Multi-Omics: Severe COVID-19 Serum Profile in Pregnancy
- Jun 22
- 4 min read
Pregnant patients were left out of nearly every early COVID-19 trial, so clinicians had little to explain why some expectant mothers deteriorated while others barely noticed the infection. Our team went after that with chemistry instead of clinical scores. In a prospective UCLA cohort, Dalton Bioanalytics scientists co-authored a multi-omics study that profiled maternal serum across proteins, lipids, and metabolites in one LC-MS workflow. Severe illness carried a serum signature the milder cases did not show: heavier complement activation alongside a clear reshuffling of circulating lipids.
Multi-Omics Profiling of Maternal Serum
The cohort was small by design and deeply phenotyped. Thirty pregnancies were analyzed, 25 with confirmed SARS-CoV-2 infection (6 asymptomatic, 13 mild or moderate, 6 severe) and 5 matched controls. Each serum sample ran through a mixed-mode RP-HILIC liquid chromatography mass spectrometry method that quantified 496 proteins, 467 lipids, and 122 small molecules from one 100 microliter aliquot. Linear regression linked analyte levels to severity, adjusted for maternal age, race, and gestational age. That single-injection design is what makes the multi-omics integration believable, since protein, lipid, and metabolite readouts come from one biofluid LC-MS workflow rather than three assays stitched together afterward.
Key Findings
Severe disease, distinct chemistry: 87 of 496 proteins shifted with severe COVID-19 (40 up, 47 down, FDR < 0.05), while mild and moderate post-infection cases looked much like controls.
Complement runs hot: upregulated proteins were enriched for complement cascade, B cell receptor signaling, and Fc receptor activation, with C3/C5 convertase, C2, C9, and C1q all rising.
Lipids reshuffle in opposite directions: of 467 lipids, 136 tracked severity (51 up, 85 down); diacylglycerols and triacylglycerols climbed while phosphatidylcholines and apolipoproteins fell.
A cheap predictor rivaled the omics: a composite of albumin, C-reactive protein, and apolipoprotein A1 reached an AUC of 0.98, edging out each marker alone (about 0.91 to 0.92).
Figure 1. Panel A shows a principal components analysis separating severe COVID-19 sera from all other participants. Panel B is a volcano plot of proteins altered in severe SARS-CoV-2 positive pregnancies. Panel C compares the severity associations against an independent multiomic COVID-19 dataset, with a correlation of 0.43. Adapted from Altendahl et al. (2022), PLOS ONE.
Why Complement Activation Stands Out
The complement signal matters more in pregnancy than the raw protein list suggests. Zinellu and Mangoni (2021) reported that falling C3 and C4 mark worse non-pregnant COVID-19, and complement activation is independently tied to adverse maternal outcomes like preeclampsia. Seeing C9 and the C3/C5 convertase rise here is confirmatory more than surprising, and it recurs across the multi-omics studies our team has published. Catching that activation early could flag mothers at risk before the clinical picture turns.
Lipid Dysregulation as an Early Signal
The lipid data hint at something usable. Phosphatidylcholines and apolipoproteins dropped while acylglycerols rose, a shift other groups have seen in fatal non-pregnant COVID-19 and that the authors validated against the Overmyer dataset. The honest caveat is sample size. With six severe cases, effect sizes need replication, and higher BMI in that group blurs obesity-driven against infection-driven lipid changes.
From Single-Sample Integration to Decisions
Reading complement proteins, lipids, and small molecules off one serum draw gets harder when each layer comes from a separate platform and aliquot. Pulling all three from a single injection, the way the Omni-MS workflow at Dalton was built to do, strips out the sample-volume and batch-correction tradeoffs that cloud cross-omics interpretation. For groups planning multi-omics biomarker discovery on scarce clinical sample, that single-sample design is the gap between a clean comparison and a noisy one, which is why we lean on it for multi-omics service work at this scale.
Frequently Asked Questions
What is multi-omics?
Multi-omics measures several molecular layers, such as proteins, lipids, and metabolites, from the same sample and analyzes them together. The aim is to see relationships no single assay would catch, like a protein change that travels with a lipid change. In serum work it often runs on a single mass spectrometry injection.
What did multi-omics profiling reveal about severe COVID-19 in pregnancy?
Severe cases showed strong complement activation and dysregulated serum lipids, while milder post-infection cases resembled healthy controls. A three-marker panel of albumin, C-reactive protein, and apolipoprotein A1 separated severe disease with an AUC of 0.98. The pattern mirrors severe COVID-19 outside pregnancy.
How much serum does a multi-omics workflow need?
This study used a 100 microliter serum aliquot per participant to quantify proteins, lipids, and metabolites in one run. A single small-volume draw keeps batch effects down and preserves precious clinical samples. That economy matters most for biobanked cohorts where volume is fixed.
Conclusion
What holds up: severe COVID-19 in pregnancy carries a genuine proteomic and lipidomic signature, close enough to the non-pregnant disease that these patients should be managed the same way. Whether the albumin, CRP, and apolipoprotein A1 trio predicts who will deteriorate is not yet proven, because six severe cases cannot settle it. The practical implication is a cheap, clinically measurable panel worth testing prospectively before severity declares itself.
Related Reading
See how we run these analyses in one lab: Dalton's multi-omics CRO services.
Citation
Altendahl, M., Mok, T., Jang, C., Yeo, S., Quach, A., & Afshar, Y. (2022). Severe COVID-19 in pregnancy has a distinct serum profile, including greater complement activation and dysregulation of serum lipids. PLOS ONE, 17(11), e0276766. https://doi.org/10.1371/journal.pone.0276766
Note
This blog post summarizes findings from the above-cited research. Figures are adapted from the original publication. For full details, please refer to the source article.
By Seungjun Yeo, CEO at Dalton Bioanalytics. Specializing in multi-omics mass spectrometry for drug discovery and biomarker research.