The logic behind the advance rests on radiochemistry that is deceptively simple. Since the mid-1970s, clinicians have labeled white blood cells with gamma emitters such as technetium-99m and indium-111, then reinjected them to locate abscesses and inflammatory lesions. Indium-111, with a physical half-life of about 2.8 days, became the workhorse for this purpose because it persists long enough to follow cells for days. Zirconium-89, with a half-life of 3.3 days, offers a nearly identical temporal window but emits positrons, enabling PET imaging, which delivers substantially higher sensitivity and better spatial and temporal resolution than SPECT. The UAB team exploited 8-hydroxyquinoline, known as oxine, a lipophilic chelator that shuttles zirconium-89 across cell membranes, allowing the radionuclide to become trapped inside the leukocytes rather than merely attached to their surfaces.

The trial enrolled four healthy adult women between 37 and 53 years of age, a deliberate choice grounded in radiation protection logic. Extrapolations from decades of indium-111 oxine dosimetry predicted a higher effective-dose coefficient for females, 1.14 mSv/MBq, than for males, 0.855 mSv/MBq, so studying women represented the more conservative test of safety. There was also a clinical rationale: the team’s longer-term goal is to image neuroinflammation in myalgic encephalomyelitis/chronic fatigue syndrome, a condition that disproportionately affects women. Each participant donated roughly 120 milliliters of blood, from which leukocytes were isolated, incubated with zirconium-89 oxine, washed, and returned intravenously within strict time limits. The mean administered activity was 10.9 plus or minus 2.9 MBq, a modest amount by nuclear medicine standards.

Quality control was rigorous. Every labeled cell preparation had to meet predefined release criteria before reinjection, including radiochemical purity of at least 95 percent, radionuclidic purity of at least 99.5 percent, verified leukocyte viability before and after labeling, sterility, and endotoxin testing. The radiolabeling itself was performed at a licensed commercial radiopharmacy under USP Chapter 797 standards, while the zirconium-89 oxine was produced in the UAB Cyclotron PET Production Facility under USP Chapter 823 guidelines. Only products passing every specification were administered, a framework designed to mirror the established clinical workflow for FDA-approved indium-111 oxine labeling and thereby ease eventual regulatory translation.

Imaging followed a demanding schedule. Each participant underwent whole-body PET/CT at five time points: immediately after injection, then at 4 to 6, 24, 48, and 72 hours. The scans captured a dynamic biological story. Early images showed the labeled cells concentrated in the liver, spleen, heart, and blood pool, as would be expected for cells still circulating. By 24 hours and increasingly at 48 and 72 hours, uptake shifted to the liver, spleen, and red marrow, particularly within the pelvis and iliac crests, reflecting the physiological homing of leukocytes to the organs where they are naturally cleared and stored. The pattern was qualitatively similar to that long documented for indium-111 oxine-labeled leukocytes and consistent with prior mouse and non-human primate studies of zirconium-89 oxine labeling.

The dosimetry numbers carry the study’s headline significance. The highest mean absorbed-dose coefficients appeared in the spleen at 3.52 plus or minus 1.38 mGy/MBq, followed by the liver at 2.1 plus or minus 0.45, the adrenals at 1.62 plus or minus 0.24, the uterus at 1.48 plus or minus 0.98, and the kidneys at 1.36 plus or minus 0.12 mGy/MBq. The mean effective-dose coefficient came out to 0.92 plus or minus 0.06 mSv/MBq, numerically lower than the predicted 1.14 mSv/MBq, a difference that reached statistical significance in an exploratory one-sample t-test with a P value of 0.006. The measured biodistribution also diverged from the assumptions used for prediction: roughly 32 percent of activity localized to the liver, 8 percent to the spleen, and 15 percent to red marrow, rather than the 30-30-34 split assumed from indium-111 data. The authors attribute much of the discrepancy to methodological differences, since PET/CT-based whole-organ dosimetry is inherently more direct than the SPECT-only methods used historically.

Safety results were unambiguous. No adverse events, clinically significant laboratory abnormalities, or pharmacological effects were observed in any participant through 72 hours, and follow-up contact 24 hours after protocol completion turned up nothing further. Vital signs remained stable throughout. One intriguing observation warrants mention: activity concentrations in plasma exceeded those in whole blood, especially at early time points, which the researchers suggest could reflect leukocyte lysis, efflux of the radionuclide from cells, or incomplete buffy coat formation during processing. This phenomenon, they note, deserves closer investigation in subsequent studies.

A key technical concern with any intracellular label is stability. Free zirconium-89 has a well-documented affinity for hydroxyapatite and accumulates in mineralized bone, so progressive cortical bone uptake would signal that the label was leaking from cells. To probe this, the team performed an exploratory skeletal analysis comparing marrow-rich regions of the spine and pelvis with cortical tibial bone. Uptake remained consistently higher in the marrow-rich compartment, while cortical tibial activity stayed low with no progressive accumulation, supporting the interpretation that skeletal signal reflects genuine leukocyte homing to red marrow rather than liberated radionuclide. The authors caution that low-level efflux beyond 72 hours cannot be fully excluded and that longer imaging windows will help settle the question.

