PO.BCS01.13 · 生物信息与计算
空间分子数据的无偏细胞类型鉴定与生物学解读
Unbiased cell type identification and biological interpretation of spatial molecular data
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摘要 Abstract
中文摘要
背景：虽然预定义的（即有偏的）表型分析算法是分析空间分子数据的常用方法，能够提供现成的生物学解读（如 CD8+ T 细胞、CD68+PD-L1+ 巨噬细胞），并在阈值选择一致的情况下实现跨实验室的可重复性，但它们存在主观性、粗糙性以及无法捕捉新兴生物学现象的缺陷。相反，无偏表型分析算法有可能解决这些局限性，但目前缺乏直接的生物学可解释性。
方法：我们的 SpaceIQ™ 多组学平台采用递归细胞分型（RCT），而非标准的层次聚类及其他基于图的算法，来实现无偏的细胞鉴定。RCT 利用空间蛋白质组学、转录组学和形态学数据中由蛋白质丰度及其他技术因素驱动的宽动态范围（方差），将其视为具有生物学洞察意义。与标准化的常规方法不同，RCT 允许方差较大的标志物驱动早期分化，而方差较小的标志物则定义后续的亚群。
结果：我们使用 SpaceIQ™ 平台在三个公开的空间数据集（包括蛋白质组学、转录组学和明场病理学）上展示了 RCT 的广泛适用性。为便于对所得无偏细胞群进行生物学解读，RCT 方法：(i) 识别用于注释每个 RCT 的判别性生物标志物特征；(ii) 计算任意无偏细胞类型内预定义表型的概率；(iii) 生成一个最小标志物面板，能够以高概率近似表征任意给定的无偏细胞类型。
结论：通过 RCT 实现的无偏细胞分型在癌症研究中至关重要。该方法确保了对所有细胞状态——稀有、丰富、阳性、阴性和过渡态——的表征，而不受抗原表达的影响。由于不依赖大量特定细胞类型的训练，它特别适合捕捉细胞异质性的全谱。
查看英文原文 English abstract
Background: While pre-defined, or biased, phenotyping algorithms are a popular approach for analyzing spatial molecular data, offering ready biological interpretation (e.g., CD8+ T-cells, CD68+PD-L1+ macrophages) and reproducibility across labs with consistent threshold choices, they suffer from subjectivity, coarseness, and an inability to capture emergent biology. Conversely, unbiased phenotyping algorithms have the potential to address these limitations, but they currently lack straightforward biological interpretability.
Methods: Our SpaceIQ™ multi-omics platform performs recursive cell typing (RCT) instead of standard hierarchical clustering and other graph-based algorithms for unbiased cell identification. RCT leverages the wide dynamic range (variance) in spatial proteomics, transcriptomics, and morphology data, driven by protein abundance and other technical factors, as biologically insightful. Unlike normalized standard methods, RCT allows markers with larger variances to drive early differentiation, with smaller-variance markers defining subsequent subpopulations.
Results: We demonstrate the broad applicability of RCT using the SpaceIQ™ platform across three publicly available spatial datasets, including proteomics, transcriptomics, and brightfield pathology. To facilitate biological interpretation of the resulting unbiased cell populations, RCT approach: (i) identifies discriminatory biomarker signatures for annotating each RCT; (ii) calculates the probability of pre-defined phenotypes within any unbiased cell type; and (iii) generates a minimal marker panel that can approximate any given unbiased cell type with high probability.
Conclusions: Unbiased cell typing, achieved through RCT, is critical in cancer research. This approach ensures the representation of all cell states-rare, abundant, positive, negative, and transitional-regardless of antigen expression. By not relying on extensive cell-type specific training, it is uniquely suited to capture the full spectrum of cellular heterogeneity.
利益披露 Disclosure
F. Pullara, None..
R. Yan, None..
B. Falkenstein, None..
A. Tosun, None..
S. Chennubhotla, None.