Understanding plant pathogen interactions using spatial and single

Communications Biology volume 6, Article number: 814 (2023) Cite this article

1723 Accesses

17 Altmetric

Metrics details

Plants are in contact with diverse pathogens and microorganisms. Intense investigation over the last 30 years has resulted in the identification of multiple immune receptors in model and crop species as well as signaling overlap in surface-localized and intracellular immune receptors. However, scientists still have a limited understanding of how plants respond to diverse pathogens with spatial and cellular resolution. Recent advancements in single-cell, single-nucleus and spatial technologies can now be applied to plant–pathogen interactions. Here, we outline the current state of these technologies and highlight outstanding biological questions that can be addressed in the future.

Plants can be infected by diverse pathogenic organisms that modulate host cells to enable their growth and dissemination. Pathogens deploy a variety of manipulation strategies that can vary during the course of infection. These strategies include plant defense suppression as well as deployment of toxins and degradative enzymes to facilitate colonization and nutrient release1. Some pathogens can directly penetrate and invade plant tissues, while others gain entry through wounds or natural openings. Vector-borne pathogens can be delivered directly into vascular tissues by different groups of piercing–sucking insects. Each mechanism of tissue invasion brings the pathogen into contact with different cell and tissue types1,2,3.

Distinct pathogen infection stages are simultaneously observed in a leaf4. Pathogen distribution in plants is not homogeneous, generating unequal symptom development5,6,7. Most previous studies investigating plant–pathogen interactions were carried out using whole plants or complex tissue types. There are significant differences between single-cell and whole-tissue responses, suggesting that the response observed at the tissue level is an average of oscillations occurring between pathogen-targeted and untargeted cells8. Recent technological advancements enable scientists to investigate plant and pathogen responses at single-cell resolution or in a spatial context9,10,11. These advancements will facilitate a more holistic understanding of cellular responses and variability within a tissue.

Plants possess an innate immune system, which consists of surface localized pattern recognition receptors (PRRs) and intracellular nucleotide-binding leucine-rich repeat receptors (NLRs)12. PRRs can recognize conserved microbe- and damage-associated molecular patterns (MAMPs and DAMPs, respectively), resulting in PRR-triggered immunity (PTI). Plant NLR immune receptors recognize secreted pathogen effectors inside cells, inducing effector-triggered immunity (ETI)12. Although PRRs and NLRs are structurally distinct and can recognize different pathogen components, they share significant overlap in downstream signaling, such as mitogen-activated protein kinase (MAPK) cascades, calcium flux, a burst of reactive oxygen species (ROS), transcriptional reprogramming, and phytohormone signaling13. Recently, it was shown that PTI and ETI mutually potentiate each other to mediate robust resistance14,15. Advancements in single-cell and spatial technologies have the potential to elucidate cellular responses in pathogen-targeted and adjacent cells.

Although immune signaling has been extensively studied, scientist still lack an understanding of the spatial expression of immune receptors across diverse plant tissues. For many years, we assumed that all plant cells were immune competent. Recently, it has been demonstrated that only a restricted subset of Arabidopsis root zones directly respond to the flagellin MAMP in the absence of damage16,17. Detailed characterization of response competent plant cells during the infection process is necessary to understand the mechanisms that regulate disease progression.

Here, we discuss the challenges and potential of studying pathogen–plant interactions from a spatial and single-cell point of view. First, we briefly introduce the current state of spatial and single-cell technologies. Next, we immerse ourselves in how these technologies can be used to address outstanding biological questions.

Studies at the level of tissues or organs have advanced our understanding of pathogen perception and plant signaling in compatible (susceptible) and incompatible (immune or non-host) interactions12,18,19. However, many important questions still remain. How do different plant cell types respond to pathogen infection? How do pathogen-targeted cells communicate with their neighbors? What transcripts, proteins and metabolites are masked in bulk analyses but play essential roles during plant–pathogen interactions? Recent advances in single-cell/-nucleus and spatial analyses facilitate the investigation of these foundational questions9,10,11,20,21,22,23.

Single-cell RNA sequencing (scRNA-seq) enables gene expression profiling in individual cells22,24,25,26. scRNA-seq was first developed to analyze the cellular transcriptome from a single mouse blastomere27. In 2012, this technology was used to profile hundreds of cells; in 2015, transcriptional profiling of 44,808 mouse cells was achieved28,29. In 2016, scRNA-seq was adopted to plant root tissue30. Since then, this technology has been used to understand plant development and acclimatization to changing environments22,31,32.

The most widely used scRNA-seq technology is droplet-based, which combines microfluidics with barcodes to allow parallel and high-throughput transcriptome profiling of individual cells (inDrop, Drop-seq, and 10× Genomics; Fig. 1)22,25,29. A plant cell consists of transcripts in both the cytoplasm and nucleus. The half-life of a transcript can be up to 24h33. Therefore, the cellular transcriptome can capture gene expression over time. RNA-seq can be performed with nuclei (snRNA-seq) or isolated individual cells (scRNA-seq). In Arabidopsis, 14% of total genes exhibited differential expression between these two methods34. snRNA-seq detected fewer transcripts than scRNA-seq, but it still profiled ~90% of the total transcripts detected in scRNA-seq. One advantage of snRNA-seq is this approach enables profiling cells that are difficult to enzymatically digest. Overall, both snRNA-seq and scRNA-seq exhibit high correlation with bulk RNA-seq (r = 0.7–0.8) and reflect the expression pattern in intact tissue34. For snRNA-seq, nuclei can be isolated using a variety of approaches on fresh or frozen tissue35,36,37. For scRNA-seq, individual cells are isolated through enzymatic digestion from plant tissues (Fig. 1)22. The protoplasting process results in transcriptional changes, which can also affect transcripts in a cell-type/state-specific manner38,39. Thus, it is essential to include bulk RNA-seq of the entire tissue as a control to exclude genes affected by the protoplasting process. Next, isolated cells are separated and encapsulated in droplets with barcoded beads using microfluidics. Subsequently, cell lysis and cDNA barcoding occur within droplets. Finally, transcripts are sequenced in parallel on the Illumina platform and demultiplexed (Fig. 1)22,26.

