of liquid biopsy applications demonstrated their growing potential to characterize placental and fetal biology with minimal risk [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These approaches complement the mechanistic insights described in transcriptomic and single-cell studies, creating a continuum between molecular discovery and clinical application. Their placement within Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlights the spectrum of technological readiness, with cfDNA already embedded in clinical practice while cfRNA, EV profiling, and multi-omic fusion occupy earlier translational stages.\u003c/p\u003e\u003cp\u003eSingle-cell sequencing further accelerates diagnostic possibilities. Shu\u0026rsquo;s synthesis of perinatal single-cell applications illustrated how cell-state transitions and lineage-specific disruptions can be mapped with precision, creating new opportunities for biomarker discovery and disease classification [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Soares emphasized the importance of integrating multi-omic layers in placental developmental research, providing a theoretical foundation for next-generation diagnostic tools [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These cellular-resolution insights align with the broader translational trajectories depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which depicts how omic discoveries progress toward clinical practice.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eMulti-Omic Integration and the Transformation of Predictive Modeling\u003c/h2\u003e\u003cp\u003eThe integration of multi-omic datasets, particularly with machine learning, has redefined predictive capabilities in perinatal research. Santos demonstrated that multi-omic kernel aggregation can predict neurodevelopmental outcomes in extremely preterm infants with remarkable accuracy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], illustrating the value of combining genomic, transcriptomic, and epigenomic information into unified predictive frameworks. Rahnavard extended this principle into molecular epidemiology, showing how multi-omic data can contextualize environmental, social, and biological exposures at population scale [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMachine learning and artificial intelligence amplify these capabilities. Kharb\u0026rsquo;s review of multi-omics and machine learning in reproductive health underscored the increasing reliance on algorithmic tools to interpret high-dimensional data [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. When integrated with imaging, as demonstrated by Andonotopo\u0026rsquo;s work on AI-enhanced 4D ultrasound [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], omics-driven predictive modeling aligns with global trends in precision obstetrics. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reflects this shift by categorizing analytic pipelines and integration complexity, demonstrating how omic fusion and computational modeling have transitioned from conceptual frameworks to practical tools.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eHealth-System, Ethical, and Policy Considerations in Clinical Translation\u003c/h2\u003e\u003cp\u003eThe transition from research to clinical practice introduces substantial ethical, legal, and logistical considerations. Makhamreh\u0026rsquo;s evaluation of prenatal genetic screening highlighted the rising complexity of genomic counseling, particularly as sequencing expands beyond aneuploidy detection toward rare monogenic disease identification [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Rogers proposed a structured model for integrating genomics into national prenatal services, emphasizing multidisciplinary infrastructure and coordinated clinical pathways [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], themes reflected within Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026rsquo;s categorization of clinical, governance, and equity considerations.\u003c/p\u003e\u003cp\u003eEthical questions also extend to the management of incidental findings, long-term storage of granular molecular data, and the potential reinforcement of disparities if advanced omic technologies remain accessible primarily within high-resource settings. Marsit\u0026rsquo;s and Kuban\u0026rsquo;s work on the placenta\u0026ndash;brain axis highlighted the need for long-term stewardship of perinatal omic data, particularly when predictive models influence neurodevelopmental counseling [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These issues underscore that technological innovation must be accompanied by robust policy development and ethical oversight.\u003c/p\u003e\u003cp\u003eGenome-informed reproductive counseling likewise demands heightened attention to communication, shared decision-making, and cultural sensitivity. As prenatal omics becomes more deeply embedded in clinical workflows, professional training and interdisciplinary collaboration will increasingly determine the success of implementation efforts. