bioRxivPlant BiologyPreprint
A calcium-fingerprint atlas infers the signaling modes of complex elicitors and reveals a salt-like response to a humic biostimulant
Milan ŽupunskiiD, Rik BrugmaniD, Martin ZauseriD, Konstantinos LampouiD, Rainer WaadtiD, Marjorie GuichardiD, Melanie KrebsiD, Karin SchumacheriD, Richard LamariD, Jürgen PahleiD, Guido GroßmanniD
Abstract
A central challenge in plant signaling is to infer, from an observed response, the mechanism that produced it, including perception and signaling components involved. Cytosolic calcium (Ca2+) is well suited to this reverse-engineering problem: it is a universal early messenger whose spatiotemporal pattern, the Ca2+ signature, is shaped by the specific channels and receptors a stimulus activates. We hypothesize that multiparametric analysis of Ca2+ signals can identify the main signaling pathways activated by complex or poorly defined elicitors such as biostimulants. Here, we establish a quantitative framework to resolve Ca2+ dynamics in roots of Arabidopsis seedlings by transforming spatiotemporal Ca2+ transients into multidimensional signatures. Using a normalization and feature extraction approach (Crestline), we define descriptors capturing the onset time, location along the root axis, duration, and propagation of Ca2+ signals tip- or shootwards, enabling robust comparison across stimuli. Integration of these descriptors reveals stimulus-specific "calcium fingerprints" that allow systematic classification of responses to biotic and abiotic cues within a structured signaling space. To test whether projecting an uncharacterized input into this space can predict its signaling mode, we analyzed a complex humic biostimulant. Its response fell within the fast-onset region of signaling space shared by DAMP and salt responses, clearly apart from MAMP responses, and was altered in moca1, a mutant impaired in salt sensing. Together, these findings establish Ca2+ dynamics as an interpretable encoding layer for environmental information and provide a scalable framework to resolve complex biological inputs.
- organism: Arabidopsis
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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