For scRNA-seq, individual cells are isolated through enzymatic digestion from intact tissue to generate protoplasts. Microfluidics are used to separate and encapsulate single protoplasts in droplets with barcoded beads. Each barcoded bead is coated with DNA probes, which contain poly (dT) to capture mRNAs, a unique molecular identifier (UMI) and a cell-specific barcode. Cell lysis then occurs, and mRNAs are hybridized to probes and reverse transcribed on beads within droplets. Finally, a library of barcoded cDNA from thousands of single cells is sequenced to generate single-cell transcriptomes. For spatial transcriptomics, freshly frozen plant tissue is sectioned onto a spatial transcriptomics slide. Capture zones on the slide contain thousands of spots. Each spot consists of probes containing a unique spatial barcode, unique molecular identifier (UMI), and poly (dT) region to capture transcripts. Transcripts are released through permeabilization, hybridized to probes and reverse transcribed to incorporate spatial barcodes. The generated cDNAs carrying spatial information are cleaved from the slide and used to prepare a library for sequencing. The sequenced reads are spatially mapped based on spatial barcodes. Created with BioRender.com.

In addition to droplet-based methods, plate-based methods are typically combined with fluorescence activated cell sorting (FACS) to sort cells of interest into individual wells in a plate22,24,25. FACS is a well-established method used in conjunction with fluorescent reporter lines to capture specific groups of cells. For example, FACS has been used to sort specific phloem and root cell types that were then analyzed by scRNA-seq40. While plate-based methods have lower numbers of profiled cells, they have the highest sensitivity to study rare transcripts or limited tissues22,24. Laser-capture microdissection is another method to capture tens or hundreds of cells under direct microscopic visualization41. Recent advancements in combinatorial barcoding strategies (SPLiT-seq and sci-RNA-seq) enable transcriptomic profiling from an exponentially scalable number of individual cells or nuclei42,43,44.

Tissue dissociation is required for single-cell isolation, but results in damage and a loss of spatial content within a tissue, which is critical to understand cellular function20. Spatial transcriptomics (ST) was developed to preserve spatial information while profiling cellular gene expression at high resolution. ST includes spot-based and fluorescence in situ hybridization (FISH)-based methods. Spot-based ST utilizes spot-specific barcodes anchored in an array to capture and label mRNAs from tissue cryosections (Fig. 1)21,45. This has been commercialized and applied in human/animal tissues at single-cell resolution46. Recently, spot-based ST has been extended to plant tissues, including Populus tremula leaf buds, Arabidopsis and Picea abies cones9,21,37,47. For the Visium spatial transcriptomics technology (10× Genomics), the center-to-center distance between neighboring spots is 100 µm which is bigger than the average size of a single plant cell (10–100 µm)21,45. The larger distance between spots will decrease resolution for a single tissue piece, but could ultimately be compensated by including additional capture areas. Another spot-based ST technology, Stereo-seq, has been applied to Arabidopsis leaves. Stereo-seq docks a nanoball as a spot, which significantly reduces the distance between neighboring spots to 500 nm47. In addition to spot-based methods, the FISH-based ST have been used in plant research11,37. For example, plant hybridization-based targeted observation of gene expression map (PHYTOMap), was developed to spatially resolve expression of dozens of genes at a single-cell level in whole-mount Arabidopsis root tissue11. PHYTOMap is more affordable than commercially available spatial transcriptomic platforms, such as Molecular Cartography (Resolve Biosciences) and MERSCOPE (Vizgen).

Transcriptomic approaches allow researchers to analyze cellular changes indirectly, but proteomic and metabolic profiling provides a more direct characterization of a cell’s functional output48. Advancements in single-cell and spatial technologies for characterizing proteomic and metabolic states are not as advanced as transcriptomic analyses due to technical challenges48,49,50. Technical challenges imposed by the nature of metabolites and proteins include: different chemistries required for different analyte types, diverse proteins and metabolites with varied abundance and the ability of metabolites to leak out of cells. Furthermore, the detection limit for protein and metabolite quantification is often higher than for transcriptional profiling and metabolite identification depends on the presence of a library of known compounds51. Recently, spatially resolved proteome analyses using laser capture microdissection coupled with nanodroplet-based sampling was used to profile proteins from ~8–15 parenchyma cells of the tomato fruit pericarp, but was limited to ~400 proteins52,53. Spatial metabolomics has enabled profiling of a single soybean root nodule inoculated with Bradyrhizobium japonicum10. Mass spectrometry imaging can resolve spatial distribution and quantification of targeted plant metabolites at as high as single-cell resolution54,55.

Plant pathogens employ a wide variety of invasion strategies and can colonize different tissue types. Most research has focused on leaf invasion through open stomata and wounds or by direct penetration of the leaf epidermis1,19. However, several devastating bacterial, fungal, and viral pathogens can invade vascular tissue2,3,56. For example, soilborne pathogens are frequently xylem colonizing and must navigate multiple cell types in the root to reach the vascular system57. Scientists still have a poor understanding of the specific cell types that are manipulated by pathogens, and how these cells can respond to pathogen infection.

While roots are immune signaling competent, different zones within the root respond differentially to MAMPs or pathogen infection58,59,60,61,62. Using fluorescent reporter lines for immune marker genes and a PRR receptor, differentiated outer cell layers in the Arabidopsis root display dampened MAMP sensitivity until they encounter damage, which locally boosts immune responsiveness16,17. These spatially restricted root responses may be due to the prevalence of microbial communities in the rhizosphere17. RNA-seq analyses also revealed NLR gene expression varies between organs in a species-specific manner63. A recent study generated a transcriptome atlas of Arabidopsis from seed-to-seed that encompasses all major organs using snRNA-seq37. This important resource can be used to query expression of PRRs, NLRs and immune signaling networks to gain greater insight into developmental and tissue specific regulation of immune competence.