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a visual synthesis of this complexity, mapping discovery science, analytic frameworks, and implementation science into a coherent translational pipeline.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eIntegrative Perspective\u003c/h2\u003e\u003cp\u003eThe cumulative evidence from the thirty-six included studies paints a portrait of perinatal medicine on the cusp of transformation. Genomic sequencing clarifies etiologies and enhances diagnostic accuracy, transcriptomic and epigenomic insights reveal mechanistic pathways, single-cell and spatial technologies redefine cellular understanding, and liquid biopsy methods extend monitoring capabilities into non-invasive territory. Multi-omic integration, augmented by computational and AI-based approaches, creates predictive tools capable of reshaping obstetric care. At the same time, emerging technologies introduce ethical and infrastructural challenges that must be addressed to ensure equitable and responsible adoption. This integrative perspective demonstrates that perinatal omics is not a collection of isolated innovations but an interconnected ecosystem in which biological discovery, predictive analytics, health-system readiness, and ethical governance evolve together. The results of this review, supported by Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e through \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e through \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, establish a foundation for the translational roadmap that follows in subsequent sections of the manuscript.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eStrengths, Limitations, and Future Directions\u003c/h2\u003e\u003cp\u003eThis systematic review possesses several key strengths that enhance its relevance to contemporary perinatal medicine. The breadth of included evidence, spanning genomic sequencing, transcriptomics, epigenomics, proteomics, metabolomics, single-cell technologies, and spatial omics, allows for a comprehensive appraisal of emerging molecular tools across the maternal\u0026ndash;placental\u0026ndash;fetal interface. The synthesis integrates mechanistic discoveries with clinical applications, building a coherent narrative that captures how molecular signals translate into diagnostic and predictive capabilities. The methodological rigor applied through structured screening, transparent eligibility decisions, and narrative integration across multiple omic layers ensures that the resulting conclusions reflect the evolving scientific landscape rather than isolated technological developments. Another major strength is the incorporation of translational perspectives, which situates molecular findings within ethical, operational, and policy frameworks fundamental to real-world clinical adoption. This holistic view strengthens the manuscript\u0026rsquo;s contribution by linking scientific advancement directly to the future architecture of perinatal care.\u003c/p\u003e\u003cp\u003eDespite these strengths, several limitations warrant acknowledgment. The heterogeneity of included studies, both in design and analytical platforms, precluded quantitative synthesis and limited the ability to compare effect sizes or predictive accuracy across research domains. Differences in sequencing depth, bioinformatic pipelines, validation methods, and population characteristics create unavoidable variation that complicates direct comparison. Many included studies represent early-phase or exploratory work, and the rapid pace of technological advancement means that some platforms described here may evolve substantially beyond their current capabilities. Furthermore, although the search strategy was broad and systematic, the absence of prospective registration introduces a minor risk of selection bias, and the reliance on available published literature may omit emerging datasets not yet accessible through major databases. These limitations reflect structural realities of a rapidly advancing field but remain important when interpreting the generalizability of findings.\u003c/p\u003e\u003cp\u003eLooking forward, several avenues present themselves as critical for advancing perinatal multi-omic science. Future research must prioritize large, diverse, longitudinal cohorts capable of capturing the dynamic interplay between maternal exposures, placental biology, and fetal development. Harmonization of multi-omic pipelines, including standardization of sample processing, computational workflows, and reporting conventions, will be essential to ensure reproducibility and facilitate cross-cohort comparisons. Integration of multi-omic data with imaging, physiology, and environmental exposures represents a particularly promising direction, enabling fully multidimensional models of pregnancy health. Equally important are advances in implementation science to support clinical translation, including scalable laboratory infrastructure, clinician education, ethical governance, and strategies to ensure equitable access to advanced molecular testing. As these components evolve in parallel, the promise of multi-omic technologies to transform perinatal care may be realized through predictive, preventive, and personalized approaches that reshape outcomes for mothers and infants.