The study combined deCODE cis-pQTL summary data with the FinnGen R12 M13_INTERVERTEB endpoint, labelled “other intervertebral disc disorders.” After harmonization, 5,820 usable SNP rows supported screening across 1,723 genes. Investigators calculated single-SNP Wald ratios, inverse-variance weighted random-effects estimates when multiple variants were available, weighted-median estimates, MR-Egger slopes, heterogeneity statistics, Egger intercepts and leave-one-out estimates. Benjamini–Hochberg correction was applied to the 1,723 primary gene-level tests. The four multi-SNP signals that passed this threshold were MST1, with an odds ratio of 1.057, 95% confidence interval 1.038–1.077 and q = 4.15 × 10⁻⁶; DLK1, with an odds ratio of 0.933, 95% CI 0.903–0.964 and q = 0.016; ITIH1, with an odds ratio of 0.926, 95% CI 0.893–0.961 and q = 0.016; and SAA1, with an odds ratio of 0.927, 95% CI 0.892–0.962 and q = 0.023. These estimates describe associations between genetically proxied protein abundance and the recorded disc-disorder outcome, not direct effects on disc tissue.

Directional agreement was seen across several analytical methods for the four correction-positive genes: the weighted-median and MR-Egger estimates pointed in the same direction as inverse-variance weighting. Leave-one-out analyses also preserved the sign of each estimate when individual variants were omitted, and MST1 and SAA1 remained nominally significant after every displayed omission. Those checks provide internal consistency but cannot replace validation of instrument independence, particularly because the supplied pQTL table did not include a reference linkage-disequilibrium panel or an auditable record of variant clumping. The authors consequently described the results as local MR screening signals. TP53I3 and BPI were also examined because they were directionally concordant and present in the single-cell matrix, but their q values of 0.13 and 0.17 placed them only in an exploratory tier.

Human nucleus pulposus tissue supplied a cell-level view of the genetic candidates. The researchers analyzed GEO dataset GSE153066, which contains eight relatively normal libraries and eight libraries from degenerated discs. Quality control filtered the original 85,205 barcodes to 70,622 cells, retaining cells with more than 200 and fewer than 5,000 detected genes and less than 20% mitochondrial counts. Marker-informed annotation resolved eight broad populations: B cells, erythroid-like cells, fibroblasts, macrophages, natural killer cells, nucleus-pulposus chondrocyte-like cells, T cells and a deliberately unresolved stress population. Low-specificity clusters were not assigned forced identities, and the cell maps were treated as descriptive because individual cells from the same donor or library are not independent patient-level replicates.

Condition comparisons were instead anchored to the 16 libraries, with eight controls and eight degenerated samples treated as unpaired observations. Counts were summed within each library and transformed into log2 counts per million plus one for pseudobulk analysis. This design exposed substantial between-library heterogeneity and prevented the much larger number of cells from being mistaken for independent clinical evidence. The control group had a median age of 34.5 years, compared with 43 years in the degenerated group, but no age-adjusted or paired estimate was substituted for the unpaired screen. The dataset is cross-sectional, so its cell states cannot establish trajectories of degeneration or reveal whether a particular population caused a change over time.

Macrophages supplied the largest positive nominal difference in cell composition between degenerated and control libraries, amounting to 20.58 percentage points, with P = 0.031 and q = 0.250. The result did not survive correction within the composition panel and was therefore treated as exploratory rather than as an FDR-supported shift. Among 12 displayed follow-up genes, BPI had the largest positive library-level expression effect, with log2 fold change 2.932, P = 0.035 and q = 0.27. GCLM was also nominally positive, with log2 fold change 0.812, P = 0.044 and q = 0.27, but neither estimate met the within-panel false-discovery threshold. These findings indicate that some molecular differences may accompany degeneration while emphasizing that sample variability and multiple testing limit the strength of the conclusions.

A prespecified six-gene ferroptosis-related pQTL panel was analyzed separately from the four correction-positive multi-SNP genes. It included TFRC, FTL, AKR1C3, GCLM, HMOX1 and HSPB1, with a broader marker program that added GPX4, SLC7A11, ACSL4, FTH1, NFE2L2 and PTGS2 for expression context. TFRC was the only member supported in the genetic screen: a single-SNP analysis produced an odds ratio of 1.471, 95% CI 1.212–1.785, P = 9.42 × 10⁻⁵ and q = 0.023. Because this result rested on one SNP rather than a multi-SNP instrument, it supports follow-up but does not demonstrate that circulating transferrin receptor 1 is a causal driver of intervertebral disc disorders. The other panel members entered the tissue analysis as expression markers, independently of genetic significance.

