Steven Altschuler, Ph.D., and Lani Wu, Ph.D.
It is often traditional to think of cells in specific tissues in the body as if they were a single type; for example, we speak of liver cells, fat cells, muscle cells in the case of solid tissue, lymphocytes or red blood cells in the case of circulating cells, and cancer cells in the case of disease states.
But, much recent research now argues that cells have individual personalities that make each of them behave in slightly different ways. How does the individuality of cells within tissues (whether normal or cancerous) contribute to tissue function and how then should we speak of models for “a cell type” if the cell type is actually an amalgamation of different cell subtypes?
Most impressively, this work has led to the finding that specific subsets of cells use their individuality to respond differently to drugs, a result that might begin to explain why the effectiveness of treatments can vary from person to person.
This research is especially exciting since it turns out that there are recognizable signatures in the profiles of single cells that can serve to predict their responses, a discovery that might lead to practical ways to personalize medical treatment.
The Altschuler/Wu Lab brings together a unique combination of experimental, computational, and mathematical approaches to understand the origins and meaning of individuality in cells. This research program has led to powerful new computer methods for automated analysis of microscopic images that for the first time begin to quantify the individuality of normal and cancer cells.
The Altschuler/WuLab has recently developed an approach called “PhenoRipper” that enables rapid exploration and interpretation of microscopy data. This software suite is available for the research community and will vastly simplify and accelerate the process of analyzing how cellular phenotypes change under perturbations.
Cells receive and process information about their external environment through large networks of proteins that are collectively called the “signaling machinery.”
These networks are truly complex; they contain tens of hundreds of enzymes and other proteins that all work somehow together to make the cell respond appropriately to external cues.
One example is the network of signaling proteins that contain immune cells in our bloodstream (called neutrophils) used to migrate towards regions of inflammation. The neutrophils sense molecules associated with inflammation and move in the direction where they are more concentrated, a process which is called “chemotaxis.” This process is critical in our ability to fight off foreign agents such as bacteria.
The Altschuler/Wu laboratory uses a combination of detailed imaging studies of migrating cells with computer modeling of the chemotaxis machinery to deduce the key chemical reactions that cells use to decide the direction of motion.
The key discovery in this research program to date is the finding that despite the large number of components and reactions involved in chemotaxis, the basic decision by the cell of choosing a direction to go is guided by a very simple core set of reactions whose action can be made mathematically clear. How does the chemotaxis machinery work to make the neutrophil move in the right direction? Answers to this question require a deep understanding of the cascade of signal processing reactions that connect the initial detection of inflammatory molecules to a complex machinery that cells use to physically move.
The recognition of such simplicity within the vast apparent complexity of the signaling machinery is a rare insight, and inspires confidence that the Altschuler/Wu laboratory might now develop general strategies for breaking down complex signaling networks into simpler units that can be understood. If so, this would represent a real breakthrough in understanding how cells work.