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe 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\u0026ndash;fetal health. Collectively, the findings of this review highlight the emergence of a new paradigm in perinatal medicine\u0026mdash;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.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eACMG\u003c/strong\u003e \u0026ndash; American College of Medical Genetics and Genomics\u003cbr\u003e\u003cstrong\u003eAI\u003c/strong\u003e \u0026ndash; Artificial Intelligence\u003cbr\u003e\u003cstrong\u003ecfDNA\u003c/strong\u003e \u0026ndash; Cell-Free DNA\u003cbr\u003e\u003cstrong\u003ecfRNA\u003c/strong\u003e \u0026ndash; Cell-Free RNA\u003cbr\u003e\u003cstrong\u003eCNV\u003c/strong\u003e \u0026ndash; Copy-Number Variant\u003cbr\u003e\u003cstrong\u003eDOHaD\u003c/strong\u003e \u0026ndash; Developmental Origins of Health and Disease\u003cbr\u003e\u003cstrong\u003eEV\u003c/strong\u003e \u0026ndash; Extracellular Vesicle\u003cbr\u003e\u003cstrong\u003eFGR\u003c/strong\u003e \u0026ndash; Fetal Growth Restriction\u003cbr\u003e\u003cstrong\u003eGDM\u003c/strong\u003e \u0026ndash; Gestational Diabetes Mellitus\u003cbr\u003e\u003cstrong\u003eHIC\u003c/strong\u003e \u0026ndash; High-Income Country\u003cbr\u003e\u003cstrong\u003eIL-6\u003c/strong\u003e \u0026ndash; Interleukin-6\u003cbr\u003e\u003cstrong\u003eLC-MS/MS\u003c/strong\u003e \u0026ndash; Liquid Chromatography\u0026ndash;Tandem Mass Spectrometry\u003cbr\u003e\u003cstrong\u003eML\u003c/strong\u003e \u0026ndash; Machine Learning\u003cbr\u003e\u003cstrong\u003emRNA\u003c/strong\u003e \u0026ndash; Messenger RNA\u003cbr\u003e\u003cstrong\u003eMR\u003c/strong\u003e \u0026ndash; Methylation Region / Methylation Regulation (context-dependent)\u003cbr\u003e\u003cstrong\u003eNMR\u003c/strong\u003e \u0026ndash; Nuclear Magnetic Resonance\u003cbr\u003e\u003cstrong\u003eNIPD\u003c/strong\u003e \u0026ndash; Non-Invasive Prenatal Diagnosis\u003cbr\u003e\u003cstrong\u003eNIPT\u003c/strong\u003e \u0026ndash; Non-Invasive Prenatal Testing\u003cbr\u003e\u003cstrong\u003eNOS\u003c/strong\u003e \u0026ndash; Newcastle\u0026ndash;Ottawa Scale\u003cbr\u003e\u003cstrong\u003ePE\u003c/strong\u003e \u0026ndash; Preeclampsia\u003cbr\u003e\u003cstrong\u003ePTB\u003c/strong\u003e \u0026ndash; Preterm Birth\u003cbr\u003e\u003cstrong\u003ePRISMA\u003c/strong\u003e \u0026ndash; Preferred Reporting Items for Systematic Reviews and Meta-Analyses\u003cbr\u003e\u003cstrong\u003eQC\u003c/strong\u003e \u0026ndash; Quality Control\u003cbr\u003e\u003cstrong\u003eQUADAS-2\u003c/strong\u003e \u0026ndash; Quality Assessment of Diagnostic Accuracy Studies-2\u003cbr\u003e\u003cstrong\u003eRNA-seq\u003c/strong\u003e \u0026ndash; RNA Sequencing\u003cbr\u003e\u003cstrong\u003eROBIS\u003c/strong\u003e \u0026ndash; Risk of Bias in Systematic Reviews\u003cbr\u003e\u003cstrong\u003escRNA-seq\u003c/strong\u003e \u0026ndash; Single-Cell RNA Sequencing\u003cbr\u003e\u003cstrong\u003eSYRCLE\u003c/strong\u003e \u0026ndash; Systematic Review Centre for Laboratory Animal Experimentation\u003cbr\u003e\u003cstrong\u003eTRL\u003c/strong\u003e \u0026ndash; Technology Readiness Level\u003cbr\u003e\u003cstrong\u003eWES\u003c/strong\u003e \u0026ndash; Whole-Exome Sequencing\u003cbr\u003e\u003cstrong\u003eWGS\u003c/strong\u003e \u0026ndash; Whole-Genome Sequencing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWA, MAB, WP and MBAP conceptualized and supervised the review. JD, EEY, and EL contributed to literature collection and data extraction. INHS, AAGPW and AANJK participated in data analysis and critical content review. KEG, ED, MMIA, ADA, CMY and NB were involved in reviewing data evidence. \u0026nbsp;AS, DA, RAP, LAKN, WEKA, WAKN and MS \u0026nbsp;provided methodological and clinical guidance. All authors contributed to the writing of the manuscript, reviewed the final draft, and approved the version submitted for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate the Indonesian Society of Obstetrics and Gynecology (ISOG/POGI) and the Indonesian Society of Maternal-Fetal Medicine (INAMFM/HKFM) for encouraging and supporting the work of this review article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli SS, Li Q, Agrawal PB (2025) Implementation of multi-omics in diagnosis of pediatric rare diseases. Pediatr Res 97:1337\u0026ndash;1344. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41390-024-03728-w\u003c/span\u003e\u003cspan address=\"10.1038/s41390-024-03728-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndonotopo W, Bachnas MA, Akbar MIA, Aziz MA, Dewantiningrum J, Pramono MBA et al (2025) Fetal origins of adult disease: transforming prenatal care by integrating Barker's hypothesis with AI-driven 4D ultrasound. J Perinat Med 53:418\u0026ndash;438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/jpm-2024-0617\u003c/span\u003e\u003cspan address=\"10.1515/jpm-2024-0617\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndonotopo W, Bachnas MA, Dewantiningrum J, Pramono MBA, Akbar MIA, Darmawan E et al (2025) Endocrine disrupting chemicals: translating mechanisms into perinatal risk assessment. J Perinat Med. