Back

Micro-biogeography greatly matters for competition: Continuous chaotic bioprinting of spatially-controlled bacterial microcosms

Carlos Fernando Ceballos-Gonzalez; Edna Johana Bolivar-Monsalve; Diego Alonso Quevedo-Moreno; Li Lu Lam-Aguilar; Karen Ixchel Borrayo-Montano; Juan Felipe Yee-de Leon; Yu Shrike Zhang; Mario Moises Alvarez; Grissel Trujillo-de Santiago

2020-07-12 bioengineering
10.1101/2020.07.12.199307 bioRxiv
Show abstract

Cells do not work alone but instead function as collaborative micro-societies. The spatial distribution of different bacterial strains (micro-biogeography) in a shared volumetric space, and their degree of intimacy, greatly influences their societal behavior. Current microbiological techniques are commonly focused on the culture of well-mixed bacterial communities and fail to reproduce the micro-biogeography of polybacterial societies. Here, fine-scale bacterial microcosms are bioprinted using chaotic flows induced by a printhead containing a static mixer. This straightforward approach (i.e., continuous chaotic bioprinting) enables the fabrication of hydrogel constructs with intercalated layers of bacterial strains. These multi-layered constructs are used to analyze how the spatial distributions of bacteria affect their social behavior. Bacteria within these biological microsystems engage in either cooperation or competition, depending on the degree of shared interface. Remarkably, the extent of inhibition in predator-prey scenarios increases when bacteria are in greater intimacy. Furthermore, two Escherichia coli strains exhibit competitive behavior in well-mixed microenvironments, whereas stable coexistence prevails for longer times in spatially structured communities. Finally, the simultaneous extrusion of four inks is demonstrated, enabling the creation of higher complexity scenarios. Thus, chaotic bioprinting will contribute to the development of a greater complexity of polybacterial microsystems, tissue-microbiota models, and biomanufactured materials.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.