The ability to profile the response to pathogen infection with spatial and cellular resolution will result in a more holistic understanding of how plants respond to diverse pathogens. What cell types are immune capable? How does cellular signaling change depending on cell and tissue type? Immune responses cannot be completely understood without considering the role of different cell types, plant age and developmental stage. Scientists have begun to reveal varying immune status in different cell types using a combination of sc/snRNA-seq coupled with FISH-based spatial mapping of transcripts64,65. For example, a significant portion of Toll/Interleukin-1 receptor NLR (TNL) genes exhibited enriched expression in the vasculature, while this was not observed for coiled-coiled NLR (CNL) and CCR domain-NLRs in Arabidopsis upon inoculation with the fungal pathogen Colletotrichum higginsianum64. Distinct patterns of immune gene expression in particular cell populations were also detected in Arabidopsis plants infected with virulent and avirulent P. syringae using a combination of snRNA-seq, snATAC-seq, and spatial transcriptome (MERFISH) analyses65. For example, genes involved in systemic acquired resistance (SAR) including ALD1, FMO1 and ILL6 also showed enhanced expression in Phloem Companion Cells, indicating that this cell type may play a unique role in regulating SAR and contribute to sending systemic immune signals65. By unraveling cell type-specific immune responses, these studies contribute to a better understanding of the complexity and coordination of plant immune responses.

Genetically encoded fluorescent reporter lines expressing immune markers in defined cell types have also been used to spatially investigate plant responses16,17. A combination of FACS and SMART-seq single-cell technologies enabled transcriptional profiling of protophloem sieve elements, metaphloem sieve elements, companion cells and phloem pole pericycle cells40. A similar approach could facilitate investigation of specific responses from rare cell types or states during pathogen infection through sc/snRNA-seq or ST (Fig. 2).

A schematic diagram showing representative research areas that can be investigated by single-cell, single-nucleus and spatial profiling. A combination of different approaches will lead to a more comprehensive understanding of plant-microbe interactions. Created with BioRender.com.

Microbial distribution is variable in and on plant tissue66,67. Moreover, multiple infection stages simultaneously exist within a tissue even under controlled conditions. For example, three distinct infection phases can be simultaneously observed in a single rice leaf sheath after inoculation with the fungal pathogen Magnaporthe oryzae (early and late biotrophic, transient necrotrophic phase)4. Spores of the fungal pathogen Zymoseptoria tritici germinate and penetrate wheat leaves for 10 days, leading to multiple asynchronized infections that exist simultaneously7,68,69. The plant response to asynchronous infection and uneven effector targeting will result in heterogeneous cellular responses that cannot be revealed using bulk RNA-seq analyses.

Not only is pathogen distribution variable within a tissue, but pathogens also exhibit cell-to-cell differences in gene expression during infection70,71,72. For example, the necrotrophic bacterial pathogen Dickeya dadantii exhibits heterogeneity in cell length and gene expression to balance virulence and vegetative growth during infection70. In Pseudomonas syringae, expression of the genes encoding the type three secretion system shows a reversible bi-stable pattern of expression that affects bacterial virulence72. The impact of transcriptional heterogeneity in isogenic bacterial populations cannot be studied using bulk RNA-seq analyses. Technical challenges have hindered adapting scRNA-seq technology to microbes; prokaryotic mRNAs are lower in abundance and are not polyadenylated. Nevertheless, several tools have been developed recently for single-cell bacterial RNA-seq in vitro, including microbial split-pool ligation transcriptomics (microSPLiT), probe-based bacterial sequencing (ProBac-seq), and prokaryotic expression profiling by tagging RNA in situ and sequencing (PETRI-seq)73,74,75. The combination of these tools coupled with efficient bacterial isolation methods from plant tissue, such as a density gradient centrifugation in the presence of an RNA protecting reagent76, could help elucidate heterogeneous microbial responses during infection.

Understanding the association between microbial distribution and the spatiotemporal regulation of plant responses during colonization remains an area that requires further exploration77. The spatial distribution of complex bacterial communities in plant tissue was recently reported using a novel technique called sequential error-robust FISH (SEER-FISH)78. Cao et al.78 investigated the microbial colonization patterns along the root and observed changes in the spatial relationships among different microbial taxa in synthetic communities. In natural environments, plants interact with diverse microbial communities that exhibit a wide range of behaviors, from pathogenic to mutualistic79. To apply the SEER-FISH method to natural communities, it is necessary to first identify the species comprising the bacterial community. Recent simultaneous detection of microbial location and plant transcripts using spatial transcriptomics highlights the heterogenicity of both microbial distribution and plant responses within the same leaf9,65. Future advancements in ST should enable profiling of both plant and pathogen transcriptomes, including the profiling of pathogen-targeted and adjacent cells (Fig. 2). By examining the transcriptomic changes occurring in both the plant and microbial cells during colonization, researchers can gain a comprehensive understanding of the molecular interactions and signaling processes involved in the establishment of plant–microbe associations.

