Plant electrical signals: A multidisciplinary challenge

Bioelectronic tools for understanding the universal language of electrical signaling across species and kingdoms

Modern bioelectronic tools are rapidly advancing to detect electric potentials within networks of electrogenic cells, such as cardiomyocytes, neurons, and pancreatic beta cells. However, it is becoming evident that electrical signaling is not limited to the animal kingdom but may be a universal form of cell-cell communication. In this review, we discuss the existing evidence of, and tools used to collect, subcellular, single-cell and network-level electrical signals across kingdoms, including bacteria, plants, fungi, and even viruses. We discuss how cellular networks employ altered electrical "circuitry" and intercellular mechanisms across kingdoms, and we assess the functionality and scalability of cutting-edge nanobioelectronics to collect electrical signatures regardless of cell size, shape, or function. Researchers today aim to design micro- and nano-topographic structures which harness mechanosensitive membrane and cytoskeletal pathways that enable tight electrical coupling to subcellular compartments within high-throughput recording systems. Finally, we identify gaps in current knowledge of inter-species and inter-kingdom electrical signaling and propose critical milestones needed to create a central theory of electrical signaling across kingdoms. Our discussion demonstrates the need for high resolution, high throughput tools which can probe multiple, diverse cell types at once in their native or experimentally-modeled environments. These advancements will not only reveal the underlying biophysical laws governing the universal language of electrical communication, but can enable bidirectional electrical communication and manipulation of biological systems.

Machine learning-assisted implantable plant electrophysiology microneedle sensor for plant stress monitoring

Plant electrical signals serve as a medium for long-distance signal transmission and are intricately linked to plant stress responses. High-fidelity acquisition and analysis of plant electrophysiological signals contribute to early stress identification, thereby enhancing agricultural production efficiency. While traditional plant electrophysiology monitoring methods like gel electrodes can capture electrical signals on plant surfaces, which face limitations due to the plant cuticle barrier, impacting signal accuracy. Moreover, the vast and intricate nature of plant electrical signal data, coupled with the absence of specialized large-scale models, impedes signal interpretation and plant physiological correlation. In light of these challenges, we engineered an implantable microneedle array using micromachining technology for monitoring and decoding plant electrical signals in a minimally invasive manner. This innovative sensor can securely adhere to plant tissue over extended periods, enabling the precise recording of electrical signals triggered by transient (mechanical injury) and long-term stresses (drought and salt stress). Based on the collected plant electrophysiological data, we utilized a machine learning model to analyze these signals for the early detection of plant stress with an accuracy of 99.29%. This sensor has great potential and is expected to revolutionize precision agricultural production and provide valuable help in managing plant stress more effectively.

Evaluating Bacterial Nanocellulose Interfaces for Recording Surface Biopotentials from Plants

The study of plant electrophysiology offers promising techniques to track plant health and stress in vivo for both agricultural and environmental monitoring applications. Use of superficial electrodes on the plant body to record surface potentials may provide new phenotyping insights. Bacterial nanocellulose (BNC) is a flexible, optically translucent, and water-vapor-permeable material with low manufacturing costs, making it an ideal substrate for non-invasive and non-destructive plant electrodes. This work presents BNC electrodes with screen-printed carbon (graphite) ink-based conductive traces and pads. It investigates the potential of these electrodes for plant surface electrophysiology measurements in comparison to commercially available standard wet gel and needle electrodes. The electrochemically active surface area and impedance of the BNC electrodes varied based on the annealing temperature and time over the ranges of 50 °C to 90 °C and 5 to 60 min, respectively. The water vapor transfer rate and optical transmittance of the BNC substrate were measured to estimate the level of occlusion caused by these surface electrodes on the plant tissue. The total reduction in chlorophyll content under the electrodes was measured after the electrodes were placed on maize leaves for up to 300 h, showing that the BNC caused only a 16% reduction. Maize leaf transpiration was reduced by only 20% under the BNC electrodes after 72 h compared to a 60% reduction under wet gel electrodes in 48 h. On three different model plants, BNC-carbon ink surface electrodes and standard invasive needle electrodes were shown to have a comparable signal quality, with a correlation coefficient of >0.9, when measuring surface biopotentials induced by acute environmental stressors. These are strong indications of the superior performance of the BNC substrate with screen-printed graphite ink as an electrode material for plant surface biopotential recordings.

