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In This Article
  Introduction
  Genomics
  Pharmacogenomics
  Metagenomics
  Epigenomics
  Transcriptomics
  Proteomics
  Metabolomics
  Lipidomics
  Cytomics
   Current Challeng...
   References
   Article Figures

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 Table of Contents    
EDITORIAL
Year : 2022  |  Volume : 54  |  Issue : 1  |  Page : 1-6
 

Integrative omics – An arsenal for drug discovery


Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India

Date of Submission18-Jan-2022
Date of Decision27-Jan-2022
Date of Acceptance29-Jan-2022
Date of Web Publication18-Mar-2022

Correspondence Address:
Dr. Bikash Medhi
Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh - 160 012
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijp.ijp_53_22

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How to cite this article:
Singh RS, Angra V, Singh A, Masih GD, Medhi B. Integrative omics – An arsenal for drug discovery. Indian J Pharmacol 2022;54:1-6

How to cite this URL:
Singh RS, Angra V, Singh A, Masih GD, Medhi B. Integrative omics – An arsenal for drug discovery. Indian J Pharmacol [serial online] 2022 [cited 2022 Dec 6];54:1-6. Available from: https://www.ijp-online.com/text.asp?2022/54/1/1/339911





  Introduction Top


The sequencing of the first human genome in 2003 marked the beginning of a new era in drug discovery.[1],[2] Genomics studies have paved the way to identify potential biomarkers and drug targets for diseases. In the postgenomic period, with high-throughput technology, it is now easier to access the information of putative biomolecules involved in a disease state in a single run. Similarly, “omics” suffix has now started to use for other data sets also, for instance, transcriptomics, proteomics, lipidomics, metabolomics, and cytomics.[3] Omics technologies have given the unparalleled ability to researchers to screen biological samples for potential drug targets at the gene, transcript, protein, and metabolite levels, and evaluate their interactions at the network level.[4],[5]


  Genomics Top


The study of the entire genome of an organism is referred to as genomics. It entails the analysis of genetic variants linked to disease and drug response to derive direct inferences. Genome-wide association study identifies numerous genetic variants in different diseases in thousands of individuals. The inference is made by comparing the genetic variants in the disease group to its respective comparator.[6] Genomics can be further classified into structural genomics and functional genomics. Structural genomics helps in analyzing the three-dimensional structure of proteins using the modeling and experimental methods. Functional genomics deals with studying the interactions of genes and proteins.[7]


  Pharmacogenomics Top


Pharmacogenomics evaluates the individual's genetic makeup in response to drug treatment.[8] The data generated helps in predicting whether the drug is effective or not and/or associated with any adverse effect for a specific individual.[7] Microarray and next-generation sequencing (NGS) are the putative high-throughput put techniques for pharmacogenomics. Microarray is a technique that provides the true picture of gene expression of the whole genome in a single experiment. Gene expression study can be extrapolated to understand the molecular signature of the disease, therapeutic targets, and drug discovery. Microarray replaced traditional northern blot, real-time polymerase chain reaction method and allows to evaluate almost lacs of interest genes expression. For gene expression analysis, complementary deoxyribonucleic acid (cDNA) microarray and oligonucleotide-based gene chip (OGC) methods can be used. In cDNA microarray, the battery of genes can be spotted on a nylon membrane or glass slide while in OGC method, oligonucleotide complementary to known genes can be synthesized in situ. The pharmacologically and toxicology-relevant gene expression on exposure to chemicals or drugs can be identified.[9],[10],[11] Earlier Sanger sequencing was used to determine nucleotide variant sequencing but that was a tedious task.[12] NGS is a new technology that enables rapid sequencing of an entire genome. Short and sheared DNA segments are read by synthesizing complementary nucleotide sequences and read as fluorescent images. The actual sequence is finally read computationally against a human reference genome.[13] Numerous public databases are available like 1000 genome projects (1000 Genomes Project Consortium 2012), NHLBI (Natural heart, lung, and blood institute) genes, exome sequencing project, which contains DNA sequence variation in a large number of populations.[14] NGS is also applied in finding rare and common variants in genes associated with drug pharmacokinetic (PK) and pharmacodynamics (PD).[15] NGS evaluation of genetic variants in Phase I and Phase II metabolic enzyme and drug receptors identified the vast majority of variations in a coding region which are rare and very rare and leads to significant functional variability.[16],[17] An example of PK variation is in the case of a prodrug that is pharmacologically inactive until activated biologically by drug-metabolizing enzymes. Clopidogrel is an antiplatelet drug activated biologically by Cytochrome P450 2C19 enzyme (CYP2C19). Loss-of-function variants of these enzymes lead to undesirable therapeutic outcomes. In heterozygotes for CYP2C19*2 where apparent activity is present, increasing the drug dose results in an antiplatelet effect. In the case of homozygous variants, the increasing dose does not show any significant effect. While gain-of-function variants (CYP2C19*17) are associated with bleeding.[18],[19] In some cases, the single biological metabolizing enzyme variants can exert a large impact during the administration of a drug having a narrow therapeutic index. For instance, loss-of-function of thiopurine S-methyltransferase, an inactivator of 6- mercaptopurine, leads to accumulation of the drug that results in life-threatening cytotoxicity.[20] Ryanodine receptor 1 (a calcium channel receptor) variation leads to inhaled anesthetics associated with malignant hyperthermia.[21] During the Second World War, variation in glucose-6-phosphate dehydrogenase resulted in hemolytic anemia in patients exposed to antimalarial drugs.[22]


