Teruel Lab

Systems Biology Of Cell Signaling & Differentiation


Systems biology of cell differentiation: Understanding the logics of the regulatory circuit of an irreversible cell fate decision.

research_pic2Figure 1: Proliferating preadipocytes are differentiated into non-dividing adipocytes capable of accumulating lipid.

Differentiated human cells are continuously replaced due to age-related damage. Adipocytes (fat cells) make up 25% of our body mass and are replaced at a rate of ~10% per year. We are using adipocytes as a model system to understand the regulatory circuit of cell differentiation, not only because adipocyte-related diseases are on the rise, but also because adipocytes can be readily studied using powerful live-cell microscopy and quantitative mass spectrometry approaches. A main goal of our lab is to apply these approaches to answer the fundamental question of how cells build regulatory circuits to make reliable and irreversible cell fate decisions.

Our current results identified a system that combines multiple positive transcriptional and signaling feedbacks to make irreversible differentiation decisions at the single cell level. At the same time, we discovered that cells use stochastic expression noise in these feedbacks loops to allow organisms to regulate the fraction of precursor cells that differentiate over time to control long term body weight. Many fundamental questions remain to be addressed about the molecular steps in this fundamental process which integrate different signaling pathways, transcription factors and chromatin remodeling proteins.

Developing cutting edge microscopy and mass spectrometry tools to investigate cell differentiation

research_pic2Figure 2: Schematic of a triple-quadropole mass spectrometer

Our current work made it clear that an ultimate understanding of this central cell fate decision problem requires better ways to systematically explore regulatory circuits and that quantitative mass spectrometry as well as fluorescent microcopy provide an ideal combined strategy. We will use novel techniques that we are developing to gain critical insights into the design of feedback circuits and into the underlying molecular regulatory mechanisms.

We already identified specific technical challenges that, if resolved, will dramatically advance our work by allowing us to 1) systematic quantify changes in protein expression and modification that control feedbacks during differentiation, 2) to measure the kinetics of the formation of chromatin regulatory complexes as well as 3) to visualize the critical regulatory steps using developed fluorescent reporters for long term live single cell imaging of differentiation decisions.

Translating our Basic Science Discoveries into Treatments for Insulin Resistance, Diabetes, Cardiovascular Disease, and Cancer

research_pic2Figure 3: Ten percent of every adult human’s fat cells are replaced each year (Spalding et al, Nature 2010). What molecular mechanisms sense and set this constant rate of cell differentiation? Can we control this rate to treat obesity and metabolic disease?

Currently more than 25% of American adults have insulin resistance, a condition which results in a substantially increased risk of developing diabetes and cardiovascular disease, the leading cause of death in the US. Of particular concern, childhood obesity is becoming more prevalent in the US, and an estimated 60% of these obese children will go on to develop insulin resistance. However, currently there are no treatments available for pediatric patients, and even for adults, the few existing treatments are often not effective. New drugs and approaches are needed to treat insulin resistance.

We are now in a unique position to apply our in vitro findings of how differentiation is controlled into studies of mouse models of adipogenesis related diseases and also by investigating adipocyte tissue from human patients. We already showed that we can use our quantitative mass spectrometry approach to determine differences in key regulatory proteins in samples from mouse models and are currently extending this approach to human patient samples. Our goal is to use our new strategies to classify patients into different insulin resistance subtypes that may be responsive to patient specific effective drug treatments. This will allow us to translate the discoveries from our in vitro studies into improved therapies for insulin resistance, diabetes, cardiovascular disease, and the many fat-related cancers.