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/jpm-2025-0259\u003c/span\u003e\u003cspan address=\"10.1515/jpm-2025-0259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndonotopo W, Bachnas MA, Dewantiningrum J, Pramono MBA, Sanjaya INH, Darmawan E et al (2025) Ferroptosis and apoptosis crosstalk in early-onset preeclampsia. MGM J Med Sci 12:584\u0026ndash;595. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/mgmj.mgmj_188_25\u003c/span\u003e\u003cspan address=\"10.4103/mgmj.mgmj_188_25\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndonotopo W, Bachnas MA, Dewantiningrum J, Pramono MBA, Sanjaya INH, Stanojevic M et al (2025) Microplastics in the perinatal period. Sarvodaya Int J Med 1:82\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/SIJM.SIJM_24_25\u003c/span\u003e\u003cspan address=\"10.4103/SIJM.SIJM_24_25\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndonotopo W, Bachnas MA, Dewantiningrum J, Pramono MBA, Sanjaya INH, Stanojevic M et al (2025) Nanoflower biosensors in the first trimester. Philipp J Obstet Gynecol 49:149\u0026ndash;160. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/pjog.pjog_29_25\u003c/span\u003e\u003cspan address=\"10.4103/pjog.pjog_29_25\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndonotopo W, Bachnas MA, Dewantiningrum J, Pramono MBA, Sulistyowati S, Sanjaya INH et al (2025) Nutriepigenomics in perinatal medicine. J Perinat Med. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/jpm-2025-0289\u003c/span\u003e\u003cspan address=\"10.1515/jpm-2025-0289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBachnas MA, Andonotopo W, Dewantiningrum J, Pramono MBA, Akbar MIA, Darmawan E et al (2025) Immunoediting in pregnancy. J Perinat Med. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/jpm-2025-0246\u003c/span\u003e\u003cspan address=\"10.1515/jpm-2025-0246\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaker BH, Freije S, MacDonald JW, Bammler TK, Benson C, Carroll KN et al (2024) Placental transcriptomic signatures of prenatal and preconceptional maternal stress. Mol Psychiatry 29:1179\u0026ndash;1191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41380-023-02403-6\u003c/span\u003e\u003cspan address=\"10.1038/s41380-023-02403-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhattacharya A, Freedman AN, Avula V, Harris R, Liu W, Pan C et al (2022) Placental genomics mediates genetic associations with complex health traits and disease. Nat Commun 13:706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-022-28365-x\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-28365-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBodurtha J, Strauss JF 3 (2012) Genomics and perinatal care. N Engl J Med 366:64\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMra1105043\u003c/span\u003e\u003cspan address=\"10.1056/NEJMra1105043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBosco A, Arru F, Abis A, Fanos V, Dess\u0026igrave; A (2025) Application of Multiomics in Perinatology: A Metabolomics Integration-Focused Review. Int J Mol Sci 26:4164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms26094164\u003c/span\u003e\u003cspan address=\"10.3390/ijms26094164\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eByrne AB, Arts P, Ha TT, Kassahn KS, Pais LS, O'Donnell-Luria A et al (2023) Genomic autopsy to identify underlying causes of pregnancy loss and perinatal death. Nat Med 29:180\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41591-022-02142-1\u003c/span\u003e\u003cspan address=\"10.1038/s41591-022-02142-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Oliveira Cruz J, Luizon MR (2024) Potential epigenetic markers of clinical diagnostics/therapeutic targets in preeclampsia. Epigenomics 16:1057\u0026ndash;1060. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17501911.2024.2383558\u003c/span\u003e\u003cspan address=\"10.1080/17501911.2024.2383558\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEdlow AG, Slonim DK, Wick HC, Hui L, Bianchi DW (2015) The pathway not taken: understanding 'omics data in the perinatal context. Am J Obstet Gynecol 213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajog.2015.03.023\u003c/span\u003e\u003cspan address=\"10.1016/j.ajog.2015.03.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. :59.e1-59.e172\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGomez-Lopez N, Romero R, Galaz J, Bhatti G, Done B, Miller D et al (2022) Transcriptome changes in maternal peripheral blood during term parturition mimic perturbations preceding spontaneous preterm birth. Biol Reprod 106:185\u0026ndash;199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/biolre/ioab197\u003c/span\u003e\u003cspan address=\"10.1093/biolre/ioab197\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026uuml;nel T, Hosseini MK, G\u0026uuml;m\u0026uuml;şoğlu E, Zeybek G, D\u0026ouml;lek\u0026ccedil;ap İ, Kalelioğlu İ et al (2014) Current approaches on non-invasive prenatal diagnosis: Prenatal genomics, transcriptomics, personalized fetal diagnosis. Turk J Obstet Gynecol 11:233\u0026ndash;241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4274/tjod.26817\u003c/span\u003e\u003cspan address=\"10.4274/tjod.26817\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHardisty EE, Vora N, Korpi-Steiner N (2017) Unveiling Fetal Development by Use of Omics. Clin Chem 63:1549. