Ken S. Lau, Ph.D.

Ken S. Lau, Ph.D.

Assistant Professor of Cell and Developmental Biology

10475 MRB IV
2215 Garland Avenue
Nashville TN 37232
(615) 936-6859

Research Description

Goal
The central goal of my lab is to understand how inflammatory microenvironments affect epithelial cell behaviors in the context of human diseases, specifically, in inflammatory bowel diseases and colorectal cancer. Aberrant cell specification is now recognized to be a major factor in various intestinal diseases. For example, dysfunctional Goblet and Paneth cells in the intestine contribute to barrier defects, while progenitor-like colorectal cancer cells are prone to therapeutic resistance and metastasis. Devising ways to control these aberrant behaviors may lead to effective therapeutic strategies to combat these complex diseases. My lab is interested in understanding the signaling mechanisms that control the differentiating cell populations in the intestine (defined loosely as transit amplifying cells) as they become exposed to various in vivo environments. We want to characterize the degree of heterogeneity and plasticity in such populations, and to leverage such properties for reversing pathological behaviors. 

Approach
In live tissues, cells must integrate dynamic mixtures of environmental cues through their signaling networks to arrive at response decisions. To understand the multivariate problem of how the microenvironment interacts with cells, we use multiplex and high throughput experimental approaches to characterize the network states (numbers and types of cells, secreted protein factors, intracellular signaling, and transcriptional changes, over time) within in vivo tissue. Our model system is the intestine of the laboratory mouse, whose state is controlled by interactions between the epithelium, immune system, and microbiota very much like in human. We use the collected datasets over different experimental conditions to build data-driven mathematical models to quantitatively describe environment-cell input/output relationships and how these relationships are integrated over time to derive cellular outcomes. Because cell populations in in vivo tissues are not homogeneous, we will focus on generating data from single cells using techniques derived from flow cytometry and microscopy.