Early detection of dark-affected plant mechanical responses using enhanced electrical signals

BACKGROUND

Mechanical damage to plants triggers local and systemic electrical signals that are eventually decoded into plant defense responses. These responses are constantly affected by other environmental stimuli in nature, for instance, light fluctuation. In recent years, studies on decoding plant electrical signals powered by various machine learning models are increasing in a sense of early prediction or detection of different environmental stresses that threaten plant growth or crop yields. However, the main bottleneck is the low-throughput nature of plant electrical signals, making it challenging to obtain a substantial amount of training data. Consequently, training these models with small datasets often leads to unsatisfactory performance.

RESULTS

In the present work, we set out to decode wound-induced electrical signals (also termed slow wave potentials, SWPs) from plants that are deprived of light to different extents. Using non-invasive electrophysiology, we separately collected sets of local and distal SWPs from the treated plants. Then, we proposed a workflow based on few-shot learning to automatically identify SWPs. This workflow incorporates data preprocessing, feature extraction, data augmentation and classifier training. We established the integral and the first-order derivative as features for efficiently classifying SWPs. We then proposed an Adversarial Autoencoder (AAE) structure to augment the SWP samples. Combining them, the Random Forest classifier allowed remarkable classification accuracies of 0.99 for both local and systemic SWPs. In addition, in comparison to two other reported methods, our proposed AAE structure enabled better classification results using our tested features and classifiers.

CONCLUSIONS

The results of this study establish new features for efficiently classifying wound-induced electrical signals, which allow for distinguishing dark-affected local and systemic plant wound responses. We also propose a new data augmentation structure to generate virtual plant electrical signals. The methods proposed in this study could be further applied to build models for crop plants using electrical signals as inputs, and also to process other small-scale signals.

Water-Responsive 3D Electronics for Smart Biological Interfaces

Three-dimensional (3D) electronic systems with their potential for enhanced functionalities often require complex fabrication processes. This paper presents a water-based, stimuli-responsive approach for creating self-assembled 3D electronic systems, particularly suited for biorelated applications. We utilize laser scribing to programmatically shape a water-responsive bilayer, resulting in smart 3D electronic substrates. Control over the deformation direction, actuation time, and surface curvature of rolling structures is achieved by adjusting laser-scribing parameters, as validated through experiments and numerical simulations. Additionally, self-locking structures maintain the integrity of the 3D systems. This methodology enables the implementation of spiral twining electrodes for electrophysiological signal monitoring in plants. Furthermore, the integration of self-rolling electrodes onto peripheral nerves in a rodent model allows for stimulation and recording of in vivo neural activities with excellent biocompatibility. These innovations provide viable paths to next-generation 3D biointegrated electronic systems for life science studies and medical applications.

Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants' signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we exposeZamioculcas zamiifoliaandSolanum lycopersicum(tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas.

Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiological data

Plant electrophysiological response contains useful signature of its environment and health which can be utilized using suitable statistical analysis for developing an inverse model to classify the stimulus applied to the plant. In this paper, we have presented a statistical analysis pipeline to tackle a multiclass environmental stimuli classification problem with unbalanced plant electrophysiological data. The objective here is to classify three different environmental chemical stimuli, using fifteen statistical features, extracted from the plant electrical signals and compare the performance of eight different classification algorithms. A comparison using reduced dimensional projection of the high dimensional features via principal component analysis (PCA) has also been presented. Since the experimental data is highly unbalanced due to varying length of the experiments, we employ a random under-sampling approach for the two majority classes to create an ensemble of confusion matrices to compare the classification performances. Along with this, three other multi-classification performance metrics commonly used for unbalanced data viz. balanced accuracy, F1-score and Matthews correlation coefficient have also been analyzed. From the stacked confusion matrices and the derived performance metrics, we choose the best feature-classifier setting in terms of the classification performances carried out in the original high dimensional vs. the reduced feature space, for this highly unbalanced multiclass problem of plant signal classification due to different chemical stress. Difference in the classification performances in the high vs. reduced dimensions are also quantified using the multivariate analysis of variance (MANOVA) hypothesis testing. Our findings have potential real-world applications in precision agriculture for exploring multiclass classification problems with highly unbalanced datasets, employing a combination of existing machine learning algorithms. This work also advances existing studies on environmental pollution level monitoring using plant electrophysiological data.