  Metagenomics Top


Metagenomics refers to the direct genetic analysis of microbial genomes obtained from different environments.[23] It is an uncultured approach that uses the biosynthetic capacity of bacterial species to synthesize drugs. Metagenomics capture environmental DNA (eDNA) to identify, isolate and induce biosynthetic gene expression in the heterologous environment to synthesize small drug molecule (antimicrobials). It employs two methodologies-sequencing-based and functional-based.[24] The sequencing-based approach generally uses shotgun sequencing and other sequencing tag tools to profile biosynthetic activity, identify targets, and recover biosynthetic pathways from eDNA cosmid library.[24] The functional-based approach evaluates clone-induced phenotype in heterologous hosts to identify biosynthetically active clones by library creation and its enrichment.[25] Biotin (Vitamin H) is currently produced through a chemical pathway in the pharmaceutical industry leading to diverse environmental impacts. Lakhdari et al.[26] produced a reporter system that detects the alteration in immune response of metagenomics clones. A metagenomic library was constructed for the patients of Crohn's disease (CD) using their fecal microbiota and was further screened for the modulatory activity of nuclear factor-kappa B subunit by using an intestinal epithelial cell line that was infected with a reporter gene. A clone showing stimulatory activity was found. The homology Bacteroids vulgatus was found to be associated with the source. Later it was found that Bacteroids vulagtus was more in patients with CD disease in comparison to the normal population.


  Epigenomics Top


Epigenomics refers to the heritable changes that do not bring any alterations in DNA sequences. Chromatin folding and attachment to nuclear matrix, covalent modifications associated with histone proteins, DNA methylation all together is known as the epigenome.[27] It is helpful in understanding the effect of DNA sequence on a particular gene function. Epigenetic changes are regulated by certain chemical modifications in the DNA itself or chromatin which are proteins, directly associated with the DNA. Epigenome varies at each cell and individual to individual, thereby modulating the gene expression. NGS evolved around the 2000s enabling researchers to sequence vast quantities of DNA in one go.[28] Epigenomics was the first one to use this technology to identify epigenetically modified regions. Other technologies used in analyzing epigenetic modifications include fluorescent in situ hybridization and chromosome conformation capture. These techniques have provided us with strong evidence about the existence of chromosomal interactions.[29] Chromatin Conformation Capture (3C) is another molecular technique that allows us to map chromosomal interactions by using formaldehyde cross-linking.[30] The biggest application of epigenomics is the early-stage detection of a disease and its therapeutic treatment. This technique has been used in the case of coronary artery disease (CAD).[31] Using integrated omics analysis several regulatory loci for CAD were investigated.[32]


