Omics Approaches Research Articles

Pathology Laboratory Archives: Conservation Quality of Nucleic Acids and Proteins for NSCLC Molecular Testing.

In the molecular era, proper archival conditions within pathology laboratories are crucial, especially for formalin-fixed paraffin-embedded (FFPE) tissue specimens retrieved years after the original diagnosis. Indeed, improper preservation can impact the integrity of nucleic acids and protein antigens. This study evaluates the quality status of stored FFPE blocks using multilevel omics approaches. FFPE blocks from 45 Non-Small Cell Lung Carcinoma (NSCLC) cases were analyzed. The blocks were collected from six different pathology archives across Italy with distinct environmental characteristics. Nucleic acids' quantity and quality, as well as protein antigens, were assessed using various techniques, including MALDI-MSI. RNA was quantitatively higher, but more fragmented, compared to DNA. DNA quantity and quality were suitable for molecular analyses in 94.4% and 62.3% of samples, respectively. RNA quantity was adequate across all samples, but it was optimal only in 22.3% of cases. DNA quality started to deteriorate after 6-8 years, whereas RNA quality diminished only after 10 years of storage. These data might suggest a particular DNA susceptibility to FFPE blocks conservation. Immunohistochemical intensity decreased significantly after 6-8 years of storage, and MALDI-MSI analysis revealed that younger tissue blocks contained more unique proteomic signals than the older ones. This study emphasizes the importance of proper FFPE archiving conditions for molecular analyses. Governance should prioritize attention to pathology archives to ensure quality preservation and optimize predictive testing. By elucidating the nuances of FFPE block storage, this research paves the way for enhanced molecular diagnostics and therapeutic insights regarding oncology and beyond.

Exploring the potential of endophyte-plant interactions for improving crop sustainable yields in a changing climate.

Climate change poses a major threat to global food security, significantly reducing crop yields as cause of abiotic stresses, and for boosting the spread of new and old pathogens and pests. Sustainable crop management as a route to mitigation poses the challenge of recruiting an array of solutions and tools for the new aims. Among these, the deployment of positive interactions between the micro-biotic components of agroecosystems and plants can play a highly significant role, as part of the agro-ecological revolution. Endophytic microorganisms have emerged as a promising solution to tackle this challenge. Among these, Arbuscular Mycorrhizal Fungi (AMF) and endophytic bacteria and fungi have demonstrated their potential to alleviate abiotic stresses such as drought and heat stress, as well as the impacts of biotic stresses. They can enhance crop yields in a sustainable way also by other mechanisms, such as improving the nutrient uptake, or by direct effects on plant physiology. In this review we summarize and update on the main types of endophytes, we highlight several studies that demonstrate their efficacy in improving sustainable yields and explore possible avenues for implementing crop-microbiota interactions. The mechanisms underlying these interactions are highly complex and require a comprehensive understanding. For this reason, omic technologies such as genomics, transcriptomics, proteomics, and metabolomics have been employed to unravel, by a higher level of information, the complex network of interactions between plants and microorganisms. Therefore, we also discuss the various omic approaches and techniques that have been used so far to study plant-endophyte interactions.

Drought stress in sorghum: physiological tools, breeding technology, Omics approaches and Genomic-assisted breeding -A review

Drought stress in sorghum: physiological tools, breeding technology, Omics approaches and Genomic-assisted breeding -A review

Prediction of pCR based on clinical-radiomic model in patients with locally advanced ESCC treated with neoadjuvant immunotherapy plus chemoradiotherapy.

The primary objective of this research is to devise a model to predict the pathologic complete response in esophageal squamous cell carcinoma (ESCC) patients undergoing neoadjuvant immunotherapy combined with chemoradiotherapy (nICRT). We retrospectively analyzed data from 60 ESCC patients who received nICRT between 2019 and 2023. These patients were divided into two cohorts: pCR-group (N = 28) and non-pCR group (N = 32). Radiomic features, discerned from the primary tumor region across plain, arterial, and venous phases of CT, and pertinent laboratory data were documented at two intervals: pre-treatment and preoperation. Concurrently, related clinical data was amassed. Feature selection was facilitated using the Extreme Gradient Boosting (XGBoost) algorithm, with model validation conducted via fivefold cross-validation. The model's discriminating capability was evaluated using the area under the receiver operating characteristic curve (AUC). Additionally, the clinical applicability of the clinical-radiomic model was appraised through decision curve analysis (DCA). The clinical-radiomic model incorporated seven significant markers: postHALP, ΔHB, post-ALB, firstorder_Skewness, GLCM_DifferenceAverage, GLCM_JointEntropy, GLDM_DependenceEntropy, and NGTDM_Complexity, to predict pCR. The XGBoost algorithm rendered an accuracy of 0.87 and an AUC of 0.84. Notably, the joint omics approach superseded the performance of solely radiomic or clinical model. The DCA further cemented the robust clinical utility of our clinical-radiomic model. This study successfully formulated and validated a union omics methodology for anticipating the therapeutic outcomes of nICRT followed by radical surgical resection. Such insights are invaluable for clinicians in identifying potential nICRT responders among ESCC patients and tailoring optimal individualized treatment plans.

