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ScienceDirect Digging deeper: high-resolution genome-scale data yields new insights into root biology Rucha Karve and Anjali S Iyer-Pascuzzi Development in multicellular organisms is the result of designated cellular programs occurring at specific points in time and space. The root is an excellent model to address how spatio-temporal complexity impacts organ development. Highresolution ‘omic’ approaches have delineated the transcriptional, proteomic, metabolomic, and small RNA profiles of multiple cell types in the Arabidopsis root. Similar approaches have shed light on root cell-type specific transcriptional programs in rice and soybean. These data are being used to identify specific spatio-temporal mechanisms of root development, dissect regulatory networks that control cell identity, and understand hormone responses in the root. Computational modeling of these data combined with new advances in imaging technologies is generating new biological insights into root growth and development. Addresses Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States Corresponding author: Iyer-Pascuzzi, Anjali S ([email protected])
data in root biology [1–8] and these studies have proved invaluable in our understanding of root biology at the cell and tissue level. The current challenge is to move from catalogs of genes, proteins, and metabolites found in different cell types to an understanding of how these work together to contribute to growth and developmental processes. Recent work using quantitative modeling of cell and tissue-specific datasets and improvements in high-resolution image analysis have advanced the field and moved us toward a systems-level view of root biology. In this review, we discuss recent progress in our understanding of root biology that has been driven by highresolution approaches. Since recent reviews [9,10] have discussed the different methods of isolating specific celltypes in plants, we address these methods only briefly in Table 1.
New insight into root–environment interactions with cell-type specific data Abiotic stress
Current Opinion in Plant Biology 2015, 24:24–30 This review comes from a themed issue on Genome studies and molecular genetics Edited by Insuk Lee and Todd C Mockler
http://dx.doi.org/10.1016/j.pbi.2015.01.007 1369-5266/# 2015 Published by Elsevier Ltd.
Introduction The growth and development of an organism is the result of specific cellular programs operating in different cell and tissue types. Knowledge of cell or tissue-specific processes is thus essential for a complete understanding of plant growth and development. High-resolution ‘omic’ data, here defined as transcriptomic, proteomic, or metabolomic data obtained at cell or tissue resolution, can provide insight into questions difficult to answer with data from whole organs, in which the effects of single celltypes are diluted [1,2]. Like most organs in multicellular organisms, roots are composed of multiple cell types (Figure 1). In recent years there has been an explosion of cell-type specific Current Opinion in Plant Biology 2015, 24:24–30
Several reports have showed that cell identity is a key determinant in a cell’s response to stress. Different celltypes and tissues have diverse transcriptional profiles after exposure to a given stress, and their responses vary depending on the environmental condition [3–5,11]. These studies focused on cell-types at just one timepoint after exposure to stress, providing a portrait of one moment in what is a dynamic growth and transcriptional response. Recent work of cell-type specific transcriptional programs at multiple time-points after exposure to salt stress has led to new understanding of the fluctuating nature of these responses [12]. Analysis of root growth using a live-imaging system identified four different phases of root growth within 24 hours after exposure to high salt stress (140 mM NaCl), suggesting that different signaling mechanisms may underlie these different phases [12]. The authors used Fluorescent Activated Cell Sorting (FACS) followed by microarray profiling of four different root cell and tissue types at 6 different time-points after exposure to high salt (140 mM) to transcriptionally dissect the response at a higher temporal and spatial resolution. Similar to their previous work that examined cell-type specific responses after 1 hour of high salt exposure [3], they found that the cortex had the highest number of genes transcriptionally altered by salt stress. Abscisic acid (ABA) signaling specifically in the endodermis (END) and pericycle (PER) was important for regulating root growth during the recovery phase. Intriguingly, the root’s response to salt www.sciencedirect.com
Insights into root biology with high-resolution data Karve and Iyer-Pascuzzi 25
Meristem
Elongation
Maturation
Figure 1
High-throughput and high-resolution phenotyping reveal that the length of the meristem (shaded cortex cells at tip) is correlated with the length of the mature cortex cell (shaded shootward cortex cell) and identify the F-box gene KUK1, which regulates both traits [40]**. FACS and microarray analysis identify 208 ‘core’ epidermal genes [31] and show that mutants without morphological phenotypes have molecular phenotypes that can help define gene function [33]**.
Phenylpropanoids and glucosinolates accumulate in the cortex [8]. A stele GRN consisting of 103 protein-DNA, protein-protein, and miRNA-mRNA interactions between 64 TFs and 8 miRNAs can tolerate expression changes without leading to changes in root morphology [32]. Lateral Root Cap Columella root cap Epidermis Cortex Endodermis Stele (pericycle, procambium, xylem, phloem cell types) Current Opinion in Plant Biology
Graphic examples of selected insights gained from high-resolution analyses and discussed in the text. Major cell types and tissues are shown as colored. Different root developmental zones (meristem, elongation, and maturation) are as labeled. Only the early maturation zone is shown.
stress depended on several hormones in addition to ABA, including jasmonate (JA), gibberellic acid (GA), and brassinosteroids (BR). The root response to high salt is thus the result of temporal changes in hormones during different phases of root growth and recovery [12]. ABA signaling in the endodermis was also shown to be important for lateral root (LR) quiescence after an intermediate level (100 mM) of salt stress [13]. Genetic analysis of primary root (PR) and LR growth in ABA response mutants exposed to 100 mM NaCl showed that only LR growth was significantly affected by defects in endogenous ABA signaling. This level of salt stress induced a period of quiescence in LRs, but not PRs. Expressing the mutant allele abi1-1, which dominantly suppresses ABA signaling, in the endodermis rescued LR growth in plants exposed to either 100 mM salt or exogenous ABA. Thus LR quiescence was induced by ABA signaling in the endodermis [13]. www.sciencedirect.com
Biotic stress
Compared to cell-type specific abiotic stress responses, much less is understood about how cell identity influences root responses to pathogens and beneficial root– microbe interactions. Much of what we know comes from analyses of root hairs, as these serve as an excellent single cell type for understanding the early interaction between roots and microorganisms. A transcriptional analysis of soybean root hairs at multiple time-points after infection with Bradyrhizobium japonicum identified 233 genes regulated at multiple stages of infection; 94 of which were enriched specifically in root hairs [14]. More recent work has used laser capture microdissection (LCM) followed by microarray analysis to determine the transcriptome of cells within root nodules at different stages of the symbiosis [15]. Arbuscular mycorrhiza (AM) fungi colonize inner cortical cells of the root and provide nutrients such as phosphate Current Opinion in Plant Biology 2015, 24:24–30
26 Genome studies and molecular genetics
Table 1 Methods for isolating specific cell and tissue-types in the root Method
Isolation of
Fluorescence activated cell sorting (FACS) [1]
Cells
Laser capture microdissection (LCM) [23]
Technique
Advantages
Disadvantages
Enzymatic digestion of cell walls to obtain protoplasts expressing fluorescent tag
High-throughput transcriptomics, proteomics, and metabolomics Relatively straightforward technique
Cells
Tissue fixed on microscope slide and desired cells are excised
Isolation of nuclei tagged in specific cell types (INTACT) [24]
Nuclei
Affinity-based method
Cell-type specific tagged lines not required Possible in almost all plant species Very useful in epigenetic work Can be done under standard laboratory settings
Cells must be protoplasted Need special instrumentation Previous knowledge of celltype specific promoters required Need special equipment Interference by tissue fixation technique
Translating ribosome affinity purification (TRAP) [11]
Ribosomes
Affinity-based method
and nitrogen to plants. One complication to understanding this interaction is that multiple infection stages are present within the root. Several studies have used LCM combined with microarray analysis to identify the transcriptional changes involved in hosting AM fungi [16–18]. LCM combined with microarray analysis has identified genes expressed in either nematode feeding cells (giant cells or syncytia) or galls (feeding cells and surrounding root tissue) (reviewed in [19]). This work has shown that different gall infection stages have distinct transcriptomic profiles. More recent work revealed that the transcriptomes of gall and feeding cells also differ from one another [20,21,22]. Comparison of the tomato giant cell (GC) transcriptome isolated with LCM to that of Arabidopsis showed conservation for hormone and specific metabolic pathways, while gene regulatory pathways diverged [22]. Thus GC formation may be under different regulatory control in distinct species [22].
