A framework to integrate live-cell imaging with single-cell sequencing and learn how cells adapt to new environments

将活细胞成像与单细胞测序相结合并了解细胞如何适应新环境的框架

• 批准号: 10337650
• 负责人: Noemi Andor
• 金额: $ 9.78万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10337650
• 关键词: Adjuvant Therapy; Alzheimer' s Disease; Bar Codes; Benchmarking; Biological; Biological Markers; Biotechnology; Cancer cell line; Cell Count; Cell Cycle; Cell Cycle Progression; Cell Line; Cell Lineage; Cells; Clinical; Clone Cells; Complement; Cytoskeleton; Cytostatics; DNA Damage; Data; Development; Diploidy; Disease; Drug Exposure; Drug Screening; Environment; Evolution; Feasibility Studies; Follow-Up Studies; Future; Gene Expression Profile; Generations; Genetic; Genetic Transcription; Genome; Genomics; Goals; Growth; Growth Factor; Heterogeneity; Hour; Human Genome Project; Image; In Vitro; Knowledge; Learning; Libraries; Light; Link; Malignant Neoplasms; Maps; Measurement; Measures; Medicine; Methods; Monitor; Nature; Nutrient; Outcome; Oxygen; Pharmaceutical Preparations; Phase; Phenotype; Population; Preparation; Primary Neoplasm; Process; Proteome; Quality Control; Research; Resolution; Speed; System; Testing; Therapeutic; Time; Time trend; Training; Work; base; cancer cell; cell age; cell injury; cellular imaging; clinical practice; clinically relevant; deep learning; deep neural network; design; drug candidate; epigenome; expectation; experience; fitness; gastric cancer cell; imaging platform; improved; in silico; in vivo; innovation; live cell imaging; pre-clinical; prediction algorithm; public health relevance; repaired; response; single cell sequencing; single-cell RNA sequencing; stressor; success; transcriptome; trend; tumor; tumor heterogeneity; virtual

Abstract Exposing cancer cells to a new environment typically influences their growth. For some cells, a moderate growth inhibition is followed by adaptation and return to normal growth rates. Others initially experience a near-complete cytostatic phenotype, only to explode in their growth during later generations, reaching growth rates well beyond baseline. This implies that one can reach opposite conclusions about the relative fitness of two cell lineages, solely depending on timing of measurement. This has implications for the time window of therapeutic success, in light of the fact that virtually all pre-clinical drug screening studies measure growth rate at a single, well defined timepoint – typically 72 hours after exposure. Despite this shortcoming, measurements of cell fitness at multiple timepoints are impractical for large-scale studies. Our long-term goal is the development of a new class of temporal biomarkers that extrapolate from a cell's transcriptome how fit its descendants will be over multiple generations. As a next step towards this goal we propose a feasibility study to collect training data of su!cient temporal reach and cellular resolution to evaluate the predictability of cell fitness. With a broad record of integrating various omics- and imaging platforms and as the developers of widely deployed drug response metrics, our team brings complementary expertise to integrate live-cell imaging with single cell sequencing for deep learning. We will record how cells divide, migrate and die, linking the recorded phenotypic di"erences between cells to di"erences between their transcriptomes. Aim 1 will use live-cell imaging to characterize the cell cycle of cancer cell clones as they adapt to new environments. Hereby we define a growth condition as the combination between founding cell and micro-environment. We hypothesize that time emphasizes di"erences between growth conditions, i.e. that cell cycle progression profiles from distinct growth conditions diverge as their cells converge on a specific path of adaptation. This temporal evolution of cell adaptation will inform which generation is optimal for single-cell RNA sequencing, namely when cell counts are su!ciently high, but the path of adaptation is not yet phenotypically evident. In aim 2 we will test the potential of the transcriptome to predict this path. To achieve this we will use a three-layered approach to map sequenced- and imaged cells in-silico. Hereby biological variability – emerging from multiple growth conditions – acts as an additional barcode during sequencing. This linking will not only match the sequenced cell's transcriptome to the one phenotype of the corresponding imaged cell, but also to adaptive phenotypes of all its ancestors. The outcome of these two aims will be training data to learn: (i) whether a snapshot of a transcriptome has the potential to forecast speed and success rate of cell cycle progression; (ii) the temporal limitations of such predictions and (iii) how much more data will be needed to train a deep neural network to make such forecasts. Integration with live-cell imaging opens the door to truly leverage the suitability of single cell sequencing for deep learning in a new way – not for solving technical challenges like segmentation and tracking, but for interpretation of genomic information. We aim for our e"orts to reduce the one-order-of-magnitude temporal chasm between in-vitro cancer cell adaptations and the time required for clinically relevant phenotypes to emerge. 2

摘要 将癌细胞暴露在新的环境中通常会促进其生长。对某些细胞来说， 抑制之后是适应和恢复正常生长速率。其他人最初经历了一个近乎完整的 细胞生长抑制表型，只有在他们的生长爆炸在后代，达到增长率远远超过 基线。这意味着人们可以得出关于两种细胞谱系的相对适合性的相反结论， 这取决于测量的时间。这对治疗成功的时间窗口有影响， 事实上，几乎所有的临床前药物筛选研究都是在一个明确定义的时间点测量生长率 - 通常在暴露后72小时。尽管有这个缺点，在多个时间点测量细胞适合性 对于大规模的研究来说是不切实际的。我们的长期目标是开发一类新的时间生物标志物 从细胞的转录组推断其后代在多代中的适应程度。作为下一个 为了实现这一目标，我们提出了一个可行性研究，以收集训练数据的苏！古时间范围和细胞 分辨率来评估细胞适合性的可预测性。在整合各种组学和成像方面有着广泛的记录， 作为广泛部署的药物反应指标的开发者，我们的团队带来了互补的专业知识， 将活细胞成像与单细胞测序相结合，用于深度学习。我们将记录细胞如何分裂，迁移， 死亡，将细胞间记录的表型差异与其转录组间的差异联系起来。目标1将 使用活细胞成像来表征癌细胞克隆适应新环境时的细胞周期。特此 我们将生长条件定义为基础细胞和微环境之间的组合。我们假设 时间强调生长条件之间的差异，即细胞周期进程来自不同的生长 当它们的细胞聚集在一条特定的适应路径上时，条件就会发生变化。细胞适应的这种时间演变将 通知哪代对于单细胞RNA测序是最佳的，即当细胞计数是sun时！最近很高，但 适应的途径在表型上还不明显。在目标2中，我们将测试转录组预测 为了实现这一点，我们将使用三层方法来在计算机上映射测序和成像的细胞。特此 生物变异性--来自多种生长条件--在测序过程中充当额外的条形码。这 连接不仅将测序细胞的转录组与相应成像细胞的一种表型相匹配， 也与所有祖先的适应性表型有关。这两个目标的结果将是训练数据以学习： 转录组的快照是否具有预测细胞周期进展的速度和成功率的潜力;（ii） 这种预测的时间限制以及（iii）需要多少数据来训练深度神经网络 做出这样的预测。与活细胞成像的集成为真正利用单细胞成像的适用性打开了大门。 以一种新的方式对深度学习进行排序-不是为了解决分割和跟踪等技术挑战，而是为了 基因组信息的解释。我们的目标是努力减少一个数量级的时间鸿沟 体外癌细胞适应性与临床相关表型出现所需的时间之间的关系。 2