Several recent studies have highlighted the importance of spatiotemporal plant responses using ST, scRNA-seq alone, or a combination of scRNA-seq and genetically encoded reporter lines36,64,65,80,81,82,83,84,85,86,87. Different cell types in Medicago truncatula roots exhibited differentially regulated gene expression in response to the bacterial symbiont Ensifer (Sinorhizobium) meliloti36. Single-cell metabolomics has also revealed metabolic heterogeneity in root nodules colonized by the symbiotic bacterium Bradyrhizobium japonicum10. Cellular heterogeneity in plant-microbe interactions can be visualized in a temporal manner using cell trajectory analyses80,81,82,83,87 (Fig. 2). For example, a continuum of disease progression from an immune to susceptible state within a leaf was revealed from single-cell transcriptomic profiling of a compatible interaction between Arabidopsis and P. syringae82. In addition, the cellular heterogeneity in plant immune response is also spatially regulated. Spatially restricted immune gene expression is correlated with pathogen distribution, with plant cells proximal to pathogen colonization exhibiting stronger immune gene expression9,64,65,82,88. P. syringae enters plant apoplast through natural openings such as stomata and accumulates in substomatal cavities. Zhu et al.82 found that the expression of the immune marker FRK1 was highly induced in cells surrounding these cavities colonized by P. syringae. Furthermore, in Arabidopsis, Liu et al.88 observed two successive peaks of transcriptomic responses during ETI induced by the AvrRpt2 effector. These peaks represented different cellular populations with distinct gene expression patterns. The first peak corresponded to cells responding cell-autonomously to AvrRpt2, indicating a localized immune response. The second peak consisted of cells surrounding the initially responding population, suggesting a coordinated response in the neighboring cells. Future advances using a combination of single-cell and high-resolution spatial approaches will provide greater insights into cellular heterogeneity and spatiotemporal dynamics occurring during plant–microbe interactions.

Hallmarks of plant immune responses (ROS, Ca2+, DAMP production) act as important signaling molecules for cell-to-cell communication89. Global reprogramming of plant tissues for defense compromises plant growth90. During natural infection, plants must be able to restrict pathogen infection without completely reprogramming entire tissues. Plants can compartmentalize defense responses by spatially restricting immune responses within a leaf to a few cell layers through a local acquired resistance (LAR)91,92. Both transcriptional reporter lines and spatial transcriptomics have revealed spatially restricted Arabidopsis leaf immune responses around bacterial colonies upon ETI activation65,82,92,93,94,95. Furthermore, three additional immune reporter lines demonstrated expression of immune transcripts in a circular pattern of several cell layers in close proximity to P. syringae colonies after surface inoculation82. Expression of plant defense genes and microbial abundance also exhibit spatial correlations on both outdoor grown and growth-chamber inoculated Arabidopsis using ST9,65.

Despite significant advances in single-cell techniques, challenges remain when studying dynamic interactions between two or more organisms. For example, identifying, isolating and profiling plant cells invaded by pathogens remains challenging. The physical interaction between plant cells and the pathogen are removed after isolation of nuclei. Advancements in ST and strategies to tag infected cells in direct contact with the pathogen are needed. These advancements will facilitate an understanding of how pathogen-targeted plant cells communicate with their neighbors. It will also be important to profile cellular signaling in response to different pathogens at different stages of infection in different tissue types across plant genotypes to obtain a holistic understanding of the repertoire of how plants respond to pathogen infection.

Simultaneous transcriptional profiling of both the plant and pathogen remain challenging at cellular resolution. Plants, as well as pathogens, exhibit heterogeneity in their responses. Current technology captures polyadenylated eukaryotic transcripts for sequencing library preparation. This step inhibits the capture of most bacterial and viral transcripts. In order to capture thousands of cells or nuclei, most studies collect tissue from multiple pooled plants, which masks plant-to-plant variation. Other challenges include cost, data analysis, and integration96. This is akin to the early stages of transcriptional profiling using microarrays and RNA-seq. Developing high throughput methods with reduced cost for ST, single-cell, and snRNA-seq will facilitate broader use of these approaches.

During plant-microbe interactions, plants have evolved a finely regulated cell-to-cell communication to combat constant exposure of microbial challenges89. Single-cell analysis has been used to probe cell-to-cell communication in plant root cells to developmental and abiotic signals, but not yet in plant-microbe interactions97,98. With the development of spatial techniques, the spatially resolved transcriptomics will facilitate an understanding of cell-to-cell communication9,65. Moreover, incorporation of spatial coordinates in a cell trajectory may resolve differences with data generated from dissociated cells46.

Future investigations using spatial technologies with single-cell resolution will enable a more comprehensive understanding of how plant cells are able to communicate with their neighbors and induce robust but spatially restricted defense responses. The identification of transcriptional factors and transcripts that are masked in bulk analyses has enabled construction of gene regulatory networks (GRNs) at single-cell resolution36,39,81. The next frontier in plant–microbe interactions will require integration of multiple ‘omic approaches with spatial and cellular resolution, including the transcriptome, epigenome, metabolome and proteome65,99 (Fig. 2). Integrated analyses using single-cell multi-omics and spatial omics will provide the opportunity to holistically investigate cell-specific responses as well as reveal the landscape of plant and pathogen responsiveness with spatial resolution22,65. The hypotheses generated from such analyses can then be experimentally tested in the context of different plant–pathogen interactions. Ultimately, a high-resolution understanding of how plants respond to diverse pathogens will identify novel regulatory mechanisms that can be harnessed for more effective disease control.

Faulkner, C. & Robatzek, S. Plants and pathogens: putting infection strategies and defence mechanisms on the map. Curr. Opin. Plant Biol. 15, 699–707 (2012).

Article CAS PubMed Google Scholar

Huang, W. et al. Bacterial vector-borne plant diseases: unanswered questions and future directions. Mol. Plant 13, 1379–1393 (2020).

Article CAS PubMed Google Scholar

Kashyap, A., Planas-Marquès, M., Capellades, M., Valls, M. & Coll, N. S. Blocking intruders: inducible physico-chemical barriers against plant vascular wilt pathogens. J. Exp. Bot. 72, 184–198 (2020).

