ArticleAnalytical chemistry2023
Using the Synergy between HPLC-MS and MALDI-MS Imaging to Explore the Lipidomics of Clear Cell Renal Cell Carcinoma.
Article in Analytical chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- Spatial metabolomics and multiomics integration for breakthroughs in precision medicine for kidney disease.Nature reviews. Nephrology · 2026Review
- Adaptations of lipid metabolism in low-grade clear cell renal cell carcinoma are linked to cholesteryl ester accumulation.Scientific reports · 2025Article
- Imaging and spatially resolved mass spectrometry applications in nephrology.Nature reviews. Nephrology · 2025Review
- Enhancing lipid production in plant cells through automated high-throughput genome engineering and phenotyping.The Plant cell · 2025Article
- Metabolic signature of renal cell carcinoma tumours and its correlation with the urinary metabolome.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Review
- Quantitative mass spectrometry imaging: therapeutics & biomolecules.Chemical communications (Cambridge, England) · 2024Review
- Recent Analytical Methodologies in Lipid Analysis.International journal of molecular sciences · 2024Review
Corrections and comments
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Authors and funding
16 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lipid imaging mass spectrometry (LIMS) has been tested in several pathological contexts, demonstrating its ability to segregate and isolate lipid signatures in complex tissues, thanks to the technique's spatial resolution. However, it cannot yet compete with the superior identification power of high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS), and therefore, very often, the latter is used to refine the assignment of the species detected by LIMS. Also, it is not clear if the differences in sensitivity and spatial resolution between the two techniques lead to a similar panel of biomarkers for a given disease. Here, we explore the capabilities of LIMS and HPLC-MS to produce a panel of lipid biomarkers to screen nephrectomy samples from 40 clear cell renal cell carcinoma patients. The same set of samples was explored by both techniques, and despite the important differences between them in terms of the number of detected and identified species (148 by LIMS and 344 by HPLC-MS in negative-ion mode) and the presence/absence of image capabilities, similar conclusions were reached: using the lipid fingerprint, it is possible to set up classifiers that correctly identify the samples as either healthy or tumor samples. The spatial resolution of LIMS enables extraction of additional information, such as the existence of necrotic areas or the existence of different tumor cell populations, but such information does not seem determinant for the correct classification of the samples, or it may be somehow compensated by the higher analytical power of HPLC-MS. Similar conclusions were reached with two very different techniques, validating their use for the discovery of lipid biomarkers.
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Registered trials
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