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

ObjectiveClassification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical “curse of dimensionality” problem, i.e. having much fewer observation, samples (n) than omics features (p). Furthermore, a major problem with multi-omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements. Methods:Among all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets. Results:We provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. To further test the extension of our approach to other omics data, we have included a transcriptomics and a proteomics data. Overall, for all datasets, we showed that applying supervised feature selection improves the performances of feature extraction methods for classification purposes. Scripts used to perform all analyses are available at: https://github.com/Plant-Net/Metabolomic_project/.

Laboratory Testing for Endocrine Hypertension: Current and Future Perspectives.

Secondary hypertension (SH) is a form of high blood pressure caused by an identifiable underlying condition. Although, it accounts for a small fraction of the overall hypertensive population, detection and management of SH is of utmost importance, because SH phenotypes carry a high cardiovascular risk and can possibly be cured by timely treatment. This review focuses on the endocrine causes of SH, such as primary aldosteronism, Cushing syndrome, thyroid disease, pheochromocytoma and paraganglioma, acromegaly, and rare monogenic forms. It discusses current biomarkers, analytical methods, and diagnostic strategies, highlighting advantages and limitations of each approach. It also explores the emerging -omics technologies that can provide a comprehensive and multidimensional assessment of SH and its underlying mechanisms. Endocrine SH is a heterogeneous and complex condition that requires proper screening and confirmatory tests to avoid diagnostic delays and improve patient outcomes. Careful biomarker interpretation is essential due to potential interferences, variability, and method-dependent differences. Liquid chromatography-tandem mass spectrometry is a superior method for measuring low-concentration hormones and metabolites involved in SH, but it requires expertise. Omics approaches have great potential to identify novel biomarkers, pathways, and targets for SH diagnosis and treatment, especially considering its multifactorial nature.

Multi-OMICS approaches in cancer biology: New era in cancer therapy

Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.

Functional insight into multi-omics-based interventions for climatic resilience in sorghum (Sorghum bicolor): a nutritionally rich cereal crop.

Thearticle highlights omics-based interventions in sorghum to combat food and nutritional scarcity in the future. Sorghum with its unique ability to thrive in adverse conditions, has become a tremendous highly nutritive, and multipurpose cereal crop. It is resistant to various types of climatic stressors which will pave its way to a future food crop. Multi-omics refers to the comprehensive study of an organism at multiple molecular levels, including genomics, transcriptomics, proteomics, and metabolomics. Genomic studies have provided insights into the genetic diversity of sorghum and led to the development of genetically improved sorghum. Transcriptomics involves analysing the gene expression patterns in sorghum under various conditions. This knowledge is vital for developing crop varieties with enhanced stress tolerance. Proteomics enables the identification and quantification of the proteins present in sorghum. This approach helps in understanding the functional roles of specific proteins in response to stress and provides insights into metabolic pathways that contribute to resilience and grain production. Metabolomics studies the small molecules, or metabolites, produced by sorghum, provides information about the metabolic pathways that are activated or modified in response to environmental stress. This knowledge can be used to engineer sorghum varieties with improved metabolic efficiency, ultimately leading to better crop yields. In this review, we have focused on various multi-omics approaches, gene expression analysis, and different pathways for the improvement of Sorghum. Applying omics approaches to sorghum research allows for a holistic understanding of its genome function. This knowledge is invaluable for addressing challenges such as climate change, resource limitations, and the need for sustainable agriculture.