  Transcriptomics Top


Transcriptomics includes the primary transcripts such as ribosomes, messenger RNA, transfer RNA, and noncoding RNA. The primary transcripts vary under the influence of the external environment. RNA synthesis is at the center of central dogma between DNA and protein, therefore the primary step in gene expression.[7] All cells have a similar genome but different cell types express different genes. Thousands of noncoding RNA are implicated in many disease conditions, for instance, cancer,[33] diabetes,[34],[35] and myocardial infarction.[36] RNA sequencing[37] and probe-based assay[38] paved the way to identify and quantify micro RNA,[39] small nuclear RNA,[40] and circular RNA[41] that plays a vital role in the disease states. Drug-induced gene expression under in vitro and in vivo conditions elucidate the therapeutic efficacy of drug target.[42],[43] Transcriptomics data is helpful in toxicogenomic studies.[5] Data of gene expression following drug therapy are vital in understanding the efficacy of a drug and is publically available such as DrugMATRIX,[44] open TG-GATE,[45] and Gene-Expression Omnibus.[46]


  Proteomics Top


Proteomics includes the identification and quantification of a set of proteins required to understand the function of cells. Due to the varied physicochemical characteristics of amino acids, posttranslational modifications (PTMs), the interconnectedness of proteins, and extremely different signaling networks, the analysis provides a difficulty.[47] Protein expression prediction at the genome level is problematic because genomic analysis ignores post-translational activities such as protein modifications and degradation. Therefore, dynamic proteomics analysis is vital along with static genomic analysis for accurate identification and quantification of biomarker and drug target studies.[48] Liquid chromatography-mass spectrometry (LC-MS) is an important tool for proteomics. The digested peptides are separated using LC and MS is used to evaluate the peptide on the basis of mass-charge ratio. The data generated helps in the identification of protein/peptide through mass spectrum and quantification of protein using mass chromatograms.[49] On the basis of methodology, proteomics is classified as quantitative and targeted proteomics. Quantitative proteomics is also known as nonbiased proteomics since it measures the expression of almost 5000 proteins in cells and tissues and 500 proteins in plasma. In addition, stable isotope labeling by metabolic incorporation of labeled amino acids in cell culture (SILAC) and isobaric labeling methods (tandem mass tags, isobaric tags), are employed to generate quantitative data.[50],[51] This approach is commonly used to find biomarkers. Targeted proteomics is another method, generally termed as a biased method to quantify specific targeted proteins/peptides.[52],[53] This method is suitable to validate the biomarkers for disease conditions. The method employs to evaluate the expression of 10-100 proteins at a single run. The biggest advantage of proteomics over genomics is that it considers PTMs. The regulation of protein function is considerably dependent on the PTMs as it affects the final molecular mass of the protein. PTM's are identified and quantified using quantitative and targeted proteomics.[54] A proteomic investigation identified a high-grade glioma-related protein in four cancer patients' tissues. The study used metabolic labeling to label an immunotoxin that targets surface sialylated glycoprotein (Ac4ManNAz). The tagged glycans were then connected to biotin-linked phosphine (Biorthogonal chemical reporting) and then enriched. To identify prospective therapeutic targets, a label-free MS technique was used. There were 52 glioblastoma-related proteins discovered, with the most important being receptor-type tyrosine-protein phosphatase zeta (PTPRZ1), angiopoietin-related protein-2 (ANGPTL2), Galectin-3-binding protein (LGALS3BP), Intercellular Adhesion Molecule-1 (ICAM1), Integrin Subunit Beta-8 (ITGB8), and interleukin-1 receptor (Interleukin-13 receptor subunit alpha-2).[55]


  Metabolomics Top


Metabolomics helps in the thorough analysis of metabolites in a cell, tissue, or organism throughout time.[56] It provides a direct signature of biochemical processes as it does not influence by any other regulatory mechanism as in the case of genes and proteins that get affected by epigenetic changes and PTM.[57] Therefore, metabolomics can directly be correlated with the phenotype. Through metabolomics one can analyze different subsets of metabolites-based compounds, functional groups, or structure similarity. This kind of analysis relies on complex instrumentation such as MS and NMR. Metabolomics helps in globally analyzing the gene expression, genetic analysis, changes in kinetic activity, and regulation of enzymes.[58] One of the most recent applications of metabolomics is in the field of oncology. Cancer cells have a high transcription and translation rate along with a high rate of proliferation in comparison to normal cells.[59] Metabolomics can be used to predict the shape of cancer cells. Pre-dose analysis of bio-fluid profile helps in predicting the efficacy of drug treatment for each sample.[60]