Identifying new genes in root development using cell-type specific data Initial studies of specific cell-types in the root focused on understanding what genes, proteins or metabolites were expressed in each cell type [1,2]. These studies gave us broad insights into root development at the cellular level. Transcriptional profiling of 15 different cell types and 12 developmental sections in the longitudinal axis of the Arabidopsis root revealed that cell identity and developmental stage govern transcriptional profiles [2]. Both this work and an analysis of LCM data from different cell types and developmental sections in rice roots [23] Current Opinion in Plant Biology 2015, 24:24–30
Identifies transcripts that are translated
Previous knowledge of celltype specific promoters required Possible differences in pools of nuclear and cellular RNA Same data set cannot be used for multiple analysis such as simultaneous transcriptomics and metabolomics Previous knowledge of celltype specific promoters required
identified genes with fluctuating expression patterns in different developmental stages of the root. Many of the rice genes were orthologs of those in Arabidopsis, suggesting that the pattern of developmental transitions may be conserved between monocot and dicots. More recently, FACS combined with proteomics [6] and small RNA profiling [7] has identified new modulators of root growth. Metabolomic analysis of specific cell types suggested a role in defense for the cortex, which accumulated glucosinolates and phenylpropanoids [8]. Recently, INTACT (isolation of nuclei tagged in specific cell types) [24] and TRAP (translating ribosome affinity purification) [11] resources for tomato roots have been developed [25], leading to exciting possibilities regarding Arabidopsis — tomato cell-type specific comparisons.
Shedding light on the role of auxin with highresolution genomic scale data The role of auxin in patterning the root meristem has been extensively documented, but primarily within the entire root apical meristem. Petersson et al. (2009) [26] used FACS combined with mass-spectrometry to measure auxin (IAA) in 14 transgenic Arabidopsis lines, each of which expressed GFP in a specific root cell or tissuetype. They identified an auxin gradient within the root apical meristem, with the highest concentrations within the quiescent center (QC), and lower concentrations in the columella and epidermis. Measurement of IAA synthesis rates showed that multiple cell types in the root apex have a high capacity for auxin synthesis, suggesting that the root’s auxin response is determined both by transport and local synthesis. These results and those www.sciencedirect.com
Insights into root biology with high-resolution data Karve and Iyer-Pascuzzi 27
of others [27–29] suggested a graded auxin pattern within the root apical meristem (RAM), which does not strictly correspond with the expression pattern of the synthetic auxin reporter DR5. Plants expressing DR5:GFP show highest expression in the QC, followed by the columella. A recent report [30] illustrates one explanation for this apparent dichotomy. Examination of the global root transcriptomic response to auxin in 4 different root tissues identified a bipartite auxin response consisting of both a graded response in the meristem and an ‘archetypal response’ that is similar to the DR5 reporter [30]. The authors speculate that the archetypal response is under control of the canonical auxin signal transduction pathway, while the graded response may be due to indirect auxin regulation by regulators that reflect the auxin concentration gradient in the RAM [30]. The negative regulation characteristic of the canonical auxin pathway may be one reason why the archetypal response does not match the measured auxin concentrations within each cell type [30].
Gene regulatory networks Recent work has demonstrated that high-resolution transcriptional data is a powerful foundation for dissecting developmental networks. FACS profiling of the epidermis of 17 root epidermal mutants followed by microarray analysis identified 208 ‘core root epidermal’ genes. Bayesian modeling with these data showed that the root epidermis differentiation network is not linear but highly branched [31]. A stele gene regulatory network (GRN) was constructed using high-throughput yeast-1-hybrid (Y1H) and yeast-2-hybrid (Y2H) of transcription factors (TFs) with enriched expression within stele cell-types (xylem, phloem, procambium, and pericycle) and miRNAs with stele-expressed targets. Mutations in approximately 65% of TFs in the stele GRN revealed a molecular phenotype, but only a morphological phenotype for 16%, suggesting that the network expression changes in the network were highly buffered to protect against morphological changes [32]. Molecular phenotypes are proving a useful way to define gene function when morphological phenotypes are not available [33]. The transcription network governing root hair development in Arabidopsis includes three sets of paralogous genes. Within each set, mutation of one gene in the pair leads to a morphological phenotype, while mutation of the other does not. In an effort to understand the function of the apparently redundant gene, Simon et al. [33] crossed the epidermal marker WER:GFP into each mutant and used FACS combined with microarrays to determine the transcriptome of developing root epidermal cells in the mutant background [33]. They identified significant levels of differential gene expression within the mutants without root hair phenotypes, and the differentially expressed genes within each mutant were overrepresented for genes found in the ‘core epidermal set’ www.sciencedirect.com
[31]. Further, based on the transparent testa glabrous2 (ttg2) mutant profile, they identified a role for TTG2 in differentiation of root hair cells, rather than non-hair cells as would be expected based on the TTG2 pattern of expression. High-resolution approaches can identify genes that function only in specific developmental contexts and show tight spatio-temporal regulation. The TFs SHORTROOT (SHR) and SCARECROW (SCR) specify and maintain the root stem cell niche [34,35] (reviewed in [6]). Inducible expression of these TFs, followed by FACS sorting and microarray analysis, identified over 800 genes induced by both TFs. Combining these data with SHR direct target data derived from a SHR chromatin immunoprecipitation–microarray (ChIP-chip) experiment identified a cell cycle gene, CYCD6;1 that was regulated by both SHR and SCR [36]. Further work showed that CYCD6;1 along with CYCLIN DEPENDENT KINASE B1 (CDKB1), phosphorylated RETINOBLASTOMA, which physically interacts with SCR [37]. Together with SHR and auxin, these proteins modulate asymmetric cell divisions in the root stem cell niche [37].
High-resolution imaging leads to insights into root development Extracting biological meaning from the vast amount of high-resolution transcriptomic, proteomic, and metabolomic data currently generated is very challenging. Imaging at the cellular level is now playing a pivotal role in our understanding of these data (reviewed in [38]). New technology for high-throughput and high-resolution imaging
The RootArray, a microfluidic device that allows for growth and confocal microscopy imaging of 64 Arabidopsis seedlings [39], was recently used to identify a new gene that controls both root meristem and cortex cell length [40]. Imaging over 1500 Arabidopsis seedling roots from 201 accessions, followed by a genome wide association study (GWAS) and quantitative RT-PCR identified KURZ UND KLEIN (KUK), an F-box protein expressed in the transition and elongation zones of the root. Analysis of mutant lines and Arabidopsis accessions with different KUK alleles showed that higher KUK expression levels were sufficient to increase meristem and mature cortical cell length [40]. High-resolution phenotyping combined with GWAS or mutant screens is likely to identify many more regulators of root growth. Microfluidic devices combined with Fo¨rster resonance energy transfer (FRET) sensors have also recently been used to explore the physiology of root cell types [41,42]. The RootChip combines live imaging of multiple Arabidopsis seedlings with the ability to rapidly alter the root growth environment [41]. The effects of different nutrients, toxins, or hormones on cell types can be examined Current Opinion in Plant Biology 2015, 24:24–30
28 Genome studies and molecular genetics
by pulsing different media through the RootChip. Using this system and plants expressing different FRETbased sensors, the dynamics of cytosolic glucose, galactose [41], Zn++ [42] concentrations, and ABA responses [43] were examined [44]. Given that many different types of sensors for different cellular metabolites are available (reviewed in [45]), microfluidic devices like the RootChip and RootArray have the potential to improve our understanding of the dynamics of metabolite and nutrient signaling on a cellular and subcellular level in roots.