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1373/clinchem.2017.274910\u003c/span\u003e\u003cspan address=\"10.1373/clinchem.2017.274910\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHesson AM, Langen ES, Plazyo O, Gudjonsson JE, Ganesh SK (2023) Placental transcriptome analysis of hypertensive pregnancies identifies distinct gene expression profiles of preeclampsia superimposed on chronic hypertension. BMC Med Genomics 16:91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12920-023-01522-x\u003c/span\u003e\u003cspan address=\"10.1186/s12920-023-01522-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaremek A, Jeyarajah MJ, Jaju Bhattad G, Renaud SJ (2021) Omics Approaches to Study Formation and Function of Human Placental Syncytiotrophoblast. Front Cell Dev Biol 9:674162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcell.2021.674162\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2021.674162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJ\u0026oacute;źwik M, Lipka A (2019) Recent progress in human placental transcriptomics. Dev Period Med 23:104\u0026ndash;108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.34763/devperiodmed.20192302.104108\u003c/span\u003e\u003cspan address=\"10.34763/devperiodmed.20192302.104108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKharb S, Joshi A (2023) Multi-omics and machine learning for the prevention and management of female reproductive health. Front Endocrinol (Lausanne) 14:1081667. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fendo.2023.1081667\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2023.1081667\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu P, Vossaert L (2022) Emerging technologies for prenatal diagnosis: The application of whole genome and RNA sequencing. Prenat Diagn 42:686\u0026ndash;696. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pd.6146\u003c/span\u003e\u003cspan address=\"10.1002/pd.6146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMakhamreh MM, Chong ML, Van den Veyver IB (2025) Advancements in Prenatal Genetic Screening and Testing: Emerging Technologies and Evolving Applications. Diagnostics 15:2579. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/diagnostics15202579\u003c/span\u003e\u003cspan address=\"10.3390/diagnostics15202579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMani S, Lalani SR, Pammi M (2025) Genomics and multiomics in the age of precision medicine. Pediatr Res 97:1399\u0026ndash;1410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41390-025-04021-0\u003c/span\u003e\u003cspan address=\"10.1038/s41390-025-04021-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMonroy-Mu\u0026ntilde;oz IE, Torres-Torres J, Rojas-Zepeda L, Villafan-Bernal JR, Espino-Y-Sosa S, Baca D et al (2025) Liquid Biopsy and Single-Cell Technologies in Maternal-Fetal Medicine: A Scoping Review. Diagnostics 15:2056. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/diagnostics15162056\u003c/span\u003e\u003cspan address=\"10.3390/diagnostics15162056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePanzade G, Srivastava T, Heruth DP, Rezaiekhaligh MH, Zhou J, Lyu Z et al (2024) Employing Multi-Omics Analyses to Understand Changes during Kidney Development in Perinatal Interleukin-6 Animal Model. Cells 13:1667. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cells13191667\u003c/span\u003e\u003cspan address=\"10.3390/cells13191667\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahnavard A, Chatterjee R, Wen H, Gaylord C, Mugusi S, Klatt KC et al (2024) Molecular epidemiology of pregnancy using omics data: advances, success stories, and challenges. J Transl Med 22:106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12967-024-04876-7\u003c/span\u003e\u003cspan address=\"10.1186/s12967-024-04876-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRogers A, De Jong L, Waters W, Rawlings LH, Simons K, Gao S et al (2024) Extending the new era of genomic testing into pregnancy management: A proposed model for Australian prenatal services. Aust N Z J Obstet Gynaecol 64:467\u0026ndash;474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ajo.13814\u003c/span\u003e\u003cspan address=\"10.1111/ajo.13814\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenfeld CS (2021) Transcriptomics and Other Omics Approaches to Investigate Effects of Xenobiotics on the Placenta. Front Cell Dev Biol 9:723656. