Omics Studies Research Articles

Drug target prediction through deep learning functional representation of gene signatures

Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute’s L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

Investigating post-traumatic syringomyelia and local fluid osmoregulation via a rat model

BackgroundSyringomyelia (SM) is characterized by the development of fluid-filled cavities, referred to as syrinxes, within the spinal cord tissue. The molecular etiology of SM post-spinal cord injury (SCI) is not well understood and only invasive surgical based treatments are available to treat SM clinically. This study builds upon our previous omics studies and in vitro cellular investigations to further understand local fluid osmoregulation in post-traumatic SM (PTSM) to highlight important pathways for future molecular interventions.MethodsA rat PTSM model consisting of a laminectomy at the C7 to T1 level followed by a parenchymal injection of 2 μL quisqualic acid (QA) and an injection of 5 μL kaolin in the subarachnoid space was utilized 6 weeks after initial surgery, parenchymal fluid and cerebrospinal fluid (CSF) were collected, and the osmolality of fluids were analyzed. Immunohistochemistry (IHC), metabolomics analysis using LC–MS, and mass spectrometry-based imaging (MSI) were performed on injured and laminectomy-only control spinal cords.ResultsWe demonstrated that the osmolality of the local parenchymal fluid encompassing syrinxes was higher compared to control spinal cords after laminectomy, indicating a local osmotic imbalance due to SM injury. Moreover, we also found that parenchymal fluid is more hypertonic than CSF, indicating establishment of a local osmotic gradient in the PTSM injured spinal cord (syrinx site) forcing fluid into the spinal cord parenchyma to form and/or expand syrinxes. IHC results demonstrated upregulation of betaine, ions, water channels/transporters, and enzymes (BGT1, AQP1, AQP4, CHDH) at the syrinx site as compared to caudal and rostral sites to the injury, implying extensive local osmoregulation activities at the syrinx site. Further, metabolomics analysis corroborated alterations in osmolality at the syrinx site by upregulation of small molecule osmolytes including betaine, carnitine, glycerophosphocholine, arginine, creatine, guanidinoacetate, and spermidine.ConclusionsIn summary, PTSM results in local osmotic disturbance that propagates at 6 weeks following initial injury. This coincides with and may contribute to syrinx formation/expansion.

A critical appreciation of pathway analysis in atherosclerotic disease. Cellular phenotypic plasticity as an illustrative example

The rapid advancements in genome-scale (omics) techniques has created significant opportunities to investigate complex disease mechanisms in tissues and cells. Nevertheless, interpreting -omics data can be challenging, and pathway enrichment analysis is a frequently used method to identify candidate molecular pathways that drive gene expression changes. With a growing number of -omics studies dedicated to atherosclerosis, there has been a significant increase in studies and hypotheses relying on enrichment analysis.This brief review discusses the benefits and limitations of pathway enrichment analysis within atherosclerosis research. We highlight the challenges of identifying complex biological processes, such as cell phenotypic switching, within -omics data. Additionally, we emphasize the need for more comprehensive and curated gene sets that reflect the biological complexity of atherosclerosis.Pathway enrichment analysis is a valuable tool for gaining insights into the molecular mechanisms of atherosclerosis. Nevertheless, it is crucial to remain aware of the intrinsic limitations of this approach. By addressing these weaknesses, enrichment analysis in atherosclerosis can lead to breakthroughs in identifying the mechanisms of disease progresses, the identification of key driver genes, and consequently, advance personalized patient care.

Developing a Radiomics Atlas Dataset of normal Abdominal and Pelvic computed Tomography (RADAPT)

Atlases of normal genomics, transcriptomics, proteomics, and metabolomics have been published in an attempt to understand the biological phenotype in health and disease and to set the basis of comprehensive comparative omics studies. No such atlas exists for radiomics data. The purpose of this study was to systematically create a radiomics dataset of normal abdominal and pelvic radiomics that can be used for model development and validation. Young adults without any previously known disease, aged > 17 and ≤ 36 years old, were retrospectively included. All patients had undergone CT scanning for emergency indications. In case abnormal findings were identified, the relevant anatomical structures were excluded. Deep learning was used to automatically segment the majority of visible anatomical structures with the TotalSegmentator model as applied in 3DSlicer. Radiomics features including first order, texture, wavelet, and Laplacian of Gaussian transformed features were extracted with PyRadiomics. A Github repository was created to host the resulting dataset. Radiomics data were extracted from a total of 531 patients with a mean age of 26.8 ± 5.19 years, including 250 female and 281 male patients. A maximum of 53 anatomical structures were segmented and used for subsequent radiomics data extraction. Radiomics features were derived from a total of 526 non-contrast and 400 contrast-enhanced (portal venous) series. The dataset is publicly available for model development and validation purposes.

