A number of Covid-19 studies with metabolomics and proteomics phenotypes are currently in the making. Here I maintain a table of these studies to keep track of what has been published so far (last updated 23 March 2021.
Should you know about any study that is missing here, please let me know.
A list of all proteins linked to Covid-19 can be found at UniProt.
| Reference | Title | Study population | Omics platform | # participants | # analyzed samples | # omics traits | General comments |
|---|---|---|---|---|---|---|---|
| Shen et al., Cell, 27 May 2020 | Proteomic and Metabolomic Characterization of COVID-19 Patient Sera | Retrospective cohort study in Taizhou Public Health Medical Center, China | Isotope labeled proteomics & UPLC-MS/MS untargeted metabolomics | 99: 46 COVID-19 and 53 controls | Single time point | 894 proteins, 941 metabolites | |
| Messner et al., Cell Systems 1 June 2020 and medRxiv 3 May 2020 | Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection | processing paper | |||||
| Lucas etal., Nature, 27 July 2020 | Longitudinal analyses reveal immunological misfiring in severe COVID-19 | Patients admitted to the Yale New Haven Hospital, USA | Immune profiling, flow cytometry | 219: 33 severe, 80 moderate, and 106 healthy | Five time points | 72 cytokines | |
| D'Alessandro et al., J. Proteome Res., 29 July 2020 and medRxiv 29 May 2020 | Serum proteomics in COVID-19 patients: Altered coagulation and complement status as a function of IL-6 level | processing paper | |||||
| Appelberg et al., Emerging Microbes & Infections, 31 Jul 2020 | Dysregulation in Akt/mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells | Study in COVID-19 infected cells | NA | NA | NA | NA | May be removed from this table, not a study on patient samples |
| Arunachalam et al., Science, 4 Sep 2020 | Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans | Hong Kong and Atlanta, USA | Single cell transcriptomics, cytokines, mass cytometry of blood leucocytes, OLINK inflammation panel, ... | 145: 76 COVID-19 patients and 69 healthy individuals | Single time point | 92 proteins (71 detected), many other (see platform) | |
| Overmyer et al., Cell Systems 7 Oct 2020 and medRxiv 19 Jul 2020 | Large-Scale Multi-omic Analysis of COVID-19 Severity | processing paper | |||||
| Su et al., Cell, 28 Oct 2020 | Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19 | North American (different ethnicities) | 5 Olink panels, Metabolon non-targeted metabolomics, single-cell multiplex secretome assay, single cell RNAseq, ... | 397: 139 COVID-19 patients + 258 healthy controls | Two time points | 464 proteins, 1050 metabolites | Data freely shared in Supplement |
| Mudd et al., Science Advances, 9 Dec 2020 | Distinct inflammatory profiles distinguish COVID-19 from influenza with limited contributions from cytokine storm | COVID-19 and influenza patients | cytokines using Luminex, single cell RNAseq, flow cytometry | 168: 79 COVID-19, 26 seasonal influenza, ... | Single time point | 35 cytokines, 12 antigens | |
| Delafiori et al., Analytical Chemistry, 20 Jan 2021 and medRxiv 27 July 2020 | Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine-Learning | Brasil | mass-spectrometry | 815: 442 COVID-19, 350 controls, 23 COVID-19 suspicious | Single time point | 19 molecules related to the disease’s pathophysiology and several discriminating features to patient’s health-related outcomes | |
| Gisby et al., eLife, 11 Mar 2021 and medRxiv, 6 Nov 2020 | Longitudinal proteomic profiling of high-risk patients with COVID-19 reveals markers of severity and predictors of fatal disease | End-stage kidney disease patients | 5 OLINK panels: ‘inflammation’, ‘immune response’, ‘cardiometabolic' 1-3 | 152: 55 pos, 51 neg; +replication 46 pos | Single time point | 436 plasma proteins (replication in serum) | Data freely shared in Supplement |
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| Tang et al., medRxiv 19 June 2020 | Proteomics Uncovers Immunosuppression in COVID-19 Patients with Long Disease Course | processing paper | |||||
| Chen et al., medRxiv 22 Jun 2020 | COVID-19 severity is associated with immunopathology and multi-organ damage | processing paper | |||||
| Patel et al., medRxiv 23 Jun 2020 | Proteomic blood profiling in mild, severe and critical COVID-19 patients | Sweden | 4 Olink panels: inflammation, autoimmune, cardiovascular, neurology | 87: 59 COVID-19 mild (n=26), severe (n=9) or critical (n=24) cases and 28 controls | Single time point | 344 unique proteins after QC | plot data using a Shiny App |
| Chen et al., medRxiv 24 June 2020 | Breath-borne VOC Biomarkers for COVID-19 | processing paper | |||||
| Soares, medRxiv 3 July 2020 | A novel specific artificial intelligence-based method to identify COVID-19 cases using simple blood exams | processing paper | uses public data | ||||
