Single-Cell Omics Approaches in Plants

CHAPTER 14

Single-Cell Omics Approaches in Plants Rohit Kambale*, Mohammed Haris Siddiqui†, Raveendran Muthurajan*, Hifzur Rahman‡ *Department of Plant Biotechnology, Tamil Nadu Agricultural University, Coimbatore, India † Department of Bioengineering, Integral University, Lucknow, India ‡ Department of Biosciences, Integral University, Lucknow, India

ABSTRACT Gene expression analysis in bulk samples containing different types of cells masks the properties of individual cell types because of averaging artifacts of the different cell types. With the advances in techniques for single-cell isolation, omics analysis of single-cells has become possible which helps in revealing the inherent properties of single-cells at high resolution. With the advent of nextgeneration sequencing platforms, single-cell RNA-sequencing protocols have been developed and are evolving day by day. Single-cell omics helps to identify expressed genes, proteins, and metabolites in individual cells of a given population. Emerging technologies for single-cell omics holds the promise not only to revolutionize our understanding of biological processes but also to help in determining the role of different cell types in plants. This will elucidate very precisely the molecular basis of plant growth and development, as well as the role of different cell types in providing tolerance against various environmental stresses.

14.1

INTRODUCTION

Plants are made up of various organs, which are further composed of different tissues, and finally of various types of cells. With the inception of nextgeneration sequencing technologies, our understanding of plant biology has increased immensely. However, this understanding is primarily at the tissue, organ, or whole plant level. Several experiments have been carried out in plants at the organ level to identify differentially expressed genes and to understand 255 Single-Cell Omics. https://doi.org/10.1016/B978-0-12-817532-3.00017-7 © 2019 Elsevier Inc. All rights reserved.

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the molecular mechanisms of different traits in several plant species (Rahman et al., 2014; Walia et al., 2007). However, these approaches suffer from several disadvantages, since the analysis of DNA, RNA, or proteins obtained from homogenized tissues, organs, or whole organisms masks and does not clearly depict the contribution of each cell type to biological functions (Simpson, 1951; Yule, 1903). In addition, the less abundant genes or proteins become difficult to detect when the analysis is performed at the tissue, organ, or whole plant level. As a result, using the data generated from complex biological samples, it is difficult to study the system biology of an organism and make predictions. Therefore, in order to fully understand a plant’s system biology, it is important to perform genomics, transcriptomics, epigenomics, proteomics, and metabolomics for each plant cell type. Using these methods, better understanding can be attained of the genes present and their expression patterns, as well as epigenetic regulations happening in each plant cell type in response to various growth and developmental processes and environmental stresses. Further, omics studies at the single-cell level can elucidate the biological networks operating at the level of single-cells. Recently, single-cell analysis has emerged as a new frontier in omics, and it has the potential to allow better understanding of the role of each cell type in systems biology. This chapter discusses the different methods used for single-cell isolation and recent omics studies that have been carried out to understand plant systems biology at the single-cell level. The single-cell omics studies involves isolation of single-cells or single-cell types ( containing a group of single-cells); their DNA, RNA, protein or metabolite extraction and further analysis through DNA/RNA sequencing, protein or metabolite identification using a combination of techniques (Fig. 14.1). Plants are highly complex and multicellular in nature, containing a functionally diverse group of cells. Each cell or cell type contributes distinctively in the plant’s developmental process. Despite the diversity among cell types, a specific type of cells shares the same genome. During the development process, a group of cells interacts with each other and in parallel with the environment (Genome X environment-interaction), making it a complex system. So, in order to understand such a complex mechanism of cellular function and development, each cell type must be evaluated at the DNA, RNA, protein, and metabolite level. The objectives of targeted single-cell studies are to obtain reliable, accurate data at the genomic level, which further leads to understanding the gene expression pattern during the developmental stages in which they are constantly exposed to various environmental signals. During environmental interaction and interaction with other cells, the plant cells may undergo different physiological and biochemical modifications, thereby altering the gene and protein expression, leading to phenotypic variation.

14.2

Single-Cell Isolation Methods in Plants

FIG. 14.1 Integration of omics technologies for studying single-cell types.

14.2

SINGLE-CELL ISOLATION METHODS IN PLANTS

In consideration of single-cell genome, transcriptome, proteome, and metabolome studies, isolation of a precise viable single-cell from a plant organ is important. Single-cell isolation is a foundation step in single-cell omics study. Here, we give an overview of the available techniques for single-cell isolation, which can also be applicable for plant systems in order to study the hidden functions of cell or tissue that have not been revealed yet. Identification of novel candidate genes, protein, and metabolites precisely can be possible only through an understanding of the -omics at the single-cell level. Several researchers have explained their methods for isolation of certain cell types from plant tissues. Gnanam and Kulandaivelu (1969) developed a procedure to isolate metabolically active mesophyll cells from mature leaves of several species of dicots and monocots. The procedure involves mild maceration of the leaves in a grinding medium (20 μmol sucrose, 10 μmol MgCl2, 20 μmol Tris-HCl buffer, pH 7.8). The homogenate was filtered using a muslin cloth and finally washed by centrifugation at low speed using the same