Article PubMed Central Google Scholar

Jones, K. et al. Disruption of the interfacial membrane leads to Magnaporthe oryzae effector re-location and lifestyle switch during rice blast disease. Front. Cell Dev. Biol. 9, 681734 (2021). This article reported that multiple infection phases simultaneously exist in a single rice leaf sheath after inoculation with the fungal pathogen Magnaporthe oryzae.

Daugherty, M. P., Lopes, J. & Almeida, R. P. P. Vector within-host feeding preference mediates transmission of a heterogeneously distributed pathogen. Ecol. Entomol. 35, 360–366 (2010).

Article Google Scholar

Xin, X.-F. et al. Bacteria establish an aqueous living space in plants crucial for virulence. Nature 539, 524–529 (2016).

Article CAS PubMed PubMed Central Google Scholar

Fantozzi, E., Kilaru, S., Gurr, S. J. & Steinberg, G. Asynchronous development of Zymoseptoria tritici infection in wheat. Fungal Genet. Biol. 146, 103504 (2021).

Article CAS PubMed PubMed Central Google Scholar

Thor, K. & Peiter, E. Cytosolic calcium signals elicited by the pathogen-associated molecular pattern flg22 in stomatal guard cells are of an oscillatory nature. N. Phytol. 204, 873–881 (2014). This article reported there are significant differences between single-cell and whole-tissue responses, suggesting that the response observed at the tissue level is an average of oscillations occurring between pathogen-targeted and untargeted cells.

Article CAS Google Scholar

Saarenpää, S. et al. Spatially resolved host-bacteria-fungi interactomes via spatial metatranscriptomics. Preprint at bioRxiv https://doi.org/10.1101/2022.07.18.496977 (2022).

Samarah, L. Z. et al. Single-cell metabolic profiling: metabolite formulas from isotopic fine structures in heterogeneous plant cell populations. Anal. Chem. 92, 7289–7298 (2020).

Article CAS PubMed Google Scholar

Nobori, T., Oliva, M., Lister, R. & Ecker, J. R. Multiplexed single-cell 3D spatial gene expression analysis in plant tissue using PHYTOMap. Nat. Plants 9, 1026–1033 (2023).

Ngou, B. P. M., Jones, J. D. G. & Ding, P. Plant immune networks. Trends Plant Sci. 27, 255–273 (2022).

Article CAS PubMed Google Scholar

Peng, Y., van Wersch, R. & Zhang, Y. Convergent and divergent signaling in PAMP-triggered immunity and effector-triggered Immunity. Mol. Plant Microbe Interact. 31, 403–409 (2018).

Article CAS PubMed Google Scholar

Ngou, B. P. M., Ahn, H.-K., Ding, P. & Jones, J. D. G. Mutual potentiation of plant immunity by cell-surface and intracellular receptors. Nature 592, 110–115 (2021).

Article CAS PubMed Google Scholar

Yuan, M., Ngou, B. P. M., Ding, P. & Xin, X. F. PTI-ETI crosstalk: an integrative view of plant immunity. Curr. Opin. Plant Biol. 62, 102030 (2021).

Article CAS PubMed Google Scholar

Zhou, F. et al. Co-incidence of damage and microbial patterns controls localized immune responses in roots. Cell 180, 440.e8–453.e8 (2020). This article reported the spatially restricted immune responses in Arabidopsis roots and demonstrated this was controlled by the co-incidence of damage signals and microbial patterns.

Article Google Scholar

Emonet, A. et al. Spatially restricted immune responses are required for maintaining root meristematic activity upon detection of bacteria. Curr. Biol. 31, 1012–1028.e1017 (2021).

Article CAS PubMed Google Scholar

Wang, Y., Pruitt, R. N., Nürnberger, T. & Wang, Y. Evasion of plant immunity by microbial pathogens. Nat. Rev. Microbiol. 20, 449–464 (2022).

Article CAS PubMed Google Scholar

Zhang, J., Coaker, G., Zhou, J. M. & Dong, X. Plant immune mechanisms: from reductionistic to holistic points of view. Mol. Plant 13, 1358–1378 (2020).

Article CAS PubMed Google Scholar

Giacomello, S. A new era for plant science: spatial single-cell transcriptomics. Curr. Opin. Plant Biol. 60, 102041 (2021).

Article CAS PubMed Google Scholar

Giacomello, S. et al. Spatially resolved transcriptome profiling in model plant species. Nat. Plants 3, 1–11 (2017). The authors applied spatial transcriptomics on three different plant tissues, describing spatially resolved transcriptomes at cellular level in plants for the first time.

Article Google Scholar

Seyfferth, C. et al. Advances and opportunities in single-cell transcriptomics for plant research. Annu. Rev. Plant Biol. 72, 847–866 (2021).

Article CAS PubMed PubMed Central Google Scholar

Depuydt, T., De Rybel, B. & Vandepoele, K. Charting plant gene functions in the multi-omics and single-cell era. Trends Plant Sci. 28, 283–296 (2023).

Birnbaum, K. D. Power in numbers: single-cell RNA-seq strategies to dissect complex tissues. Annu. Rev. Genet. 52, 203–221 (2018).

Article CAS PubMed PubMed Central Google Scholar

Rich-Griffin, C. et al. Single-cell transcriptomics: a high-resolution avenue for plant functional genomics. Trends Plant Sci. 25, 186–197 (2020).

Article CAS PubMed Google Scholar

Denyer, T. & Timmermans, M. C. Crafting a blueprint for single-cell RNA sequencing. Trends Plant Sci. 27, 92–103 (2022).

Article CAS PubMed Google Scholar

Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009). The authors analyzed the whole transcriptome from a single mouse blastomere and detected 64% of all expressed genes for the first time in a single cell, highlightling the intricate and diverse nature of the transcript variants across the entire genome within individual cells.