The researchers also sought experimental support for selected findings in a human monocytic cell line. They differentiated THP-1 cells with 100 nM phorbol 12-myristate 13-acetate for 24 hours, allowed a 24-hour recovery, and then exposed the macrophage-like cells to 10 ng/mL each of interleukin-1 beta and tumor necrosis factor alpha for 24 hours. Relative to controls, the inflammatory condition produced higher MST1 expression, measured at 1.34 ± 0.07, higher BPI at 3.46 ± 0.28 and higher GCLM at 1.70 ± 0.12, while SAA1 expression was lower at 0.31 ± 0.03. Each comparison used four independent biological replicates per group and produced a two-sided Welch P value below 0.001. The directions were consistent with the corresponding library-level single-cell results, providing cross-platform concordance for four selected genes.

That laboratory agreement nevertheless has clear boundaries. THP-1 cells are a macrophage-like in vitro model, not primary nucleus-pulposus cells, and the PMA differentiation and recovery conditions can shape inflammatory responsiveness. The experiment therefore supports the plausibility of the measured expression directions in this selected system, not a mechanism of disc degeneration or a clinical effect in patients. Replication would require primary or disc-relevant models, prespecified stimulation conditions, independent biological replication and fuller qPCR reporting, including raw Ct values, primer details and verified amplification efficiency. The genetic analysis likewise needs independent instruments with documented linkage disequilibrium, broader outcome phenotypes, ancestry-diverse data and separate single-cell cohorts. Taken together, the study offers a layered candidate map linking circulating protein signals, cellular context and an inflammatory cell model, but it does not yet establish causal pathways, therapeutic targets or clinical interventions.

Subject of Research: Genetic and single-cell evidence for protein signals and ferroptosis-related states in human intervertebral disc degeneration

Article Title: Genetically supported protein signals and ferroptosis-related cell states in human intervertebral disc degeneration: local pQTL–FinnGen screening integrated with nucleus pulposus single-cell RNA sequencing

Article References: Tang, G., Wu, X., Lin, Y., Tang, Z., & Liang, H. (2026). Genetically supported protein signals and ferroptosis-related cell states in human intervertebral disc degeneration: local pQTL–FinnGen screening integrated with nucleus pulposus single-cell RNA sequencing. Frontiers in Cell and Developmental Biology, 14, Article 1944218. https://doi.org/10.3389/fcell.2026.1944218

Image Credits: AI Generated

DOI: 10.3389/fcell.2026.1944218

Keywords: intervertebral disc degeneration, Mendelian randomization, protein quantitative trait loci, nucleus pulposus, single-cell RNA sequencing, ferroptosis, TFRC, MST1, SAA1, THP-1 cells, FinnGen, Genetically

The research, published in the open-access journal Microplastics and Nanoplastics, focuses on a technical but crucial distinction in laboratory toxicology: the difference between the administered dose and the delivered dose. The administered dose is simply the amount of plastic a scientist pours into a cell culture dish. The delivered dose is the fraction of that plastic that actually settles onto, or otherwise contacts, the cells over the course of an experiment. When particles are suspended in liquid culture medium, they do not all reach the cell layer at the same speed, and some may never fully deposit at all. Ignoring this distinction, the researchers show, can distort toxicity rankings and lead to conclusions that are wrong by a factor of two or more.

To investigate the problem systematically, the team, led by M. Kloukinioti and colleagues including co-senior authors H. Braakhuis and L. A. Parker, used fluorescent aminated polystyrene microspheres of two very different sizes: one micrometer and ten micrometers in diameter. Polystyrene is one of the most widely used plastics in consumer products and is also the standard material for laboratory test particles, making it a sensible model for the broader microplastics problem. The fluorescent labels allowed the researchers to track exactly where the particles went over time, simply by measuring how much fluorescence appeared at the bottom of the wells in a cell-free system.

The experimental results were striking. Over a standard 24-hour exposure period, neither particle size achieved complete deposition. In other words, even after a full day of settling, a significant portion of the plastic added to the dish never made contact with the surface where cells would normally grow. This finding alone challenges a widespread assumption in in vitro toxicology: that the amount of material pipetted into a well is a reasonable proxy for the amount the cells experience. For small particles suspended in viscous biological media, reality is considerably more complicated.

