Travis A. Lee

Spatial omics technologies provide new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organized cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalize. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.

Transcriptome analysis via RNA sequencing (RNAseq) has become a ubiquitous method of molecular characterization from whole organisms, dissected tissues, and single cells. These experiments continue to provide an extraordinary volume of data describing molecular states and responses to many conditions. However, standard approaches to RNAseq analysis commonly use expression-level filters that eliminate potentially useful data in the service of decreasing noise. Here we describe the implementation of a coefficient of variation-based filter for RNAseq gene expression data. This filter prioritizes consistent data across replicates, allowing lowly expressed genes with low-variation measurements to be retained for downstream analysis. We show, using two independent Arabidopsis RNAseq datasets, that this filter allows for the inclusion of many more transcription factors than even a low-stringency expression level filter. This effect is independent of sequencing depth. We find that these lowly-expressed genes mark specific cell clusters (CCs) in our single-nucleus (sn)RNAseq dataset and may facilitate future characterization of currently unknown cell types or states. We further characterize communities of co-expressed genes, sampled across the day at two growth temperatures, in relation to snRNAseq CCs, finding evidence for a highly photosynthetic cell population and a cell state marked by high cell division and translation. These methods can be expanded to RNAseq analysis in many systems, facilitating the construction of more detailed models of tissue-specific gene regulatory networks.

How tissues generate asymmetric growth from spatially and temporally restricted signals remains a fundamental challenge in developmental biology. We show that transient cell states encode positional information that instructs asymmetric growth in plants. Using spatial single-cell transcriptomics and multi-omics, we uncover a surprisingly complex landscape of transient cell states in the apical hook, including a previously unrecognized population in the apical hypocotyl region that integrates developmental and hormonal cues and bifurcates into opposing growth trajectories. Gene regulatory network analysis and functional perturbation establish GATA TRANSCRIPTION FACTOR 2 (GATA2) as a central regulator that promotes cell elongation through gibberellin signaling. These results reveal that diverse, spatially localized transient cell states function as regulatory hubs that convert positional information into divergent growth programs, providing a unifying framework for how dynamic cellular states shape organ morphology.

Transcriptome analysis via RNA sequencing (RNAseq) has become a ubiquitous method of molecular characterization from whole organisms, dissected tissues, and single cells. These experiments have provided an extraordinary volume of data describing the molecular states and responses to many conditions. However, standard approaches to RNAseq analysis commonly use expression level filters that eliminate potentially useful data in the service of decreasing noise. Here we describe the implementation of a coefficient of variation-based filter for RNAseq gene expression data. This filter prioritizes consistent data across replicates, allowing lowly-expressed genes with low-variation measurements to be retained for downstream analysis. We show that, in our Arabidopsis RNAseq data set, this filter allows for the inclusion of many more transcription factors than even a low-stringency expression level filter. We find that these lowly-expressed genes mark specific cell clusters in our single-nucleus (sn)RNAseq dataset. We further characterize communities of co-expressed genes, sampled across the day at two growth temperatures, in relation to snRNAseq cell clusters, finding evidence for a highly photosynthetic cell population, and a cell state marked by high cell division and translation. These methods can be expanded to RNAseq analysis in many systems, facilitating the construction of more detailed models of tissue-specific gene regulatory networks.

All organisms experience stress as an inevitable part of life, from single-celled microorganisms to complex multicellular beings. The ability to recover from stress is a fundamental trait that determines the overall resilience of an organism, yet stress recovery is understudied. To investigate how plants recover from drought, we examine a fine-scale time series of RNA sequencing starting 15 min after rehydration following moderate drought. We reveal that drought recovery is a rapid process involving the activation of thousands of recovery-specific genes. To capture these rapid recovery responses in different Arabidopsis thaliana ( A. thaliana ) leaf cell types, we perform a single-nucleus transcriptome analysis at the onset of drought recovery, identifying a cell type-specific transcriptional state developing independently across cell types. To further validate the cell-type specific transcriptional changes observed during drought recovery, we employ spatial transcriptomics using multiplexed error-robust fluorescence in situ hybridization (MERFISH), revealing anatomical localization of recovery-induced gene expression programs across Arabidopsis leaf tissues. Furthermore, we reveal a recovery-induced activation of the immune system that occurs autonomously, and which enhances pathogen resistance in vivo in A. thaliana , wild tomato ( Solanum pennellii) and domesticated tomato ( Solanum lycopersicum cv. M82). Since rehydration promotes microbial proliferation and thereby increases the risk of infection, the activation of drought recovery-induced immunity may be crucial for plant survival in natural environments. These findings indicate that drought recovery coincides with a preventive defense response, unraveling the complex regulatory mechanisms that facilitate stress recovery in different plant cell types.