High resolution imaging of an auxin sensor combined with modeling reveals new insight into auxin transport in the root
Models of auxin transport in the meristem have been based primarily on the role of the polarly localized PIN auxin efflux proteins [28,29,46]. However, work with auxin transporter mutants suggested that both PIN efflux proteins and nonpolar auxin influx (AUX1/ LAX) carriers were important for determining auxin flow in the root tip [47] (reviewed in [48]). A recent study combines modeling with high-resolution imaging to show the importance of both types of transporters for auxin transport in the root tip. Band et al. [49] used root cell geometries from segmented confocal images to model the flow of auxin in the root meristem. They tested their predictions using the auxin sensor DIIVenus [50,51], which allows for quantitative measurements of auxin levels [50,51]. When Band et al. used only PIN proteins in the model, the model prediction did not match the pattern of auxin reported by DII-Venus. But once both PINs and AUX1/LAX proteins were included, the model more closely matched the pattern of auxin reported by DII-Venus in the root tip. Removing PINs from the model predicted a significantly smaller flux of auxin through the meristem, but did not change the spatial distribution. This work suggests that PINs regulate the direction of auxin flow through the meristem while AUX1/LAX proteins control its spatial distribution [49].
Conclusions and future prospects The complexity of development in multicellular organisms necessitates knowledge of transcriptomic, proteomic, and metabolomics data at both the cell-type and whole organ levels. We now have tremendous ability to test hypotheses generated from these data, build networks, and identify new genes controlling root development. A systems-level view into root development in Arabidopsis is emerging from modeling these data and imaging studies. The tools necessary for high-resolution analyses in crop species such as tomato are becoming available [25]. Future work should address questions of conservation of root spatio-temporal programs between Arabidopsis and crop species. Current Opinion in Plant Biology 2015, 24:24–30
Acknowledgements We thank Purdue University for support, Abhijit Karve for manuscript review, and Erin Sparks for title suggestions.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
Birnbaum K, Shasha DE, Wang JY, Jung JW, Lambert GM, Galbraith DW, Benfey PN: A gene expression map of the Arabidopsis root. Science 2003, 302:1956-1960.
2.
Brady SM, Orlando DA, Lee JY, Wang JY, Koch J, Dinneny JR, Mace D, Ohler U, Benfey PN: A high-resolution root spatiotemporal map reveals dominant expression patterns. Science 2007, 318:801-806.
3.
Dinneny JR, Long TA, Wang JY, Jung JW, Mace D, Pointer S, Barron C, Brady SM, Schiefelbein J, Benfey PN: Cell identity mediates the response of Arabidopsis roots to abiotic stress. Science 2008, 320:942-945.
4.
Gifford ML, Dean A, Gutierrez RA, Coruzzi GM, Birnbaum KD: Cellspecific nitrogen responses mediate developmental plasticity. Proc Natl Acad Sci U S A 2008, 105:803-808.
5.
Iyer-Pascuzzi AS, Jackson T, Cui H, Petricka JJ, Busch W, Tsukagoshi H, Benfey PN: Cell identity regulators link development and stress responses in the Arabidopsis root. Dev Cell 2011, 21:770-782.
6.
Petricka JJ, Schauer MA, Megraw M, Breakfield NW, Thompson JW, Georgiev S, Soderblom EJ, Ohler U, Moseley MA, Grossniklaus U et al.: The protein expression landscape of the Arabidopsis root. Proc Natl Acad Sci U S A 2012, 109:6811-6818.
7.
Breakfield NW, Corcoran DL, Petricka JJ, Shen J, Sae-Seaw J, Rubio-Somoza I, Weigel D, Ohler U, Benfey PN: High-resolution experimental and computational profiling of tissue-specific known and novel miRNAs in Arabidopsis. Genome Res 2012, 22:163-176.
8.
Moussaieff A, Rogachev I, Brodsky L, Malitsky S, Toal TW, Belcher H, Yativ M, Brady SM, Benfey P, Aharoni A: Highresolution metabolic mapping of cell types in plant roots. Proc Natl Acad Sci U S A 2013. E1232–I1241.
9.
Wang D, Mills ES, Deal RB: Technologies for systems-level analysis of specific cell types in plants. Plant Sci 2012, 197:21-29.
10. Bailey-Serres J: Microgenomics: genome-scale, cell-specific monitoring of multiple gene regulation tiers. Annu Rev Plant Biol 2013, 64:293-325. 11. Mustroph A, Zanetti ME, Jang CJ, Holtan HE, Repetti PP, Galbraith DW, Girke T, Bailey-Serres J: Profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis. Proc Natl Acad Sci U S A 2009, 106:18843-18848. 12. Geng Y, Wu R, Wee CW, Xie F, Wei X, Chan PM, Tham C, Duan L, Dinneny JR: A spatio-temporal understanding of growth regulation during the salt stress response in Arabidopsis. Plant Cell 2013, 25:2132-2154. The authors dissect the salt stress response by FACS and microarray profiling of different cell-types at 6 time-points after exposure to high salt stress. Their results highlight the importance of cell-type specific hormone signaling for root growth and recovery after stress. 13. Duan L, Dietrich D, Ng CH, Chan PM, Bhalerao R, Bennett MJ, Dinneny JR: Endodermal ABA signaling promotes lateral root quiescence during salt stress in Arabidopsis seedlings. Plant Cell 2013, 25:324-341. This work shows the importance of ABA signaling in the endodermis for the LR response to salt stress. 14. Libault M, Farmer A, Brechenmacher L, Drnevich J, Langley RJ, Bilgin DD, Radwan O, Neece DJ, Clough SJ, May GD et al.: Complete transcriptome of the soybean root hair cell, www.sciencedirect.com
Insights into root biology with high-resolution data Karve and Iyer-Pascuzzi 29
a single-cell model, and its alteration in response to Bradyrhizobium japonicum infection. Plant Physiol 2010, 152:541-552.
response to drive Arabidopsis root meristem growth. Curr Biol 2011, 21:1918-1923.
15. Limpens E, Moling S, Hooiveld G, Pereira PA, Bisseling T, Becker JD, Kuster H: Cell- and tissue-specific transcriptome analyses of Medicago truncatula root nodules. PLOS ONE 2013, 8:e64377.
30. Bargmann BO, Vanneste S, Krouk G, Nawy T, Efroni I, Shani E, Choe G, Friml J, Bergmann DC, Estelle M et al.: A map of cell type-specific auxin responses. Mol Syst Biol 2013, 9:688. Identifies bipartite auxin responses in the Arabidopsis root and nicely demonstrates the insight that comes from high-resolution work.
16. Gaude N, Bortfeld S, Duensing N, Lohse M, Krajinski F: Arbuscule-containing and non-colonized cortical cells of mycorrhizal roots undergo extensive and specific reprogramming during arbuscular mycorrhizal development. Plant J 2012, 69:510-528.
31. Bruex A, Kainkaryam RM, Wieckowski Y, Kang YH, Bernhardt C, Xia Y, Zheng X, Wang JY, Lee MM, Benfey P et al.: A gene regulatory network for root epidermis cell differentiation in Arabidopsis. PLoS Genet 2012, 8:e1002446.
