Original data of the paper Temporal integration of auxin information for the regulation of patterning (eLife, May 7, 2020; doi.org/10.7554/eLife.55832)
Plants, like most multicellular organisms, rely on molecular signals to control the architecture of their bodies — determining when and where new organs are produced. In plants, a hormone called auxin plays a central role in this process: it accumulates at precise locations within the shoot apical meristem (SAM), a small group of stem cells at the growing tip, to trigger the formation of new leaves and flowers in the characteristic spiraling arrangements observed in many species.
Although auxin had long been recognized as a key positional signal, the precise dynamics of its distribution in space and time remained inaccessible. In this study, we combined a quantitative ratiometric auxin biosensor (qDII-VENUS), live confocal imaging, and a purpose-built computational analysis pipeline to map auxin with high spatiotemporal resolution in Arabidopsis thaliana shoot apical meristems.
The analysis of dozens of meristems reveals that auxin distribution follows a remarkably precise and reiterative spatial pattern. Time-lapse imaging further shows that auxin maxima are not static: they move centrifugally through the tissue as rhythmic waves, traveling faster than tissue growth. As a result, individual cells experience a rapidly changing auxin signal rather than a sustained local maximum. Functional experiments demonstrate that this temporal dimension is not merely incidental — cells require exposure to elevated auxin over time to activate the transcriptional response associated with organ initiation. Taken together, these findings reveal that auxin encodes positional information not only in space but also in time, providing a mechanism that contributes to the rhythmic, self-organizing nature of organ patterning at the shoot apex.
Raw confocal image stacks (CZI format, Zeiss) from the Arabidopsis thaliana transgenic lines used in this study (qDII-pCLV3-pDR5 and qDII-pCLV3-PIN1) are deposited on Zenodo: doi:10.5281/zenodo.3737795. The image analysis pipeline, sam_spaghetti (SAM Sequence Primordia Alignment, GrowtH Estimation, tracking & temporal indexation), developed alongside this paper, is available on GitHub and on the Inria GitLab. The processed image datasets used to generate the figures of the paper are browsable below.
