Antibody Fingerprint in Blood Reveals Why Plasma Proteomes Differ Between People

Blood plasma has long been the workhorse of clinical diagnostics, a fluid so rich in molecular information that a single draw can reveal infection, inflammation, organ dysfunction and metabolic state. Yet for all its diagnostic promise, plasma remains one of the most analytically punishing proteomes in biology. Its protein concentrations span more than ten orders of magnitude, from albumin, which alone accounts for roughly 55 percent of total plasma protein, down to trace-level cytokines that flirt with the detection limits of the most sensitive instruments. A new longitudinal study published in Molecular Systems Biology has now tackled this complexity head-on, tracking the plasma proteomes of 51 healthy volunteers monthly for a full year and analyzing roughly 600 samples in two independent laboratories. The result is a strikingly clear picture of what makes each person’s blood proteome unique, and the answer, unexpectedly, lies in the antibodies.

The study, led by teams at University Medicine Greifswald in Germany and Utrecht University in the Netherlands, exploited the TIMES cohort, an acronym for Tracking Individuals Monthly for Evaluating Stability. Participants, 32 women and 19 men with a mean age of about 34 years, donated blood once a month for twelve months between June 2017 and May 2018. This design is unusual in plasma proteomics, where most biomarker studies still rely on cross-sectional snapshots of a single time point per subject. By sampling repeatedly, the researchers could separate the variation that occurs within a person over time from the variation that exists between people, a distinction that is fundamental to interpreting any clinical measurement.

What makes the study particularly persuasive is its dual-laboratory architecture. The Greifswald site and the Utrecht site processed and analyzed the same samples using deliberately different workflows. One laboratory used a bead-based SP3 digestion protocol, the other a conventional liquid-phase digest. One measured peptides on a timsTOF Pro2 mass spectrometer, the other on the newer timsTOF HT, which features a fourth-generation trapped ion mobility analyzer with fivefold greater ion charge capacity. Even the software pipelines diverged: Greifswald searched raw data with Spectronaut against a large database of more than 42,000 human protein entries, while Utrecht used DIA-NN with a compact plasma-specific database of just 2,445 entries built from the Human Protein Atlas. Quantification strategies differed too, with Greifswald favoring intensity-based absolute quantification and Utrecht using MaxLFQ normalization.

Despite this deliberate methodological divergence, the two datasets agreed remarkably well. After filtering out contaminants and the hypervariable regions of immunoglobulins, Greifswald quantified 274 proteins and Utrecht 188, with 163 proteins overlapping. Comparing the two laboratories sample by sample, the mean Spearman correlation coefficient across all samples was 0.908 and the mean Pearson correlation was 0.824, indicating strong agreement in both the ranking of proteins and their absolute abundances. To make the numbers clinically meaningful, both sites converted relative intensities into absolute concentrations using a panel of 37 housekeeping plasma proteins with well-established reference values. The resulting estimates landed squarely within known clinical ranges, a benchmark that mass spectrometry workflows have historically struggled to meet.

With the analytical foundation secured, the team turned to the central biological question: how stable is a person’s plasma proteome over a year, and how different are people from one another? The answer came vividly from an unsupervised dimensionality reduction technique called UMAP, which projects high-dimensional protein data into two dimensions while preserving neighborhood structure. When all monthly samples from all donors were projected, each person’s twelve samples clustered tightly together, while the clusters of different donors sat far apart. In other words, your plasma proteome in January looks far more like your own proteome in December than like anyone else’s at any time of year.

To quantify this, the researchers built a machine learning classifier, a support vector machine with a radial basis kernel, and challenged it to identify which donor a blinded sample came from. Across 1,000 iterations, the model achieved a median classification accuracy of 98 percent. The proteome, in effect, carries a personal signature as distinctive as a fingerprint. Notably, statistical adjustment for age, sex and body mass index did not significantly reduce the inter-donor variability, suggesting that these common demographic confounders do not drive the observed differences in this cohort. The clustering was so robust that it even exposed a suspected sample mix-up: the December sample labeled as donor 27 sat squarely within donor 4’s cluster, prompting the team to exclude it as a probable aliquoting or labeling error.