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medium. Plant single-cells have been isolated by enzymatic hydrolysis from the leaves of potato, tobacco, etc. To enable cell separation from each other, cells can be treated with cellulose and pectinase in order to isolate a single-cell or protoplast (Takebe et al., 1968; Zhang et al., 2004). Despite the difficulties involved in isolating shoot apical meristematic cells, since they are a group of stem cells and are also surrounded by highly differentiating cells, Yadav et al. (2009) successfully developed and implemented a florescent marker technique to isolate distinct cell types from shoot apical meristem. During the early days of omics studies, single-cell isolation faced several obstacles; however, currently there are different tools/techniques available for targeted single-cell isolation and analysis. These tools enable cell isolation without disturbing the cellular profile of the samples. Some of these techniques include FACS (fluorescence activated cell sorting), LCM (laser capture microdissection), manual cell sorting, dilution, and microfluidics. Each of these techniques has its advantages and limitations (Table 14.1). A wide literature survey of single-cell isolation methods indicated that the most commonly used is FACS (41%), followed by microfluidic/lab-on-a-chip (29%), manual cell picking/micromanipulation (12%), laser microdissection (4%), random seeding or liquid dilution into microplates, and noncontact dispensing methods (e.g., inkjet, solenoid valves, or acoustic) (John Comley, HTStec Ltd. 2017; Gross et al., 2014).

14.2.1

Fluorescence Activated Cell Sorting

FACS was first reported by Bonner et al. (1972). It is a rapid cell-sorting method in which the live cells are stained (fluorescent), analyzed concurrently, and then separated. FACS requires transgenic lines expressing a Table 14.1 Single-Cell Isolation Technologies: Advantages and Limitations Technique

Advantage

Limitations

Reference

Flow cytometry/ Fluorescence activated cell sorting (FACS) Magnetic activated cell sorting (MACS)

Versatile analysis that allows isolation of RNA, protein, and metabolites.

Specialized transgenic lines for each cell type of interest need to be generated Nonspecific cell capture

Gross et al. (2014), Rogers et al. (2012) Hu et al. (2016), Welzel et al. (2015) Nakazono et al. (2003)

Dilution

Can be used to isolate protein. High throughput, low loss. Simple and cost-effective Isolation of large number of the homogeneous cell population. Can also isolate rare cell Low cost, less time needed

Manual cell sorting

Low cost

Laser capture microdissection (LCM)

Ice crystals are formed in the air spaces between cells High possibility of contamination of unwanted cells Manual, labor intensive, and requires more time

Hu et al. (2016) Hu et al. (2016)

14.2

Single-Cell Isolation Methods in Plants

fluorescent marker in a specific cell type. Green fluorescent protein (GFP) is used comprehensively in FACS studies. A protoplast of the targeted cell can be isolated from a group of cells using FACS, and can further be used for omics profiling (Dinneny et al., 2008; Gifford et al., 2008; Long, 2011). Brady et al. (2007) used FACS to generate a high-resolution root spatiotemporal map to understand the expression patterns of genes. Birnbaum et al. (2003) created a gene expression map of Arabidopsis root using FACS. Similarly, with the advances in FACS, this technique can be utilized to generate any organism’s transcriptional map. A pure cell population has been isolated from Arabidopsis root quiescent center using FACS (Nawy et al., 2005). With a limited number of single-cell types, it can be challenging to isolate DNA, RNA, or protein for high throughput omics studies. FACS has made it possible to identify and isolate similar kinds of cells in enough quantity to get a sufficient amount of nucleic acids and protein to carry out transcriptomic and proteomics analysis. Bargmann and Birnbaum (2010) have developed a protocol to isolate plant protoplast for use in FACS. They have used cell type specific fluorescent reporter lines of Arabidopsis thaliana root as PSCR:: GFP (endodermis and quiescent center) and PWOX5::GFP (quiescent center). For cellular splitting, they have used enzymatic digestion of cell wall and made use of high osmolarity-induced plasmolysis. The proplasting solution included 1.25% w/v cellulase, 0.3% w/v Macerozyme, 0.4 M D-mannitol, 20 mM MES, and 20 mM KCl. They further cleared the cells by treating the cellular solution at 55°C and flowed by addition of 0.1% w/v BSA (bovine serum albumin), 10 mM CaCl2, and 5 mM β-mercaptoethanol. The cleared cell solution was filtered by 40 μm cell strainer and the protoplasts were sedimented by centrifugation at 500G and resuspended to be directly used for FACS. The FACS setup depends on the experiment and requirement of cell densities. Bargmann and Birnbaum (2010) used FACSAria (BD) using a flow stream with a 100 μm nozzle at 20 psi pressure to sort up to 10,000,000 cells/mL.