Classification of various nutrient deficiencies in tomato plants through electrophysiological signal decomposition and sample space reduction

Plants leave testimonies of undergoing physical state by depicting distinct variations in their electrophysiological data. Adequate nutrition of plants signifies their role in the growth and a plentiful harvest. Plant signal data carries enough information to detect and analyse nutrient deficiency. Classification of nutrient deficiencies through signal decomposition and bilevel measurements has not been reported earlier. The proposed work explores tomato plants in four-time cycles (Early Morning, Morning, After Noon, Night) of macronutrients Calcium (Ca), Nitrogen (N) and micronutrients Manganese (Mn), Iron (Fe). Using the Empirical Mode Decomposition method (EMD), signals are decomposed into Intrinsic Mode Functions (IMF) in 10-levels. Further, Intrinsic mode functions are grouped into two clusters to extract descriptive data statistics and bi-level measurements. Then a novel sample selection method is proposed to achieve a better classification rate by reducing sample space. A binary classification model is built to train and test 15 features individually using discriminant analysis and naïve-Bayes classifier variants. The reported results achieved a classification rate up to 98% after 5-fold cross-validation. Attained findings endorse novel pathways for detection and classification of nutrient deficiencies in the early stages, consequently promoting prevention and treatment approaches earliest to the appearance of symptoms, also helping to enhance plant growth.

Rapid systemic responses to herbivory

Rapid systemic signals travel within the first seconds and minutes after herbivore infestation to mount defense responses in distal tissues. Recent studies have revealed that wound-induced hydraulic pressure changes play an important role in systemic electrical signaling and subsequent calcium and reactive oxygen species waves. These insights raise new questions about signal specificity, the role of insect feeding guild and feeding style and the impact on longer term plant defenses. Here, we integrate the current molecular understanding of wound-induced rapid systemic signaling in the framework of insect-plant interactions.

Benchmarking organic electrochemical transistors for plant electrophysiology

Plants are able to sense and respond to a myriad of external stimuli, using different signal transduction pathways, including electrical signaling. The ability to monitor plant responses is essential not only for fundamental plant science, but also to gain knowledge on how to interface plants with technology. Still, the field of plant electrophysiology remains rather unexplored when compared to its animal counterpart. Indeed, most studies continue to rely on invasive techniques or on bulky inorganic electrodes that oftentimes are not ideal for stable integration with plant tissues. On the other hand, few studies have proposed novel approaches to monitor plant signals, based on non-invasive conformable electrodes or even organic transistors. Organic electrochemical transistors (OECTs) are particularly promising for electrophysiology as they are inherently amplification devices, they operate at low voltages, can be miniaturized, and be fabricated in flexible and conformable substrates. Thus, in this study, we characterize OECTs as viable tools to measure plant electrical signals, comparing them to the performance of the current standard, Ag/AgCl electrodes. For that, we focused on two widely studied plant signals: the Venus flytrap (VFT) action potentials elicited by mechanical stimulation of its sensitive trigger hairs, and the wound response of Arabidopsis thaliana. We found that OECTs are able to record these signals without distortion and with the same resolution as Ag/AgCl electrodes and that they offer a major advantage in terms of signal noise, which allow them to be used in field conditions. This work establishes these organic bioelectronic devices as non-invasive tools to monitor plant signaling that can provide insight into plant processes in their natural environment.