Spectralomics – Towards a holistic adaptation of label free spectroscopy

Vibrational spectroscopy, largely based on infrared absorption and Raman scattering techniques, is much vaunted as a label free approach, delivering a high content, holistic characterisation of a sample, with demonstrable applications in a broad range of fields, from process analytical technologies and preclinical drug screening, to disease diagnostics, therapeutics, prognostics and personalised medicine. However, in the analysis of such complex systems, a trend has emerged in which spectral analysis is reduced to the identification of individual peaks, based on reference tables of assignments derived from literature, which are then interpreted as biomarkers. More sophisticated analysis attempts to unmix the spectrum of the complex mixture into constituent components, which are then used to characterise the biochemistry of a sample and changes to it, in terms of its constituent components. Data mining the spectra, and in particular change due to kinetic processes, remains a challenge, and it is proposed that the rate of temporal evolution of the combination spectrum can be used in itself as a label by which to guide the spectral analysis. Ultimately, it is argued that the true potential of label free spectroscopy is best harnessed in a truly “spectralomic” approach, by which the spectral signature of an “event”, such as drug intercalation in the DNA of the nucleus of a cell, or a key stage of a cellular pathway such as oxidative stress, is presented. It is envisioned that, in the future, such Spectralomics pathway analysis will be fully integrated with similar omics approaches, potentially ultimately through deep learning algorithms, and underpinned by systems biology kinetic models, to provide a living human cell atlas, describing the function and dysfunction of organism at a cellular level, as the basis for improved healthcare.

Evaluation of the aroma and taste contributions of star anise (I. Verum hook. f.) in braised duck leg via flavor omics combined with multivariate statistics

To promote the rationalized and standardized application of star anise in braised poultry products, the effects of different concentrations of star anise (0 %, 0.1 %, 0.2 %, 0.3 %, and 0.4 %) on the aroma and taste compounds intensities of braised duck legs from the perspective of flavor were evaluated by using flavor omics approach combined with multivariate statistics. The volatile flavor results showed that there were 17 key aroma compounds with odor activity values (OAVs) > 1, including aldehydes, alcohols, ketones, furans, hydrocarbons, and ethers. Most of the aroma compounds related to lipid oxidation were significantly inhibited when the concentration of star anise reached 0.2 %, especially inhibited the concentrations of the unpleasant off-odorants containing hexanal, heptanal, 1-octen-3-ol, and 2-pentyl-furan by 30.27 %, 15.08 %, 30.30 %, and 41.63 %, respectively. And the flavor intensities of these compounds were negatively correlated with the concentration of star anise. Additionally, star anise gave braised duck legs characteristic aroma such as floral and herbal notes. The taste results revealed that the maximum umami value (4.36 g MSG/100 g) of braised duck legs was observed when the concentration of star anise reached 0.2 %. Six flavor markers were obtained via PLS-DA model, and the flavors of braised duck legs with different concentrations of star anise were distinguished. This study provided a vital theoretical basis for the rational application and flavor control of star anise in braised poultry products.

Integrated Omics Approach: Revealing the Mechanism of Auxenochlorella pyrenoidosa Protein Extract Replacing Fetal Bovine Serum for Fish Muscle Cell Culture.

The process of producing cell-cultured meat involves utilizing a significant amount of culture medium, including fetal bovine serum (FBS), which represents a considerable portion of production expense while also raising environmental and safety concerns. This study demonstrated that supplementation with Auxenochlorella pyrenoidosa protein extract (APE) under low-serum conditions substantially increased Carassius auratus muscle (CAM) cell proliferation and heightened the expression of Myf5 compared to the absence of APE. An integrated intracellular metabolomics and proteomics analysis revealed a total of 13 and 67 differentially expressed metabolites and proteins, respectively, after supplementation with APE in the medium containing 5%FBS, modulating specific metabolism and signaling pathways, which explained the application of APE for passage cell culture under low-serum conditions. Further analysis revealed that the bioactive factors in the APE were protein components. Moreover, CAM cells cultured in reconstructed serum-free media containing APE, l-ascorbic acid, insulin, transferrin, selenium, and ethanolamine exhibited significantly accelerated growth in a scale-up culture. These findings suggest a promising alternative to FBS for fish muscle cell culture that can help reduce production costs and environmental impact in the production of cultured meat.

Aphid gene expression following polerovirus acquisition is host species dependent.