17. Hogekamp C, Arndt D, Pereira PA, Becker JD, Hohnjec N, Kuster H: Laser microdissection unravels cell-type-specific transcription in arbuscular mycorrhizal roots, including CAATbox transcription factor gene expression correlating with fungal contact and spread. Plant Physiol 2011, 157:2023-2043. 18. Hogekamp C, Kuster H: A roadmap of cell-type specific gene expression during sequential stages of the arbuscular mycorrhiza symbiosis. BMC Genomics 2013, 14:306. 19. Escobar C, Sigal B, Mitchum M: Transcriptomic and proteomic analysis of the plant response to namatode infection. In Genomics and molecular genetics of plant-nematode interactions. Edited by Jones , Gheysen , Fenoll . Springer; 2011:157-176. 20. Barcala M, Garcia A, Cabrera J, Casson S, Lindsey K, Favery B, Garcia-Casado G, Solano R, Fenoll C, Escobar C: Early transcriptomic events in microdissected Arabidopsis nematode-induced giant cells. Plant J 2010, 61:698-712. 21. Ji H, Gheysen G, Denil S, Lindsey K, Topping JF, Nahar K, Haegeman A, De Vos WH, Trooskens G, Van Criekinge W et al.: Transcriptional analysis through RNA sequencing of giant cells induced by Meloidogyne graminicola in rice roots. J Exp Bot 2013, 64:3885-3898. 22. Portillo M, Cabrera J, Lindsey K, Topping J, Andres MF, Emiliozzi M, Oliveros JC, Garcia-Casado G, Solano R, Koltai H et al.: Distinct and conserved transcriptomic changes during nematode-induced giant cell development in tomato compared with Arabidopsis: a functional role for gene repression. New Phytol 2013, 197:1276-1290. Comparison of giant cell transcriptomes in Arabidopsis and tomato shows similarity between some metabolic pathways (such as lignin decomposition) but little conservation of genes related to the regulation of gene expression. The authors speculate that genes controlling giant cell differentiation and/or maintenance may be specific to each species. 23. Jiao Y, Tausta SL, Gandotra N, Sun N, Liu T, Clay NK, Ceserani T, Chen M, Ma L, Holford M et al.: A transcriptome atlas of rice cell types uncovers cellular, functional and developmental hierarchies. Nat Genet 2009, 41:258-263. 24. Deal RB, Henikoff S: The INTACT method for cell type-specific gene expression and chromatin profiling in Arabidopsis thaliana. Nat Protoc 2011, 6:56-68. 25. Ron M, Kajala K, Pauluzzi G, Wang D, Reynoso MA, Zumstein K, Garcha J, Winte S, Masson H, Inagaki S et al.: Hairy root transformation using Agrobacterium rhizogenes as a tool for exploring cell type-specific gene expression and function using tomato as a model. Plant Physiol 2014, 166:455-469. 26. Petersson SV, Johansson AI, Kowalczyk M, Makoveychuk A, Wang JY, Moritz T, Grebe M, Benfey PN, Sandberg G, Ljung K: An auxin gradient and maximum in the Arabidopsis root apex shown by high-resolution cell-specific analysis of IAA distribution and synthesis. Plant Cell 2009, 21:1659-1660. 27. Galinha C, Hofhuis H, Luijten M, Willemsen V, Blilou I, Heidstra R, Scheres B: PLETHORA proteins as dose-dependent master regulators of Arabidopsis root development. Nature 2007, 449:1053-1057. 28. Grieneisen VA, Xu J, Maree AF, Hogeweg P, Scheres B: Auxin transport is sufficient to generate a maximum and gradient guiding root growth. Nature 2007, 449:1008-1013. 29. Santuari L, Scacchi E, Rodriguez-Villalon A, Salinas P, Dohmann EM, Brunoud G, Vernoux T, Smith RS, Hardtke CS:: Positional information by differential endocytosis splits auxin www.sciencedirect.com
32. Brady SM, Zhang L, Megraw M, Martinez NJ, Jiang E, Yi CS, Liu W, Zeng A, Taylor-Teeples M, Kim D et al.: A stele-enriched gene regulatory network in the Arabidopsis root. Mol Syst Biol 2011, 7:459. 33. Simon M, Bruex A, Kainkaryam RM, Zheng X, Huang L, Woolf PJ, Schiefelbein J: Tissue-specific profiling reveals transcriptome alterations in Arabidopsis mutants lacking morphological phenotypes. Plant Cell 2013, 25:3175-3185. Using high-resolution profiling of epidermal mutants, the authors show that many mutants with no morphological phenotype have a significant molecular phenotype. Cell-type specific profiling can be used to assign functions to genes which have no phenotypes when mutated. 34. Di Laurenzio L, Wysocka-Diller J, Malamy JE, Pysh L, Helariutta Y, Freshour G, Hahn MG, Feldmann KA, Benfey PN: The SCARECROW gene regulates an asymmetric cell division that is essential for generating the radial organization of the Arabidopsis root. Cell 1996, 86:423-433. 35. Helariutta Y, Fukaki H, Wysocka-Diller J, Nakajima K, Jung J, Sena G, Hauser MT, Benfey PN: The SHORT-ROOT gene controls radial patterning of the Arabidopsis root through radial signaling. Cell 2000, 101:555-567. 36. Sozzani R, Cui H, Moreno-Risueno MA, Busch W, Van Norman JM, Vernoux T, Brady SM, Dewitte W, Murray JA, Benfey PN: Spatiotemporal regulation of cell-cycle genes by SHORTROOT links patterning and growth. Nature 2010, 466:128-132. 37. Cruz-Ramirez A, Diaz-Trivino S, Blilou I, Grieneisen VA, Sozzani R, Zamioudis C, Miskolczi P, Nieuwland J, Benjamins R, Dhonukshe P et al.: A bistable circuit involving SCARECROWRETINOBLASTOMA integrates cues to inform asymmetric stem cell division. Cell 2012, 150:1002-1015. 38. Sozzani R, Busch W, Spalding EP, Benfey PN: Advanced imaging techniques for the study of plant growth and development. Trends Plant Sci 2014, 19:304-310. 39. Busch W, Moore BT, Martsberger B, Mace DL, Twigg RW, Jung J, Pruteanu-Malinici I, Kennedy SJ, Fricke GK, Clark RL et al.: A microfluidic device and computational platform for highthroughput live imaging of gene expression. Nat Methods 2012, 9:1101-1106. A new device for simultaneous growth and cellular imaging of Arabidopsis roots. 40. Meijo´n M, Satbhai SB, Tsuchimatsu T, Busch W: Genome-wide association study using cellular traits identifies a new regulator of root development in Arabidopsis. Nat Genet 2014, 46:77-81. A beautiful illustration of the use of high-throughput and high-resolution cellular imaging to identify novel genes that control root development. 41. Grossmann G, Guo WJ, Ehrhardt DW, Frommer WB, Sit RV, Quake SR, Meier M: The RootChip: an integrated microfluidic chip for plant science. Plant Cell 2011, 23:4234-4240. A device similar to that in [38] but in which growth media can be altered during growth. 42. Lanquar V, Grossmann G, Vinkenborg JL, Merkx M, Thomine S, Frommer WB: Dynamic imaging of cytosolic zinc in Arabidopsis roots combining FRET sensors and RootChip technology. New Phytol 2014, 202:198-208. 43. Jones AM, Danielson JA, Manojkumar SN, Lanquar V, Grossmann G, Frommer WB: Abscisic acid dynamics in roots detected with genetically encoded FRET sensors. Elife 2014, 3:e01741. Current Opinion in Plant Biology 2015, 24:24–30
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44. Waadt R, Hitomi K, Nishimura N, Hitomi C, Adams SR, Getzoff ED, Schroeder JI: FRET-based reporters for the direct visualization of abscisic acid concentration changes and distribution in Arabidopsis. Elife 2014, 3:e01739. 45. Swanson SJ, Choi WG, Chanoca A, Gilroy S: In vivo imaging of Ca2+, pH, and reactive oxygen species using fluorescent probes in plants. Annu Rev Plant Biol 2011, 62:273-297. 46. Grieneisen VA, Scheres B, Hogeweg P, Mare´e AF: Morphogengineering roots: comparing mechanisms of morphogen gradient formation. BMC Syst Biol 2012, 6:37. 47. Peret B, Swarup K, Ferguson A, Seth M, Yang Y, Dhondt S, James N, Casimiro I, Perry P, Syed A et al.: AUX/LAX genes encode a family of auxin influx transporters that perform distinct functions during Arabidopsis development. Plant Cell 2012, 24:2874-2885.
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48. Swarup R, Peret B: AUX/LAX family of auxin influx carriers — an overview. Front Plant Sci 2012, 3:225. 49. Band LR, Wells DM, Fozard JA, Ghetiu T, French AP, Pound MP, Wilson MH, Yu L, Li W, Hijazi HI et al.: Systems analysis of auxin transport in the Arabidopsis root apex. Plant Cell 2014, 26:862-875. This work shows the importance of both PIN and AUX1/LAX transporters for auxin transport in the root. 50. Vernoux T, Brunoud G, Farcot E, Morin V, Van den Daele H, Legrand J, Oliva M, Das P, Larrieu A, Wells D et al.: The auxin signalling network translates dynamic input into robust patterning at the shoot apex. Mol Syst Biol 2011, 7:508. 51. Brunoud G, Wells DM, Oliva M, Larrieu A, Mirabet V, Burrow AH, Beeckman T, Kepinski S, Traas J, Bennett MJ et al.: A novel sensor to map auxin response and distribution at high spatiotemporal resolution. Nature 2012, 482:103-106.
