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<p>Long non-coding RNAs (lncRNAs) are widely nominated as biomarkers from bulk-tissue data, which cannot reveal which cell type carries a transcript or whether a disease change is cell-intrinsic or compositional. Using the lncRNA <i>RMST</i>, I show that bulk expression can misrepresent where a lncRNA resides in <i>both</i> directions, and that cell-of-origin attribution should precede biomarker claims. I combined GTEx with &gt; 40 million single cells and nuclei from the CZ CELLxGENE Census. (i) <i>Under-representation</i>: at single-cell resolution <i>RMST</i> is most broadly detected in brain (17.2% of ~ 29 million cells; 35% of neurons, up to 74% of dopaminergic neurons), confirming its neural role, yet bulk brain expression is low (0.7 TPM) because <i>RMST</i> is broadly but lowly expressed and nuclear-enriched. (ii) <i>Tissue-dependent peripheral carriers</i>: across six non-brain tissues <i>RMST</i>-positive cells are 85.5% stromal/epithelial (hepatocyte 0.02%), and the dominant carrier switches by tissue (cardiac/vascular fibroblasts vs renal/pulmonary/hepatic epithelium); in heart, fibroblasts carry it (79–91% of positive cells), but a disease per-cell change is not robust (bootstrap CI spans zero; within-study flat-to-negative), so I report attribution, not direction. A broad control (<i>KCNQ1OT1</i>: 6.2 vs <i>RMST</i> 0.5 lineages/tissue) confirms <i>RMST</i>’s restriction is gene-specific. (iii) <i>Over-attribution</i>: <i>RMST</i> is undetected in all 411,742 diseased-liver nuclei (<i>NEAT1</i> and <i>MALAT1</i>: ~ 90% of the same nuclei) and near-absent across liver cells, so a reported liver biomarker — including my own prior report — is unattributable. Cell-of-origin attribution, which both rescues an under-counted neural transcript and retracts an over-counted liver biomarker, belongs in the standard workflow for lncRNA biomarkers.</p>

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A single-cell cell-of-origin audit of the lncRNA RMST: bulk expression under-represents its broad neural expression and over-attributes a liver biomarker

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • Hidenori Tani

摘要

Long non-coding RNAs (lncRNAs) are widely nominated as biomarkers from bulk-tissue data, which cannot reveal which cell type carries a transcript or whether a disease change is cell-intrinsic or compositional. Using the lncRNA RMST, I show that bulk expression can misrepresent where a lncRNA resides in both directions, and that cell-of-origin attribution should precede biomarker claims. I combined GTEx with > 40 million single cells and nuclei from the CZ CELLxGENE Census. (i) Under-representation: at single-cell resolution RMST is most broadly detected in brain (17.2% of ~ 29 million cells; 35% of neurons, up to 74% of dopaminergic neurons), confirming its neural role, yet bulk brain expression is low (0.7 TPM) because RMST is broadly but lowly expressed and nuclear-enriched. (ii) Tissue-dependent peripheral carriers: across six non-brain tissues RMST-positive cells are 85.5% stromal/epithelial (hepatocyte 0.02%), and the dominant carrier switches by tissue (cardiac/vascular fibroblasts vs renal/pulmonary/hepatic epithelium); in heart, fibroblasts carry it (79–91% of positive cells), but a disease per-cell change is not robust (bootstrap CI spans zero; within-study flat-to-negative), so I report attribution, not direction. A broad control (KCNQ1OT1: 6.2 vs RMST 0.5 lineages/tissue) confirms RMST’s restriction is gene-specific. (iii) Over-attribution: RMST is undetected in all 411,742 diseased-liver nuclei (NEAT1 and MALAT1: ~ 90% of the same nuclei) and near-absent across liver cells, so a reported liver biomarker — including my own prior report — is unattributable. Cell-of-origin attribution, which both rescues an under-counted neural transcript and retracts an over-counted liver biomarker, belongs in the standard workflow for lncRNA biomarkers.