The researchers then turned to two computational models that attempt to predict how particles settle in liquid. The first is Stokes’ Law, the classic nineteenth-century physics equation that describes the sedimentation of spherical particles in a fluid, taking into account the particle’s radius and density, the density of the fluid, and its viscosity. The second is the RiskGONE model, a more recent dosimetry tool developed within a European nanosafety project, which incorporates additional parameters such as particle size distributions and can account for more complex settling behavior. Both models require detailed input about the culture system, including the density and viscosity of the medium and the physical characteristics of the particles themselves, which the team measured carefully using techniques such as static light scattering.

What emerged was a surprising split verdict. The sedimentation behavior of the smaller, one-micrometer particles was accurately predicted by the RiskGONE model, while the settling of the larger, ten-micrometer particles aligned closely with the classical Stokes’ Law. This size-dependent divergence matters because it means there is no single, one-size-fits-all formula for calculating delivered dose in microplastics experiments. A researcher who applies the wrong model to the wrong particle size could introduce systematic errors into their dose calculations, and those errors would propagate directly into their toxicity conclusions.

The most consequential part of the study came when the team connected their dosimetry findings to actual biological measurements. In parallel with the cell-free sedimentation experiments, the researchers exposed THP-1 cells, a widely used human immune cell line derived from monocytes, to the same polystyrene particles under identical conditions of medium composition, plate setup, and exposure volume. They then assessed cell viability using standard assays and compared the hazard estimates obtained when calculations were based on the administered dose versus the delivered dose.

The result was unambiguous: relying solely on the administered dose led to an underestimation of the microplastics’ hazard potential by approximately a factor of two. Because fewer particles reached the cells than had been pipetted into the wells, the true toxicity per delivered particle was roughly twice as high as the administered-dose calculation suggested. In practical terms, a plastic particle that appears relatively harmless when judged by the amount poured into the dish may in fact be considerably more damaging to the cells it actually contacts. For regulators and risk assessors trying to rank different plastics by hazard, such a systematic bias could shuffle the entire leaderboard.

This finding has implications that extend well beyond polystyrene spheres. Efforts to compare the toxicity of different polymer types, including polypropylene, polyvinyl chloride, polyethylene terephthalate, and polyamide, are accelerating worldwide, and many of these comparisons rest on in vitro data. If dosimetry is not properly accounted for, a plastic that settles quickly might appear more toxic than one that stays suspended, not because it is intrinsically more hazardous but simply because more of it reached the cells. Conversely, a genuinely dangerous polymer could be wrongly exonerated if most of its particles never arrived at the cell layer during the experiment. The study provides a technical roadmap for avoiding these pitfalls, emphasizing that researchers should characterize their particles’ size distribution and effective density, measure their medium’s properties, and apply an appropriate sedimentation model before interpreting any biological endpoint.

The work also highlights a broader lesson about the limits of computational models in new scientific domains. Both Stokes’ Law and the RiskGONE model were validated here against direct experimental measurements, and neither was universally correct. The authors’ approach, testing model predictions against fluorescence-based deposition data before trusting either one, offers a template that other laboratories can follow. As the field of microplastics toxicology matures and moves toward benchmark dose modeling and quantitative risk assessment, the distinction between what is poured in and what lands on the cells may prove to be one of the most important numbers in the entire experiment. The study was conducted as part of the Dutch MOMENTUM research program on microplastics and health, with funding from ZonMw, Health-Holland, and Plastics Europe, though the funding organizations had no influence on the work. The authors note that the article was shared early as a citable, peer-reviewed accepted manuscript, with a final version of record to follow, and the research stands as a reminder that in the fast-moving world of microplastics science, the most important measurements are sometimes the ones nobody thought to make.

Subject of Research: In vitro dosimetry of microplastics and the distinction between administered and delivered dose in cell toxicity testing

Article Title: In vitro dosimetry of microplastics: the importance of understanding the delivered dose in toxicity testing

Article References: Kloukinioti, M., Neijenhuis, F., Nijman, J. E., Kooter, I. M., van de Steeg, E., Braakhuis, H., & Parker, L. A. (2026). In vitro dosimetry of microplastics: the importance of understanding the delivered dose in toxicity testing. Microplastics and Nanoplastics. https://doi.org/10.1186/s43591-026-00234-0

Image Credits: AI Generated

DOI: 10.1186/s43591-026-00234-0

Keywords: microplastics, dosimetry, in vitro toxicology, polystyrene, delivered dose, administered dose, Stokes' Law, RiskGONE model, THP-1 cells, sedimentation, hazard assessment, nanotoxicology