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ScienceDirect Digging deeper: high-resolution genome-scale data yields new insights into root biology Rucha Karve and Anjali S Iyer-Pascuzzi Development in multicellular organisms is the result of designated cellular programs occurring at specific points in time and space. The root is an excellent model to address how spatio-temporal complexity impacts organ development. Highresolution ‘omic’ approaches have delineated the transcriptional, proteomic, metabolomic, and small RNA profiles of multiple cell types in the Arabidopsis root. Similar approaches have shed light on root cell-type specific transcriptional programs in rice and soybean. These data are being used to identify specific spatio-temporal mechanisms of root development, dissect regulatory networks that control cell identity, and understand hormone responses in the root. Computational modeling of these data combined with new advances in imaging technologies is generating new biological insights into root growth and development. Addresses Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, United States Corresponding author: Iyer-Pascuzzi, Anjali S ([email protected])
data in root biology [1–8] and these studies have proved invaluable in our understanding of root biology at the cell and tissue level. The current challenge is to move from catalogs of genes, proteins, and metabolites found in different cell types to an understanding of how these work together to contribute to growth and developmental processes. Recent work using quantitative modeling of cell and tissue-specific datasets and improvements in high-resolution image analysis have advanced the field and moved us toward a systems-level view of root biology. In this review, we discuss recent progress in our understanding of root biology that has been driven by highresolution approaches. Since recent reviews [9,10] have discussed the different methods of isolating specific celltypes in plants, we address these methods only briefly in Table 1.
New insight into root–environment interactions with cell-type specific data Abiotic stress
Current Opinion in Plant Biology 2015, 24:24–30 This review comes from a themed issue on Genome studies and molecular genetics Edited by Insuk Lee and Todd C Mockler
http://dx.doi.org/10.1016/j.pbi.2015.01.007 1369-5266/# 2015 Published by Elsevier Ltd.
Introduction The growth and development of an organism is the result of specific cellular programs operating in different cell and tissue types. Knowledge of cell or tissue-specific processes is thus essential for a complete understanding of plant growth and development. High-resolution ‘omic’ data, here defined as transcriptomic, proteomic, or metabolomic data obtained at cell or tissue resolution, can provide insight into questions difficult to answer with data from whole organs, in which the effects of single celltypes are diluted [1,2]. Like most organs in multicellular organisms, roots are composed of multiple cell types (Figure 1). In recent years there has been an explosion of cell-type specific Current Opinion in Plant Biology 2015, 24:24–30
Several reports have showed that cell identity is a key determinant in a cell’s response to stress. Different celltypes and tissues have diverse transcriptional profiles after exposure to a given stress, and their responses vary depending on the environmental condition [3–5,11]. These studies focused on cell-types at just one timepoint after exposure to stress, providing a portrait of one moment in what is a dynamic growth and transcriptional response. Recent work of cell-type specific transcriptional programs at multiple time-points after exposure to salt stress has led to new understanding of the fluctuating nature of these responses [12]. Analysis of root growth using a live-imaging system identified four different phases of root growth within 24 hours after exposure to high salt stress (140 mM NaCl), suggesting that different signaling mechanisms may underlie these different phases [12]. The authors used Fluorescent Activated Cell Sorting (FACS) followed by microarray profiling of four different root cell and tissue types at 6 different time-points after exposure to high salt (140 mM) to transcriptionally dissect the response at a higher temporal and spatial resolution. Similar to their previous work that examined cell-type specific responses after 1 hour of high salt exposure [3], they found that the cortex had the highest number of genes transcriptionally altered by salt stress. Abscisic acid (ABA) signaling specifically in the endodermis (END) and pericycle (PER) was important for regulating root growth during the recovery phase. Intriguingly, the root’s response to salt www.sciencedirect.com
Insights into root biology with high-resolution data Karve and Iyer-Pascuzzi 25
Meristem
Elongation
Maturation
Figure 1
High-throughput and high-resolution phenotyping reveal that the length of the meristem (shaded cortex cells at tip) is correlated with the length of the mature cortex cell (shaded shootward cortex cell) and identify the F-box gene KUK1, which regulates both traits [40]**. FACS and microarray analysis identify 208 ‘core’ epidermal genes [31] and show that mutants without morphological phenotypes have molecular phenotypes that can help define gene function [33]**.
Phenylpropanoids and glucosinolates accumulate in the cortex [8]. A stele GRN consisting of 103 protein-DNA, protein-protein, and miRNA-mRNA interactions between 64 TFs and 8 miRNAs can tolerate expression changes without leading to changes in root morphology [32]. Lateral Root Cap Columella root cap Epidermis Cortex Endodermis Stele (pericycle, procambium, xylem, phloem cell types) Current Opinion in Plant Biology
Graphic examples of selected insights gained from high-resolution analyses and discussed in the text. Major cell types and tissues are shown as colored. Different root developmental zones (meristem, elongation, and maturation) are as labeled. Only the early maturation zone is shown.
stress depended on several hormones in addition to ABA, including jasmonate (JA), gibberellic acid (GA), and brassinosteroids (BR). The root response to high salt is thus the result of temporal changes in hormones during different phases of root growth and recovery [12]. ABA signaling in the endodermis was also shown to be important for lateral root (LR) quiescence after an intermediate level (100 mM) of salt stress [13]. Genetic analysis of primary root (PR) and LR growth in ABA response mutants exposed to 100 mM NaCl showed that only LR growth was significantly affected by defects in endogenous ABA signaling. This level of salt stress induced a period of quiescence in LRs, but not PRs. Expressing the mutant allele abi1-1, which dominantly suppresses ABA signaling, in the endodermis rescued LR growth in plants exposed to either 100 mM salt or exogenous ABA. Thus LR quiescence was induced by ABA signaling in the endodermis [13]. www.sciencedirect.com
Biotic stress
Compared to cell-type specific abiotic stress responses, much less is understood about how cell identity influences root responses to pathogens and beneficial root– microbe interactions. Much of what we know comes from analyses of root hairs, as these serve as an excellent single cell type for understanding the early interaction between roots and microorganisms. A transcriptional analysis of soybean root hairs at multiple time-points after infection with Bradyrhizobium japonicum identified 233 genes regulated at multiple stages of infection; 94 of which were enriched specifically in root hairs [14]. More recent work has used laser capture microdissection (LCM) followed by microarray analysis to determine the transcriptome of cells within root nodules at different stages of the symbiosis [15]. Arbuscular mycorrhiza (AM) fungi colonize inner cortical cells of the root and provide nutrients such as phosphate Current Opinion in Plant Biology 2015, 24:24–30
26 Genome studies and molecular genetics
Table 1 Methods for isolating specific cell and tissue-types in the root Method
Isolation of
Fluorescence activated cell sorting (FACS) [1]
Cells
Laser capture microdissection (LCM) [23]
Technique
Advantages
Disadvantages
Enzymatic digestion of cell walls to obtain protoplasts expressing fluorescent tag
High-throughput transcriptomics, proteomics, and metabolomics Relatively straightforward technique
Cells
Tissue fixed on microscope slide and desired cells are excised
Isolation of nuclei tagged in specific cell types (INTACT) [24]
Nuclei
Affinity-based method
Cell-type specific tagged lines not required Possible in almost all plant species Very useful in epigenetic work Can be done under standard laboratory settings
Cells must be protoplasted Need special instrumentation Previous knowledge of celltype specific promoters required Need special equipment Interference by tissue fixation technique
Translating ribosome affinity purification (TRAP) [11]
Ribosomes
Affinity-based method
and nitrogen to plants. One complication to understanding this interaction is that multiple infection stages are present within the root. Several studies have used LCM combined with microarray analysis to identify the transcriptional changes involved in hosting AM fungi [16–18]. LCM combined with microarray analysis has identified genes expressed in either nematode feeding cells (giant cells or syncytia) or galls (feeding cells and surrounding root tissue) (reviewed in [19]). This work has shown that different gall infection stages have distinct transcriptomic profiles. More recent work revealed that the transcriptomes of gall and feeding cells also differ from one another [20,21,22]. Comparison of the tomato giant cell (GC) transcriptome isolated with LCM to that of Arabidopsis showed conservation for hormone and specific metabolic pathways, while gene regulatory pathways diverged [22]. Thus GC formation may be under different regulatory control in distinct species [22].
