TABLE 1.

Summary of MS‐based proteomic and metabolic studies analysing human plasma

S/N	Sample size	Methodology	Results	References
1	The results of a primary exploratory cohort (31 COVID‐19 patients) were validated on a smaller cohort of 17 independent patients and 15 healthy volunteers	Ultra‐high‐throughput clinical proteomics using complementary LC systems coupled to a TripleTOF 6600. Data acquisition using sequential window acquisition of all theoretical fragment ion spectra (SWATH) or data‐independent acquisition (DIA)	Identification of 27 potential biomarkers that are differentially expressed depending on COVID‐19 disease severity	(Messner et al., 2020)
2	Standard Reference Material (SRM 1950)	Non‐targeted metabolomics using nano, conventional and UHPLC coupled to a Q‐TOF MS or Orbitrap MS	Highly reproducible results when using a single platform; but significantly different metabolite profiles when using different platforms	(Telu et al., 2016)
3	42 participants	LC‐MS‐based clinical proteomics	Comparison of longitudinal proteomes of women and men in a weight loss study revealed that estrogen‐regulated proteins were significantly higher in women	(Geyer et al., 2021)
4	572 chronic kidney disease patients	MS‐based proteomics platform using iTRAQ labelling and Matrix‐Assisted Laser Desorption/Ionization (MALDI) MS using a 4800 TOF/TOF system	Albuminuria, renal impairment (eGFR), and chronic kidney disease staging (CKD Stage 1, ROC curve of 0.77) were all found to be significantly associated with diabetic kidney disease	(Bringans et al., 2017)
5	46 COVID‐19 patients and 53 control individuals	Stable isotope labeled proteomics and UPLC‐MS/MS untargeted metabolomic profiling using a Q‐Exactive HF‐X hybrid Quadrupole‐Orbitrap	The study observed that 105 proteins were differentially expressed in COVID‐19 patients' sera but not in control patients' sera. In severe patients, 93 proteins showed specific modulation	(Shen et al., 2020)
6	40 urine specimens that passed quality check, including 32 healthy controls, 6 COVID‐19 patients and 2 corresponding recovery persons	LC‐MS/MS analysis on an Orbitrap Fusion Lumos coupled with EASY‐nLC 1200	The correlation of samples within the health and recovery groups was shown to be greater than the correlation of samples between the healthy and patient groups. Overall, the study demonstrated that urine proteomics can reliably and sensitively differentiate COVID‐19 patients from healthy individuals	(Li et al., 2020)
7	Undepleted plasma samples from 8 COVID‐19 patients, including 3 non‐severe (mild) and 5 severe cases	LC–MS/MS analyses using Quadrupole Orbitrap MSs in data‐dependent acquisition (DDA) mode	The study discovered 91 differentially expressed plasma proteins between the mild and severe COVID‐19 groups, demonstrating the utility of plasma proteome signatures. Furthermore, the bioinformatics analysis revealed that several inflammatory modulators, particularly IL‐6, IL‐1B, and TNF, have a high specificity	(Park et al., 2020)
8	32 patients diagnosed with CKD (aged 3–18 years) and 26 control patients (aged 6–19 years)	Untargeted metabolomics based on LC‐QTOF‐MS	Control and CKD paediatric patients' metabolic fingerprints were compared, and 5 metabolites that showed a significant change in both data analysis procedures were identified	(Benito et al., 2018)
9	48 individuals with various urea cycle disorders	Untargeted mass spectrometry‐based metabolomics using a Waters ACQUITY UPLC and a Thermo Scientific Q‐Exactive MS	Plasma metabolomic analysis identified multiple potentially neurotoxic arginine metabolites in arginase deficiency, which may be useful in monitoring treatment efficacy in arginase deficiency. Furthermore, multiple biochemical perturbations in all UCDs were detected that likely reflect clinical management	(Burrage et al., 2019)
10	76 subjects that included 26 healthy controls and 50 COVID‐19 patients of differing disease severity (mild, moderate, and severe)	Targeted lipidomics and untargeted lipidomics using high‐resolution TOF MS on a 5600 TripleTOF Plus	Untargeted metabolomics detected an initial pool of 1,552 metabolite peaks with coefficients of variation of 20% across quality control samples. The consolidated plasma metabolome contained 1,002 metabolites (598 lipids and 404 polar metabolites) quantified using 71 internal standards after structural confirmation using MS/MS spectra	(Song et al., 2020)
11	9 patients with fatal outcome, 11 patients with severe symptoms, 14 patients with mild symptoms, and 10 healthy individuals	Sample extracts of hydrophilic compounds were analyzed using an LC‐ESI‐MS/MS system	In total, 431 metabolites and 698 lipids were identified and quantified, and both the metabolome and the lipidome revealed dramatic changes in the plasma of COVID‐19 patients. When comparing patients who died with healthy volunteers, malic acid and glycerol 3‐phosphate showed the greatest reduction, but they also showed dramatic reduction in groups with severe and mild symptoms	(Wu et al., 2020)
12	5 patients with fatal outcome, 7 patients with severe symptoms, 10 patients with mild symptoms, and 8 healthy individuals	Proteomics profiling of plasma using LC‐MS/MS analysis on a Q Exactive HF‐X MS coupled with an Easy‐nLC 1200 system	Eleven biomarkers and a set of biomarker combinations were developed that can accurately differentiate or predict different COVID‐19 outcomes. For the cohort, 530 proteins were mutually quantified in >70% of the samples	(Shu et al., 2020)