Incorporation of optical profilometry volume correction in quantitative elemental bioimaging workflows.
- Publisher:
- ELSEVIER
- Publication Type:
- Journal Article
- Citation:
- Talanta, 2026, 298, (Pt B), pp. 128923
- Issue Date:
- 2026-02-01
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Quantitative elemental bioimaging workflows are well established and typically rely on matrix-matched standards for comparable and repeatable calibration. However, variations in tissue thickness, microtome cutting artefacts, and cell density are usually assumed to have negligible impact on method uncertainties. Common mitigation strategies include endogenous signal normalization with 12C or 31P, complete ablation of thin sections, or analysis of thicker specimens under stable laser fluences. While effective for homogeneous specimens, these approaches may fail in heterogeneous tissues with disparate anatomical structures, variable cell populations, or inconsistencies in sectioning, leading to uncontrolled anomalies and potentially misleading interpretation of elemental distributions. To overcome these limitations, here we incorporate optical profilometry into quantitative bioimaging workflows to directly measure tissue surface topographies and correct for thickness variations. Topographic maps were acquired for gelatin standards and representative tissues, including murine kidney, multi-organ arrays, human meningioma, and emphysematous lung, revealing substantial deviations from nominal thicknesses and heterogeneous surface roughness. These data were registered against LA-ICP-MS elemental images and applied for volume normalization. Volume correction significantly altered elemental quantification. In kidney, Cu, Fe, and Zn increased 4-5 fold, whereas in meningioma, Cu and Fe increased 8-10 fold, with Zn similarly elevated. Correlations of endogenous signals with thickness were tissue-dependent: in the kidney, 12C strongly correlated (R = 0.66) and 31P moderately (R = 0.44), while in the meningioma, both were weak (12C R = 0.21; 31P R = 0.09), though 31P moderately tracked cell density (R = 0.51). These results demonstrate that profilometry-based volume correction improves accuracy, reproducibility, and interpretability of elemental bioimaging, particularly in heterogeneous tissues.
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