# Individual variability in plants: From intra- to inter-individual variability and its response to the environment

Clémentine Coroenneª, Charlotte Lecuyerª,
Antoine Martin and Sandra Cortijo

<figure><img src="image_1.png" /></figure>

Individual variability refers to the differences observed among genetically identical plants or cells grown in the same environment. Phenotypic and transcriptional variability have been extensively described in unicellular organisms and mammalian cells. However, increasing evidence now points to both intra- and inter-individual variability in plants. Cell-to-cell variability in gene expression within a single plant (intra-individual variability) is now recognised as a key factor contributing to the robustness of plant development as well as environmental responses. At a broader scale, multiple studies strongly suggest that inter-individual variability, often involving gene expression differences between individual seedlings, can be associated with an adaptive value at the population level under challenging environmental conditions. This review first aims to describe what is currently known about intra- and inter-individual variability in plants, with a main focus on gene expression variation and highlighting the importance of chromatin modifications. We then illustrate how the extent of inter-individual variability can differ depending on environmental conditions, and discuss how the plasticity of such variability may enhance the ability of plants to respond to challenging situations. These observations finally underline the relevance of investigating individual variability in the context of agriculture.

## Addresses

IPSiM, Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France

Corresponding author: Cortijo, Sandra (sandra.cortijo@cnrs.fr)

## Keywords

Variability, Noise, Heterogeneity.

## Introduction

Genetically identical cells in the same environment can show differences in gene expression, known as transcriptional heterogeneity or noise. This has been well documented in microorganisms and mammalian cells and contributes to processes like cell differentiation, environmental adaptation, and population robustness [1–3]. However, in plants, knowledge about the sources and impact of transcriptional noise between cells remains limited [4]. Some studies, particularly in the model species *Arabidopsis thaliana*, have analysed transcriptional noise between cells in plants, their role and the mechanisms behind. Moreover, it has been shown that many traits like lateral root [5–7], petal [8,9], leaves [10,11] and sepal [12] as well as for the transcriptome, proteome and metabolome display variability among genetically identical plants grown in the same environment. In this review, the term noise will be used to refer to the cell-to-cell differences and the term variability for differences between plants, while heterogeneity is also used as an umbrella term.

Since cell-to-cell noise and inter-individual variability have been seen for many traits in plants, studies are now focused on understanding the mechanisms behind this heterogeneity as well as its consequences. In this review, we will discuss the latest papers on this topic as well as a novel aspect of that field, which is to study how cell-to-cell noise and inter-individual variability respond to various environmental constraints in plants.

## Intra-individual heterogeneity in plants

While noise in gene expression among cells is better characterised in unicellular organisms, several studies have nonetheless explored it in plants (Table 1). From the extensive analyses in unicellular organisms, we know that transcriptional noise arises from the stochastic nature of the gene expression process itself and can be

**Current Opinion in Plant Biology** 2026, 89:102833

This review comes from a themed issue on **VSI: Epigenetics and gene regulation_2025**