  Lipidomics Top


Lipidomics is a type of lipid-targeted metabolomics that studies the global lipid profile. It studies structure, function, and lipid interactions with other biomolecules.[61],[62] Lipidomics studies revealed the vital role of lipids in numerous neurodegenertive and metabolic diseases.[62],[63],[64] European Union is supporting “Lipidomics Expertise Platform,” that collects lipidomics data from institutions and houses databases for further investigational purpose.[63] Gas chromatography with polar group derivatization or LC with MS is the most regularly utilized techniques for lipidomic analysis (GC-MS or LC-MS). Some lipid indicators for early illness detection have been identified using lipidomics analysis based on MS. As a result, lipidomic research mainly focuses on the detection of lipid changes at the system level that are symptomatic of illness, environmental disturbances, food, medications, and toxins, as well as genetics.[65] It opens up a whole new way of looking at how lipids work in biological systems.[66] Lipidomics can be utilized to develop biomarkers for early detection and prognosis of various diseases.[67]


  Cytomics Top


Cytomics is the study of cytome heterogeneity, or more specifically, the study of molecular single-cell phenotypes arising from genotype and exposure combined with extensive bioinformatics knowledge extraction. Cells and their interconnections, rather than genes or macromolecules, are the fundamental functional components of organisms, according to the cytomics concept. Cytomics relies on sensitive, minimally invasive fluorescence-based multiparametric techniques such as flow cytometry (FCM), microscopic techniques, bioimaging techniques to unravel the activity of cells and tissue associated with disease.[68],[69],[70],[70] To view and quantify the interaction of treatments in cell populations, high-content screening bioimaging uses automated microscopy, fluorescence detection, and multiparameter algorithms.[71] Microscopic technologies, on the other hand, are superior to FCM in terms of high content analysis. All molecules of individual cells, including their intracellular location, can be quantified with the right technique. These instruments allow for simultaneous screening of many drug targets and actions in the same specimen.[72],[73] A photonic microscopic robot can tag and scan hundreds of distinct molecular components, such as proteins, in morphologically intact preserved cells and tissue.[74] Different omics approaches along with their tools and applications are summarized in [Figure 1].
Figure 1: Different omics approaches with their tools and applications applied to drug discovery

Click here to view



  Current Challenges and Future Insights Top


Data processing

The data generated through omics approach has an issue of data diversification. Further, there is a database redundancy due to the huge amount of data available from different omics sources. Lack of uniform data description standards also increased the complexity.[7] The presentation of data in network biology presents the solution of data processing. Network biology represents biological systems in a simplistic manner. Nodes in network biology represent intracellular processes (enzyme, protein, gene, metabolites) and edges represent relation between nodes.[75]

Nature of data

The data obtained from different data sets (genomic, transcriptomic, proteomics, etc.,) are dynamic in nature and not static. This causes biasness, low repeatability and the results fail in clinics. Therefore, it is important to consider time factor and sampling can be performed at different time points.[7]

Heterogeneity of the sample preparation

Omics studies compare data from healthy and disease and try to apply on the whole disease population but in actual both the population are heterogeneous. The approach should be to use a similar population. However, the challenge is that the confounding factors are not known. Therefore integrative omics approach should be employed that elucidates large number of variables.[6]

Single omic dataset

In general, a disease is a multifactorial means results of variation in genes, proteins, and metabolites. Single omic dataset is not sufficient as it provides reactive processes rather than causative,[6] therefore, integrative approach is required which disclose underlying mechanism that regulate complex disease.

