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.

项目总结 尽管在单细胞图谱、机器学习和系统生物学方面取得了显著进展，但我们利用 由于缺乏适当的框架来对它们进行建模和分析，这些测量受到了限制。在这 应用，我提出了将实验技术开发、数学建模、 和机器学习算法的创新，超越了传统的描述性和仅仅是统计的 从单个细胞到机理的分析以及细胞命运在时间和空间上的预测模型，以及 跨越转录、表观遗传和蛋白质组水平。首先，为了揭示监管网络， 管理干细胞和祖细胞的维护，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的关键调节因素，例如造血干细胞状态， 通过对重构后的矢量场进行灵敏度分析。此外，我将在目前取得的成功的基础上， 用我们的最小作用路径方法预测广泛的造血细胞命运转变，以将其扩展到 研究其他生物系统，如胰腺内分泌。为了验证这些预测，我将 继续我与Vijay Sankran博士的实验室(共同导师实验室)的持续合作，以首次实施代谢 使用10倍铬系统标记基于scRNA-seq的序列，并将其与由 魏斯曼实验室(我的导师实验室)来测试预测的因素在维持HSC状态方面的有效性。第二，我 将开发新的方法来无缝集成多组学并使短期RNA速度与 长期的血统追踪。通过这样做，我们可以对单个细胞的命运转变进行更准确的建模 认为由尖端单细胞基因组提供的世系决定的、表观遗传的蛋白质组动力学 技术和前沿的深度学习方法。最后，我将利用我早期使用鼠标的优势 通过我与华大基因研究的密切合作，用功能强大的Stereo-seq分析了胚胎发生数据集 建立哺乳动物器官发生的三维电子时空模型。我也会训练自己去学习其他 最先进的原位测序方法，例如我的合作者Dr。 远大的小王。通过这项拟议的职业发展计划的K99阶段，我将开发新的 计算工具包，进一步加强了我的实验技能，既研究了人体造血，又扰动了序贯 和空间转录组。当将这些新技能与我在系统生物学方面的严格培训结合起来时， 单细胞基因组学，我将更好地准备过渡到顶级研究的独立研究员 上大学。毫无疑问，我在K99阶段和向R00过渡期间的研究和职业发展 由于怀特黑德研究所、布罗德研究所和 哈佛干细胞研究所。总而言之，我提出的研究将为我未来开展跨学科研究铺平道路 该团队致力于建立细胞命运转变的机械性和预测性模型，重点放在人类 造血术。