Measuring Electrical Responses during Acute Exposure of Roots and Rhizoids of Plants to Compounds Using a Flow-Through System

Monitoring electrical signals in plants allows the examination of their acute and chronic physiological changes and responses to stimuli. Understanding how plant roots/rhizoids respond to chemical cues in their environment will provide insight into how these structures acquire resources. Chronic exposure to L-glutamate alters root growth and is known to alter Ca²⁺ flux inside roots. The ionic flux can be detected by electrical changes. A rapid and relatively easy approach is presented to screen the electrical sensitivity of roots/rhizoids to compounds such as amino acids and known agonists/antagonists to receptors and ion channels. The approach uses a background-flow system of basal salt or water; then, the administered compounds are added to the roots/rhizoids while monitoring their electrical responses. As a proof of concept, the response to flow-through of glutamate (1 mM) was targeted at the root/rhizoids of three plants (Arabidopsis thaliana, Pisum sativum and Marchantia inflexa). Both Arabidopsis thaliana and Pisum sativum produced rapid depolarization upon exposure to glutamate, while M. inflexa did not show an electrical response. In some experiments, simultaneous recordings with impedance measures for acute changes and glass electrodes for chronic electrical potential changes were used. The effect of potassium chloride (300 mM) as a depolarizing stimulus produced responses in both P. sativum and M. inflexa. The protocol presented can be used to screen various compounds in a relatively rapid manner for responsiveness by the roots/rhizoids of plants.

Impedance Measures for Detecting Electrical Responses during Acute Injury and Exposure of Compounds to Roots of Plants

Electrical activity is widely used for assessing a plant's response to an injury or environmental stimulus. Commonly, a differential electrode recording between silver wire leads with the reference wire connected to the soil, or a part of the plant, is used. One method uses KCl-filled glass electrodes placed into the plant, similar to recording membrane/cell potentials in animal tissues. This method is more susceptible to artifacts of equipment noise and photoelectric effects than an impedance measure. An impedance measure using stainless steel wires is not as susceptible to electrically induced noises. Impedance measurements are able to detect injury in plants as well as exposure of the roots to environmental compounds (glutamate). The impedance measures were performed in 5 different plants (tomato, eggplant, pepper, liverwort, and Coleus scutellarioides), and responses to mechanical movement of the plant, as well as injury, were recorded. Monitoring electrical activity in a plant that arises in a distant plant was also demonstrated using the impedance method. The purpose of this report is to illustrate the ease in using impedance measures for monitoring electrical signals from individual plants or aggregates of plants for potentially scaling for high throughput and monitoring controlled culturing and outdoor field environments.

Wound-Induced Systemic Responses and Their Coordination by Electrical Signals

Wounding not only induces the expression of damage-responsive genes, but also initiates physiological changes, such as tissue repair, vascular reconnection, and de novo organogenesis in locally damaged tissues. Wound-induced signals also propagate from the site of wounding to distal organs to elicit a systemic response. Electrical signaling, which is the most conserved type of systemic signaling in eukaryotes, is triggered by wound-induced membrane potential changes. Changes in membrane potential spread toward systemic tissues in synergy with chemical and hydraulic signals. Here, we review current knowledge on wound-induced local and systemic responses in plants. We focus particularly on how wound-activated plasma membrane-localized ion channels and pumps propagate systemic information about wounding to induce downstream molecular responses in distal tissues. Finally, we propose future studies that could lead to a better understanding of plant electrical signals and their role in physiological responses to wounding.

Influence of Burning-Induced Electrical Signals on Photosynthesis in Pea Can Be Modified by Soil Water Shortage

Local damage to plants can induce fast systemic physiological changes through generation and propagation of electrical signals. It is known that electrical signals influence numerous physiological processes including photosynthesis; an increased plant tolerance to actions of stressors is a result of these changes. It is probable that parameters of electrical signals and fast physiological changes induced by these signals can be modified by the long-term actions of stressors; however, this question has been little investigated. Our work was devoted to the investigation of the parameters of burning-induced electrical signals and their influence on photosynthesis under soil water shortage in pea seedlings. We showed that soil water shortage decreased the amplitudes of the burning-induced depolarization signals (variation potential) and the magnitudes of photosynthetic inactivation (decreasing photosynthetic CO₂ assimilation and linear electron flow and increasing non-photochemical quenching of the chlorophyll fluorescence and cyclic electron flow around photosystem I) caused by these signals. Moreover, burning-induced hyperpolarization signals (maybe, system potentials) and increased photosynthetic CO₂ assimilation could be observed under strong water shortage. It was shown that the electrical signal-induced increase of the leaf stomatal conductance was a potential mechanism for the burning-induced activation of photosynthetic CO₂ assimilation under strong water shortage; this mechanism was not crucial for photosynthetic response under control conditions or weak water shortage. Thus, our results show that soil water shortage can strongly modify damage-induced electrical signals and fast physiological responses induced by these signals.