The study also showcased a modern dosimetry workflow. Organ regions of interest were generated automatically on CT using an in-house deep learning segmentation tool, then manually refined and propagated across time points. This automation reduces interobserver variability and could make dosimetry scalable across larger, multi-institution cohorts. Time-integrated activity coefficients were computed with Simpson’s rule, and doses were calculated in OLINDA 2.1 using standard adult female phantoms and ICRP 103 tissue weighting factors. Beyond the final 72-hour scan, activity was assumed to decay only physically, a conservative assumption that likely overestimates doses for organs still clearing activity biologically.

Looking ahead, the implications stretch well beyond infection imaging. The same labeling strategy could track CAR T-cells and other cell-based therapies as they home to tumors, offering oncologists a real-time window into treatment delivery. It may prove especially valuable in neurology: animal models of stroke, traumatic brain injury, multiple sclerosis, and Alzheimer’s and Parkinson’s diseases all show leukocyte invasion of the brain, but these events have never been directly visualized in living humans because available PET tracers decay too quickly, while the critical window of immune cell infiltration spans three to four days. Zirconium-89’s half-life fits that window precisely. The research team plans to apply the technique to patients with suspected central nervous system inflammation, including multiple sclerosis and ME/CFS, while working to reduce the blood volume needed for labeling and to improve labeling efficiency. Limitations remain, including the small all-female cohort and the absence of functional testing of labeled cells beyond viability, but the foundation is now laid: for the first time, human dosimetry data confirm that PET imaging of a patient’s own immune cells is both safe and feasible, opening a new chapter in molecular imaging of the immune system.

Subject of Research: First-in-human radiation dosimetry and biodistribution of zirconium-89 oxine-labeled autologous leukocytes imaged with PET/CT

Article Title: First-in-human dosimetry and biodistribution of [89Zr]Zr-oxine-labeled autologous leukocytes using PET/CT

Article References: Gultekin, K., Bartels, J. L., Cardenas, C. E., Kumar, Y., Gimblet, G. R., Jones, C. L., Bankston, C., Jeffers, C. D., White, S. L., Lapi, S. E., Younger, J. W., & McConathy, J. E. (2026). First-in-human dosimetry and biodistribution of [89Zr]Zr-oxine-labeled autologous leukocytes using PET/CT. European Journal of Nuclear Medicine and Molecular Imaging. https://doi.org/10.1007/s00259-026-08158-w

Image Credits: AI Generated

DOI: 10.1007/s00259-026-08158-w

Keywords: zirconium-89, PET/CT, leukocytes, cell tracking, dosimetry, radiopharmaceuticals, nuclear medicine, oxine, immunimaging, neuroinflammation, CAR T-cells, first-in-human trial

The research, led by Mari Tolonen, Ziwei Xu, and Jakub Sedzinski of the University of Copenhagen together with Bianca Dumitrascu’s group at Columbia University, tackled a problem that has long frustrated developmental biologists: how to quantify the behavior of thousands of individual cells inside a densely packed, actively remodeling tissue. Morphology is often treated as a downstream consequence of fate decisions, a passive readout of the genetic program. The team set out to test a bolder idea, namely that the dynamic geometry of a cell might serve as a complementary, and potentially predictive, indicator of its state, in the same way that transcriptomic profiles define molecular identity.

Their experimental system was the Xenopus mucociliary epithelium, a bilayered embryonic skin that closely resembles the cellular composition of the mammalian airway. The researchers dissected animal cap explants from embryos at early blastula stages, when the tissue was still multipotent, and mounted them on fibronectin-coated dishes where they flattened into stratified epithelia amenable to long-term optical access. Embryos had been injected with messenger RNAs encoding membrane-localized mNeonGreen and nuclear H2B-RFP, allowing both cell boundaries and nuclei to be visualized. Volumetric confocal time-lapse imaging then captured the full course of differentiation, from NF stage 9 through stage 30, with z-stacks acquired every three to five minutes over 16 to 22 hours across three independent experiments.

Turning those raw movies into quantitative data required a substantial computational pipeline. Nuclei were segmented in three dimensions using a custom-trained StarDist model, while membrane signals were first denoised with a CARE network and then segmented slice by slice with Cellpose, with individual 2D contours stitched into 3D volumes based on maximal pairwise overlap. Nuclear objects were tracked over time using TrackMate, and the team integrated a convolutional neural network called Oneat to detect cell division events, which were used to impose branching points on trajectories. This combination substantially improved mitosis detection compared with TrackMate’s native algorithm, raising branching correctness from 0.122 to 0.328. On average, trajectories spanned at least 9.5 hours in a third of cells, providing sufficient temporal coverage for the analyses that followed.

From the segmentations and tracks, the researchers computed a rich set of time-resolved features for each cell: three-dimensional shape descriptors derived from point clouds resampled from surface meshes, including surface area, eccentricity, and orientation; conventional two-dimensional descriptors such as area, Feret diameter, and solidity taken from each membrane object’s central slice; nuclear shape metrics; spatial position; and instantaneous movement. The result was a high-dimensional morphodynamic feature space, conceptually analogous to a transcriptomic state vector, in which each cell’s identity at each moment was represented by dozens of quantitative measurements.