Article CAS PubMed Google Scholar

Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

Article PubMed PubMed Central Google Scholar

Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

Article CAS PubMed PubMed Central Google Scholar

Efroni, I. et al. Root regeneration triggers an embryo-like sequence guided by hormonal interactions. Cell 165, 1721–1733 (2016). This article reported root regeneration using a combination of transgenic reporter lines and single-cell RNA-seq, which was the first study to dissect plant root development at single-cell resolution.

Article CAS PubMed PubMed Central Google Scholar

Shahan, R. et al. A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants. Dev. Cell 57, 543–560.e549 (2022).

Article CAS PubMed PubMed Central Google Scholar

Tenorio Berrío, R. et al. Single-cell transcriptomics sheds light on the identity and metabolism of developing leaf cells. Plant Physiol. 188, 898–918 (2022).

Article PubMed Google Scholar

Sorenson, R. S., Deshotel, M. J., Johnson, K., Adler, F. R. & Sieburth, L. E. Arabidopsis mRNA decay landscape arises from specialized RNA decay substrates, decapping-mediated feedback, and redundancy. Proc. Natl Acad. Sci. USA 115, E1485–E1494 (2018).

Article CAS PubMed Google Scholar

Guillotin, B. et al. A pan-grass transcriptome reveals patterns of cellular divergence in crops. Nature 617, 785–791 (2023).

Article CAS PubMed Google Scholar

Farmer, A., Thibivilliers, S., Ryu, K. H., Schiefelbein, J. & Libault, M. Single-nucleus RNA and ATAC sequencing reveals the impact of chromatin accessibility on gene expression in Arabidopsis roots at the single-cell level. Mol. Plant 14, 372–383 (2021).

Article CAS PubMed Google Scholar

Cervantes-Pérez, S. A. et al. Cell-specific pathways recruited for symbiotic nodulation in the Medicago truncatula legume. Mol. Plant 15, 1868–1888 (2022).

Article PubMed Google Scholar

Lee, T. A. et al. A single-nucleus atlas of seed-to-seed development in Arabidopsis. Preprint at bioRxiv https://doi.org/10.1101/2023.03.23.533992 (2023). This article reported Arabidopsis seed-to-seed development using single-nucleus RNA-seq and spatial transcriptomics, enabling us to understand the transcriptional conservation and heterogeneity of different cells in different organs at single-cell resolution.

Birnbaum, K. et al. A gene expression map of the Arabidopsis root. Science 302, 1956–1960 (2003).

Article CAS PubMed Google Scholar

Denyer, T. et al. Spatiotemporal developmental trajectories in the Arabidopsis root revealed using high-throughput single-cell RNA sequencing. Dev. Cell 48, 840–852.e845 (2019).

Article CAS PubMed Google Scholar

Otero, S. et al. A root phloem pole cell atlas reveals common transcriptional states in protophloem-adjacent cells. Nat. Plants 8, 954–970 (2022).

Article CAS PubMed Google Scholar

Espina, V., Heiby, M., Pierobon, M. & Liotta, L. A. Laser capture microdissection technology. Expert Rev. Mol. Diagn. 7, 647–657 (2007).

Article CAS PubMed Google Scholar

Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).

Article CAS PubMed PubMed Central Google Scholar

Martin, B. K. et al. Optimized single-nucleus transcriptional profiling by combinatorial indexing. Nat. Protoc. 18, 188–207 (2023).

Article CAS PubMed Google Scholar

Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

Article CAS PubMed PubMed Central Google Scholar

Giacomello, S. & Lundeberg, J. Preparation of plant tissue to enable spatial transcriptomics profiling using barcoded microarrays. Nat. Protoc. 13, 2425–2446 (2018).

Article CAS PubMed Google Scholar

Palla, G., Fischer, D. S., Regev, A. & Theis, F. J. Spatial components of molecular tissue biology. Nat. Biotechnol. 40, 308–318 (2022).

Article CAS PubMed Google Scholar

Xia, K. et al. The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves. Dev. Cell 57, 1299.e4–1310.e4 (2022).

Article Google Scholar

Taylor, M. J. et al. Optical microscopy-guided laser ablation electrospray ionization ion mobility mass spectrometry: ambient single cell metabolomics with increased confidence in molecular identification. Metabolites 11, 200 (2021).

Article CAS PubMed PubMed Central Google Scholar

de Souza, L. P., Borghi, M. & Fernie, A. Plant single-cell metabolomics—challenges and perspectives. Int. J. Mol. Sci. 21, 8987 (2020).

Article PubMed PubMed Central Google Scholar

Clark, N. M., Elmore, J. M. & Walley, J. W. To the proteome and beyond: advances in single-cell omics profiling for plant systems. Plant Physiol. 188, 726–737 (2021).

Article PubMed Central Google Scholar

Single-cell proteomics: challenges and prospects. Nat. Methods 20, 317–318 (2023).

Liang, Y. et al. Spatially resolved proteome profiling of <200 cells from tomato fruit pericarp by integrating laser-capture microdissection with nanodroplet sample preparation. Anal. Chem. 90, 11106–11114 (2018).

Article CAS PubMed Google Scholar

Balasubramanian, V. K. et al. Cell‐type‐specific proteomics analysis of a small number of plant cells by integrating laser capture microdissection with a nanodroplet sample processing platform. Curr. Protoc. 1, e153 (2021).

Article CAS PubMed Google Scholar

Hu, W. et al. Mass spectrometry imaging for direct visualization of components in plants tissues. J. Sep. Sci. 44, 3462–3476 (2021).

Article CAS PubMed Google Scholar

de Menezes Daloso, D., Morais, E. G., Oliveira e Silva, K. F. & Williams, T. C. R. Cell-type-specific metabolism in plants. Plant J. https://doi.org/10.1111/tpj.16214 (2023).

Kappagantu, M., Collum, T. D., Dardick, C. & Culver, J. N. Viral hacks of the plant vasculature: the role of phloem alterations in systemic virus infection. Annu. Rev. Virol. 7, 351–370 (2020).