Edited by Dr. Sara Farrona and Dr. Ralf Müller-Xing

For a complete overview see the **Issue** and the **Editorial**

Available online xxx

https://doi.org/10.1016/j.pbi.2025.102833

1369-5266/© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

<footnote>ª CC and CL contributed equally to the article.</footnote>
<table><caption>Table 1</caption><caption>Sources of variability and molecular mechanisms described in plants.</caption><thead><tr><th></th><th>Variability gene expression</th><th>Phenotypic heterogeneity or robustness</th><th>Molecular mechanisms</th><th>References</th></tr></thead><tbody><tr><td rowspan="5">Cell-to-cell</td><td>GL3</td><td><strong>Trichome patterning</strong><br>Growth</td><td>Histone modifications<br>Cell wall elasticity, microtubules, turgor pressure</td><td>[24]<br>[17]</td></tr><tr><td>ATML1</td><td>Size and shape</td><td>Cell division<br>Threshold</td><td>[17]<br>[23]</td></tr><tr><td>Genome-wide</td><td><strong>Differentiation of giant cells in sepal</strong><br>Regeneration from protoplasts</td><td>Chromatin accessibility</td><td>[25]</td></tr><tr><td>FLC</td><td>Flowering in response to cold</td><td>Cell size, chromatin and long noncoding RNA</td><td>[27,28]</td></tr><tr><td>DR5, AHP6, DOF5.8 buffer</td><td>Meristem flowers</td><td></td><td>[15]</td></tr><tr><td rowspan="2">Inter-organ</td><td>Genome-wide</td><td>Leaf development</td><td>m6A RNA methylation</td><td>[11]</td></tr><tr><td>Buffer</td><td><strong>Heat and fertility</strong><br>Organ robustness</td><td>ROS, microtubule, Paf1c</td><td>[32]<br>[20–22]</td></tr><tr><td>Inter-organ or inter-individuals</td><td>Cell wall genes</td><td>Sepal size and shape<br>Lateral root architecture</td><td></td><td>[12,31]<br>[7]</td></tr><tr><td rowspan="10">Inter-individuals</td><td rowspan="10">Response to environmental stress genes<br>Leaf transcriptome<br>NRT2.1</td><td>Petal number</td><td>APETALA1 (AP1)</td><td>[8,9]</td></tr><tr><td>Leaf development</td><td></td><td>[10]</td></tr><tr><td>Transcriptome</td><td>Chromatin environment</td><td>[43]</td></tr><tr><td>General development</td><td>HSP90</td><td>[35,36]</td></tr><tr><td>Sepal initiation</td><td>DRMY1 control of TOR activity and auxin-cytokinin patterning</td><td>[37]</td></tr><tr><td>Plant metabolism</td><td>ELF3 identified by QTL</td><td>[38,40,42]</td></tr><tr><td>Iron levels: molybdenum</td><td>MOT1 identified by QTL</td><td>[39]</td></tr><tr><td>Flowering</td><td>FRI-FLC identified by QTL</td><td>[39]</td></tr><tr><td>Timing in seed germination</td><td>ABA sensitivity identified by QTL</td><td>[41]</td></tr><tr><td>Transcriptional variability</td><td>MET1-dependant methylation</td><td>[45]</td></tr><tr><td>Primary root and hypocotyl length</td><td>Chromatin remodeling by ATPase CHR23</td><td>[46]</td></tr><tr><td>Yield and development</td><td></td><td></td></tr><tr><td>Nitrate influx, Root architecture</td><td></td><td></td></tr></tbody></table>

This table contains the examples of phenotypic heterogeneity and robustness as well as variability in gene expression and molecular mechanisms among cells, organs and individuals. Examples in bold correspond to cases where transcriptional heterogeneity and phenotypic robustness or variability are connected. The text in green represents the cases that described phenotypic robustness.

amplified or buffered by several mechanisms such as chromatin, the promoter architecture, the regulatory network structure as well as posttranscriptional and posttranslational regulation [4]. In plants, it has been shown that the level of noise in gene expression, and the mechanisms at play, varies across different regulatory pathways and among different tissues and organs. This has been studied for genes involved in heat shock and phosphate using RNA-labelling live imaging, and auxin

responses by confocal live imaging [13–15]. Also, it has been proposed that differences in the level of transcriptional noise among genes in the same pathway can be linked to promoter architecture, such as the number and location of transcription factor binding sites [15]. Moreover, it has been demonstrated by single-cell RNA-seq on root quiescent centre cells that gene length, gene duplications, and gene body methylation are negatively correlated with transcriptional noise among cells [16].
However, it is important to note that this study is based on pools of plants, which makes it impossible to distinguish between inter-cellular and inter-individual heterogeneities. It would thus be interesting to conduct this type of experiment on a single organism in order to be sure that the cell-to-cell noise is indeed analysed. Moreover, for future studies on cell-to-cell transcriptional noise, it would be important to ensure they are carried out on a specific cell type so as not to confuse cell-to-cell noise with a difference in expression among cell types. In this regard, microscopy is preferable as it maintains the positional information.