A Set of Screening Techniques for a Quick Overview of the Neutrophil Function.

Neutrophils are known as one of the first lines of defense in the innate immune response and can perform many particular cellular functions, such as chemotaxis, reverse migration, phagocytosis, degranulation of cytotoxic enzymes and metabolites, and release of DNA as neutrophil extracellular traps (NETs). Neutrophils not only have tightly regulated signaling themselves, but also participate in the regulation of other components of the immune system. As fresh neutrophils are terminally differentiated, short-lived, and highly variable among individuals, it is important to make the most of the collected samples. Researchers often need to perform screening assays to assess an overview of the many neutrophil functions that may be affected by specific conditions under evaluation. A set of tests following a single isolation process of normal density neutrophils was developed to address this need, seeking a balance between speed, comprehensiveness, cost, and accuracy. The results can be used to reason and guide in-depth follow-up studies. This procedure can be carried out in an average time of 4 h and includes the evaluation of cell viability, reactive oxygen species (ROS) production, real-time migration, and phagocytosis of yeast on glass slides, leaving enough cells for more detailed approaches like omics studies. Moreover, the procedure includes a way to easily observe a preliminary suggestion of NETs after fast panoptic staining observed by light microscopy, with a lack of specific markers, albeit enough to indicate if further efforts in that way would be worthwhile. The diversity of functions tested combines common points among tests, reducing the analysis time and expenses. The procedure was named NeutroFun Screen, and although havinglimitations, it balances the aforementioned factors. Furthermore, the aim of this work is not a definite test set, but rather a guideline that can easily be adjusted to each lab's resources and demands.

Exploiting open source omics data to advance pancreas research.

The "omics" revolution has transformed the biomedical research landscape by equipping scientists with the ability to interrogate complex biological phenomenon and disease processes at an unprecedented level. The volume of "big" data generated by the different omics studies such as genomics, transcriptomics, proteomics, and metabolomics has led to the concurrent development of computational tools to enable in silico analysis and aid data deconvolution. Considering the intensive resources and high costs required to generate and analyze big data, there has been centralized, collaborative efforts to make the data and analysis tools freely available as "Open Source," to benefit the wider research community. Pancreatology research studies have contributed to this "big data rush" and have additionally benefitted from utilizing the open source data as evidenced by the increasing number of new research findings and publications that stem from such data. In this review, we briefly introduce the evolution of open source omics data, data types, the "FAIR" guiding principles for data management and reuse, and centralized platforms that enable free and fair data accessibility, availability, and provide tools for omics data analysis. We illustrate, through the case study of our own experience in mining pancreatitis omics data, the power of repurposing open source data to answer translationally relevant questions in pancreas research.

Multi-Chemical Omics Analysis of the Symbiodiniaceae Durusdinium trenchii under Heat Stress.

The urgency of responding to climate change for corals necessitates the exploration of innovative methods to swiftly enhance our understanding of crucial processes. In this study, we employ an integrated chemical omics approach, combining elementomics, metabolomics, and volatilomics methodologies to unravel the biochemical pathways associated with the thermal response of the coral symbiont, Symbiodiniaceae Durusdinium trenchii. We outline the complimentary sampling approaches and discuss the standardised data corrections used to allow data integration and comparability. Our findings highlight the efficacy of individual methods in discerning differences in the biochemical response of D. trenchii under both control and stress-inducing temperatures. However, a deeper insight emerges when these methods are integrated, offering a more comprehensive understanding, particularly regarding oxidative stress pathways. Employing correlation network analysis enhanced the interpretation of volatile data, shedding light on the potential metabolic origins of volatiles with undescribed functions and presenting promising candidates for further exploration. Elementomics proves to be less straightforward to integrate, likely due to no net change in elements but rather elements being repurposed across compounds. The independent and integrated data from this study informs future omic profiling studies and recommends candidates for targeted research beyond Symbiodiniaceae biology. This study highlights the pivotal role of omic integration in advancing our knowledge, addressing critical gaps, and guiding future research directions in the context of climate change and coral reef preservation.

Wekemo Bioincloud: A user-friendly platform for meta-omics data analyses.

