Engineered 3D tissue models for cell-laden microfluidic channels
Young S. Song, Richard L. Lin, Grace Montesano, Naside G. Durmus, Grace Lee, Seung-Schik Yoo, Emre Kayaalp, Edward Hæggström, Ali Khademhosseini, Utkan Demirci
http://www.springerlink.com/content/550231201lq76j78/fulltext.pdf
Introduction
An important part of tissue engineering technologies in areas such as organ transplantation and tissue regeneration are the 3D scaffolds on which cells are grown. These scaffolds need to be mechanically strong enough to support tissue growth but must also be porous enough to allow for the diffusion of fresh nutrients. This paper compares 1D perfusion and 2D perfusion models on agarose and aims to find the best geometry for the microfluidic channels that introduce nutrients by diffusion for highest cell viability. Theoretical models are compared to concentrations of nutrients and cell viabilities in actual 1D and 2D perfusion agarose cell scaffolds.
Summary
Fig. 1
The authors of the paper compared a 1D perfusion model with a 2D microchannel perfusion model. For the 1D model, 3T3 cells in agarose were mixed, deposited into a Petri dish, allowed to gel and covered with 3T3 medium. For the 2D perfusion model, one model included a single 300um microchannel through the center of a tube containing agarose-cell mixture and the second model included two microchannels of different separations and radii. The microchannels were constructed by letting the hot agarose gel around a small glass capillary tube. The tube was then pulled out enough to leave 1cm of the tube inside the agarose for flowing of media.
Fig. 2
Before the actual cell culture experiments were run, numerical simulations of normalized concentration were performed. The variables examined were the radius of the single microchannel in the 1D perfusion model (0.2mm to 0.8mm), radii of both microchannels in the 2D perfusion model (0.2mm to 0.8mm) and the distance between the two microchannels of the 2D perfusion model(2mm to 8mm) to determine the nutrient concentration distribution. The nutrient concentrations were normalized using the concentration at the top of the agarose.
Fig. 3
Theoretical analyses of the distribution of nutrients was also run across 3 days. Figure 3 A-C show contours of the distribution of nutrients at day 1 for the three different perfusion models. The contours suggest that the 2D perfusion model with dual channel is the best geometry to maintain high cell viability. Figure 3 D-F show theoretical normalized nutrient concentration for day 1, 2 and 3. Again, with the dual channel 2D perfusion model, the concentration distribution is the highest, allowing for cells as far away from the nutrient source as 5mm to be viable.
Fig. 4
To validate theoretical predictions, first the 1D perfusion model was tested with an initial cell viability in the Petri dish of 89%. As seen in Fig. 4 the cell viability decreased both with longer perfusion time and distance from the hydrogel surface. These results were consistent with theory. Cell viability was measured using a Live/Dead kit on 500um increments of slices of the hydrogel.
Fig. 5
After the 1D perfusion model, both of the 2D perfusion model conditions were tested on three consecutive days of culture and the viability at increasing distances from the perfusion channel was measured on each day. The results indicate that the dual channel configuration maintains cell viability over 80% after 3 days and up to 8mm away from the nutrient source while with the single channel configuration the cell viability drops to 50% after 3 days at the same distance. These results of different 3D tissue engineering approaches provide a theoretical and experimental understanding of nutrient diffusion in tissue culture for cell viability.
Discussion
While this paper compared simple 3D culture with 1D perfusion to 3D culture with 2D perfusion, it did not go far enough. Other configurations should have been tested, especially after the authors suggested that there is an ideal distance and configuration that would give the highest cell viability. In addition, cultures should have been tested after more than 3 days since the drop in viability may be significant. Experiments involving different concentrations could have been done to eliminate that as a factor. The theoretical simulations were close to half of the paper’s content and could have been combined into one figure to allow for space for more relevant experiments.
6 comments:
it seems like they assumed some symmetry when they counted the live cells (1 channel) as the data show a perfect symmetry distribution. How did they ensure the cells were evenly distributed across the culture? In figure 5c (single channel), it seems like the cell viability in the region very close to the channel was lower than the one next to it (i.e. ~0.9 mm vs. 1.9 mm). Assuming their theory was correct, this closer region should have higher cell viability (Figure 5d); did they address this?
Do you know why they choose to use agarose? Since agarose leads to non-adherent cells, perhaps another gel material, like collagen or matrigel, would have led to a more proliferative cell culture.
I notice that their 2D design is very similar to a hollow fiber bioreactor. They seem to be comparing a flat plate bioreactor to a hollow fiber one. The pros and cons of either design are rather well known, what is it about this design that makes it different. Also, they seem to have picked agarose over a alginate gel which is more commonly used. I just wonder why they wanted the cells to be less mobile.
that's pretty impressive, if they were able to create theoretical models of nutrient perfusion, and then verify it experimentally. usually parameters are hard to find or difficult to estimate, resulting in a theoretical model inconsistent with subsequent experimental results.
do you know how the cell viability percentages were calculated? were the green and red fluorescent intensities determined by machine or by eye, and was that intensity then converted to cell number, and divided by the initial cell count? if so, couldn't the cells have reproduced over the 3-day period? i think these numbers would be more reliable calculated using FACS.
The paper seems to gloss over the comparison of theoretical to experimental data. Was there any comment as to how great of a predictor the theoretical data was for experimental data? Also, since the theoretical and experimental data were deemed close enough, did future work include exploration of other geometries using experimental methods since it is less time-intensive?
The article focused on cell viability, but I wonder if there is a difference in the rate of cell growth between the 1D and the 2D model.
Also, after reading about how they designed their 2D perfusion models, I wonder how difficult it would be to create a simple 3D perfusion model with the same method (6 glass tubes in the hydrogel with one or two inlets and the rest as outlets). Exploring other designs might be interesting.
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