A well-described aspect of transcriptional noise in plants is its role in patterning and organ development which has been studied mostly by live imaging, quantitative image analyses and modelling. Although plant development is highly robust, some stochasticity exists which may be caused at least partly by noisy gene expression. As reviewed by Hong et al., cells can exhibit growth heterogeneity due to cell wall elasticity, microtubules and turgor pressure variation, but also to cell division [17]. Counterintuitively, growth cell-to-cell heterogeneity was shown to be required for robustness of sepal shape and size [18]. A buffer of noise, by a precise timing of organ initiation or via VIP3 (VERNALISATION INDEPENDENCE 3), a subunit of polymerase-associated factor 1 complex (Paf1C), can also ensure shape robustness [19,20]. Recent studies have shown that ROS (Reactive Oxygen Species) homeostasis and microtubule dynamics contribute to robust morphogenesis [21,22], which could explain the more heterogeneous phenotype of the *vip3* mutant where a patchy ROS pattern was observed. While on one hand cell-to-cell transcriptional noise can be buffered, on the other hand, it can also be exploited for fate exploration and commitment, helping in the creation of patterns. In plants, noise in the expression of the transcription factor *ATML1* (*ARABIDOPSIS THALIANA MERISTEM LAYER 1*) between cells mediates the differentiation of giant cells in sepals [23] (Figure 1a). In leaves, it has been shown through mathematical modelling based on publicly available single-cell RNA-seq data and the study of trichome patterning in several ecotypes that the heterogeneity in the trichome distribution pattern was associated with noise in *GL3* (*GLABRA 3*) expression, a gene critical for cell fate determination during trichome formation. It turns out that this pattern and *GL3* expression noise were both affected in the *arp6-1* mutant, which is impaired in the deposition of the histone variant H2A.Z in the chromatin [24]. Moreover, it has been shown by live imaging, single-cell transcriptomics and ATAC-seq (transposase-accessible chromatin sequencing) assays that protoplast isolation induces a genome-wide increase in chromatin accessibility and transcriptional noise, which promotes cell fate reprogramming and plant regeneration from protoplasts [25].)

<figure><img src="image_2.png" /><figcaption>**Figure 1**</figcaption></figure>

**Example of transcriptional noise involvement in developmental patterns and in response to the environment in Arabidopsis.**

(a) In sepal primordia (images from Refs. [23,37]), the *ATML1* gene, a transcription factor involved in cell differentiation into giant cells, has noisy expression between cells. Within a cell at the G2 stage, if the expression of *ATML1* exceeds a certain threshold, the cell stops dividing and enters endoreduplication to become a giant cell. This noisy expression between cells therefore leads to heterogeneity in the number and location of giant cells within the mature sepal. Adapted from Ref. [23].
(b) For genotypes that require vernalisation to flower, cold causes the PRC2 complex to deposit the repressive mark H3K27me3 at the *FLC* locus, a repressor of flowering [26]. The slow and asynchronous expression of the *FLC* gene switches from ON to OFF due to the deposition of H3K27me3 during cold [29]. This leads to noise in *FLC* expression from cell to cell within a tissue as some cells will express it (ON stage) and others will not (OFF stage). This noise allows a quantitative response over time and memory of cold at the individual plant level. Created in https://BioRender.com.

Cell-to-cell transcriptional noise was also observed for genes not involved in cell differentiation or patterning, but in response to the environment, like vernalisation, heat and phosphate responses [13,14,26]. For example, in the case of FLC, a major regulator of flowering whose expression is affected by vernalisation, it has been shown that there is a quantitative cell-to-cell transcriptional noise due to cell size in absence of cold measured using single-molecule RNA fluorescence in situ hybridisation [27] (Figure 1B). A long noncoding RNA antisense to *FLOWERING LOCUS C (FLC)*, *COOLAIR*, which represses *FLC*, has also been shown to play a role in the cell-to-cell noise of *FLC*. Moreover, in response to cold, the chromatin modification
H3K27me3 is involved in an asynchronous switch from an expression ON to OFF for each gene copy of *FLC* (digital switch), causing cell-to-cell noise [28]. During cold, *COOLAIR* is more expressed and H3K27me3 is deposited at the *FLC* locus. The increase in the number of cells repressing *FLC* over time is thus allowing a quantitative response to cold [26,29]. The response at the plant level is linked to the mobility of the FLC signal [30], which has not been shown yet for the response to heat and phosphate where transcriptional noise between cells was also observed.