Upon acquisition of persistent circulative viruses such as poleroviruses, the virus particles transcytose through membrane barriers of aphids at the midgut and salivary glands via hemolymph. Such intricate interactions can influence aphid behavior and fitness and induce associated gene expression in viruliferous aphids. Differential gene expression can be evaluated by omics approaches such as transcriptomics. Previously conducted aphid transcriptome studies used only one host species as the source of virus inoculum. Viruses typically have alternate hosts. Hence, it is not clear how alternate hosts infected with the same virus isolate alter gene expression in viruliferous vectors. To address the question, this study conducted a transcriptome analysis of viruliferous aphids that acquired the virus from different host species. A polerovirus, cotton leafroll dwarf virus (CLRDV), which induced gene expression in the cotton aphid, Aphis gossypii Glover, was assessed using four alternate hosts, viz., cotton, hibiscus, okra, and prickly sida. Among a total of 2,942 differentially expressed genes (DEGs), 750, 310, 1,193, and 689 genes were identified in A. gossypii that acquired CLRDV from infected cotton, hibiscus, okra, and prickly sida, respectively, compared with non-viruliferous aphids that developed on non-infected hosts. A higher proportion of aphid genes were overexpressed than underexpressed following CLRDV acquisition from cotton, hibiscus, and prickly sida. In contrast, more aphid genes were underexpressed than overexpressed following CLRDV acquisition from okra plants. Only four common DEGs (heat shock protein, juvenile hormone acid O-methyltransferase, and two unannotated genes) were identified among viruliferous aphids from four alternate hosts. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations indicated that the acquisition of CLRDV induced DEGs in aphids associated with virus infection, signal transduction, immune systems, and fitness. However, these induced changes were not consistent across four alternate hosts. These data indicate that alternate hosts could differentially influence gene expression in aphids and presumably aphid behavior and fitness despite being infected with the same virus isolate.

Comparative metabarcoding and biodiversity of gut-associated fungal assemblages of Dendroctonus species (Curculionidae: Scolytinae).

The genus Dendroctonus is a Holarctic taxon composed of 21 nominal species; some of these species are well known in the world as disturbance agents of forest ecosystems. Under the bark of the host tree, these insects are involved in complex and dynamic associations with phoretic ectosymbiotic and endosymbiotic communities. Unlike filamentous fungi and bacteria, the ecological role of yeasts in the bark beetle holobiont is poorly understood, though yeasts were the first group to be recorded as microbial symbionts of these beetles. Our aim was characterize and compare the gut fungal assemblages associated to 14 species of Dendroctonus using the internal transcribed spacer 2 (ITS2) region. A total of 615,542 sequences were recovered yielding 248 fungal amplicon sequence variants (ASVs). The fungal diversity was represented by 4 phyla, 16 classes, 34 orders, 54 families, and 71 genera with different relative abundances among Dendroctonus species. The α-diversity consisted of 32 genera of yeasts and 39 genera of filamentous fungi. An analysis of β-diversity indicated differences in the composition of the gut fungal assemblages among bark beetle species, with differences in species and phylogenetic diversity. A common core mycobiome was recognized at the genus level, integrated mainly by Candida present in all bark beetles, Nakazawaea, Cladosporium, Ogataea, and Yamadazyma. The bipartite networks confirmed that these fungal genera showed a strong association between beetle species and dominant fungi, which are key to maintaining the structure and stability of the fungal community. The functional variation in the trophic structure was identified among libraries and species, with pathotroph-saprotroph-symbiotroph represented at the highest frequency, followed by saprotroph-symbiotroph, and saprotroph only. The overall network suggested that yeast and fungal ASVs in the gut of these beetles showed positive and negative associations among them. This study outlines a mycobiome associated with Dendroctonus nutrition and provides a starting point for future in vitro and omics approaches addressing potential ecological functions and interactions among fungal assemblages and beetle hosts.

Integrative Multiomics Approach to Skin: The Sinergy between Individualised Medicine and Futuristic Precision Skin Care?