The increasing application of meta-omics approaches to investigate the structure, function, and intercellular interactions of microbial communities has led to a surge in available data. However, this abundance of human and environmental microbiome data has exposed new scalability challenges for existing bioinformatics tools. In response, we introduce Wekemo Bioincloud-a specialized platform for -omics studies. This platform offers a comprehensive analysis solution, specifically designed to alleviate the challenges of tool selection for users in the face of expanding data sets. As ofnow, Wekemo Bioincloud has been regularly equipped with 22 workflows and 65 visualization tools, establishing itself as a user-friendly and widely embraced platform for studying diverse data sets. Additionally, the platform enables the online modification of vector outputs, and the registration-independent personalized dashboard system ensures privacy and traceability. Wekemo Bioincloud is freely available at https://www.bioincloud.tech/.

Integrated histopathology and transcriptome metabolome profiling reveal the toxicity mechanism of phenazine-1-carboxylic acid in zebrafish

Phenazine-1-carboxylic acid (PCA) is a new type of agrochemical used to prevent plant diseases, but its effects on aquatic organisms are unclear. To comprehensively assess the impacts of PCA for aquatic organisms and its associated environmental risks, this study investigated, taking zebrafish as the research object, the toxicological mechanism of PCA by means of optical microscopy, hematoxylin and eosin (HE) staining, ultrastructural observation, physiological and biochemical testing, transcriptome sequencing, metabolome analysis, fluorescence quantitative PCR and molecular simulation. The results indicated that PCA was detrimental to zebrafish embryos, larvae and adults, with LC50 values at 96 h of 3.9093 mg/L, 8.5075 mg/L, and 13.6388 mg/L, respectively. PCA caused abnormal spontaneous movement, slowed the heart rate, delayed hatching, shortened the body length, slowed growth, and caused malformations. PCA mainly affected the brain, liver, heart, and ovaries. PCA distorted cell morphology, damaged mitochondrial membranes, disintegrated mitochondrial ridges, and dissociated nuclear membranes. PCA inhibited the enzyme activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-PX), decreased the malondialdehyde (MDA) content and disrupted antioxidant effects. The results of omics studies confirmed that PCA interfered with the transcriptional and metabolic network of zebrafish, downregulating most genes and metabolites. PCA mainly affected functions related to mitochondrial steroids, lipids, sterols, oxidoreductase activity and pathways involving cofactors, steroids, porphyrin, cytochromes, which specifically bound to targets such as panx3, agmat, and ace2. PCA was moderately toxic to zebrafish, and its usage should be strictly controlled to reduce toxic effects on aquatic organisms. The results of this study provide a new insights for ecotoxicology research.

Global omics study of Tetraselmis chuii reveals time-related metabolic adaptations upon oxidative stress

Microalgae species encounter oxidative stress in their natural environments, prompting the development of species-specific adaptation mechanisms. Understanding these mechanisms can offer valuable insights for biotechnological applications in microalgal metabolic manipulation. In this study, we investigated the response of Tetraselmis chuii, an industrially important microalga, to H2O2-induced oxidative stress. Exposure to 0.5-mM H2O2 resulted in reduced cell viability, and higher concentrations led to a drastic decline. After 1 h of exposure to H2O2, photosynthetic capacity (Qy) was negatively impacted, and this reduction intensified after 6 h of continuous stress. Global multi-omics analysis revealed that T. chuii rapidly responded to H2O2-induced oxidative stress within the first hour, causing significant changes in both transcriptomic and metabolomic profiles. Among the cellular functions negatively affected were carbon and energy flow, with photosynthesis-related PSBQ having a 2.4-fold downregulation, pyruvate kinase decreased by 1.5-fold, and urea content reduced by threefold. Prolonged exposure to H2O2 incurred a high energy cost, leading to unsuccessful attempts to enhance carbon metabolism, as depicted, for example, by the upregulation of photosystems-related PETC and PETJ by more than twofold. These findings indicate that T. chuii quickly responds to oxidative stress, but extended exposure can have detrimental effects on its cellular functions.Key points• 0.5-mM H2O2–induced oxidative stress strongly affects T. chuii• Distinct short- and long-term adaptation mechanisms are induced• Major metabolic adaptations occur within the first hour of exposure

Machine learning and multi-omics data in chronic lymphocytic leukemia: the future of precision medicine?

