Joshy Dennis M, Yi Soojin V
Department of Mechanical Engineering, University of California, Santa Barbara, 93106, USA.
Neuroscience Research Institute, University of California, Santa Barbara, 93106, USA.
bioRxiv. 2025 Aug 26:2025.08.26.672469. doi: 10.1101/2025.08.26.672469.
Cell type markers have been instrumental to physiological and molecular investigation of the human brain and remain essential for annotating cell type clusters in single-cell expression data and for targeted validation studies. However, expression of canonical markers in the target cell type (which we termed as the expression 'fidelity') as well as expression in unrelated cell types (which we termed as the 'background expression') across cortical regions remains poorly characterized. Using nearly 500,000 high-quality single-nucleus profiles from 19 studies, we quantified marker fidelity, revealing substantial regional variability. We developed a statistical framework that aggregates annotated barcodes into pseudo-bulk profiles, applied rigorous performance metrics, and identified markers with improved fidelity, reduced background, and consistent expression across regions. This approach extended the canonical marker set for six major brain cell types and yielded superior subtype-specific markers. The resulting marker lists, and a user-friendly analysis interface, provide a valuable resource for cell type annotation and validation in neurological research.
细胞类型标志物对人类大脑的生理学和分子研究起到了重要作用，并且对于注释单细胞表达数据中的细胞类型簇以及进行靶向验证研究仍然至关重要。然而，跨皮质区域的目标细胞类型中典型标志物的表达（我们称之为表达“保真度”）以及在不相关细胞类型中的表达（我们称之为“背景表达”）仍未得到充分表征。利用来自19项研究的近50万个高质量单核图谱，我们对标志物保真度进行了量化，揭示了显著的区域变异性。我们开发了一个统计框架，将注释的条形码聚合为伪批量图谱，应用了严格的性能指标，并识别出保真度提高、背景降低且跨区域表达一致的标志物。这种方法扩展了六种主要脑细胞类型的典型标志物集，并产生了更优的亚型特异性标志物。所得的标志物列表以及用户友好的分析界面为神经学研究中的细胞类型注释和验证提供了宝贵资源。
