. 2022 Nov 15;37(10):2007–2019. doi: 10.1038/s41433-022-02307-9

Table 2.

Characteristics of biofluids, and artificial intelligence/bioinformatics analysis methods and aims.

Author(s) year	Biofluid sample type	Biofluids (Significant/Total)	Sample collection method	Statistical/ AI Model Type	AI Application / Bioinformatic type: Bioinformatic Purpose
Dry Eye, Sjögren’s Syndrome, Meibomian Gland Dysfunction
Aqrawi et al., (2017)	Tears Saliva	Saliva: (30/500) † Ig kappa chain C region, Calmodulin, Annexin A1, Alpha-enolase, Hemopexin, L-lactate dehydrogenase B chain, Granulins, Plastin-2, etc. Tears: (197/900) † Galectin-3, Fibrinogen beta chain, Copine-1, Calpastatin, Ig gamma-1 chain C region, Calpastatin, Ig gamma-3 chain C region*.	Schirmer strip	Prediction Classification	Proteomics, Scaffold, STRING, GO analysis with DAVID: protein identification, functional pathway identification, protein-protein interaction
González et al., (2020)	Tears	(2/-)†† lipocalin-1*, lysozyme C.	Tear collection with glass capillaries	Classification	Multilayer perceptron neural network, stepwise discriminant analysis, nonlinear iterative partial least squares.
González et al., (2020)	Tears	(2/-)†† lipocalin-1*, lysozyme C.	Tear collection with glass capillaries	Classification	Proteomics with MASCOT: identify proteins, cluster biofluids based on similarities.
Grus et al., (2005)	Tears	(7/††)? (3700 Da),? (3916 Da), nasopharyngeal* carcinoma-associated proline-rich protein, proline-rich protein 4, alpha-1-antitrypsin, c-terminal fragment, proline rich protein 3, calgranulin A.	Schirmer strip	Prediction Classification	Discriminant analysis, artificial neural network MLFN with back-propagation training algorithm.
Grus et al., (2005)	Tears		Schirmer strip	Prediction Classification	Proteomics: classify biofluids and make predictions about their function.
Huang et al., (2018)	Tears	(18/50) † Albumin, Lactotransferrin Lysozyme* Transferrin, Lipocalin 1Zinc-alpha-2-glycoprotein, Prolactin-induced protein, Keratin 1, Secretoglobin, family 2 A, member 1,* Apolipoprotein A-I, Serpin peptidase inhibitor 1, Polymeric immunoglobulin receptor, Complement component 3, S100 calcium binding protein, A8, Haptoglobin,S100 calcium binding protein A9, Lacritin, Cystatin S, Proline rich, lacrimal 1, Orosomucoid 1, Keratin 10, Keratin 2, Clusterin, Annexin A1, Secretoglobin, family 1D, member 1 Hemopexin, Immunoglobulin J, polypeptide alpha-2-HS-glycoprotein, Apolipoprotein H (beta-2-glycoprotein I), Heat shock 27 kDa protein 1, Glutathione S-transferase,Filamin A interacting protein 1-like POTE ankyrin domain family, member F Alpha-2-macroglobulin Transglutaminase 3Proline rich 4 (lacrimal), CCCTC-binding factor (zinc finger protein)-like	Schirmer strip	Classification	Proteomics, Proteome Discoverer, GO analysis, STRING: identify proteins and their biological processes
Ji et al., (2019)	Tears	(0/794) †Haemoglobin subunit delta, Haemoglobin subunit alpha, Haemoglobin subunit beta, Transitional endoplasmic reticulum ATPase, Vimentin, Coronin-1A, Tubulin beta-4B chain, etc.	Schirmer strip	Classification Prediction	GO with DAVID, KEGG pathway mapping, functional annotation clustering, protein-protein interaction with STRING database: identify function of biofluids, assess similarities with existing ones, and determine biological pathways.
Jiang et al., (2020)	Tears	(48/51) † Thiodiacetic acid* Uridine* Octadecanamide* Phthalic anhydride, 3-Acrylamidopropyl trimethylammonium, Triglyme, N-Heptane, 1-Piperidinecarboxaldehyde, 2-Methylbutyroylcarnitine, Palmitic amide Diglyme, N-(3-Indolylac etyl)-L-isoleucine N,N?-Dicyclohexylurea (S)-Desoxy-D2PM* Tuckolide, Alanyl-Alanine Dihydroterrein* Indoline*, N-methyl corydaldine (-).	Schirmer strip	Prediction	Least absolute shrinkage and selection operator regression.
Jiang et al., (2020)	Tears		Schirmer strip	Prediction	Proteomics, KEGG and Metaboanalyst: identify biofluids associated with increased risk of disease
Piyacomm et al., (2019)	Tears	(1/2) IL-Ra*, IL-6.	Schirmer strip	Prediction	Multilevel mixed-effect linear regression: predict which biomarkers are associated with treatment response
