Stochastic Choice and Regulation
By combining new high-throughput techniques such as single cell RNA sequencing and genetic lineage tracing with quantitative data analysis and biophysical approaches, in our lab we aim to uncover the fundamental principles of cell differentiation and how cells acquire their identity during embryonic development. Our favourite model systems range from 2D differentiation towards each germ layer to cardioids and gastruloids.
Are you interested in uncovering gene regulatory network architectures mediating cell-fate differentiation? Can we quantify the gene-to-gene interactions necessary to establish the proper cell populations at the right time? Do you believe that cells' responses to external perturbations can be quantified?
Drop us an email at a.alemany[at]lumc.nl!
Cells differentiate as a result of the integration between extracellular signals and gene expression programs. A better understanding of the signaling events that cells experience during fate commitment (in embryonic development, but also disease context) will lead to more effective designs of in vitro differentiation protocols and drug therapies.
| single-cell RNA sequencing | Lineage tracing | Statistical physics |
With this in mind, we combine single-cell RNA sequencing technologies with genetic lineage tracing and statistical physics in order to develop methods to quantify the integration between extracellular signals and gene regulatory networks during cell differentiation.
Our ultimate goal is to understand how cells explore the Waddington landscape during cell-fate commitment decisions and translate our findings to improved regeneration therapies.