Arabidopsis has been pivotal in uncovering fundamental principles of plant biology, yet a comprehensive, high-resolution understanding of its cellular identities throughout the entire life cycle remains incomplete. Here we present a single-nucleus and spatial transcriptomic atlas spanning ten developmental stages, encompassing over 400,000 nuclei from all organ systems and tissues—from seeds to developing siliques. Leveraging paired single-nucleus and spatial transcriptomic datasets, we annotate 75% of identified cell clusters, revealing striking molecular diversity in cell types and states across development. Our integrated approach identified conserved transcriptional signatures among recurrent cell types, organ-specific heterogeneity and previously uncharacterized cell-type-specific markers validated spatially. Moreover, we uncover dynamic transcriptional programs governing secondary metabolite production and differential growth patterns, exemplified by detailed spatial profiling of the compact yet complex apical hook structure; this profiling revealed transient cellular states linked to developmental progression and hormonal regulation, highlighting the hidden complexity underlying plant morphogenesis. Functional validation of genes uniquely expressed within specific cell contexts confirmed their essential developmental roles, underscoring the predictive power of our atlas. Collectively, this comprehensive resource provides an invaluable foundation for exploring cellular differentiation, environmental responses and genetic perturbations at high resolution, advancing our understanding of plant biology.

Plants lack specialized and mobile immune cells. Consequently, any cell type that encounters pathogens must mount immune responses and communicate with surrounding cells for successful defence. However, the diversity, spatial organization and function of cellular immune states in pathogen-infected plants are poorly understood¹. Here we infect Arabidopsis thaliana leaves with bacterial pathogens that trigger or supress immune responses and integrate time-resolved single-cell transcriptomic, epigenomic and spatial transcriptomic data to identify cell states. We describe cell-state-specific gene-regulatory logic that involves transcription factors, putative cis-regulatory elements and target genes associated with disease and immunity. We show that a rare cell population emerges at the nexus of immune-active hotspots, which we designate as primary immune responder (PRIMER) cells. PRIMER cells have non-canonical immune signatures, exemplified by the expression and genome accessibility of a previously uncharacterized transcription factor, GT-3A, which contributes to plant immunity against bacterial pathogens. PRIMER cells are surrounded by another cell state (bystander) that activates genes for long-distance cell-to-cell immune signalling. Together, our findings suggest that interactions between these cell states propagate immune responses across the leaf. Our molecularly defined single-cell spatiotemporal atlas provides functional and regulatory insights into immune cell states in plants.

C₄ photosynthesis is used by the most productive plants on the planet, and compared with the ancestral C₃ pathway, it confers a 50% increase in efficiency¹. In more than 60 C₄ lineages, CO₂ fixation is compartmentalized between tissues, and bundle-sheath cells become photosynthetically activated². How the bundle sheath acquires this alternate identity that allows efficient photosynthesis is unclear. Here we show that changes to bundle-sheath gene expression in C₄ leaves are associated with the gain of a pre-existing cis-code found in the C₃ leaf. From single-nucleus gene-expression and chromatin-accessibility atlases, we uncover DNA binding with one finger (DOF) motifs that define bundle-sheath identity in the major crops C₃ rice and C₄ sorghum. Photosynthesis genes that are rewired to be strongly expressed in the bundle-sheath cells of C₄ sorghum acquire cis-elements that are recognized by DOFs. Our findings are consistent with a simple model in which C₄ photosynthesis is based on the recruitment of an ancestral cis-code associated with bundle-sheath identity. Gain of such elements harnessed a stable patterning of transcription factors between cell types that are found in both C₃ and C₄ leaves to activate photosynthesis in the bundle sheath. Our findings provide molecular insights into the evolution of the complex C₄ pathway, and might also guide the rational engineering of C₄ photosynthesis in C₃ crops to improve crop productivity and resilience^3,4.

Summary: In multicellular systems changes to the patterning of gene expression drive modifications in cell function and trait evolution. One striking example is found in more than sixty plant lineages where compartmentation of photosynthesis between cell types allowed the evolution of the efficient C ₄ pathway from the ancestral C ₃ state. The molecular events enabling this transition are unclear. We used single nuclei sequencing to generate atlases for C ₃ rice and C ₄ sorghum during photomorphogenesis. Our analysis revealed that initiation of photosynthesis gene expression is conditioned by cell identity. In both species a conserved cistrome defines each cell type, and photosynthesis genes switching expression from mesophyll in rice to bundle sheath in sorghum acquire hallmarks of bundle sheath identity. The sorghum bundle sheath has also acquired gene networks associated with C ₃ guard cells. We conclude C ₄ photosynthesis is based on rewiring in cis that exapts cell identity networks of C ₃ plants.