Implementation of Multi-omics approach at bench side

For different omics approaches different type of sample processing is required. If limited patient sample is available, then it is not feasible to carry out the analysis. Implementation of multi-omics approach to the clinics requires standardization of analytical method, streamline sample collection, storage process, cost of analysis at each omic level.[76],[77]



 
  References Top

1.
Cavalli-Sforza LL. The human genome diversity project: Past, present and future. Nat Rev Genet 2005;6:333-40.  Back to cited text no. 1
    
2.
Kandpal R, Saviola B, Felton J. The era of 'omics unlimited. Biotechniques 2009;46:351-2, 354-5.  Back to cited text no. 2
    
3.
Yadav SP. The wholeness in suffix-omics, -omes, and the word om. J Biomol Tech 2007;18:277.  Back to cited text no. 3
    
4.
Matthews H, Hanison J, Nirmalan N. “Omics”-informed drug and biomarker discovery: Opportunities, challenges and future perspectives. Proteomes 2016;4:28.  Back to cited text no. 4
    
5.
Paananen J, Fortino V. An omics perspective on drug target discovery platforms. Brief Bioinform 2020;21:1937-53.  Back to cited text no. 5
    
6.
Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol 2017;18:83.  Back to cited text no. 6
    
7.
Yan SK, Liu RH, Jin HZ, Liu XR, Ye J, Shan L, et al. “Omics” in pharmaceutical research: Overview, applications, challenges, and future perspectives. Chin J Nat Med 2015;13:3-21.  Back to cited text no. 7
    
8.
Nebert DW. Pharmacogenetics and pharmacogenomics: Why is this relevant to the clinical geneticist? Clin Genet 1999;56:247-58.  Back to cited text no. 8
    
9.
Chin KV, Kong AN. Application of DNA microarrays in pharmacogenomics and toxicogenomics. Pharm Res 2002;19:1773-8.  Back to cited text no. 9
    
10.
Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270:467-70.  Back to cited text no. 10
    
11.
Lipshutz RJ, Fodor SP, Gingeras TR, Lockhart DJ. High density synthetic oligonucleotide arrays. Nat Genet 1999;21:20-4.  Back to cited text no. 11
    
12.
Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A 1977;74:5463-7.  Back to cited text no. 12
    
13.
Nielsen R, Paul JS, Albrechtsen A, Song YS. Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet 2011;12:443-51.  Back to cited text no. 13
    
14.
Tennessen JA, Bigham AW, O'Connor TD, Fu W, Kenny EE, Gravel S, et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 2012;337:64-9.  Back to cited text no. 14
    
15.
Gordon AS, Fulton RS, Qin X, Mardis ER, Nickerson DA, Scherer S. PGRNseq: A targeted capture sequencing panel for pharmacogenetic research and implementation. Pharmacogenet Genomics 2016;26:161-8.  Back to cited text no. 15
    
16.
Fujikura K, Ingelman-Sundberg M, Lauschke VM. Genetic variation in the human cytochrome P450 supergene family. Pharmacogenet Genomics 2015;25:584-94.  Back to cited text no. 16
    
17.
Kozyra M, Ingelman-Sundberg M, Lauschke VM. Rare genetic variants in cellular transporters, metabolic enzymes, and nuclear receptors can be important determinants of interindividual differences in drug response. Genet Med 2017;19:20-9.  Back to cited text no. 17
    
18.
Mega JL, Hochholzer W, Frelinger AL 3rd, Kluk MJ, Angiolillo DJ, Kereiakes DJ, et al. Dosing clopidogrel based on CYP2C19 genotype and the effect on platelet reactivity in patients with stable cardiovascular disease. JAMA 2011;306:2221-8.  Back to cited text no. 18
    
19.
Sibbing D, Koch W, Gebhard D, Schuster T, Braun S, Stegherr J, et al. Cytochrome 2C19*17 allelic variant, platelet aggregation, bleeding events, and stent thrombosis in clopidogrel-treated patients with coronary stent placement. Circulation 2010;121:512-8.  Back to cited text no. 19
    