Article CAS PubMed Google Scholar

Xue, H., Lozano-Durán, R. & Macho, A. P. Insights into the root invasion by the plant pathogenic bacterium Ralstonia solanacearum. Plants 9, 516 (2020).

Article CAS PubMed PubMed Central Google Scholar

Millet, Y. A. et al. Innate immune responses activated in Arabidopsis roots by microbe-associated molecular patterns. Plant Cell 22, 973–990 (2010).

Article CAS PubMed PubMed Central Google Scholar

Beck, M. et al. Expression patterns of FLAGELLIN SENSING 2 map to bacterial entry sites in plant shoots and roots. J. Exp. Bot. 65, 6487–6498 (2014).

Article CAS PubMed PubMed Central Google Scholar

Wyrsch, I., Domínguez-Ferreras, A., Geldner, N. & Boller, T. Tissue-specific FLAGELLIN-SENSING 2 (FLS2) expression in roots restores immune responses in Arabidopsis fls2 mutants. N. Phytol. 206, 774–784 (2015).

Article CAS Google Scholar

Poncini, L. et al. In roots of Arabidopsis thaliana, the damage-associated molecular pattern AtPep1 is a stronger elicitor of immune signalling than flg22 or the chitin heptamer. PLoS ONE 12, e0185808 (2017).

Article PubMed PubMed Central Google Scholar

Fröschel, C. et al. Plant roots employ cell-layer-specific programs to respond to pathogenic and beneficial microbes. Cell Host Microbe 29, 299.e7–310.e7 (2021).

Article Google Scholar

Munch, D. et al. The Brassicaceae family displays divergent, shoot-skewed NLR resistance gene expression. Plant Physiol. 176, 1598–1609 (2017).

Article PubMed PubMed Central Google Scholar

Tang, B., Feng, L., Ding, P. & Ma, W. Cell type-specific responses to fungal infection in plants revealed by single-cell transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2023.04.03.535386 (2023).

Nobori, T. et al. Time-resolved single-cell and spatial gene regulatory atlas of plants under pathogen attack. Preprint at bioRxiv https://doi.org/10.1101/2023.04.10.536170 (2023). This article profiled single-cell transcriptome, epigenome and spatial transcriptome in Arabidopsis leaf responding to both virulent and avirulent bacterial pathogens, revealing spatial heterogeneity of plant immune responses associated with pathogen distribution and providing a spatiotemporal map of plant-microbe interactions at the cellular level.

Leveau, J. H. J. in Principles of Plant-Microbe Interactions: Microbes for Sustainable Agriculture (ed. Lugtenberg, B.) Ch, 4 (Springer, 2015).

Desprez-Loustau, M.-L. et al. From leaf to continent: the multi-scale distribution of an invasive cryptic pathogen complex on oak. Fungal Ecol. 36, 39–50 (2018).

Article Google Scholar

Fones, H. N., Eyles, C. J., Kay, W., Cowper, J. & Gurr, S. J. A role for random, humidity-dependent epiphytic growth prior to invasion of wheat by Zymoseptoria tritici. Fungal Genet. Biol. 106, 51–60 (2017).

Article PubMed PubMed Central Google Scholar

Haueisen, J. et al. Highly flexible infection programs in a specialized wheat pathogen. Ecol. Evol. 9, 275–294 (2019).

Article PubMed Google Scholar

Cui, Z. et al. Cell-length heterogeneity: a population-level solution to growth/virulence trade-offs in the plant pathogen Dickeya dadantii. PLoS Pathog. 15, e1007703 (2019).

Article CAS PubMed PubMed Central Google Scholar

Striednig, B. & Hilbi, H. Bacterial quorum sensing and phenotypic heterogeneity: how the collective shapes the individual. Trends Microbiol. 30, 379–389 (2022).

Article CAS PubMed Google Scholar

Rufián, J. S. et al. Pseudomonas syringae differentiates into phenotypically distinct subpopulations during colonization of a plant host. Environ. Microbiol. 18, 3593–3605 (2016).

Article PubMed Google Scholar

Kuchina, A. et al. Microbial single-cell RNA sequencing by split-pool barcoding. Science 371, eaba5257 (2021).

Article CAS PubMed Google Scholar

McNulty, R. et al. Probe-based bacterial single-cell RNA sequencing predicts toxin regulation. Nat. Microbiol. 8, 934–945 (2023).

Article CAS PubMed PubMed Central Google Scholar

Blattman, S. B., Jiang, W., Oikonomou, P. & Tavazoie, S. Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing. Nat. Microbiol. 5, 1192–1201 (2020).

Article CAS PubMed Google Scholar

Chapelle, E. et al. A straightforward and reliable method for bacterial in planta transcriptomics: application to the Dickeya dadantii/Arabidopsis thaliana pathosystem. Plant J. 82, 352–362 (2015).

Article CAS PubMed Google Scholar

Tsai, H.-H., Wang, J., Geldner, N. & Zhou, F. Spatiotemporal control of root immune responses during microbial colonization. Curr. Opin. Plant Biol. 74, 102369 (2023).

Article CAS PubMed Google Scholar

Cao, Z. et al. Spatial profiling of microbial communities by sequential FISH with error-robust encoding. Nat. Commun. 14, 1477 (2023). This article provides a useful method for profiling the spatial ecology of complex microbial communities in Arabidopsis roots.

Article CAS PubMed PubMed Central Google Scholar

Velásquez, A. C., Huguet-Tapia, J. C. & He, S. Y. Shared in planta population and transcriptomic features of nonpathogenic members of endophytic phyllosphere microbiota. Proc. Natl Acad. Sci. USA 119, e2114460119 (2022).