Sembler-Møller et al., (2020)	Saliva, plasma, salivary gland tissue	Saliva (40/1013) †, Matrix Gla protein, Basic salivary proline-rich protein 1, Basic salivary proline-rich protein 2, Histatin-3, Basic salivary proline-rich protein 4, Histatin-1, Neutrophil elastase, Calreticulin, Tripartite motif-containing protein 29, Clusterin, Vitronectin, Catalase, Complement factor B*, etc.; Plasma (0/219) ††; Salivary gland tissue (0/2773) ††.	Blood draw, sialometry, labial salivary gland biopsy	Classification	Hierarchical clustering, PCA.
Sembler-Møller et al., (2020)	Saliva, plasma, salivary gland tissue		Blood draw, sialometry, labial salivary gland biopsy	Classification	Proteomics, GO analysis, KEGG analysis.
Soria et al., (2013)	Tears	(5/5)100A6, annexin A1, annexin A11, cystatin-S, phospholipase A2-activatingprotein*.	Schirmer strip Glass capillaries Merocel sponge	Classification	K-nearest neighbour, support vector machine, classification trees, random forest, naive bayes.
Soria et al., (2013)	Tears		Schirmer strip Glass capillaries Merocel sponge	Classification	Proteomics using GO and DAIVD, protein-protein interactions: identification of protein function, biological process, and classification into disease groups.
Srinivasan et al., (2012)	Tears	(33/386)†Cystatin-S, Ig lambda chain C region, Lipocalin-1, Putative lipocalin 1-like protein, Secretoglobin family, zinc-alpha-2-glycoprotein, mammaglobin-b, Polymeric immunoglobulin receptor, arin, lysozyme, Zymogen granule protein-16, etc.	Schirmer strip	Classification	Proteomics with MASCOT, GO analysis: identification or proteins and their function
Tong et al., (2017)	Tears	(8/400) †† Glutathione synthetase, IL-1RN, ADH1C, AGT, CHRNA7, HIST1H4E, LCP1, H3P3A.	Schirmer strip	Classification Prediction	Hierarchical clustering, logistic regression.
Tong et al., (2017)	Tears		Schirmer strip	Classification Prediction	Proteomics: classify cytokines into groups based on similarity and assess how change in cytokines predicts treatment response.
Zou et al., (2020)	Tears	(3/1922) †† Adult, lysozyme C, zinc-alpha-2-glycoprotein, DNA J homolog subfamily C member 3. (1/2709) †† Child, Phosphoglycerate kinase 1.	Schirmer strip	Classification	Weighted correlation network analysis.
Zou et al., (2020)	Tears		Schirmer strip	Classification	Proteomics, GO/KEGG: identify and cluster biofluids based on similarities between and within groups.
Keratoconus and other Corneal Diseases
Borges et al., (2020)	Tears	(38/208 KC), (29/322 pterygium), (79/517 GVHD)†, Keratin, type I cytoskeletal 13, Immunoglobulin heavy vari-able 5–10–1, Immunoglobulin heavy variable 5–51, Proline-rich protein 27, Immunoglobulin heavy variable 3–23, Histone H2B type 1-A, Apolipoprotein, prolactin-inducible protein, S100-A8, annexin A2, cystatin-C, lipocalin-1, lysosome C, lysosome C,etc.	Micropipette	Classification	Partial Least Squares analysis with Metaboanalyst, PCA
Borges et al., (2020)	Tears		Micropipette	Classification	Proteomics with KEGG: identify and cluster biomarkers in different groups based on similarities.
Fodor et al., (2009)	Tears	(5/6)/IL-8, IL-1β, IL-6, TNF-α, IL-10, and IL-12p70.	Schirmer strip	Prediction	Locally weighted regression: compare cytokines of groups over time
Fodor et al., (2021)	Tears	(8/13) IL-6, IL-10, IL-13, IL-17A, CXCL8, IL-8, CCL5, RANTES, IFN-gamma, MMP-9, MMP-13, TIMP-1, NGF, t-PA, PAI-1.	Tear collection with glass capillaries	Prediction	Logistic Regression: predict change in biomarkers at follow-up
Kim et al., (2014)	Pterygium Healthy conjunctiva	(40/230) † aldehyde dehydrogenase, dimeric NADP- preferring, protein disulphide-isomerase A3, peroxiredoxin-2, Isoform 1 of Protein-glutamine gamma-glutamyltransferase 2 TYMP, FH Isoform Mitochondrial of Fumarate hydratase, mitochondrial IGHV4–31, Putative uncharacterized protein IGKC Ig kappa chain C region, etc.	Excision	Classification	Proteomics, GO with DAVID: protein classification
Leonardi et al., (2014)	Tears Serum	(3/78) † serum albumin, lactotransferrin, lysozyme, lacritin, secretoglobin 1D, mammoglobin B, lipocalin-1, proline-rich, protein 4, cistatine-S, hemopexin, serotransferrin*, Ig a-1 chain	Tear collection with glass capillaries	Prediction	Stepwise linear regression.
Leonardi et al., (2014)	Tears Serum		Tear collection with glass capillaries	Prediction	iTRAQ proteomics with Mascot engine: identify differences in biomarker expression and predict group differences.