At the organ level, it was shown by RNA-seq on single sepals, gene regulatory network analyses and morphology data that changes in expression between sepals for cell wall-related genes are correlated with its morphology on one hand [12], but that highly expressed cell wall-related genes are associated with the robustness of sepal size on the other hand [31]. Also, it has been shown that gene expression variability was lower between flowers than between leaves by comparing RNA-seq biological replicates, and that it is at least partly caused by m6A RNA methylation, which is linked to thermotolerance and fertility [32]. They also found that lowly variable genes are more associated with stress granules, which are biomolecular condensates in the cytosol composed of proteins and RNA formed in response to stress [33].

## Inter-individual variability in plants

As described in the first part, heterogeneity exists between cells and organs in a single plant. However, genetically identical plants in the same environment also display phenotypic and gene expression differences (Table 1), here referred to as inter-individual variability [4]. To illustrate the relevance of this mechanism, examples known so far of inter-individual variability in roots are illustrated in Figure 2.

Although identified, variability has not always attracted the interest of the community and in opposition, the attention was initially focused on understanding robustness mechanisms. Therefore, genes defined as phenotypic capacitors (i.e. favouring robustness) have been mostly described [34]. The most studied one is the chaperone protein heat shock protein 90 (HSP90), which buffers phenotypic instability [35] while its inhibition reveals hidden phenotypic variation [36]. Recently, a study identified the transcription factor DEVELOPMENT-RELATED MYB-LIKE 1 (DRMY1) as a buffer for sepal initiation instability through Target Of Rapamycin (TOR) signalling and auxin-cytokinin patterning [37].

Similarly, it has been demonstrated that phenotypic variability is under genetic control. By using a quantitative genetics approach, studies on *Arabidopsis thaliana*

<figure><img src="image_3.png" /><figcaption>**Figure 2**</figcaption></figure>

### Inter-individual variability in *Arabidopsis thaliana* roots.

Genetically identical plants grown in the same environment display phenotypic and transcriptomic inter-individual variability in roots. The lateral root architecture heterogeneity was described for different phenotypes: lateral root growth rate, length, density and angle [7]. Variability in root hair was also observed [7]. The high affinity nitrate transporter *NRT2.1* was found to have high transcriptional variability. This *NRT2.1* expression heterogeneity could partly explain variability in primary root length and NRT2.1 nitrate uptake between seedlings [49]. The over-expression of the ATPase CHROMATIN REMODELING 23 (CHR23), through chromatin remodelling was found to increase variability of primary root length and transcriptional variability of growth and environment response genes. Among those genes, a member of the 3-Ketoacyl-CoA family named KCS2/DAISY, which is involved in root suberin biosynthesis and osmotic and drought stress response [46], is an interesting candidate for further studies.

In the figure, examples of phenotypic variability are in green and examples of transcriptional variability in orange. The solid arrows represent direct effects while the dashed ones illustrate indirect effects. Created in https://BioRender.com.

found QTLs responsible for phenotypic variability [38–40]. The most complete work focuses on variability for the timing of seed germination. Using a multi-parent recombinant inbred population (MAGIC) they identified two loci that affect only the variability of germination time and contain genes known to be involved in abscisic acid (ABA) sensitivity [41]. In seeds, ABA acts as an inhibitor of germination while gibberellins (GA) function as an activator. Therefore, the ABA/GA ratio is important to trigger seed germination. As a confirmation, the treatment with exogenous ABA increased the variability level of seed germination time in lines known to have low variability. However, none of these studies succeeded in clearly identifying genes and molecular
mechanisms controlling inter-individual variability and
more work is needed to do so.