The most consequential finding concerns the immunoglobulins, the antibody family that constitutes roughly 20 percent of total plasma protein mass. Because antibodies are extraordinarily polymorphic, with variable regions numbering in the millions of possible sequences, most plasma proteomics studies either physically deplete them or quietly ignore them in data analysis. This study did the opposite, retaining and quantifying them using peptides unique to the constant regions of each antibody class and subclass. The measurements showed that immunoglobulins vary enormously between donors but barely at all within a donor over twelve months. Total IgG averaged 9.75 grams per liter across the cohort with a standard deviation of 2.64 grams per liter, a 27 percent coefficient of variation, while the average intra-donor fluctuation was only about 1.11 grams per liter. The same pattern held for IgG subclasses IgG1 through IgG4, as well as IgM, IgA1, IgA2 and IgD, and it was mirrored by CD5L, a protein known to travel in complex with IgM.

The mass spectrometry-derived subclass distribution also matched the literature closely. IgG1 averaged 6.12 milligrams per liter of blood plasma, representing about 62.8 percent of total IgG, compared with a literature value of roughly 63.6 percent. IgG2 came in at 19.1 percent versus an expected 24.5 percent, IgG3 at 9.3 percent versus 5.7 percent, and IgG4 at 8.7 percent versus 6.3 percent. These numbers matter because they demonstrate that untargeted mass spectrometry can now deliver antibody measurements of clinical grade without the targeted immunoassays traditionally required. They also align with recent work showing that the dominant antibody clones in plasma are produced by long-lived plasma cells, which would explain why circulating immunoglobulin levels remain so stable over months.

Not everything in plasma stood still. C-reactive protein, the classic inflammation marker, behaved in a completely different manner. Most donors kept CRP concentrations low throughout the year, but a few individuals showed dramatic spikes, with one donor ranging from a basal 0.16 nanomolar to a peak of 721 nanomolar, a roughly 4,500-fold swing. Others, including six donors, maintained persistently elevated basal CRP of around 50 nanomolar across all twelve months, hinting at low-grade chronic inflammation. The episodic peaks were mirrored by sister inflammation markers SAA1, SAA2, S100A8 and S100A9, strengthening the interpretation that they reflect acute inflammatory episodes such as minor infections. The contrast between rock-steady antibodies and volatile CRP illustrates a key principle for biomarker research: some proteins define who you are, while others report what is happening to you right now.

The study also delivered an unplanned clinical vignette. One donor, identified as donor 26, showed immunoglobulin levels four to ten times below the cohort average for IgG1, IgG2, IgG3, IgA1 and IgM, all falling below the 2.5th percentile, combined with high body mass index and chronically elevated CRP. This pattern is suggestive of common variable immunodeficiency, the most prevalent primary immunodeficiency in humans, although the donor reported no increased susceptibility to infections. The researchers emphasize that this chance observation, surfaced purely through longitudinal proteomic screening, illustrates the diagnostic potential of the approach. More broadly, the work argues that biomarker discovery needs longitudinal designs, donor-specific baselines and cross-laboratory validation, and that a universal plasma reference material or isotopically labeled internal standards should accompany multi-center studies. If a single blood draw can be read against a person’s own stable proteomic baseline rather than a population average, the sensitivity of future diagnostics could improve substantially.

Subject of Research: Longitudinal mass spectrometry analysis of inter-donor variability in the human plasma proteome, focusing on immunoglobulin subclass stability

Article Title: Immunoglobulin sub-class levels define inter-donor plasma variability: a longitudinal dual-lab study

Article References: Michalik, S., Kalaidopoulou Nteak, S., Drouin, N., Gesell Salazar, M., Hammer, E., Dhople, V. M., Holtfreter, S., Weiss, S., van den Toorn, H. W. P., Bröker, B. M., Domańska, G., Völker, U., & Heck, A. J. R. (2026). Immunoglobulin sub-class levels define inter-donor plasma variability: a longitudinal dual-lab study. Molecular Systems Biology, 22(8), 1312-1332. https://doi.org/10.1038/s44320-026-00218-5

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00218-5

Keywords: plasma proteomics, immunoglobulins, mass spectrometry, longitudinal study, biomarkers, C-reactive protein, IgG subclasses, inter-donor variability, machine learning, clinical diagnostics, TIMES cohort, proteome stability