| Maras et al., medRxiv 7 Jul 2020 | Multi-Omics integration analysis of respiratory specimen characterizes baseline molecular determinants associated with COVID-19 diagnosis. | processing paper | |||||
| Wu et al., medRxiv 22 July 2020 | The Trans-omics Landscape of COVID-19 | processing paper | |||||
| Nie et al., medRxiv 19 August 2020 | Multi-organ Proteomic Landscape of COVID-19 Autopsies | processing paper | |||||
| Cai et al., medRxiv 8 Sep 2020 | Kynurenic acid underlies sex-specific immune responses to COVID-19 | processing paper | |||||
| Reyes et al., medRxiv 18 September 2020 | Proteomics identifies a type I IFN, prothrombotic hyperinflammatory circulating COVID-19 neutrophil signature distinct from non-COVID-19 ARDS | processing paper | |||||
| Bernardes et al., medRxiv 18 Sep 2020 | Longitudinal multi-omics analysis identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories | processing paper | |||||
| Cardoso et al., medRxiv 4 Oct 2020 | Prognostic accuracy of MALDI mass spectrometric analysis of plasma in COVID-19 | processing paper | |||||
| Huang, medRxiv, 5 Oct 2020 | Comparing biomarkers for COVID-19 disease with commonly associated preexisting conditions and complications | See table entry below (Filbin et al.) | See below | See below | See below | See below | This paper appears to describe the same study as Filbin et al., bioRxiv 2020 |
| Garcia et al., medRxiv 14 Oct 2020 | Innate lymphoid cell composition associates with COVID-19 disease severity | processing paper | |||||
| Petrović et al., medRxiv 20 Oct 2020 | Composition of the immunoglobulin G glycome associates with the severity of COVID-19 | processing paper | glycomics | ||||
| Filbin et al., bioRxiv 3 Nov 2020 (v1) | Plasma proteomics reveals tissue-specific cell death and mediators of cell-cell interactions in severe COVID-19 patients | Patients presenting with Cov19 symptoms at Massachusetts General Hospital | OLINK Explore 1536 | 384: 306 positive, 78 negative | Three time points: 383 (day0), 218 (day3), 136 (day7), 44 (event driven) | 1472 unique plasma proteins | Study has also collected SOMAscan data; OLINK data freely shared on web-site |
| Livanos et al., medRxiv 11 Nov 2020 | Gastrointestinal involvement attenuates COVID-19 severity and mortality | processing paper | |||||
| Dierckx et al. medRxiv 12 Nov 2020 | The metabolic fingerprint of COVID-19 severity | processing paper | |||||
| Demichev et al., medRxiv 12 Nov 2020 | A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome | processing paper | |||||
| Meoni etal., medRxiv 13 Nov 2020 | Metabolomic/lipidomic profiling of COVID-19 and individual response to tocilizumab | processing paper | |||||
| Giron et al., medRxiv 16 Nov 2020 | Severe COVID-19 Is Fueled by Disrupted Gut Barrier Integrity | processing paper | |||||
| Crunfli et al., medRxiv 18 Nov 2020 | SARS-CoV-2 infects brain astrocytes of COVID-19 patients and impairs neuronal viability | processing paper | |||||
| Chioh et al., medRxiv 18 Nov 2020 | Convalescent COVID-19 patients are susceptible to endothelial dysfunction due to persistent immune activation | processing paper | |||||
| Spick et al., medRxiv 28 Nov 2020 | Changes to the sebum lipidome upon COVID-19 infection observed via non-invasive and rapid sampling from the skin | processing paper | skin lipidomics | ||||
| Sindelar et al., medRxiv 8 Feb 2021 | Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity | Barnes Jewish Hospital, Christian Hospital, and Washington University | untargeted metabolomics | 341: 67 negative, 274 positive, thereof 253 admitted to the hospital, 129 ended up in ICU, 48 Covid deaths | >700 samples, up to six longitudinal time points | 25 metabolites measured at admission predict disease severity | raw LC/MS data and processed metabolic profiles with corresponding deidentified metadata will be made publicly available on the Metabolomics Workbench repository |
| Geyer et al., medRxiv 23 Feb 2021 | High-resolution longitudinal serum proteome trajectories in COVID-19 reveal patients-specific seroconversion | German University hospital | unbiased MS-based proteomics, five different SARS-CoV-2 antibody immunoassays | 293: 263 controls + 31 patients | 720 proteomes of 262 controls and 458 longitudinal samples (average of 31 days) of 31 patients | 502 quantified proteins | see also Buchholtz et al., medXriv 23 Feb 2021 for companion paper on seroconversion in the same cohort |
| Völlmy et al., medRxiv 13 Mar 2021 | Is there a serum proteome signature to predict mortality in severe COVID-19 patients? | University Hospital of Ferrara, Italy | serum proteome profiles (DIA-MS) | 33 COVID-19 patients admitted to respiratory and intensive care, 17 survivors, 16 deceased | up to three time points | 452 proteins quantified on average |
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