Explaining phenotypic variability through molecular
mechanisms is quite complex due to the numerous
steps and molecular factors involved. Consequently,
more studies have focused on the transcriptomic level as
it is relatively easier to analyse. However, other prom-
ising omics approaches such as studies of metabolic
variability have been explored. In this context, it has
been shown that inter-individual variability in the root
metabolome exhibits greater variance than natural
variation [42] suggesting that metabolic variability may
partly account for phenotypic variability. More recently,
Arabidopsis transcriptome inter-individual variability
has been studied. Single seedling RNA-seq revealed
widespread transcriptional variability between identical
plants and that highly variable genes (HVGs) are
enriched in environmental response genes [43]. In other
eukaryotes than plants, it has been shown that chro-
matin modifications could control transcriptional vari-
ability [44]. Similarly, in plants, HVGs are enriched in
chromatin repressive marks (H3K27me1 and
H3K27me3) [43]. In the *methyltransferase 1* (*met1*)
mutant, the decrease of CpG methylation and the
modification of chromatin accessibility come with tran-
scriptomic heterogeneity in the expression of a large
group of genes which were similarly expressed in the
Wild-Type (WT). This suggests a buffer role of MET1-
dependent CG methylation for gene expression [45].
Chromatin remodelling through the ATPase CHRO-
MATIN REMODELING 23 (CHR23) was also found to
have a genome-wide effect on the variability of gene
expression as well as primary root and hypocotyl length
[46]. All those results strongly suggest that chromatin-
based mechanisms have an important role in control-
ling the level of expression variability.

Variability in gene expression has been associated with
phenotypic variation in certain studies. Meyer and col-
leagues used single plant leaf transcriptome variability
and machine learning models to predict final pheno-
types and yield for the rapeseed *Brassica napus* [47].
Among the transcriptional variability measured, the
HVGs were enriched in genes related to photosynthesis
and responses to stress. Another similar experiment with
the same conclusions has been conducted on maize
[48]. However, those studies used field-grown plants
which makes it difficult to determine the part of vari-
ability due to microenvironmental differences. So far,
regarding the lack of studies focusing on this question, it
remains unclear to what extent gene expression vari-
ability could explain phenotypic variability. In this
regard, a recent work found a correlation between single
seedling transcriptional variability of *NITRATE
TRANSPORTER 2.1* (*NRT2.1*), root architecture and
nitrate influx [49]. *NRT2.1* transcriptional variability was

quantified both by RT-qPCR and live imaging with a
luciferase promoter reporter line. In theory, inter-
individual variability between plants could be an
advantage in an unpredictable environment as a bet-
hedging strategy [4]. Seed germination time is the
most studied example of bet-hedging in plants [50].
Indeed, while not formally demonstrated yet, it was
proposed that variability of the timing of seed germi-
nation would allow the survival of a population in case of
sudden and brief stress [51]. The potential-positive
effects of phenotypic variability are well studied but we
still lack works exploring the possible drawbacks. In the
agronomic context, investigating inter-individual vari-
ability in relation to mineral nutrition and resistance to
biotic and abiotic stresses might provide rele-
vant information.

The plasticity of intra- and inter-individual variability

It has been observed that genes with high levels of vari-
ability are also genes with changes in expression in
response to environmental fluctuation [52]. However,
most of the studies of how plants respond to the envi-
ronment have been done using pools of plants or organs,
and have rarely focused on the variability. Below, we will
discuss recent works that analysed how variability among
plants, organs and cells respond to the environment.