Article PubMed PubMed Central Google Scholar

Ku, C. et al. A single-cell view on alga-virus interactions reveals sequential transcriptional programs and infection states. Sci. Adv. 6, eaba4137 (2020).

Article CAS PubMed PubMed Central Google Scholar

Bai, Y. et al. Development of a single-cell atlas for woodland strawberry (Fragaria vesca) leaves during early Botrytis cinerea infection using single-cell RNA-seq. Hortic. Res. 9, uhab055 (2022).

Article PubMed PubMed Central Google Scholar

Zhu, J. et al. Single-cell profiling of Arabidopsisleaves to Pseudomonas syringae infection. Cell Reports 42, e112676 (2023). This article reported plant response to bacterial infection using scRNA-seq and confocal imaging, revealing a single leaf exhibits cellular clusters programmed for immunity and susceptibility in a compatible interaction.

Ye, Q. et al. Differentiation trajectories and biofunctions of symbiotic and un-symbiotic fate cells in root nodules of Medicago truncatula. Mol. Plant 15, 1852–1867 (2022).

Article CAS Google Scholar

Wang, L. et al. Single cell-type transcriptome profiling reveals genes that promote nitrogen fixation in the infected and uninfected cells of legume nodules. Plant Biotechnol. J. 20, 616–618 (2022).

Article CAS PubMed PubMed Central Google Scholar

Kawa, D. & Brady, S. M. Root cell types as an interface for biotic interactions. Trends Plant Sci. 27, 1173–1186 (2022).

Article CAS PubMed Google Scholar

Üstüner, S., Schäfer, P. & Eichmann, R. Development specifies, diversifies and empowers root immunity. EMBO Rep. 23, e55631 (2022).

Article PubMed PubMed Central Google Scholar

Liu, Z. et al. Integrated single-nucleus and spatial transcriptomics captures transitional states in soybean nodule maturation. Nat. Plants 9, 515–524 (2023).

Article CAS PubMed Google Scholar

Liu, X. et al. Dynamic decomposition of transcriptome responses during plant effector-triggered immunity revealed conserved responses in two distinct cell populations. Preprint at bioRxiv https://doi.org/10.1101/2022.12.30.522333 (2022).

Tabassum, N. & Blilou, I. Cell-to-cell communication during plant-pathogen interaction. Mol. Plant Microbe Interact. 35, 98–108 (2022).

Article CAS PubMed Google Scholar

He, Z., Webster, S. & He, S. Y. Growth-defense trade-offs in plants. Curr. Biol. 32, R634–R639 (2022).

Article CAS PubMed Google Scholar

Ross, A. F. Systemic acquired resistance induced by localized virus infections in plants. Virology 14, 340–358 (1961).

Article CAS PubMed Google Scholar

Betsuyaku, S. et al. Salicylic acid and jasmonic acid pathways are activated in spatially different domains around the infection site during effector-triggered immunity in Arabidopsis thaliana. Plant Cell Physiol. 59, 8–16 (2018).

Article CAS PubMed Google Scholar

Salguero-Linares, J. et al. Robust transcriptional indicators of immune cell death revealed by spatiotemporal transcriptome analyses. Mol. Plant 15, 1059–1075 (2022).

Article CAS PubMed Google Scholar

Jacob, P. et al. Broader functions of TIR domains in Arabidopsis immunity. Proc. Natl Acad. Sci. USA 120, e2220921120 (2023).

Article CAS PubMed PubMed Central Google Scholar

Calabria, J. et al. Spatially distinct phytohormone responses of individual Arabidopsis thaliana root cells to infection and colonization by Fusarium oxysporum. Preprint at bioRxiv https://doi.org/10.1101/2022.12.20.521292 (2022).

Cervantes-Pérez, S. A., Thibivillliers, S., Tennant, S. & Libault, M. Review: Challenges and perspectives in applying single nuclei RNA-seq technology in plant biology. Plant Sci. 325, 111486 (2022).

Article PubMed Google Scholar

Wendrich, J. R. et al. Vascular transcription factors guide plant epidermal responses to limiting phosphate conditions. Science 370, eaay4970 (2020).

Article CAS PubMed PubMed Central Google Scholar

Ortiz-Ramírez, C. et al. Ground tissue circuitry regulates organ complexity in maize and Setaria. Science 374, 1247–1252 (2021).

Article PubMed PubMed Central Google Scholar

Yu, X., Liu, Z. & Sun, X. Single-cell and spatial multi-omics in the plant sciences: technical advances, applications, and perspectives. Plant Commun. 4, 100508 (2022).

Article PubMed PubMed Central Google Scholar

Download references

We thank members of the Coaker Laboratory, Benjamin Cole, and Tatsuya Nobori for critical reading of the manuscript. This work was supported by the National Institutes of Health [grant number R35GM136402] to G.C. A.M.-P. was supported by a Postdoctoral fellowship from Fundación Alfonso Martín Escudero.

These authors contributed equally: Jie Zhu, Alba Moreno-Pérez.

Department of Plant Pathology, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA

Jie Zhu, Alba Moreno-Pérez & Gitta Coaker

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

All authors contributed to writing the manuscript and the ideas presented. G.C. led the structure and finalized the writing. J.Z. generated Figs. 1 and 2.

Correspondence to Gitta Coaker.

The authors declare no competing interests.

Communications Biology thanks Andre Velasquez and the other anonymous reviewers for their contribution to the peer review of this work. Primary handling editors: Shahid Mukhtar, George Inglis.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

Zhu, J., Moreno-Pérez, A. & Coaker, G. Understanding plant pathogen interactions using spatial and single-cell technologies. Commun Biol 6, 814 (2023). https://doi.org/10.1038/s42003-023-05156-8

Download citation

Received: 07 April 2023

Accepted: 18 July 2023

Published: 04 August 2023

DOI: https://doi.org/10.1038/s42003-023-05156-8

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.