基于单细胞表征实现开放式生物学发现
Operationalizing open-ended biological discovery across single-cell representations
AI总结：
PROSPECTor框架可在单细胞常规表达表征与基础模型嵌入中搜索可重复生物学结构，经检验其提名的假设在独立队列中可转移或重现，为开放式生物学发现提供可审计的端到端工具。
AI中文摘要：
单细胞研究通常从预先定义的研究问题启动，导致现有数据中编码的大量生物学信息未被挖掘。我们将开放式发现形式化为一种分析范式，即先识别数据衍生的信号，再探究其生物学背景，随后根据其证明前瞻性实验投入的潜力进行评估。在此，我们开发了PROSPECTor，这是一个端到端框架，可在常规表达表征和各类基础模型嵌入中搜索可重复的生物学结构，将稳健信号转化为可定量检验的候选假设。投影到未见数据集可评估其泛化性和表型关联，为值得前瞻性验证的候选者提供可扩展筛选。经支持的信号来自不同表征空间和搜索策略。随后在独立生物学场景中检验PROSPECTor提名的假设：成纤维细胞细胞外基质程序可转移至具有干预背景的独立小鼠队列，而患者解析的胃癌T细胞程序在单细胞、 bulk及空间队列中重现。PROSPECTor建立了一个可审计框架，用于在不断扩展的表征空间中系统地重新审视单细胞数据集，将回顾性集合转化为可激发新研究问题的生物学发现前瞻性资源。
英文摘要：
Single-cell studies are typically initiated from predefined research questions, leaving much of the biological information encoded within existing data unexplored. We formalize open-ended discovery as an analytical paradigm, in which data-derived signals are identified before biological context is interrogated and subsequently evaluated according to their potential to justify prospective experimental investment. Here we develop PROSPECTor, an end-to-end framework that searches for reproducible biological structures across conventional expression representations and diverse foundation-model embeddings, translating robust signals into quantitatively testable candidate hypotheses. Projection into unseen datasets then evaluates their generalizability and phenotype association, providing a scalable screen for candidates that warrant prospective validation. Supported signals emerged from different representation spaces and search strategies. PROSPECTor-nominated hypotheses were then examined in independent biological settings: fibroblast extracellular-matrix programmes demonstrated transferability to an independent mouse cohort with an intervention context, while a patient-resolved gastric-cancer T-cell programme recurred across single-cell, bulk and spatial cohorts. PROSPECTor establishes an auditable framework for systematically revisiting single-cell datasets across expanding representation spaces, turning retrospective collections into prospective resources for biological discovery that can motivate new research questions.