Identifying new genes in root development using cell-type specific data Initial studies of specific cell-types in the root focused on understanding what genes, proteins or metabolites were expressed in each cell type [1,2]. These studies gave us broad insights into root development at the cellular level. Transcriptional profiling of 15 different cell types and 12 developmental sections in the longitudinal axis of the Arabidopsis root revealed that cell identity and developmental stage govern transcriptional profiles [2]. Both this work and an analysis of LCM data from different cell types and developmental sections in rice roots [23] Current Opinion in Plant Biology 2015, 24:24–30
Identifies transcripts that are translated
Previous knowledge of celltype specific promoters required Possible differences in pools of nuclear and cellular RNA Same data set cannot be used for multiple analysis such as simultaneous transcriptomics and metabolomics Previous knowledge of celltype specific promoters required
identified genes with fluctuating expression patterns in different developmental stages of the root. Many of the rice genes were orthologs of those in Arabidopsis, suggesting that the pattern of developmental transitions may be conserved between monocot and dicots. More recently, FACS combined with proteomics [6] and small RNA profiling [7] has identified new modulators of root growth. Metabolomic analysis of specific cell types suggested a role in defense for the cortex, which accumulated glucosinolates and phenylpropanoids [8]. Recently, INTACT (isolation of nuclei tagged in specific cell types) [24] and TRAP (translating ribosome affinity purification) [11] resources for tomato roots have been developed [25], leading to exciting possibilities regarding Arabidopsis — tomato cell-type specific comparisons.
Shedding light on the role of auxin with highresolution genomic scale data The role of auxin in patterning the root meristem has been extensively documented, but primarily within the entire root apical meristem. Petersson et al. (2009) [26] used FACS combined with mass-spectrometry to measure auxin (IAA) in 14 transgenic Arabidopsis lines, each of which expressed GFP in a specific root cell or tissuetype. They identified an auxin gradient within the root apical meristem, with the highest concentrations within the quiescent center (QC), and lower concentrations in the columella and epidermis. Measurement of IAA synthesis rates showed that multiple cell types in the root apex have a high capacity for auxin synthesis, suggesting that the root’s auxin response is determined both by transport and local synthesis. These results and those www.sciencedirect.com
Insights into root biology with high-resolution data Karve and Iyer-Pascuzzi 27
of others [27–29] suggested a graded auxin pattern within the root apical meristem (RAM), which does not strictly correspond with the expression pattern of the synthetic auxin reporter DR5. Plants expressing DR5:GFP show highest expression in the QC, followed by the columella. A recent report [30] illustrates one explanation for this apparent dichotomy. Examination of the global root transcriptomic response to auxin in 4 different root tissues identified a bipartite auxin response consisting of both a graded response in the meristem and an ‘archetypal response’ that is similar to the DR5 reporter [30]. The authors speculate that the archetypal response is under control of the canonical auxin signal transduction pathway, while the graded response may be due to indirect auxin regulation by regulators that reflect the auxin concentration gradient in the RAM [30]. The negative regulation characteristic of the canonical auxin pathway may be one reason why the archetypal response does not match the measured auxin concentrations within each cell type [30].
Gene regulatory networks Recent work has demonstrated that high-resolution transcriptional data is a powerful foundation for dissecting developmental networks. FACS profiling of the epidermis of 17 root epidermal mutants followed by microarray analysis identified 208 ‘core root epidermal’ genes. Bayesian modeling with these data showed that the root epidermis differentiation network is not linear but highly branched [31]. A stele gene regulatory network (GRN) was constructed using high-throughput yeast-1-hybrid (Y1H) and yeast-2-hybrid (Y2H) of transcription factors (TFs) with enriched expression within stele cell-types (xylem, phloem, procambium, and pericycle) and miRNAs with stele-expressed targets. Mutations in approximately 65% of TFs in the stele GRN revealed a molecular phenotype, but only a morphological phenotype for 16%, suggesting that the network expression changes in the network were highly buffered to protect against morphological changes [32]. Molecular phenotypes are proving a useful way to define gene function when morphological phenotypes are not available [33]. The transcription network governing root hair development in Arabidopsis includes three sets of paralogous genes. Within each set, mutation of one gene in the pair leads to a morphological phenotype, while mutation of the other does not. In an effort to understand the function of the apparently redundant gene, Simon et al. [33] crossed the epidermal marker WER:GFP into each mutant and used FACS combined with microarrays to determine the transcriptome of developing root epidermal cells in the mutant background [33]. They identified significant levels of differential gene expression within the mutants without root hair phenotypes, and the differentially expressed genes within each mutant were overrepresented for genes found in the ‘core epidermal set’ www.sciencedirect.com
[31]. Further, based on the transparent testa glabrous2 (ttg2) mutant profile, they identified a role for TTG2 in differentiation of root hair cells, rather than non-hair cells as would be expected based on the TTG2 pattern of expression. High-resolution approaches can identify genes that function only in specific developmental contexts and show tight spatio-temporal regulation. The TFs SHORTROOT (SHR) and SCARECROW (SCR) specify and maintain the root stem cell niche [34,35] (reviewed in [6]). Inducible expression of these TFs, followed by FACS sorting and microarray analysis, identified over 800 genes induced by both TFs. Combining these data with SHR direct target data derived from a SHR chromatin immunoprecipitation–microarray (ChIP-chip) experiment identified a cell cycle gene, CYCD6;1 that was regulated by both SHR and SCR [36]. Further work showed that CYCD6;1 along with CYCLIN DEPENDENT KINASE B1 (CDKB1), phosphorylated RETINOBLASTOMA, which physically interacts with SCR [37]. Together with SHR and auxin, these proteins modulate asymmetric cell divisions in the root stem cell niche [37].
High-resolution imaging leads to insights into root development Extracting biological meaning from the vast amount of high-resolution transcriptomic, proteomic, and metabolomic data currently generated is very challenging. Imaging at the cellular level is now playing a pivotal role in our understanding of these data (reviewed in [38]). New technology for high-throughput and high-resolution imaging
The RootArray, a microfluidic device that allows for growth and confocal microscopy imaging of 64 Arabidopsis seedlings [39], was recently used to identify a new gene that controls both root meristem and cortex cell length [40]. Imaging over 1500 Arabidopsis seedling roots from 201 accessions, followed by a genome wide association study (GWAS) and quantitative RT-PCR identified KURZ UND KLEIN (KUK), an F-box protein expressed in the transition and elongation zones of the root. Analysis of mutant lines and Arabidopsis accessions with different KUK alleles showed that higher KUK expression levels were sufficient to increase meristem and mature cortical cell length [40]. High-resolution phenotyping combined with GWAS or mutant screens is likely to identify many more regulators of root growth. Microfluidic devices combined with Fo¨rster resonance energy transfer (FRET) sensors have also recently been used to explore the physiology of root cell types [41,42]. The RootChip combines live imaging of multiple Arabidopsis seedlings with the ability to rapidly alter the root growth environment [41]. The effects of different nutrients, toxins, or hormones on cell types can be examined Current Opinion in Plant Biology 2015, 24:24–30
28 Genome studies and molecular genetics
by pulsing different media through the RootChip. Using this system and plants expressing different FRETbased sensors, the dynamics of cytosolic glucose, galactose [41], Zn++ [42] concentrations, and ABA responses [43] were examined [44]. Given that many different types of sensors for different cellular metabolites are available (reviewed in [45]), microfluidic devices like the RootChip and RootArray have the potential to improve our understanding of the dynamics of metabolite and nutrient signaling on a cellular and subcellular level in roots.