When the team explored this feature space with unsupervised methods, the picture was sobering. Principal component analysis of more than 4,200 cell trajectories revealed no distinct clusters corresponding to cell types or developmental stages. Cells were distributed continuously, with only slight shifts over time, and standard clustering metrics such as the Adjusted Rand Index and Normalized Mutual Information were uniformly low. A simple K-nearest-neighbor classifier reached a mean accuracy of just 65.8 percent against a stratified baseline of 54.0 percent, with balanced accuracy of only 28.1 percent. Unlike cultured cells, where shape heterogeneity is high and morphology alone can resolve distinct states, cells in this densely packed epithelium share constrained shapes and largely collective movement, making them hard to distinguish by any single feature.

The breakthrough came from supervised learning combined with lineage backtracking. After live imaging ended, the samples were fixed and immunostained for markers of multiciliated cells, goblet cells, small secretory cells, and basal cells, while ionocytes were identified by their characteristic morphology and lack of marker expression. By aligning the immunostained images with the final live-imaging frame, the team assigned terminal fates to roughly 75 percent of all tracked trajectories, producing a ground-truth dataset of 2,576 curated lineages. They then trained an XGBoost gradient-boosted classifier and a multinomial logistic regression model, treating each cell at each time point as an independent observation labeled by its track’s endpoint fate, with absolute experimental time included as an additional input.

The supervised models achieved moderate but consistent performance across 20 independent train-test splits. XGBoost reached a mean accuracy of 80.6 percent and a balanced accuracy of 61.4 percent, while logistic regression achieved 70.3 percent accuracy with a comparable balanced accuracy of 62.9 percent. Performance was strongest for the abundant epithelial lineages: basal cells scored an F1 of 0.90, goblet cells 0.72, and multiciliated cells 0.56 under XGBoost, whereas the rare ionocytes and small secretory cells remained difficult to classify despite class balancing with SMOTE-based oversampling and undersampling. Notably, classifier confidence and accuracy rose steadily during development, with mean confidence increasing from 0.80 at around five hours to 0.89 by 22 hours, and class-balanced accuracy climbing from 50.4 to 75.1 percent. Intriguingly, predictions for multiciliated cells peaked at roughly 15 hours, with 80.6 percent accuracy, and then declined, suggesting these cells reach a morphodynamic plateau before terminal maturation, after which their features increasingly overlap with those of goblet cells.

Feature importance analysis revealed which aspects of cellular behavior carried the predictive signal. Normalized Z position, reflecting a cell’s height within the apico-basal axis of the epithelium, was the single most informative feature, consistent with the invariant layer preferences of different lineages: goblet cells remain in the superficial layer, basal stem cells stay confined to the base, and intercalating lineages ascend. The nucleus-to-membrane centroid offset was the most predictive morphological variable in the XGBoost model, likely capturing the transition from a flat, migratory configuration to a vertically oriented shape during radial intercalation. Absolute experimental time was also a strong predictor, particularly for small secretory cells, which emerge late in development. Perhaps most strikingly, movement features contributed almost nothing to fate prediction, apparently because migration within the tissue is largely collective rather than fate-specific. Nuclear shape features proved more informative than membrane shape, which the authors attribute to the higher segmentation fidelity of nuclei.

The broader significance of this work lies in what it suggests about the nature of cell state itself. Fate, the study indicates, is not defined by discrete morphological states that cells snap into; instead, morphodynamic signatures of identity emerge gradually along continuous, overlapping trajectories, and only become legible through time-integrated, supervised analysis. Because morphodynamic tracking provides dense temporal sampling with positional context, something neither single-cell RNA sequencing nor spatial transcriptomics can fully deliver, it could complement molecular fate mapping by filling temporal and spatial gaps. The authors note that their pipeline, built from open tools including StarDist, Cellpose, TrackMate, and Oneat, can be extended to other developing tissues and combined with transcriptomics or targeted perturbations to uncover causal links between gene expression, signaling, and phenotype. Future models that incorporate neighboring cells and local tissue geometry may sharpen predictions further. For now, the message is clear: the shape a cell takes, where it sits, and when it does so are not mere consequences of its destiny but readable, quantitative clues to what that destiny will be.

Subject of Research: Machine learning prediction of cell fate from single-cell morphodynamic features during Xenopus mucociliary epithelial differentiation

Article Title: Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation

Article References: Tolonen, M., Xu, Z., Beker, O., Kapoor, V., Dumitrascu, B., & Sedzinski, J. (2026). Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation. Molecular Systems Biology, 22(7), 1040-1069. https://doi.org/10.1038/s44320-026-00212-x

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00212-x

Keywords: cell fate, cell differentiation, Xenopus laevis, mucociliary epithelium, live imaging, morphodynamics, machine learning, XGBoost, cell tracking, segmentation, radial intercalation, developmental biology