Predictive modeling of mammalian cell fate transitions over time and space with single-cell genomics

利用单细胞基因组学预测哺乳动物细胞命运随时间和空间转变的模型

• 批准号: 10572855
• 负责人: Xiaojie Qiu
• 金额: $ 8.5万
• 依托单位: WHITEHEAD INSTITUTE FOR BIOMEDICAL RES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10572855
• 关键词: 3-Dimensional; Address; Architecture; Atlases; Big Data; Biological; CRISPR/Cas technology; Caenorhabditis elegans; Cell Differentiation process; Cell model; Cells; Chromatin; Chromium; Collaborations; Cues; Data; Data Set; Development; Development Plans; Differential Equation; Digit structure; Embryo; Embryonic Development; Environment; Epigenetic Process; Future; Genetic; Genomics; Hematopoiesis; Hematopoietic; Hematopoietic stem cells; Hour; Human; Kinetics; Label; Learning; Link; Machine Learning; Maintenance; Mammalian Cell; Maps; Measurement; Measures; Mentors; Metabolic; Methods; Microscopy; Modeling; Modernization; Mus; Organic Synthesis; Organogenesis; Pancreas; Paper; Paracrine Communication; Pattern; Phase; Positioning Attribute; Predictive Factor; Proteomics; Publishing; RNA; Regenerative Medicine; Research; Research Personnel; Side; Signal Transduction; Statistical Data Interpretation; Sum; System; Systems Biology; Technology; Testing; Time; Tracer; Training; Universities; Work; biological systems; career development; deep learning; dynamic system; experimental study; field study; improved; in silico; in situ sequencing; in vivo; innovation; insight; kernel methods; learning strategy; machine learning algorithm; mathematical model; mouse model; multimodality; multiple omics; neural; neural network; novel strategies; prediction algorithm; predictive modeling; progenitor; research and development; simulation; single cell analysis; single cell technology; single-cell RNA sequencing; skills; spatiotemporal; stem cells; success; technology development; three-dimensional modeling; transcription factor; transcriptome; transcriptomics; vector

Project summary Despite remarkable advances in single-cell profiling, machine learning and systems biology, our ability to exploit these measurements is limited by the lack of an appropriate framework to model and analyze them. In this application, I propose an organic synthesis of experimental technological development, mathematical modeling, and machine learning algorithm innovations to move beyond conventional descriptive and merely statistical analyses of single cells to mechanistic and predictive modeling of cell fate transition over time and space, and across transcriptomic, epigenetic and proteomic levels. Firstly, in order to unveil the regulatory networks that govern the maintenance of stem cells and progenitors, I will extend the dynamo framework that published recently to predict key regulators that stabilize or destabilize cells states, e.g. the hematopoietic stem cell state, via sensitivity analyses of the reconstructed vector field. In addition, I will build upon the current success of predicting a broad range of hematopoietic cell fate transitions with our least action path approach to extend it to study other biological systems, such as pancreatic endocrinogenesis. To validate these predictions, I will continue my ongoing collaboration with Dr. Vijay Sankran’s lab (co-mentor lab) to first implemented metabolic labeling based scRNA-seq with the 10x chromium system and integrate it with perturb-seq that championed by the Weissman lab (my mentor lab) to test the predicted factors’ efficacy in maintaining the HSC state. Second, I will develop new approaches to seamlessly integrate multi-omics and harmonize short-term RNA velocities with long-term lineage tracing. By doing so, we can enable even more accurate modeling of single cell fate transitions that consider lineage-resolved, epigenetic, proteomic kinetics, offered by cutting-edge single-cell genomic technologies and cutting-edge deep learning methods. Lastly, I will take advantage of my early access of mouse embryogenesis dataset profiled with the powerful Stereo-seq through my close collaboration with BGI research to build 3D in silico spatiotemporally models of mammalian organogenesis. I will also train myself to study other state-of-the-art in-situ sequencing approaches, for example the STAR-map method from my collaborator, Dr. Xiao Wang from Broad. Through the K99 phase of this proposed career development plan, I will develop new computational toolkits and further strengthen my experiment skills, both in human hematopoiesis, Perturb-seq and spatial transcriptomics. When combining these new skills with my rigorous training in systems biology, and single cell genomics, I will be better prepared to transition into an independent investigator in a top-tier research university. Undoubtedly, my research and career development during both K99 phase and my transition to R00 phase will be greatly facilitated thanks to the excellent research environment in Whitehead institute, Broad and Harvard stem cell institute. To sum up, my proposed study will pave the road to launch my future interdisciplinary team that aims at building mechanistic and predictive models of cell fate transitions with a focus in human hematopoiesis.

项目摘要 尽管在单细胞分析、机器学习和系统生物学方面取得了显着进展，但我们利用 由于缺乏一个适当的框架来模拟和分析这些数据，这些测量受到限制。在这 应用，我提出了实验技术开发，数学建模， 和机器学习算法创新，超越传统的描述性和仅仅统计性 从单细胞分析到随时间和空间的细胞命运转变的机制和预测建模，以及 在转录组学、表观遗传学和蛋白质组学水平上进行研究。首先，为了揭开监管网络的面纱， 管理干细胞和祖细胞的维持，我将扩展发表的发电机框架， 最近，为了预测稳定或不稳定细胞状态，例如造血干细胞状态， 通过对重构矢量场的灵敏度分析。此外，我将在目前成功的基础上， 预测广泛的造血细胞命运的转变与我们的最小行动路径的方法，以扩大它， 研究其他生物系统，如胰腺内分泌发生。为了验证这些预测，我将 我继续与Vijay Sankran博士的实验室（共同导师实验室）进行合作， 使用10 x铬系统标记基于scRNA-seq，并将其与由 Weissman实验室（我的导师实验室）来测试预测因素在维持HSC状态方面的功效。二我 将开发新的方法来无缝整合多组学，并协调短期RNA速度， 长期追踪血统通过这样做，我们可以更准确地建模单细胞命运转变 考虑到谱系分辨、表观遗传、蛋白质组动力学，由尖端单细胞基因组学提供， 技术和尖端的深度学习方法。最后，我将利用我的抢先体验的鼠标 通过我与BGI研究的密切合作，使用强大的Stereo-seq分析胚胎发生数据集 来建立哺乳动物器官发生的三维计算机时空模型。我也会训练自己去学习其他的 最先进的原位测序方法，例如来自我的合作者Dr. 远大的小王。通过K99阶段的职业发展计划，我将开发新的 计算工具包，并进一步加强我的实验技能，无论是在人类造血，扰动序列 和空间转录组学当我将这些新技能与系统生物学的严格训练相结合时， 单细胞基因组学，我将更好地准备过渡到一个顶级研究的独立调查员 大学毫无疑问，我在K99阶段和向R 00过渡期间的研究和职业发展 由于怀特黑德研究所，布罗德和 哈佛干细胞研究所。综上所述，我的研究将为我未来跨学科的发展铺平道路 该团队旨在建立细胞命运转变的机制和预测模型，重点是人类 造血