High resolution imaging of an auxin sensor combined with modeling reveals new insight into auxin transport in the root
Models of auxin transport in the meristem have been based primarily on the role of the polarly localized PIN auxin efflux proteins [28,29,46]. However, work with auxin transporter mutants suggested that both PIN efflux proteins and nonpolar auxin influx (AUX1/ LAX) carriers were important for determining auxin flow in the root tip [47] (reviewed in [48]). A recent study combines modeling with high-resolution imaging to show the importance of both types of transporters for auxin transport in the root tip. Band et al. [49] used root cell geometries from segmented confocal images to model the flow of auxin in the root meristem. They tested their predictions using the auxin sensor DIIVenus [50,51], which allows for quantitative measurements of auxin levels [50,51]. When Band et al. used only PIN proteins in the model, the model prediction did not match the pattern of auxin reported by DII-Venus. But once both PINs and AUX1/LAX proteins were included, the model more closely matched the pattern of auxin reported by DII-Venus in the root tip. Removing PINs from the model predicted a significantly smaller flux of auxin through the meristem, but did not change the spatial distribution. This work suggests that PINs regulate the direction of auxin flow through the meristem while AUX1/LAX proteins control its spatial distribution [49].
Conclusions and future prospects The complexity of development in multicellular organisms necessitates knowledge of transcriptomic, proteomic, and metabolomics data at both the cell-type and whole organ levels. We now have tremendous ability to test hypotheses generated from these data, build networks, and identify new genes controlling root development. A systems-level view into root development in Arabidopsis is emerging from modeling these data and imaging studies. The tools necessary for high-resolution analyses in crop species such as tomato are becoming available [25]. Future work should address questions of conservation of root spatio-temporal programs between Arabidopsis and crop species. Current Opinion in Plant Biology 2015, 24:24–30
Acknowledgements We thank Purdue University for support, Abhijit Karve for manuscript review, and Erin Sparks for title suggestions.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
Birnbaum K, Shasha DE, Wang JY, Jung JW, Lambert GM, Galbraith DW, Benfey PN: A gene expression map of the Arabidopsis root. Science 2003, 302:1956-1960.
2.
Brady SM, Orlando DA, Lee JY, Wang JY, Koch J, Dinneny JR, Mace D, Ohler U, Benfey PN: A high-resolution root spatiotemporal map reveals dominant expression patterns. Science 2007, 318:801-806.
3.
Dinneny JR, Long TA, Wang JY, Jung JW, Mace D, Pointer S, Barron C, Brady SM, Schiefelbein J, Benfey PN: Cell identity mediates the response of Arabidopsis roots to abiotic stress. Science 2008, 320:942-945.
4.
Gifford ML, Dean A, Gutierrez RA, Coruzzi GM, Birnbaum KD: Cellspecific nitrogen responses mediate developmental plasticity. Proc Natl Acad Sci U S A 2008, 105:803-808.
5.
Iyer-Pascuzzi AS, Jackson T, Cui H, Petricka JJ, Busch W, Tsukagoshi H, Benfey PN: Cell identity regulators link development and stress responses in the Arabidopsis root. Dev Cell 2011, 21:770-782.
6.
Petricka JJ, Schauer MA, Megraw M, Breakfield NW, Thompson JW, Georgiev S, Soderblom EJ, Ohler U, Moseley MA, Grossniklaus U et al.: The protein expression landscape of the Arabidopsis root. Proc Natl Acad Sci U S A 2012, 109:6811-6818.
7.
Breakfield NW, Corcoran DL, Petricka JJ, Shen J, Sae-Seaw J, Rubio-Somoza I, Weigel D, Ohler U, Benfey PN: High-resolution experimental and computational profiling of tissue-specific known and novel miRNAs in Arabidopsis. Genome Res 2012, 22:163-176.
8.
Moussaieff A, Rogachev I, Brodsky L, Malitsky S, Toal TW, Belcher H, Yativ M, Brady SM, Benfey P, Aharoni A: Highresolution metabolic mapping of cell types in plant roots. Proc Natl Acad Sci U S A 2013. E1232–I1241.
9.
Wang D, Mills ES, Deal RB: Technologies for systems-level analysis of specific cell types in plants. Plant Sci 2012, 197:21-29.
10. Bailey-Serres J: Microgenomics: genome-scale, cell-specific monitoring of multiple gene regulation tiers. Annu Rev Plant Biol 2013, 64:293-325. 11. Mustroph A, Zanetti ME, Jang CJ, Holtan HE, Repetti PP, Galbraith DW, Girke T, Bailey-Serres J: Profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis. Proc Natl Acad Sci U S A 2009, 106:18843-18848. 12. Geng Y, Wu R, Wee CW, Xie F, Wei X, Chan PM, Tham C, Duan L, Dinneny JR: A spatio-temporal understanding of growth regulation during the salt stress response in Arabidopsis. Plant Cell 2013, 25:2132-2154. The authors dissect the salt stress response by FACS and microarray profiling of different cell-types at 6 time-points after exposure to high salt stress. Their results highlight the importance of cell-type specific hormone signaling for root growth and recovery after stress. 13. Duan L, Dietrich D, Ng CH, Chan PM, Bhalerao R, Bennett MJ, Dinneny JR: Endodermal ABA signaling promotes lateral root quiescence during salt stress in Arabidopsis seedlings. Plant Cell 2013, 25:324-341. This work shows the importance of ABA signaling in the endodermis for the LR response to salt stress. 14. Libault M, Farmer A, Brechenmacher L, Drnevich J, Langley RJ, Bilgin DD, Radwan O, Neece DJ, Clough SJ, May GD et al.: Complete transcriptome of the soybean root hair cell, www.sciencedirect.com
Insights into root biology with high-resolution data Karve and Iyer-Pascuzzi 29
a single-cell model, and its alteration in response to Bradyrhizobium japonicum infection. Plant Physiol 2010, 152:541-552.
response to drive Arabidopsis root meristem growth. Curr Biol 2011, 21:1918-1923.
15. Limpens E, Moling S, Hooiveld G, Pereira PA, Bisseling T, Becker JD, Kuster H: Cell- and tissue-specific transcriptome analyses of Medicago truncatula root nodules. PLOS ONE 2013, 8:e64377.
30. Bargmann BO, Vanneste S, Krouk G, Nawy T, Efroni I, Shani E, Choe G, Friml J, Bergmann DC, Estelle M et al.: A map of cell type-specific auxin responses. Mol Syst Biol 2013, 9:688. Identifies bipartite auxin responses in the Arabidopsis root and nicely demonstrates the insight that comes from high-resolution work.
16. Gaude N, Bortfeld S, Duensing N, Lohse M, Krajinski F: Arbuscule-containing and non-colonized cortical cells of mycorrhizal roots undergo extensive and specific reprogramming during arbuscular mycorrhizal development. Plant J 2012, 69:510-528.
31. Bruex A, Kainkaryam RM, Wieckowski Y, Kang YH, Bernhardt C, Xia Y, Zheng X, Wang JY, Lee MM, Benfey P et al.: A gene regulatory network for root epidermis cell differentiation in Arabidopsis. PLoS Genet 2012, 8:e1002446.