Chronic lymphocytic leukemia is a complex and heterogeneous hematological malignancy. The advance of high-throughput multi-omics technologies has significantly influenced chronic lymphocytic leukemia research and paved the way for precision medicine approaches. In this review, we explore the role of machine learning in the analysis of multi-omics data in this hematological malignancy. We discuss recent literature on different machine learning models applied to single omic studies in chronic lymphocytic leukemia, with a special focus on the potential contributions to precision medicine. Finally, we highlight the recently published machine learning applications in multi-omics data in this area of research as well as their potential and limitations.

Omics Studies of Tumor Cells under Microgravity Conditions.

Cancer is defined as a group of diseases characterized by abnormal cell growth, expansion, and progression with metastasis. Various signaling pathways are involved in its development. Malignant tumors exhibit a high morbidity and mortality. Cancer research increased our knowledge about some of the underlying mechanisms, but to this day, our understanding of this disease is unclear. High throughput omics technology and bioinformatics were successful in detecting some of the unknown cancer mechanisms. However, novel groundbreaking research and ideas are necessary. A stay in orbit causes biochemical and molecular biological changes in human cancer cells which are first, and above all, due to microgravity (µg). The µg-environment provides conditions that are not reachable on Earth, which allow researchers to focus on signaling pathways controlling cell growth and metastasis. Cancer research in space already demonstrated how cancer cell-exposure to µg influenced several biological processes being involved in cancer. This novel approach has the potential to fight cancer and to develop future cancer strategies. Space research has been shown to impact biological processes in cancer cells like proliferation, apoptosis, cell survival, adhesion, migration, the cytoskeleton, the extracellular matrix, focal adhesion, and growth factors, among others. This concise review focuses on publications related to genetic, transcriptional, epigenetic, proteomic, and metabolomic studies on tumor cells exposed to real space conditions or to simulated µg using simulation devices. We discuss all omics studies investigating different tumor cell types from the brain and hematological system, sarcomas, as well as thyroid, prostate, breast, gynecologic, gastrointestinal, and lung cancers, in order to gain new and innovative ideas for understanding the basic biology of cancer.

Molecular Basis and Engineering Strategies for Transcription Factor-Mediated Reproductive-Stage Heat Tolerance in Crop Plants

Heat stress (HS) is a major threat to crop productivity and is expected to be more frequent and severe due to climate change challenges. The predicted increase in global temperature requires us to understand the dimensions of HS experienced by plants, particularly during reproductive stages, as crop productivity is majorly dependent on the success of plant reproduction. The impact of HS on crop productivity is relatively less-studied than the other abiotic stresses, such as drought and salinity. Plants have evolved diverse mechanisms to perceive, transduce, respond, and adapt to HS at the molecular, biochemical, and physiological levels. Unraveling these complex mechanisms underlying plant HS response and tolerance would facilitate designing well-informed and effective strategies to engineer HS tolerance in crop plants. In this review, we concisely discuss the molecular impact of HS on plant reproductive processes and yield, with major emphasis on transcription factors. Moreover, we offer vital strategies (encompassing omics studies, genetic engineering and more prominently gene editing techniques) that can be used to engineer transcription factors for enhancing heat tolerance. Further, we highlight critical shortcomings and knowledge gaps in HS tolerance research that should guide future research investigations. Judicious studies and a combination of these strategies could speed up the much-needed development of HS-resilient crop cultivars.

Cell type-specific roles of APOE4 in Alzheimer disease.

The ɛ4 allele of the apolipoprotein E gene (APOE), which translates to the APOE4 isoform, is the strongest genetic risk factor for late-onset Alzheimer disease (AD). Within the CNS, APOE is produced by a variety of cell types under different conditions, posing a challenge for studying its roles in AD pathogenesis. However, through powerful advances in research tools and the use of novel cell culture and animal models, researchers have recently begun to study the roles of APOE4 in AD in a cell type-specific manner and at a deeper and more mechanistic level than ever before. In particular, cutting-edge omics studies have enabled APOE4 to be studied at the single-cell level and have allowed the identification of critical APOE4 effects in AD-vulnerable cellular subtypes. Through these studies, it has become evident that APOE4 produced in various types of CNS cell - including astrocytes, neurons, microglia, oligodendrocytes and vascular cells - has diverse roles in AD pathogenesis. Here, we review these scientific advances and propose a cell type-specific APOE4 cascade model of AD. In this model, neuronal APOE4 emerges as a crucial pathological initiator and driver of AD pathogenesis, instigating glial responses and, ultimately, neurodegeneration. In addition, we provide perspectives on future directions for APOE4 research and related therapeutic developments in the context of AD.

Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies.

Sarcoidosis is a chronic granulomatous disorder characterized by unknown etiology, undetermined mechanisms, and non-specific therapies except TNF blockade. To improve our understanding of the pathogenicity and to predict the outcomes of the disease, the identification of new biomarkers and molecular endotypes is sorely needed. In this study, we systematically evaluate the biomarkers identified through Omics and non-Omics approaches in sarcoidosis. Most of the currently documented biomarkers for sarcoidosis are mainly identified through conventional "one-for-all" non-Omics targeted studies. Although the application of machine learning algorithms to identify biomarkers and endotypes from unbiased comprehensive Omics studies is still in its infancy, a series of biomarkers, overwhelmingly for diagnosis to differentiate sarcoidosis from healthy controls have been reported. In view of the fact that current biomarker profiles in sarcoidosis are scarce, fragmented and mostly not validated, there is an urgent need to identify novel sarcoidosis biomarkers and molecular endotypes using more advanced Omics approaches to facilitate disease diagnosis and prognosis, resolve disease heterogeneity, and facilitate personalized medicine.

Understanding grain development in the Poaceae family by comparing conserved and distinctive pathways through omics studies in wheat and maize.

The Poaceae family, commonly known as the grass family, encompasses a diverse group of crops that play an essential role in providing food, fodder, biofuels, environmental conservation, and cultural value for both human and environmental well-being. Crops in Poaceae family are deeply intertwined with human societies, economies, and ecosystems, making it one of the most significant plant families in the world. As the major reservoirs of essential nutrients, seed grain of these crops has garnered substantial attention from researchers. Understanding the molecular and genetic processes that controls seed formation, development and maturation can provide insights for improving crop yield, nutritional quality, and stress tolerance. The diversity in photosynthetic pathways between C3 and C4 plants introduces intriguing variations in their physiological and biochemical processes, potentially affecting seed development. In this review, we explore recent studies performed with omics technologies, such as genomics, transcriptomics, proteomics and metabolomics that shed light on the mechanisms underlying seed development in wheat and maize, as representatives of C3 and C4 plants respectively, providing insights into their unique adaptations and strategies for reproductive success.

Biomarkers of heart failure: advances in omics studies.

Heart failure is a complex syndrome characterized by progressive circulatory dysfunction, manifesting clinically as pulmonary and systemic venous congestion, alongside inadequate tissue perfusion. The early identification of HF, particularly at the mild and moderate stages (stages B and C), presents a clinical challenge due to the overlap of signs, symptoms, and natriuretic peptide levels with other cardiorespiratory pathologies. Nonetheless, early detection coupled with timely pharmacological intervention is imperative for enhancing patient outcomes. Advances in high-throughput omics technologies have enabled researchers to analyze patient-derived biofluids and tissues, discovering biomarkers that are sensitive and specific for HF diagnosis. Due to the diversity of HF etiology, it is insufficient to study the diagnostic data of early HF using a single omics technology. This study reviewed the latest progress in genomics, transcriptomics, proteomics, and metabolomics for the identification of HF biomarkers, offering novel insights into the early clinical diagnosis of HF. However, the validity of biomarkers depends on the disease status, intervention time, genetic diversity and comorbidities of the subjects. Moreover, biomarkers lack generalizability in different clinical settings. Hence, it is imperative to conduct multi-center, large-scale and standardized clinical trials to enhance the diagnostic accuracy and utility of HF biomarkers.

Advances in Physalis molecular research: applications in authentication, genetic diversity, phylogenetics, functional genes, and omics.

