PREreview of WAChRs are excitatory opsins sensitive to indoor lighting
- Published
- DOI
- 10.5281/zenodo.17889080
- License
- CC BY 4.0
Summary
This study reports the discovery and characterization of a class of highly light-sensitive, excitatory opsins termed “WAChRs” that were engineered from the potassium-selective channelrhodopsin WiChR. WAChRs were found to respond to irradiances as low as 15 nW/mm2, which are achievable even with indoor ambient office light and at least an order of magnitude more sensitive than previously reported opsins. The authors adapted a commercially available automated patch-clamp system to enable high-throughput measurements of light-evoked currents in mammalian cells across 7 different wavelengths. They used this platform to characterize the optogenetic performance of a small set (~20) of known opsins, which uncovered that a F240A mutation that converts WiChR from K+-selective to more broadly cation permeable results in a highly light-sensitive, excitatory opsin dubbed “WAChR”. They then used this dataset in conjunction with prior work in the literature to train protein language models to suggest mutations in WAChR to improve functional properties such as activation threshold, amplitude, and speed. The authors characterized these variants using their high-throughput optogenetic automated patch clamp, and further characterized a subset using manual patch clamp to demonstrate the enhanced sensitivity of WAChRs relative to existing opsins. Finally, the authors expressed WAChRs in vivo in the mouse brain and demonstrated that WAChR evokes a larger increase in firing rate in response to light compared to a state-of-the-art opsin, ChRmine. Overall, this study presents a valuable new tool that represents an improvement over existing opsins. However, the study could be strengthened by characterizing existing opsins with the same trafficking tags and promoters as WAChRs to address potential mechanisms by which WAChRs outperform existing tools. Nevertheless, the comprehensive benchmarking of a large set of opsins represents an important resource for the field, and the novel opsins presented in this work should help unlock new avenues for both basic research and translational applications.
Major points
1. Figure 5: The authors note that ex3mV1Co and CoChR-3M were not fused with any signal peptides, whereas other opsins were fused with the standard GFSE cassette. While the authors justify this difference by noting that prior work did not use these peptides, it would be helpful if the authors could also characterize these two opsins with the same GFSE fusion to determine the extent to which trafficking of the opsins accounts for differences in the threshold and other properties. This would be particularly useful for ex3mV1Co where the threshold, sensitivity, and amplitude are closer to those of the WAChRs (Fig. 5B).
2. Figure 7: For in vivo experiments, the authors drive expression of WAChR using the EF1a promoter but drive expression of ChRmine using the CAG promoter. In the Methods, the authors recognize that this is less than ideal, but the in vivo results would be more compelling and thorough if the authors also assessed the performance of AAV-EF1a-ChRmine. Moreover, to determine the extent to which the observed increase in firing for WAChR relative to ChRmine may be attributed to differences in expression, the authors should consider performing a quantitative assessment of relative expression levels of each opsin for both AAV-CAG-ChRmine and AAV-EF1a-WAChR-m, either through fluorescence or Western blotting.
Minor points
1. Figure S1: The text indicates that the cleavable LucyRho (LR) tag was prepended in some cases to enhance surface expression. Could the authors clarify the experiments/constructs in which the LR tag was used?
2. Figure 2: The sample size for WiChR F240A is listed as n = 2, whereas the sample size for ChRmine is n = 39. The interpretability of these data would be improved if the authors could comment on why the sample size for WiChR F240A is so low, as well as any other variants for which the sample size is low (assuming this is not a typo). For instance, does this reflect variability in expression/toxicity, or is this a result of some aspect of the manual patch clamp platform?
3. Figure 5/S6: In Fig. S6, it appears from the representative images of opsin expression that AAV-CoChR-3M may be expressed at a lower level compared to the other opsins. It would be helpful if the authors could provide quantification of expression level (either based on fluorescence or Western blot) for the opsins in Figs. 5 and S6 to better assess any impacts of expression level on the patch clamp results.
4. Figure S6C: There may be a typo on the label for the P2A construct in the figure, which does not currently match what is written in the figure caption (GFS vs GSE). Could the authors double check the figure and correct the label if necessary?
5. Figure 7A: Most of the rest of Figure 7 presents data directly comparing ChRmine with WAChR-m, so the authors may consider moving Fig. 7A to the Supplementary Information to enhance consistency.
6. Figure 7C: The representative raster plots seem to suggest that there may be a higher level of baseline firing in AAV-CAG-ChRmine compared to AAV-EF1a-WAChR-m, which may not be captured by the Z-score metrics in Figs. 7D, S9, and S10. If this is a broad trend across all recorded units for ChRmine, it would be helpful for the authors to mention this in the text and provide some discussion for why this might be the case. If instead this is a feature of the specific unit selected, then it would help with clarity for the authors provide representative units for ChRmine and WAChR-m that are more closely matched in baseline firing. Additionally, displaying more of the pre-stimulation window in the data plotted in Fig. 7C would also allow for better visualization of baseline firing.
Competing interests
The authors declare that they have no competing interests.
Competing interests
The authors declare that they have no competing interests.
Use of Artificial Intelligence (AI)
The authors declare that they did not use generative AI to come up with new ideas for their review.