How tissues self-construct

The process of development is an astounding journey from simplicity to complexity.  You start with a single cell, the fertilized egg, and you end up with a complete multicellular organism, made up of tissues that self-organize from many individual cells of different types.  The question of how cells know who to be and where to go has many layers to it, starting with the question of how you lay down the basic body plan (head here, tail there, which side is left and where does the heart go?) and continuing on down to microscopic structures, with questions such as how and where to form the small tubes that will allow blood to permeate through apparently solid tissues.  This kind of self-organizing behavior is deeply interesting to robotics researchers (who would love to copy it) and tissue engineers (who would like to manipulate it).

A recent paper (Parsa et al. 2011. Uncovering the behaviors of individual cells within a multicellular microvascular community.  PNAS doi:10.1073/pnas.1007508108) takes a close look at self-organization on the micro level.  It turns out that if you take human endothelial cells and put them in a soft gel, they will spontaneously move around and form small tubes.  Parsa et al. tracked the behavior and morphology of individual cells from the moment they were seeded into the gel to the point when they have formed a connected network that will eventually turn into capillary-like structures.  This wasn’t an easy task: the cells move in three dimensions in the gel, the gel itself can shrink over time, and each cell is making many contacts with other cells; in many cases the cells are literally crawling over each other. To help track individual cells, they made a mixed population of cells that were labeled with 6 different combinations of fluorescent dyes, so that they had a good chance of being able to distinguish two neighboring cells using color.  And to shape the gel in a way that gives reasonable optical imaging they designed a PDMS mold with a removable cap that was used to flatten the gel’s top surface.

Once the authors could track the movement and shapes of individual cells, they were able to define 5 general stages that most cells go through: rearrangement and aggregation, in which the cells move around to variable degrees; spreading, in which the area the cell covers increases; elongation and formation of a polygonal network of cell-cell contacts; stabilization of the network; and reorganization.  They find that while all cells undergo the same types of behaviors — moving, spreading, making contacts — there are clear differences in the behavioral patterns of different cells.  For example, on average cells increase in area early on, before the network begins to form: after making some contacts, the cells elongate and the area that they cover shrinks.  But the average behavior is very misleading, because no individual cell follows the average trajectory.  Some cells spread early, others late.  Some move quickly through the gel at first and then slow down, others do the opposite. Parsa et al. next assigned numbers to the different characteristics (movement, size, shape, direction, branching, etc.) each cell showed at different time points during the formation of the network, amassing a set of 3,641 measurements on over 300 individual cells.

Despite the tremendous variability in the paths followed by individual cells, the authors hoped to find patterns in their data that might provide insight into how the network forms.  And luckily, the patterns were there to find.  Using a clustering algorithm, they identified groups of cells that behaved similarly to each other with respect to specific sets of behavioral parameters.  For example, looking at the pattern of how the area of a cell grows and shrinks allowed the authors to define three major clusters of cells that accounted for about 2/3 of the cells in their study.  In the same way, they could define subsets of cells that moved through the gel in similar ways.  Although these clusters are rather broadly defined, they seem to be telling us something important about differences between the cells in the different subsets; the subset of cells that spread early (with areas showing a peak at 60 or 120 minutes) are more likely to end up as connection points in the network, while the cells that spread late (300 minutes) tend to end up as branches between the connection points.

What causes this bias in cell fate?  It doesn’t obviously correlate with the environment the cell finds itself in to start with.  Does the behavior of a cell determine its eventual decision to become a node or a branch? Or is there some difference between these groups of cells at the beginning of the experiment, which drives both the behavioral pattern and the eventual fate?  To gain some insight into causality, the authors forced a change in cell behavior using a myosin II inhibitor (blebbistatin, a compound discovered by the Mitchison lab), together with an inhibitor of Rho-associated kinase.  These drugs affect the interactions of cells with the gel, by inhibiting the formation of stress fibers and focal adhesions — though they undoubtedly have other effects as well.  When treated cells are mixed with normal cells, the treated cells are biased towards a particular pattern of dynamic behavior, and disproportionately end up taking the role of network node. Which tends to suggest that the behavior is important in helping to determine fate, and that fates can be changed by manipulating behavior.

Being able to make quantitative measurements of the behaviors of individual cells as they interact and make decisions about how to cooperate opens all kinds of doors.   For example, it’ll be interesting to see what happens when the cells are treated with drugs that modulate pathways involved in angiogenesis (such as the VEGF pathway); and one can imagine using changes in cell behavior as a way to screen for new drugs or growth factors that affect endothelial cell behavior.  And soon it may be possible to look at what’s happening inside the cells at the same time as watching how they behave.  Another exciting window on the wonderful world of biology.

Parsa H, Upadhyay R, & Sia SK (2011). Uncovering the behaviors of individual cells within a multicellular microvascular community. Proceedings of the National Academy of Sciences of the United States of America, 108 (12), 5133-8 PMID: 21383144