PO.BCS01.09 · 生物信息与计算

选取代表性组织学切片以实现经济高效的3D空间转录组学与肿瘤微环境重建

Selecting representative histologic sections for cost-efficient 3D spatial transcriptomics and tumor microenvironment reconstruction

编号 1470 展板 9 时间 4/20 09:00–12:00 区域 Section 5 主讲 Minh-Khang Le, PhD

分会场 Integrative Computational Approaches 1

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作者与单位 Authors & Affiliations

Minh-Khang Le¹, James Evans², Harsimran Kaur², Sojung Lee², Vivek Pujara¹, Alan Joey Simmons², Seungwoo Kang², Jai Mehta³, Louis J. Vaickus⁴, Ken Lau², Joshua Jay Levy¹

¹Cedars-Sinai Medical Center, Los Angeles, CA,²Vanderbilt University School of Medicine, Nashville, TN,³University of Tennessee Knoxville, Knoxville, TN,⁴Dartmouth-Hitchcock Medical Center, Lebanon, NH

摘要 Abstract

中文摘要

背景：三维空间转录组学（ST）对于表征肿瘤微环境（TME）异质性至关重要，但对每一张连续切片进行分析成本过高。既往研究已探索选取切片子集以在交错位置对ST进行插补。然而，这些方法并未确定应对哪些切片进行分析才能最高效地保留3D结构。最优策略必须在组成相似的区域强化高置信度推断，同时确保纳入形态上不同的区域。我们开发了一个计算框架，用于选取能够最优保留3D TME结构的组织学切片，同时最大限度减少需要ST分析的切片数量。方法：采集了4个3-mm的FFPE结直肠癌组织芯（每位患者2个），以捕获肿瘤-间质界面和三级淋巴结构。每个组织芯约300张连续5-μm H&E切片（深度约1500 μm）以40倍分辨率成像；保留组织充足的切片（各处报告的组织丢失为：TL 1150 μm、TR 350 μm、BL 1005 μm、BR 1100 μm）。保留的切片使用VALIS进行配准。使用图神经网络分割出11种组织类别——包括肿瘤、促纤维增生性间质（DS）、坏死、平滑肌、免疫聚集体和黏膜。对每张切片，我们提取Prov-GigaPath嵌入和组织类别面积比例。整合组织学特征和z轴距离的成对相似度用于指导设施选址子模优化算法，以选取k张代表性切片（k=3-30）。选取的子集用于通过类别特异性alpha-shape网格和体素化重建3D TME结构。重建保真度采用相对于全切片重建的体素化交并比（IoU）进行评估。结果：各组织芯的组成存在差异——TL：正常黏膜50%、平滑肌26%、间质11%、DS 10%；TR：肿瘤49%、DS 49%；BL：平滑肌43%、肿瘤32%、间质23%、炎症2%；BR：平滑肌71%、DS 14%、间质11%、肿瘤3%。加权IoU随k增加而升高；混合效应模型显示每张切片使IoU提高0.014（p<2×10⁻¹⁶），核心特异性斜率分别为TL 0.015、TR 0.019、BL 0.016、BR 0.013。饱和分别出现在TL 18张、TR 12张、BL 17张、BR 16张切片，最大加权IoU分别为0.49、0.64、0.68和0.50。类别特异性IoU与组织类别丰度相关（ρ=0.81，p=3.6x10⁻⁵），丰度高的类别重建效果最佳。结论：切片选取算法能够识别出可保留3D TME结构的小规模H&E切片子集，从而实现经济高效的ST分析。尽管重建保真度反映了组织类别丰度，但15-20张切片已足以捕获3D结构。未来工作将优化选取策略，并将选取的ST切片与交错的H&E切片整合，以增强3D重建。

查看英文原文 English abstract

Background: Three-dimensional spatial transcriptomics (ST) is essential for characterizing tumor-microenvironment (TME) heterogeneity but profiling every serial section is cost-prohibitive. Prior work has explored selecting subsets of sections for imputing ST at interleaved positions. However, these approaches do not identify which sections to profile to most efficiently preserve the 3D architecture. An optimal strategy must reinforce high-confidence inference in regions with similar composition while ensuring inclusion of morphologically distinct areas. We developed a computational framework to select histologic sections that optimally preserve 3D TME structure while minimizing the number requiring ST profiling. Methods: Four 3-mm FFPE colorectal cancer cores (two per patient) were collected to capture tumor-stroma interfaces and tertiary lymphoid structures. ~300 serial 5-µm H&E sections per core (~1500 µm depth) were imaged at 40x resolution; sections with sufficient tissue were retained (reported tissue loss at: TL 1150 µm, TR 350 µm, BL 1005 µm, BR 1100 µm). Retained sections were co-registered with VALIS. Eleven tissue classes-including tumor, desmoplastic stroma (DS), necrosis, smooth muscle, immune aggregates, and mucosa-were segmented using a graph neural network. For each section, we extracted Prov-GigaPath embeddings and tissue class area proportions. Pairwise similarities, incorporating histology features and z-distance, informed a facility location submodular optimization algorithm to select k representative sections (k=3-30). Selected subsets were used to reconstruct 3D TME structure via class-specific alpha-shape meshes and voxelization. Reconstruction fidelity was assessed using voxelized intersection-over-union (IoU) relative to full-section reconstructions. Results: Cores differed in composition-TL: normal mucosa 50%, smooth muscle 26%, stroma 11%, DS 10%; TR: tumor 49%, DS 49%; BL: smooth muscle 43%, tumor 32%, stroma 23%, inflammation 2%; BR: smooth muscle 71%, DS 14%, stroma 11%, tumor 3%. Weighted IoU increased with k; mixed-effects models showed each section improved IoU by 0.014 (p<2×10⁻¹⁶), with core-specific slopes TL 0.015, TR 0.019, BL 0.016, BR 0.013. Saturation occurred at TL 18, TR 12, BL 17, BR 16 sections, with maximum weighted IoUs of 0.49, 0.64, 0.68, and 0.50. Class-specific IoUs correlated with tissue class prevalence (ρ=0.81, p=3.6x10⁻⁵), with abundant classes reconstructing best. Conclusion: Section-selection algorithms can identify small subsets of H&E sections that preserve 3D TME structure for cost-efficient ST profiling. Although reconstruction fidelity reflects tissue class abundance, 15-20 sections sufficiently capture 3D architecture. Future work will refine selection strategies and integrate selected ST sections with interleaved H&E sections to enhance 3D reconstruction.

利益披露 Disclosure

M. Le, None.. J. Evans, None.. H. Kaur, None.. S. Lee, None.. V. Pujara, None.. A. Simmons, None.. S. Kang, None.. J. Mehta, None.. L. J. Vaickus, None.. K. Lau, None.. J. J. Levy, None.

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