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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要尽管单细胞分析，机器学习和系统生物学取得了显着进步，但我们的利用能力这些测量值受到缺乏适当的框架来建模和分析它们的限制。在这个应用，我提出了实验技术发展，数学建模的有机综合，和机器学习算法创新，超越了传统的描述性和统计分析单细胞对随时间和空间的机械和预测模型的分析，以及跨转录组，表观遗传和蛋白质组学水平。首先，为了揭示监管网络控制干细胞和祖细胞的维护，我将扩展发表的发电机框架最近，要预测稳定或破坏细胞状态的关键调节剂，例如造血干细胞状态，通过重构矢量场的灵敏度分析。此外，我将基于目前的成功通过我们最少的动作路径方法来预测广泛的造血细胞脂肪转变，以扩展到研究其他生物系统，例如胰腺内分泌生成。为了验证这些预测，我将继续我与Vijay Sankran博士（Co-Incertor Lab）进行的持续合作，以首次实施代谢基于10倍铬系统标签的SCRNA-Seq，并将其与withurb-seq集成在一起魏斯曼实验室（我的心理实验室）测试了预测因素维持HSC状态的效率。第二，我将开发新的方法，以无缝整合多词并将短期RNA速度协调长期谱系跟踪。通过这样做，我们可以实现更精确的单细胞脂肪过渡的建模考虑了尖端单细胞基因组提供的谱系分辨，表观遗传学，蛋白质组学动力学技术和最先进的深度学习方法。最后，我将利用我早期访问鼠标的优势胚胎发生数据集通过我与BGI研究的密切合作与强大的立体声隔板进行了介绍在计算机空间中构建3D的哺乳动物器官发生模型。我还将训练自己学习其他最先进的原位测序方法，例如我的合作者Dr.的Star-MAP方法王的小王。通过该提议的职业发展计划的K99阶段，我将开发新的计算工具包，并进一步增强我的实验技巧和空间转录组学。在将这些新技能与我在系统生物学方面的严格培训相结合时，单细胞基因组学，我将更好地准备在顶级研究中过渡到独立研究者大学。毫无疑问，我在K99阶段的研究和职业发展以及向R00的过渡由于Whitehead Institute，Broad和哈佛干细胞研究所。总而言之，我拟议的研究将为启动我未来的跨学科的道路铺平道路旨在建立细胞脂肪过渡的机械和预测模型的团队，重点是人类造血。