20.
Relling MV, Schwab M, Whirl-Carrillo M, Suarez-Kurtz G, Pui CH, Stein CM, et al. Clinical pharmacogenetics implementation consortium guideline for thiopurine dosing based on TPMT and NUDT15 genotypes: 2018 update. Clin Pharmacol Ther 2019;105:1095-105.  Back to cited text no. 20
    
21.
Gonsalves SG, Dirksen RT, Sangkuhl K, Pulk R, Alvarellos M, Vo T, et al. Clinical pharmacogenetics implementation consortium (CPIC) guideline for the use of potent volatile anesthetic agents and succinylcholine in the context of RYR1 or CACNA1S genotypes. Clin Pharmacol Ther 2019;105:1338-44.  Back to cited text no. 21
    
22.
Relling MV, McDonagh EM, Chang T, Caudle KE, McLeod HL, Haidar CE, et al. Clinical pharmacogenetics implementation consortium (CPIC) guidelines for rasburicase therapy in the context of G6PD deficiency genotype. Clin Pharmacol Ther 2014;96:169-74.  Back to cited text no. 22
    
23.
Thomas T, Gilbert J, Meyer F. Metagenomics – A guide from sampling to data analysis. Microb Inform Exp 2012;2:3.  Back to cited text no. 23
    
24.
Charlop-Powers Z, Milshteyn A, Brady SF. Metagenomic small molecule discovery methods. Curr Opin Microbiol 2014;19:70-5.  Back to cited text no. 24
    
25.
Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 2013;31:814-21.  Back to cited text no. 25
    
26.
Lakhdari O, Cultrone A, Tap J, Gloux K, Bernard F, Ehrlich SD, et al. Functional metagenomics: A high throughput screening method to decipher microbiota-driven NF-κB modulation in the human gut. PLoS One 2010;5:e13092.  Back to cited text no. 26
    
27.
Wang KC, Chang HY. Epigenomics: Technologies and applications. Circ Res 2018;122:1191-9.  Back to cited text no. 27
    
28.
Mardis ER. A decade's perspective on DNA sequencing technology. Nature 2011;470:198-203.  Back to cited text no. 28
    
29.
Miele A, Dekker J. Long-range chromosomal interactions and gene regulation. Mol Biosyst 2008;4:1046-57.  Back to cited text no. 29
    
30.
Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science 2002;295:1306-11.  Back to cited text no. 30
    
31.
Schunkert H, König IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat Genet 2011;43:333-8.  Back to cited text no. 31
    
32.
Miller CL, Pjanic M, Wang T, Nguyen T, Cohain A, Lee JD, et al. Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci. Nat Commun 2016;7:12092.  Back to cited text no. 32
    
33.
Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 2010;464:1071-6.  Back to cited text no. 33
    
34.
Morán I, Akerman I, van de Bunt M, Xie R, Benazra M, Nammo T, et al. Human β cell transcriptome analysis uncovers lncRNAs that are tissue-specific, dynamically regulated, and abnormally expressed in type 2 diabetes. Cell Metab 2012;16:435-48.  Back to cited text no. 34
    
35.
Arnes L, Akerman I, Balderes DA, Ferrer J, Sussel L. βlinc1 encodes a long noncoding RNA that regulates islet β-cell formation and function. Genes Dev 2016;30:502-7.  Back to cited text no. 35
    
36.
Ishii N, Ozaki K, Sato H, Mizuno H, Saito S, Takahashi A, et al. Identification of a novel non-coding RNA, MIAT, that confers risk of myocardial infarction. J Hum Genet 2006;51:1087-99.  Back to cited text no. 36
    
37.
Ozsolak F, Milos PM. RNA sequencing: Advances, challenges and opportunities. Nat Rev Genet 2011;12:87-98.  Back to cited text no. 37
    
38.
Schulze A, Downward J. Navigating gene expression using microarrays – A technology review. Nat Cell Biol 2001;3:E190-5.  Back to cited text no. 38
    
39.
Lee CY, Lin SJ, Wu TC. miR-548j-5p regulates angiogenesis in peripheral artery disease. Sci Rep 2022;12:838.  Back to cited text no. 39
    