The plants of the genus Physalis L. have been extensively utilized in traditional and indigenous Chinese medicinal practices for treating a variety of ailments, including dermatitis, malaria, asthma, hepatitis, and liver disorders. The present review aims to achieve a comprehensive and up-to-date investigation of the genus Physalis, a new model crop, to understand plant diversity and fruit development. Several chloroplast DNA-, nuclear ribosomal DNA-, and genomic DNA-based markers, such as psbA-trnH, internal-transcribed spacer (ITS), simple sequence repeat (SSR), random amplified microsatellites (RAMS), sequence-characterized amplified region (SCAR), and single nucleotide polymorphism (SNP), were developed for molecular identification, genetic diversity, and phylogenetic studies of Physalis species. A large number of functional genes involved in inflated calyx syndrome development (AP2-L, MPF2, MPF3, and MAGO), organ growth (AG1, AG2, POS1, and CNR1), and active ingredient metabolism (24ISO, DHCRT, P450-CPL, SR, DUF538, TAS14, and 3β-HSB) were identified contributing to the breeding of novel Physalis varieties. Various omic studies revealed and functionally identified a series of reproductive organ development-related factors, environmental stress-responsive genes, and active component biosynthesis-related enzymes. The chromosome-level genomes of Physalis floridana Rydb., Physalis grisea (Waterf.) M. Martínez, and Physalis pruinosa L. have been recently published providing a valuable resource for genome editing in Physalis crops. Our review summarizes the recent progress in genetic diversity, molecular identification, phylogenetics, functional genes, and the application of omics in the genus Physalis and accelerates efficient utilization of this traditional herb.

Isolation of Protoplasts from Tissues of Mexican Prickly Poppy (Argemone mexicana L.): An Alkaloid-Producing Medicinal Plant.

Protoplasts are plant cells from which the pectocellulosic cell wall has been removed, thus keeping the plasma membrane intact. For plant secondary metabolites research, this system is a powerful tool to study the metabolites' dynamics inside the cells, such as the subcellular localization of proteins, characterization of gene function, transcription factors involved in metabolite pathways, protein transport machinery, and to perform single-cell omics studies. Due to its lack of a cell wall, better images of the interior of the cell can be obtained compared to the whole tissue. This allows the identification of specific cell types involved in the accumulation of specialized metabolites, such as alkaloids, given their autofluorescence properties. Here is a simplified protocol to obtain protoplasts from leaves and in vitro cell cultures from Argemone mexicana, which produces the pharmacologically important alkaloids berberine and sanguinarine.

Multi ‐ omics studies of eye and lens development

Early eye development involves formation of a symmetric pair of optic vesicles from the anterior neuroectoderm and parallel formation of closely apposed lens placodes. Their reciprocal invaginations generate the 3D‐foundation of the optic cups. Embryonic lens development is initiated within the border between the anterior neuroectoderm and naïve ectoderm, called the anterior pre‐placodal region. The earliest stages of lens development require coordinated actions of DNA‐binding transcription factor Pax6, signalling growth factor BMP4 and a pair of chromatin remodelling enzymes p300 and CBP. Studies in model vertebrates postulated a common Pax6+ progenitor cell population to generate both the lens and olfactory placodes. Recent progress in technologies to probe chromatin structure, landscape of transcription factor binding, DNA methylation, coding (mRNA) and noncoding (e.g. eRNAs and ncRNAs) transcriptomes and chromatin conformation have been shown to generate unprecedented mechanistic insights into the earliest cell fate decisions and tissue organization. To examine lens development at E8.5, E9.5 and E10.5 mouse embryos, single cell RNA‐seq (scRNA‐seq) experiments using the 10XGenomics Chromium platform were conducted. E9.5 Pax6 null embryos and controls were also included. Whole genome bisulfite sequencing (WGBS) was performed in E14.5 and P0.5 microdissected mouse lenses to map DNA methylation and integrate the data with corresponding ATAC‐ and RNA‐seq data sets. Open chromatin and DNA methylation changes were defined through identification of differentially accessible regions (DARs), unmethylated/low methylated regions (UMRs/LMRs) coupled with differential gene expression during lens differentiation. To probe 3D‐organization of lens chromatin and to identify temporally and spatially regulated lens‐specific distal enhancers, Hi‐C was used in conjunction with ChIP‐seq analysis of transcription factor CTCF in microdissected newborn lenses. Previous ChIP‐seq data using whole newborn lenses include H3K27ac peaks to identify super‐enhancers. Pax6 proteins were found both in open and closed chromatin domains, including both closed and methylated regions. In vitro binding of Pax6 proteins was not inhibited by 5‐methylcytosines within one or two CpG dinucleotides present in the Pax6 binding sites. In summary, cell fate decisions are governed by complex changes in chromatin landscape controlled by sequence‐specific DNA‐binding transcription factors and their recruitment of chromatin remodelling complexes. A combination of different –omics is a powerful approach to examine molecular mechanisms governing eye development. These studies also reveal additional levels of genetic code complexity and stimulate follow up mechanistic studies of the individual cellular and molecular processes of lens morphogenesis and their underlying GRNs and identification of cataract‐causing mutation in non‐coding regulatory sequences.