The Glutamate Receptor Plays a Role in Defense against Botrytis cinerea through Electrical Signaling in Tomato

Plant glutamate-like receptor genes (GLRs) are homologous to mammalian ionotropic glutamate receptors genes (iGluRs). Although GLRs have been implicated in plant defenses to biotic stress, the relationship between GLR-mediated plant immunity against fungal pathogens and electrical signals remains poorly understood. Here, we found that pretreatment with a GLR inhibitor, 6,7-dinitriquinoxaline-2,3-dione (DNQX), increased the susceptibility of tomato plants to the necrotrophic fungal pathogen Botrytis cinerea. Assessment of the glr3.3, glr3.5 and glr3.3/glr3.5 double-mutants upon B. cinerea infection showed that tomato GLR3.3 and GLR3.5 are essential for plant immunity against B. cinerea, wherein GLR3.3 plays the main role. Analysis of the membrane potential changes induced by glutamate (Glu) or glycine (Gly) revealed that amplitude was significantly reduced by knocking out GLR3.3 in tomato. While treatment with Glu or Gly significantly increased immunity against B. cinerea in wild-type plants, this effect was significantly attenuated in glr3.3 mutants. Thus, our data demonstrate that GLR3.3- and GLR3.5-mediated plant immunity against B. cinerea is associated with electrical signals in tomato plants.

Can Electrophysiological Parameters Substitute for Growth, and Photosynthetic Parameters to Characterize the Response of Mulberry and Paper Mulberry to Drought?

Drought is a key factor restricting plant survival, growth and development. The physiological parameters of plants are commonly used to determine the water status, in order to irrigate appropriately and save water. In this study, mulberry (Morus alba L.) and paper mulberry (Broussonetia papyrifera (L.) Vent.) seedlings were used as experimental materials, and four soil moisture treatments were set up for both plant species: 70–75% (CK: the control group, referred to as T0), 55–60% (T1: mild drought), 40–45% (T2: moderate drought), and 25–30% (T3: severe drought). The growth parameter of the plants was measured every two days from the onset of the treatment, the photosynthetic and electrophysiological parameters of the plants were measured every other week for a total of five times. The physiological responses and electrophysiological traits of leaves under different treatment levels were analyzed. The results showed that the photosynthetic and electrophysiological parameters could characterize the response of mulberry growth and development to soil water, and the growth and electrophysiological parameters could characterize the response of paper mulberry growth and development to soil water. Mild drought had no significant effects on the growth and development of mulberry and paper mulberry.

Electrical Signals, Plant Tolerance to Actions of Stressors, and Programmed Cell Death: Is Interaction Possible?

In environmental conditions, plants are affected by abiotic and biotic stressors which can be heterogenous. This means that the systemic plant adaptive responses on their actions require long-distance stress signals including electrical signals (ESs). ESs are based on transient changes in the activities of ion channels and H⁺-ATP-ase in the plasma membrane. They influence numerous physiological processes, including gene expression, phytohormone synthesis, photosynthesis, respiration, phloem mass flow, ATP content, and many others. It is considered that these changes increase plant tolerance to the action of stressors; the effect can be related to stimulation of damages of specific molecular structures. In this review, we hypothesize that programmed cell death (PCD) in plant cells can be interconnected with ESs. There are the following points supporting this hypothesis. (i) Propagation of ESs can be related to ROS waves; these waves are a probable mechanism of PCD initiation. (ii) ESs induce the inactivation of photosynthetic dark reactions and activation of respiration. Both responses can also produce ROS and, probably, induce PCD. (iii) ESs stimulate the synthesis of stress phytohormones (e.g., jasmonic acid, salicylic acid, and ethylene) which are known to contribute to the induction of PCD. (iv) Generation of ESs accompanies K⁺ efflux from the cytoplasm that is also a mechanism of induction of PCD. Our review argues for the possibility of PCD induction by electrical signals and shows some directions of future investigations in the field.