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