17. Hogekamp C, Arndt D, Pereira PA, Becker JD, Hohnjec N, Kuster H: Laser microdissection unravels cell-type-specific transcription in arbuscular mycorrhizal roots, including CAATbox transcription factor gene expression correlating with fungal contact and spread. Plant Physiol 2011, 157:2023-2043. 18. Hogekamp C, Kuster H: A roadmap of cell-type specific gene expression during sequential stages of the arbuscular mycorrhiza symbiosis. BMC Genomics 2013, 14:306. 19. Escobar C, Sigal B, Mitchum M: Transcriptomic and proteomic analysis of the plant response to namatode infection. In Genomics and molecular genetics of plant-nematode interactions. Edited by Jones , Gheysen , Fenoll . Springer; 2011:157-176. 20. Barcala M, Garcia A, Cabrera J, Casson S, Lindsey K, Favery B, Garcia-Casado G, Solano R, Fenoll C, Escobar C: Early transcriptomic events in microdissected Arabidopsis nematode-induced giant cells. Plant J 2010, 61:698-712. 21. Ji H, Gheysen G, Denil S, Lindsey K, Topping JF, Nahar K, Haegeman A, De Vos WH, Trooskens G, Van Criekinge W et al.: Transcriptional analysis through RNA sequencing of giant cells induced by Meloidogyne graminicola in rice roots. J Exp Bot 2013, 64:3885-3898. 22. Portillo M, Cabrera J, Lindsey K, Topping J, Andres MF, Emiliozzi M, Oliveros JC, Garcia-Casado G, Solano R, Koltai H et al.: Distinct and conserved transcriptomic changes during nematode-induced giant cell development in tomato compared with Arabidopsis: a functional role for gene repression. New Phytol 2013, 197:1276-1290. Comparison of giant cell transcriptomes in Arabidopsis and tomato shows similarity between some metabolic pathways (such as lignin decomposition) but little conservation of genes related to the regulation of gene expression. The authors speculate that genes controlling giant cell differentiation and/or maintenance may be specific to each species. 23. Jiao Y, Tausta SL, Gandotra N, Sun N, Liu T, Clay NK, Ceserani T, Chen M, Ma L, Holford M et al.: A transcriptome atlas of rice cell types uncovers cellular, functional and developmental hierarchies. Nat Genet 2009, 41:258-263. 24. Deal RB, Henikoff S: The INTACT method for cell type-specific gene expression and chromatin profiling in Arabidopsis thaliana. Nat Protoc 2011, 6:56-68. 25. Ron M, Kajala K, Pauluzzi G, Wang D, Reynoso MA, Zumstein K, Garcha J, Winte S, Masson H, Inagaki S et al.: Hairy root transformation using Agrobacterium rhizogenes as a tool for exploring cell type-specific gene expression and function using tomato as a model. Plant Physiol 2014, 166:455-469. 26. Petersson SV, Johansson AI, Kowalczyk M, Makoveychuk A, Wang JY, Moritz T, Grebe M, Benfey PN, Sandberg G, Ljung K: An auxin gradient and maximum in the Arabidopsis root apex shown by high-resolution cell-specific analysis of IAA distribution and synthesis. Plant Cell 2009, 21:1659-1660. 27. Galinha C, Hofhuis H, Luijten M, Willemsen V, Blilou I, Heidstra R, Scheres B: PLETHORA proteins as dose-dependent master regulators of Arabidopsis root development. Nature 2007, 449:1053-1057. 28. Grieneisen VA, Xu J, Maree AF, Hogeweg P, Scheres B: Auxin transport is sufficient to generate a maximum and gradient guiding root growth. Nature 2007, 449:1008-1013. 29. Santuari L, Scacchi E, Rodriguez-Villalon A, Salinas P, Dohmann EM, Brunoud G, Vernoux T, Smith RS, Hardtke CS:: Positional information by differential endocytosis splits auxin www.sciencedirect.com
32. Brady SM, Zhang L, Megraw M, Martinez NJ, Jiang E, Yi CS, Liu W, Zeng A, Taylor-Teeples M, Kim D et al.: A stele-enriched gene regulatory network in the Arabidopsis root. Mol Syst Biol 2011, 7:459. 33. Simon M, Bruex A, Kainkaryam RM, Zheng X, Huang L, Woolf PJ, Schiefelbein J: Tissue-specific profiling reveals transcriptome alterations in Arabidopsis mutants lacking morphological phenotypes. Plant Cell 2013, 25:3175-3185. Using high-resolution profiling of epidermal mutants, the authors show that many mutants with no morphological phenotype have a significant molecular phenotype. Cell-type specific profiling can be used to assign functions to genes which have no phenotypes when mutated. 34. Di Laurenzio L, Wysocka-Diller J, Malamy JE, Pysh L, Helariutta Y, Freshour G, Hahn MG, Feldmann KA, Benfey PN: The SCARECROW gene regulates an asymmetric cell division that is essential for generating the radial organization of the Arabidopsis root. Cell 1996, 86:423-433. 35. Helariutta Y, Fukaki H, Wysocka-Diller J, Nakajima K, Jung J, Sena G, Hauser MT, Benfey PN: The SHORT-ROOT gene controls radial patterning of the Arabidopsis root through radial signaling. Cell 2000, 101:555-567. 36. Sozzani R, Cui H, Moreno-Risueno MA, Busch W, Van Norman JM, Vernoux T, Brady SM, Dewitte W, Murray JA, Benfey PN: Spatiotemporal regulation of cell-cycle genes by SHORTROOT links patterning and growth. Nature 2010, 466:128-132. 37. Cruz-Ramirez A, Diaz-Trivino S, Blilou I, Grieneisen VA, Sozzani R, Zamioudis C, Miskolczi P, Nieuwland J, Benjamins R, Dhonukshe P et al.: A bistable circuit involving SCARECROWRETINOBLASTOMA integrates cues to inform asymmetric stem cell division. Cell 2012, 150:1002-1015. 38. Sozzani R, Busch W, Spalding EP, Benfey PN: Advanced imaging techniques for the study of plant growth and development. Trends Plant Sci 2014, 19:304-310. 39. Busch W, Moore BT, Martsberger B, Mace DL, Twigg RW, Jung J, Pruteanu-Malinici I, Kennedy SJ, Fricke GK, Clark RL et al.: A microfluidic device and computational platform for highthroughput live imaging of gene expression. Nat Methods 2012, 9:1101-1106. A new device for simultaneous growth and cellular imaging of Arabidopsis roots. 40. Meijo´n M, Satbhai SB, Tsuchimatsu T, Busch W: Genome-wide association study using cellular traits identifies a new regulator of root development in Arabidopsis. Nat Genet 2014, 46:77-81. A beautiful illustration of the use of high-throughput and high-resolution cellular imaging to identify novel genes that control root development. 41. Grossmann G, Guo WJ, Ehrhardt DW, Frommer WB, Sit RV, Quake SR, Meier M: The RootChip: an integrated microfluidic chip for plant science. Plant Cell 2011, 23:4234-4240. A device similar to that in [38] but in which growth media can be altered during growth. 42. Lanquar V, Grossmann G, Vinkenborg JL, Merkx M, Thomine S, Frommer WB: Dynamic imaging of cytosolic zinc in Arabidopsis roots combining FRET sensors and RootChip technology. New Phytol 2014, 202:198-208. 43. Jones AM, Danielson JA, Manojkumar SN, Lanquar V, Grossmann G, Frommer WB: Abscisic acid dynamics in roots detected with genetically encoded FRET sensors. Elife 2014, 3:e01741. Current Opinion in Plant Biology 2015, 24:24–30
30 Genome studies and molecular genetics
44. Waadt R, Hitomi K, Nishimura N, Hitomi C, Adams SR, Getzoff ED, Schroeder JI: FRET-based reporters for the direct visualization of abscisic acid concentration changes and distribution in Arabidopsis. Elife 2014, 3:e01739. 45. Swanson SJ, Choi WG, Chanoca A, Gilroy S: In vivo imaging of Ca2+, pH, and reactive oxygen species using fluorescent probes in plants. Annu Rev Plant Biol 2011, 62:273-297. 46. Grieneisen VA, Scheres B, Hogeweg P, Mare´e AF: Morphogengineering roots: comparing mechanisms of morphogen gradient formation. BMC Syst Biol 2012, 6:37. 47. Peret B, Swarup K, Ferguson A, Seth M, Yang Y, Dhondt S, James N, Casimiro I, Perry P, Syed A et al.: AUX/LAX genes encode a family of auxin influx transporters that perform distinct functions during Arabidopsis development. Plant Cell 2012, 24:2874-2885.
Current Opinion in Plant Biology 2015, 24:24–30
48. Swarup R, Peret B: AUX/LAX family of auxin influx carriers — an overview. Front Plant Sci 2012, 3:225. 49. Band LR, Wells DM, Fozard JA, Ghetiu T, French AP, Pound MP, Wilson MH, Yu L, Li W, Hijazi HI et al.: Systems analysis of auxin transport in the Arabidopsis root apex. Plant Cell 2014, 26:862-875. This work shows the importance of both PIN and AUX1/LAX transporters for auxin transport in the root. 50. Vernoux T, Brunoud G, Farcot E, Morin V, Van den Daele H, Legrand J, Oliva M, Das P, Larrieu A, Wells D et al.: The auxin signalling network translates dynamic input into robust patterning at the shoot apex. Mol Syst Biol 2011, 7:508. 51. Brunoud G, Wells DM, Oliva M, Larrieu A, Mirabet V, Burrow AH, Beeckman T, Kepinski S, Traas J, Bennett MJ et al.: A novel sensor to map auxin response and distribution at high spatiotemporal resolution. Nature 2012, 482:103-106.
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