Printing and Characterizing RNA Gradient Generators

Living organisms detect and produce gradients for a variety of different tasks, including morphogenesis^1,2, chemotaxis^3,4, and distributed computation⁵. Synthetic gradient generators have been developed to recreate the spatially heterogenous chemical environments seen in biological systems. Some previously developed methods for gradient generation include using droplet mixers, hydrogel secretion, membrane channels, and pipette injectors⁶. However, these methods generally create gradients that are difficult to reproduce, are transient (lasting no more than 2 hours), introduce convection, or are difficult to model. Stable gradient generators have been reliably produced using microfluidic convection-based microchannels⁷ which produce gradients using a replenishing reservoir of a source and sink. However, convection-based microchannel gradients are difficult to integrate into multiple independently produced gradients capable of interacting with each other within the same chamber. Additionally, the gradient generator developed produces RNA, which can be coupled to fluorescent reporters and to control downstream dynamic processes^8,9. To build a tunable RNA gradient generator, we anchor transcription templates within hydrogel posts photopatterned within a microfluidic flow cell. These posts act as a chemical source, constantly producing RNA. The RNA diffuses out of the hydrogel posts where it is degraded in solution. We use T7 RNAP and RNase A/T1 to drive local RNA transcription and global degradation, respectively. Tuning the location and size of the hydrogel posts, along with T7 RNAP and RNAse A/T1 concentrations, enables the building of a tunable set of RNA gradients. To measure these gradients, we use hydrogels with attached “reporter” complexes that react with the RNA and produce a fluorescent signal. Multiple photopatterned source hydrogels could also be used to form more complex additive gradients, more closely recreating environments in cell populations². This platform makes it possible to direct local self-assembly of nucleic acid responsive nanostructures and nanoparticles, and for the study of complex microenvironments in cell cultures.<br/><br/><br/>Bibliography<br/>1. Ellison, D. <i>et al.</i> Cell–cell communication enhances the capacity of cell ensembles to sense shallow gradients during morphogenesis. <i>PNAS</i> <b>113</b>, E679–E688 (2016).<br/>2. Grant, P. K. <i>et al.</i> Interpretation of morphogen gradients by a synthetic bistable circuit. <i>Nat Commun</i> <b>11</b>, 5545 (2020).<br/>3. Alon, U., Surette, M. G., Barkai, N. & Leibler, S. Robustness in bacterial chemotaxis. <i>Nature</i> <b>397</b>, 168–171 (1999).<br/>4. Sourjik, V. & Wingreen, N. S. Responding to chemical gradients: bacterial chemotaxis. <i>Current Opinion in Cell Biology</i> <b>24</b>, 262–268 (2012).<br/>5. Alberghini, S. <i>et al.</i> Consequences of relative cellular positioning on quorum sensing and bacterial cell-to-cell communication. <i>FEMS Microbiology Letters</i> <b>292</b>, 149–161 (2009).<br/>6. Somaweera, H., Ibraguimov, A. & Pappas, D. A review of chemical gradient systems for cell analysis. <i>Analytica Chimica Acta</i> <b>907</b>, 7–17 (2016).<br/>7. Hosokawa, M. <i>et al.</i> Microfluidic Device with Chemical Gradient for Single-Cell Cytotoxicity Assays. <i>Anal. Chem.</i> <b>83</b>, 3648–3654 (2011).<br/>8. Matsuura, S. <i>et al.</i> Synthetic RNA-based logic computation in mammalian cells. <i>Nat Commun</i> <b>9</b>, 4847 (2018).<br/>9. English, M. A. <i>et al.</i> Programmable CRISPR-responsive smart materials. <i>Science</i> <b>365</b>, 780–785 (2019).