40.
Köhler J, Schuler M, Gauler TC, Nöpel-Dünnebacke S, Ahrens M, Hoffmann AC, et al. Circulating U2 small nuclear RNA fragments as a diagnostic and prognostic biomarker in lung cancer patients. J Cancer Res Clin Oncol 2016;142:795-805.  Back to cited text no. 40
    
41.
Chen Y, Li C, Tan C, Liu X. Circular RNAs: A new frontier in the study of human diseases. J Med Genet 2016;53:359-65.  Back to cited text no. 41
    
42.
Alexander-Dann B, Pruteanu LL, Oerton E, Sharma N, Berindan-Neagoe I, Módos D, et al. Developments in toxicogenomics: Understanding and predicting compound-induced toxicity from gene expression data. Mol Omics 2018;14:218-36.  Back to cited text no. 42
    
43.
Sawada R, Iwata M, Tabei Y, Yamato H, Yamanishi Y. Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures. Sci Rep 2018;8:156.  Back to cited text no. 43
    
44.
Natsoulis G, El Ghaoui L, Lanckriet GR, Tolley AM, Leroy F, Dunlea S, et al. Classification of a large microarray data set: Algorithm comparison and analysis of drug signatures. Genome Res 2005;15:724-36.  Back to cited text no. 44
    
45.
Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, et al. Open TG-GATEs: A large-scale toxicogenomics database. Nucleic Acids Res 2015;43:D921-7.  Back to cited text no. 45
    
46.
Clough E, Barrett T. The gene expression omnibus database. Methods Mol Biol 2016;1418:93-110.  Back to cited text no. 46
    
47.
Altelaar AF, Munoz J, Heck AJ. Next-generation proteomics: Towards an integrative view of proteome dynamics. Nat Rev Genet 2013;14:35-48.  Back to cited text no. 47
    
48.
Okerberg ES, Wu J, Zhang B, Samii B, Blackford K, Winn DT, et al. High-resolution functional proteomics by active-site peptide profiling. Proc Natl Acad Sci U S A 2005;102:4996-5001.  Back to cited text no. 48
    
49.
Masuda T, Mori A, Ito S, Ohtsuki S. Quantitative and targeted proteomics-based identification and validation of drug efficacy biomarkers. Drug Metab Pharmacokinet 2021;36:100361.  Back to cited text no. 49
    
50.
Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 2002;1:376-86.  Back to cited text no. 50
    
51.
Hogrebe A, von Stechow L, Bekker-Jensen DB, Weinert BT, Kelstrup CD, Olsen JV. Benchmarking common quantification strategies for large-scale phosphoproteomics. Nat Commun 2018;9:1045.  Back to cited text no. 51
    
52.
Anderson L, Hunter CL. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics 2006;5:573-88.  Back to cited text no. 52
    
53.
Borràs E, Sabidó E. What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry. Proteomics. 2017;17(17-18).  Back to cited text no. 53
    
54.
Yoneyama T, Ohtsuki S, Tachikawa M, Uchida Y, Terasaki T. Scrambled internal standard method for high-throughput protein quantification by matrix-assisted laser desorption ionization tandem mass spectrometry. J Proteome Res 2017;16:1556-65.  Back to cited text no. 54
    
55.
Autelitano F, Loyaux D, Roudières S, Déon C, Guette F, Fabre P, et al. Identification of novel tumor-associated cell surface sialoglycoproteins in human glioblastoma tumors using quantitative proteomics. PLoS One 2014;9:e110316.  Back to cited text no. 55
    
56.
Clish CB. Metabolomics: An emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud 2015;1:a000588.  Back to cited text no. 56
    
57.
Patti GJ, Yanes O, Siuzdak G. Innovation: Metabolomics: The apogee of the omics trilogy. Nat Rev Mol Cell Biol 2012;13:263-9.  Back to cited text no. 57
    
58.
van der Greef J, van Wietmarschen H, van Ommen B, Verheij E. Looking back into the future: 30 years of metabolomics at TNO. Mass Spectrom Rev 2013;32:399-415.  Back to cited text no. 58
    
59.
Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000;100:57-70.  Back to cited text no. 59
    