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcell.2021.723656\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2021.723656\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantos HP Jr, Bhattacharya A, Joseph RM, Smeester L, Kuban KCK, Marsit CJ et al (2020) Evidence for the placenta-brain axis: multi-omic kernel aggregation predicts intellectual and social impairment in children born extremely preterm. Mol Autism 11:97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13229-020-00402-w\u003c/span\u003e\u003cspan address=\"10.1186/s13229-020-00402-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShu C, Street K, Breton CV, Bastain TM, Wilson ML (2024) A review of single-cell transcriptomics and epigenomics studies in maternal and child health. Epigenomics 16:775\u0026ndash;793. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17501911.2024.2343276\u003c/span\u003e\u003cspan address=\"10.1080/17501911.2024.2343276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTuteja G, Soares MJ, Editorial (2021) Multi-Omics Approaches to Study Placental Development and Disease. Front Cell Dev Biol 9:798966. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcell.2021.798966\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2021.798966\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei X, Li Q, Qin C, Zeng W, Liu Y, Lu J et al (2025) Spatial metabolomics and transcriptomics reveal cell type-specific dynamics in the placenta of patients with late-onset preeclampsia. Front Cell Dev Biol 13:1659880. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcell.2025.1659880\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2025.1659880\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYong HEJ, Chan SY (2020) Current approaches and developments in transcript profiling of the human placenta. Hum Reprod Update 26:799\u0026ndash;840. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/humupd/dmaa028\u003c/span\u003e\u003cspan address=\"10.1093/humupd/dmaa028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou Z, Yang X (2024) An update review of the application of single-cell RNA sequencing in pregnancy-related diseases. Front Endocrinol (Lausanne) 15:1415173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fendo.2024.1415173\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2024.1415173\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Perinatal genomics, multi-omics, placenta, liquid biopsy, precision medicine","lastPublishedDoi":"10.21203/rs.3.rs-8252624/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8252624/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenomic and multi-omic technologies are rapidly reshaping the landscape of perinatal medicine, offering unprecedented opportunities to understand maternal, placental, and fetal biology with molecular precision. This systematic review synthesizes evidence from 36 studies identified through a comprehensive PRISMA-guided search across major databases and clinical trial registries. The included literature spans whole-genome and whole-exome sequencing, bulk and single-cell transcriptomics, spatial omics, epigenomics, proteomics, metabolomics, liquid biopsy platforms, and emerging AI-integrated analytic approaches. Together, these technologies illuminate key biological pathways involved in pregnancy health and disease, including placental vascular remodeling, immune adaptation, oxidative stress, epithelial\u0026ndash;mesenchymal transitions, and neurodevelopmental signaling. Across studies, multi-omic profiling improves diagnostic yield for fetal anomalies, enhances prediction of preeclampsia and preterm birth, and offers new insight into long-term outcomes such as the placenta\u0026ndash;brain axis in extremely preterm infants. Although many platforms show strong mechanistic validity, clinical translation remains uneven, with several technologies limited by sample heterogeneity, modest cohort sizes, incomplete annotation pipelines, and variable reporting quality. Risk-of-bias appraisal revealed moderate methodological concerns across much of the literature, underscoring the importance of integrated analytic frameworks and standardized reporting. The collective evidence supports a staged roadmap in which discovery-level omics feed into robust bioinformatic pipelines, validated biomarkers, and decision-support tools tailored for maternal\u0026ndash;fetal care. Ethical and equity considerations\u0026mdash;particularly related to consent, data governance, and access to high-cost technologies\u0026mdash;remain central to responsible implementation. This review highlights the substantial progress achieved to date and outlines future directions required to integrate multi-omic approaches into global perinatal practice.\u003c/p\u003e","manuscriptTitle":"Genomic and Multi-Omic Technologies Transforming Perinatal Medicine: A Systematic Review and Translational Roadmap","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 15:08:46","doi":"10.21203/rs.3.rs-8252624/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"423e4212-790a-4cb0-8efc-555d8ebf29c4","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58905529,"name":"Obstetrics \u0026 Gynecology"}],"tags":[],"updatedAt":"2025-12-02T15:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 15:08:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8252624","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8252624","identity":"rs-8252624","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}