It was shown thanks to transcriptomics on single in-
dividuals in response to the environment that the level
of variability can change between day and night, some-
times independently of gene expression levels [43].
More recently, Krzyszton et al. analysed the variability
among single-seed transcriptomes during stress, which
causes a secondary dormancy as well as during recovery
from this stress [53]. They found an increase in the
number of HVGs at the early stages of the stress
response (after one day), which then decreased over
time and became lower than before stress (after five
days). Upon recovery from stress, which alleviated the
secondary dormancy, the number of HVGs increased
again (Figure 3). In another study, a reduction of the
variability of the metabolome and proteome was shown
in response to acute nonlethal stresses, which increased
back to the original level during recovery [54]
(Figure 3). It thus suggests that a reduction of inter-
individual variability upon stress is a common feature.
An open question is whether this change in variability in
response to stress could help plants to survive or if it is a
mere secondary effect of the transcriptional response to
stress. As a first answer, a mutant with a reduced level of
inter-individual transcriptional variability in flowers was
shown to be more sensitive to heat [32]. However, more
studies of the response to environmental constraints for
mutants affecting inter-individual variability in response
are needed to have a more global picture.
<figure><img src="image_4.png" /><figcaption>Figure 3</figcaption></figure>

### Response of inter-individual variability to environmental stresses

Transcriptomic inter-seed variability was shown to transiently increase in response to heat or during secondary dormancy and then reduce to a level lower than before stress during longer exposure to stress [53]. Also, proteomic and metabolomic inter-individual variability has been shown to reduce in response to several acute stresses [54]. In both cases, the level of variability returns to its original level upon recovery from the stress. Further studies of the variability response to stresses will be needed to define if the transient increase during early stress is a common feature or if it is specific for seeds' secondary dormancy. Moreover, we still need to identify the molecular mechanisms involved in the response of inter-individual variability to stresses and if it helps plants to survive a stress. Created in https://BioRender.com.

It was also found that intra- and/or inter-individual variability in plants for several traits like fruit quality [55], leaf phenotypes, plant height [56], trichome patterning [24] and chlorophyll fluorescence [57] changed when facing different environmental conditions. In particular, it was shown that plants subjected to a less predictable environment during their late growth had a higher intra- and inter-individual variability for flowering time [58]. Similarly, Wang et al. studied for many plant species the impact of an alternation of flooding and drought on intra- and inter-individual variability for leaf fluctuation asymmetry and leaf size [59]. They could find in some species both increased and decreased variability in plants subjected to temporally heterogeneous watering. We, however, need to keep in mind that genetic differences might exist among plants in some of these studies, meaning inter-individual variability could be confounded with genetic effects. We would need more studies with species that allow analysis of variability among genetically identical individuals.

On a smaller scale, cell to cell variability was also measured in response to changes in the environment. On top of the now well-described case of FLC [26,29],

whose noisy response to cold we described above, it was also shown that transcriptional activation of a heat shock protein in response to heat [13] and the transcriptional repression of SPX DOMAIN GENE 1 (SPXI) during phosphate resupply [14] are also heterogeneous between cells. However, we still do not know if the level of cell-to-cell variability is different before and during stress for these genes.

### Conclusion and future directions

We are now at a stage where we have solid evidence of intra- and inter-individual variability in plants. It is also clear that the level of this variability can change in response to the environment. However, we are still missing the molecular mechanisms behind this variability in most cases as well as whether it can help plants survive environmental stresses. To identify the mechanisms at play, similar strategies that were used to study the response of plants to the environment will be needed, this time focusing on the plasticity of variability: genetic screens, GWAS (Genome-Wide Association Studies) and QTL (Quantitative Trait Locus) as well as omics approaches and modelling. These methods will need to be scaled to populations of individuals, organs or cells, instead of averaging them out by pooling plants as it is usually the case. It will be interesting to define if mechanisms known to be involved in the response to the environment such as the chromatin, posttranscriptional regulation of mRNAs and posttranslational regulation of proteins are also at play in the response of variability to the environment.

In the future, spatial and temporal climatic variability will increase [60,61] and models show it will have a negative impact on crop yield and quality [60]. In this context, it is urgent to include intra- and inter-individual variability of plants in these models to inform strategies for crop selection and agricultural practices in order to develop a resilient agriculture in face of climate change.

### Funding

This work was supported by a grant from the National Research Agency (ANR-22-CE20-0020, ChromaVari).

### Declaration of competing interest

The authors declare the following financial interests/ personal relationships which may be considered as potential competing interests:

Sandra Cortijo reports financial support was provided by French National Research Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
**Data availability**

No data was used for the research described in the article.

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