Linghu et al., (2017)	Ptergyia Healthy conjunctiva	(156/156) †† Fibrinogen alpha chain, fibrinogen gamma chain, microfibril- associated glycoprotein 4, fibrinogen beta chain, fibronectin1, collagen alpha-3, MMP-1, −8, −13, MMP-3, −10, −21, −22, CD34, CD3*.	Cohen forceps and iridodialysis spatula	Classification	Proteomics, GO analysis with DAVID, KEGG pathway analysis: identify function of proteins, putative biological pathways.
Menegay et al., (2008)	Cornea	(105/105) 14-3-3 Protein gamma, 14-3-3 Protein sigma, 14-3-3 Protein zeta/delta, 24 kDa Protein, 60 S Ribosomal protein L3, Actin, alpha skeletal muscle, Actin, cytoplasmic 1, Actin-like protein 2, Actin-related protein 2/3 complex subunit 1B, Aldehyde dehydrogenase, Alpha 3 type VI collagen isoform 1, Alpha-actinin-4, etc	Sharp scissors and fine forceps, capturing an area of the droplets	Classification	KEGG Pathway Database/ Proteomics: identify proteins against reference healthy cornea.
O’Leary et al., (2020)	Tears Serum	(13/785) Phosphoglycerate mutase 1, Keratin type I, cytoskeletal 9, Keratin type 2, cytoskeletal 1, Fatty acid binding protein, epidermal Profilin-1, Immunoglobulin κ constant, Dermicidin, Protein S100-A4 Lysozyme C, Polymeric immunoglobulin receptor, Glyceraldehyde 3 phosphate dehydrogenase, Serum albumin, Gelsolin.	Schirmer strip	Prediction Classification	Random forest, logistic regression.
O’Leary et al., (2020)	Tears Serum		Schirmer strip	Prediction Classification	Proteomics, GO analysis: classification of severity, identification of predictive biofluids, functional annotation of biological pathways.
Soria et al., (2015)	Aqueous humour	(16/137) haemoglobin subunit beta, haptoglobin, plasma protease C1 inhibitor, alpha-2-HS-glycoprotein, basement membrane-specific heparan sulphate proteoglycan core protein* haemoglobin subunit delta, carbonic anhydrase 1, ceruloplasmin, hemopexin, apolipoprotein A-II, prostaglandin-H2 D-isomerase, actin cytoplasmic 2, semaphoring-7A, alpha-1-acid glycoprotein 1, latent transforming growth factor beta-binding protein 2, Ig kappa chain V-I region EU.	Paracentesis KC patients during keratoplasty Controls during phakic intraocular Lens implantation	Classification	PCA, hierarchical clustering, k-nearest neighbour.
Soria et al., (2015)	Aqueous humour			Classification	Proteomics, APEX, MASCOT with Proteome Discover, GO analysis: determine the overlap and differences in expression of biofluids between groups.
Wojakoswka et al., (2020)	Corneal buttons	(13/377) ††Benzoic acid, Glycolic acid, Succinic acid, Gluconic acid, Linoleic acid, Myristic acid, Palmitic acid, Pentadecanoic acid, Stearic acid, trans-13-Octadecenoic acid, Petroselinic acid, Cholesta-3,5-diene—isomer 1, Cholesta-3,5-diene—isomer 2, Cholesterol, Cholesterol propionate, Hexadecanol, Phosphoric acid*.	Penetrating corneal transplantation surgery	Classification	PCA, hierarchical clustering.
Wojakoswka et al., (2020)	Corneal buttons		Penetrating corneal transplantation surgery	Classification	Metabolomics, MSEA: classify cytokines in multiple groups and differentiate between healthy cornea and keratoconus
Yawata et al., (2020)	Tears	(11/51) IL-1a, IL-2Ra, IL-3, IL-12 (p40), IL-16, IL-18, CTAK, GRO-a, HGF, IFN-a2, LIF, MCP-1, M-CSF, MIF, MIG, NGF, SCF, SCGF-b, SDF-1a, TNF-b, TRAIL, IL-1b, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12p70, IL-13, IL-15, IL-17, Eotaxin, FGF basic, G-CSF, GM-CSF, IFN-g, IP-10, MCP-1, MIP1-a, PDGF-bb, MIP-1b, RANTES, TNF-a, VEGF, TGF-b1, TGF-b2*, TGF-b3.	Schirmer strip	Classification	PCA.
Yawata et al., (2020)	Tears		Schirmer strip	Classification	Proteomics; classify the cytokines into multiple groups to identify common patterns.

†All available biofluids included in the respective article.

“–“ indicates that information was not available.

*That had significant implications as determined by statistical analysis.

††Full list of proteins not available

? unknown name.

MSEA metabolite set enrichment analysis, PCA principal component analysis, MLFN multiple-layer feed-forward network, Da Dalton, GO gene ontology analysis, IL-Ra interleukin-1 receptor agonist, IL-6 interleukin receptor, DAVID Database for Annotation, Visualization and Integrated Discovery, STRING Search Tool for the Retrieval of Interacting Genes/Proteins, MASCOT Mascot Daemon by MatrixScience version 2.2.2 (Boston, MA).