The skin is a complex ecosystem colonized by millions of microorganisms, the skin microbiota, which are crucial in regulating not only the physiological functions of the skin but also the metabolic changes underlying the onset of skin diseases. The high microbial colonization together with a low diversity at the phylum level and a high diversity at the species level of the skin is very similar to that of the gastrointestinal tract. Moreover, there is an important communication pathway along the gut-brain-skin axis, especially associated with the modulation of neurotransmitters by the microbiota. Therefore, it is evident that the high complexity of the skin system, due not only to the genetics of the host but also to the interaction of the host with resident microbes and between microbe and microbe, requires a multi-omics approach to be deeply understood. Therefore, an integrated analysis, with high-throughput technologies, of the consequences of microbial interaction with the host through the study of gene expression (genomics and metagenomics), transcription (transcriptomics and meta-transcriptomics), and protein production (proteomics and meta-proteomics) and metabolite formation (metabolomics and lipidomics) would be useful. Although to date very few studies have integrated skin metabolomics data with at least one other 'omics' technology, in the future, this approach will be able to provide simple and fast tests that can be routinely applied in both clinical and cosmetic settings for the identification of numerous skin diseases and conditions. It will also be possible to create large archives of multi-omics data that can predict individual responses to pharmacological treatments and the efficacy of different cosmetic products on individual subjects by means of specific allotypes, with a view to increasingly tailor-made medicine. In this review, after analyzing the complexity of the skin ecosystem, we have highlighted the usefulness of this emerging integrated omics approach for the analysis of skin problems, starting with one of the latest 'omics' sciences, metabolomics, which can photograph the expression of the genome during its interaction with the environment.

Polystyrene Micro- and Nanoplastics (PS MNPs): A Review of Recent Advances in the Use of -Omics in PS MNP Toxicity Studies on Aquatic Organisms

In recent years, micro- and nanoplastics (MNPs) have represented an emerging threat for the aquatic environment due to their persistence and widespread distribution. Indeed, their small size and increased surface area lead to a high biological reactivity, which can be crucial for the health status of biota. To date, several studies have investigated lethal and sublethal endpoints of MNPs, while one of the main challenges is to understand their mechanisms of toxicity in a comprehensive manner. With this aim, the field of aquatic ecotoxicology can now benefit from the -omics approaches. This review focuses on the recent advances related to the use of transcriptomics, proteomics, and metabolomics to deeply investigate the molecular and biochemical responses of aquatic organisms, both fishes and aquatic invertebrates, to pristine polystyrene (PS) MNPs. The literature reviewed revealed that transcriptomics and metabolomics are the most frequently used -omic approaches. Overall, the studies taken into consideration shed light on the events triggered by PS MNPs at molecular and cellular levels, identifying as mechanisms of toxicity the pathways involved in oxidative stress, energy metabolism, immune response, and the nervous system. Future studies should therefore focus on the use and integration of multi-omics approaches for a more comprehensive understanding of the mechanisms involved in MNPs toxicity.

Advances in single-cell omics and multiomics for high-resolution molecular profiling.

Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

A new piece of the microbiota pie: Mining ‘omics for DNA inversion states

In this issue of Cell Host & Microbe, Carasso etal. survey invertible DNA sites in Bacteroidales from patients with inflammatory bowel disease (IBD) and healthy control individuals. They identify complex functional interactions between Bacteroides fragilis, an invertible promoter, a capsular polysaccharide, a bacteriophage, and the human host. The establishment of 'omics approaches to characterizing genomic targets and functional roles is still required.

Invited Commentary: Omics Approaches to Mechanistic, Biomarker, and Therapeutic Development for Right Ventricular Failure in Congenital Right-Sided Obstructive Lesions: A Brave New World?

Invited Commentary: Omics Approaches to Mechanistic, Biomarker, and Therapeutic Development for Right Ventricular Failure in Congenital Right-Sided Obstructive Lesions: A Brave New World?

Plant based metabolomics: a future prospective and versatile tool for metabolite databases of Curcuma longa

Sophisticated analytical instruments are used for quality evaluation and scientific validation to compliance the market demands. Recently, increase in demand of Curcuma longa has led to a high swing in small and medium food industry. The present review is devoted to an in-depth information on metabolite database of C. longa from different analytical techniques such as high-performance thin-layer chromatography (HPTLC), liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry and nuclear magnetic resonance. This work highlights different extraction methods, phytochemistry along with structure activity relationship, nutritional profiling, market viability and pharmacology of C. longa. The bioactives of C. longa depends on various factors such as plants growth, temperature, humidity, soil type, collection, drying and extraction process. These factors can contribute in quality evaluation of the food supplements if need to be included. The introduction of omics approaches can helpful to explore the quality and identification of potential lead of C. longa. The results could play an essential role in the area of food-phyto based profiling. Further, metabolomics techniques may provide an analytical platform by combination of different “omics” data sources for future research to food and herbal based products.