60.
Balashova EE, Maslov DL, Lokhov PG. A metabolomics approach to pharmacotherapy personalization. J Pers Med 2018;8:28.  Back to cited text no. 60
    
61.
Quehenberger O, Armando AM, Brown AH, Milne SB, Myers DS, Merrill AH, et al. Lipidomics reveals a remarkable diversity of lipids in human plasma. J Lipid Res 2010;51:3299-305.  Back to cited text no. 61
    
62.
Agatonovic-Kustrin S, Morton DW, Smirnov V, Petukhov A, Gegechkori V, Kuzina V, et al. Analytical strategies in lipidomics for discovery of functional biomarkers from human saliva. Dis Markers 2019;2019:6741518.  Back to cited text no. 62
    
63.
Hu C, van der Heijden R, Wang M, van der Greef J, Hankemeier T, Xu G. Analytical strategies in lipidomics and applications in disease biomarker discovery. J Chromatogr B Analyt Technol Biomed Life Sci 2009;877:2836-46.  Back to cited text no. 63
    
64.
Palacios G, Bowling JJ, Abdolvahabi A. The growing landscape of metabolomics and lipidomics: Applications to medicinal chemistry and drug discovery. Future Med Chem 2019;11:495-8.  Back to cited text no. 64
    
65.
Han X, Gross RW. Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: A bridge to lipidomics. J Lipid Res 2003;44:1071-9.  Back to cited text no. 65
    
66.
Watson AD. Thematic review series: Systems biology approaches to metabolic and cardiovascular disorders. Lipidomics: A global approach to lipid analysis in biological systems. J Lipid Res 2006;47:2101-11.  Back to cited text no. 66
    
67.
Han X. Lipid alterations in the earliest clinically recognizable stage of Alzheimer's disease: Implication of the role of lipids in the pathogenesis of Alzheimer's disease. Curr Alzheimer Res 2005;2:65-77.  Back to cited text no. 67
    
68.
Tárnok A. Slide-based cytometry for cytomics – A minireview. Cytometry A 2006;69:555-62.  Back to cited text no. 68
    
69.
Ecker RC, Rogojanu R, Streit M, Oesterreicher K, Steiner GE. An improved method for discrimination of cell populations in tissue sections using microscopy-based multicolor tissue cytometry. Cytometry A 2006;69:119-23.  Back to cited text no. 69
    
70.
Lima R, Wada S, Tsubota KI, Yamaguchi T. Confocal micro-PIV measurements of three-dimensional profiles of cell suspension flow in a square microchannel. Meas Sci Technol 2006;17:797.  Back to cited text no. 70
    
71.
Jan E, Byrne SJ, Cuddihy M, Davies AM, Volkov Y, Gun'ko YK, et al. High-content screening as a universal tool for fingerprinting of cytotoxicity of nanoparticles. ACS Nano 2008;2:928-38.  Back to cited text no. 71
    
72.
Mittag A, Lenz D, Gerstner AO, Tárnok A. Hyperchromatic cytometry principles for cytomics using slide based cytometry. Cytometry A 2006;69:691-703.  Back to cited text no. 72
    
73.
Hennig C, Adams N, Hansen G. A versatile platform for comprehensive chip-based explorative cytometry. Cytometry A 2009;75:362-70.  Back to cited text no. 73
    
74.
Schubert W. Cytomics in characterizing toponomes: Towards the biological code of the cell. Cytometry A 2006;69:209-11.  Back to cited text no. 74
    
75.
Haoudi A, Bensmail H. Bioinformatics and data mining in proteomics. Expert Rev Proteomics 2006;3:333-43.  Back to cited text no. 75
    
76.
Kopczynski D, Coman C, Zahedi RP, Lorenz K, Sickmann A, Ahrends R. Multi-OMICS: A critical technical perspective on integrative lipidomics approaches. Biochim Biophys Acta Mol Cell Biol Lipids 2017;1862:808-11.  Back to cited text no. 76
    
77.
Misra BB, Langefeld CD, Olivier M, Cox LA. Integrated Omics: Tools, Advances, and Future Approaches. J Mol Endocrinol 2018;62:21-45.  Back to cited text no. 77
    


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