From intra to intercellular regulatory networks that define cell type identity

Project Abstract
Cell fate engineering, for example the directed differentiation of pluripotent stem cells or
the direct conversion among somatic cell types, holds great promise to improve disease
modeling, drug screening, and to lead to regenerative medicine therapies. However, our
ability to engineering cell fate with fidelity has been impeded by an incomplete
understanding of inter- and intracellular networks that govern differentiation, and by the
lack of adequate computational tools to distill accurate and testable hypothesis from the
mountains of data coming from single cell omics technologies. In the initial period of
funding under the MIRA, we started to address these challenges by developing novel
theoretical and computational methods to define cell type identity from single cell RNA-
Seq (scRNA-Seq) data with an emphasis on developmental cell types that emerge
during mesoderm development and subsequent commitment to lineages of the synovial
joint. As part of this work, we generated scRNA-Seq data of the developing synovial
joint, we adopted a pluripotent stem cell-to-chondrocyte differentiation protocol, and we
invented a generally applicable platform for assessing cell type identity at the single cell
level of resolution. We also developed a computational method to infer dynamic
regulatory networks accurately and to integrate them with signaling pathways. Now, we
propose to address the following unanswered questions and unmet challenges. First,
we will substantially improve and extend our computational methods that assess the
outcomes of cell fate engineering by extending them to more data types, and thus
increasing the comprehensiveness of its results, and by predicting not only cell identity
but function. Second, we will substantially improve and extend our regulatory network
tools so that their predictions are statistically calibrated and so that they can be applied
to chromatin accessibility and expression data simultaneously with the intention of
discovering binding site motifs of orphan transcription factors. Third, we will devise and
experimentally test computational methods to generate reliable cell fate engineering
recipes that account for not only transcriptional networks but also how signaling
pathways inform them, and that account for temporal dynamics. Finally, we will follow
up on observations from applying our dynamic network tool to in vitro gastrulation that
indicates that some signaling pathways influence differentiation more by re-wiring
network topology than by directly impacting expression of effector target genes.
Collectively meeting these goals will help to make cell fate engineering more reliable
and controllable, and it will shed light on how signaling pathways and intracellular
regulatory networks interact during development.

Grant Number: 5R35GM124725-09
NIH Institute/Center: NIH
Principal Investigator: Patrick Cahan