14.2.2

Laser-Capture Microdissection

A large number of a homogeneous cell population from tissues can also be isolated using LCM. This technology uses a microscope to select specific cells from tissue on the basis of cell morphology or histology (Rogers et al., 2012; Asano et al., 2002). LCM is among the first methods developed to isolate a single-cell type (Rogers et al., 2012). As compared to FACS, LCM does not require expression of any fluorescent marker. LCM has been significantly used to study the gene expression in animal cells (Luo et al., 1999; Sgroi et al., 1999; Trogan et al., 2002), but it met with two challenges with plant cells: a cell wall that makes it challenging to separate it from surrounding cells, and the formation of ice crystals in the air spaces between cells while preparing frozen sections.

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Nakazono et al. (2003) overcame these limitations by using a cryoprotectant before freezing and an adhesive-coated slide system, which made it possible to capture a large number of vascular bundles and bundle sheath cells from ethanol:acetic acid-fixed coleoptiles of maize. LCM has been successfully implemented and used to study individual cell types of roots and shoots of many plants (Nakazono et al., 2003; Woll et al., 2005; Jiao et al., 2009). Epidermal cells and vascular tissues of maize were isolated using LCM for cDNA microarray analysis, resulting in the identification of approximately 130 of 8791 of the genes that were preferentially expressed in epidermal cells compared with vascular tissues and/or total coleoptiles (Woll et al., 2005). Nakazono et al. (2003) has optimized a protocol for isolation of cells of epidermis and vascular tissues. Nakazono et al. (2003) used a Zea mays inbred line and after germination for 7 days, 5 μm-thick cross-sections of root were fixed with fixative containing 75% ethanol and 25% acetic acid followed by infiltration under vacuum (400 mmHg) and were swirled on a rotator at 4°C. Formation of ice crystals was avoided by a Suc solution (diethyl pyrocarbonate-treated PBS buffer). Further cryosectioning and dehydration of the sections were carried out and the targeted cells were laser captured and microdissected using the PixCell II LCM system.

14.2.3

Magnetic Activated Cell Sorting

A simple and flexible magnetic cell-sorting system from complex cells was described by utilizing specific surface markers (Miltenyi et al., 1990). Two methods involving magnetization for cell sorting were utilized: in the first type of cells, containing a high amount of para-magnate such as hemoglobin, bacteria containing the magnetic particle were used, and in the second type tagging of diamagnetic cells with a magnetic label was carried out to achieve the paramagnetism (H€afeli et al., 2013). MACS is based on the principle of cell labeling and sorting using a high-gradient magnetic field. The cells can be labeled with magnetic particles of diameter >0.5 μm. Miltenyi et al. (1990) separated >109 cells stained with biotinylated antibodies, fluorochrome-conjugated avidin, and 100 nm diameter superparamagnetic biotinylated microparticles by passing through high magnetic columns. In addition to this, Adams et al. (2008) used a multitarget magnetic activated cell sorter (MT-MACS) in combination with microfluidics technology to achieve simultaneous sorting of multiple targeted bacterial cells, which resulted in purification of cells in independent outlets with multiple bacterial cell types with >90% purity and >500-fold enrichment at a throughput of 109 cells per hour. So far, several researchers have worked on isolation and separation of candidate cells using MACS from animal tissues (Geens et al., 2006; Handgretinger et al., 1998; Fong et al., 2009; Lee et al., 2010; Ravelo et al., 2018), but there are a limited number of reports in plants.

14.3

14.3

Single-Cell Genomics in Plants

SINGLE-CELL GENOMICS IN PLANTS

Plant organs are composed of various types of cells that are specialized both physiologically and biochemically. Analyzing a group of cells at the population level represents summarized information about various cell types, which can be heterogeneous; although the results used to be informative at the bulk level, but are comparatively less informative when analysis is carried our at single-cell level. In the earlier era of scientific research the vascular sap of anatomically similar cells of the upper and lower epidermis of the barley leaf were studied for analyzing osmolality, and it was concluded that the lower and upper epidermis exhibited the distinct pattern of vascular solutes (Fricke et al., 1994). However, during vegetative and reproductive stages and during various biotic/abiotic stresses, the function as well as gene expression pattern drastically changes in plants, making it crucial to have brief and targeted information about the genetic changes that occur in these cells. Although limited research has been carried out on single-cell plant genomics, it has had a major impact in understanding single-cell type genomics. Single-cell genomic studies include isolation of genomic DNA from a single cell or singlecell types and then sequencing to understand the presence/absence of genes or variations in genes. Single-cell RNA sequencing is widely used in population studies, averaging the cell transcripts (Saliba et al., 2014); single-cell genomics has been widely carried out in Arabidopsis and other cereal crops. A high-resolution expression map was developed to reveal the gene expression networks of individual domains of shoot apical meristem and led to the identification of rare transcripts (Yadav et al., 2009). Similarly, analyzing the gene expression from 15 different zones of the root resembling different cell types at diverse developmental stages resulted in determining localized expression of >22,000 genes in the Arabidopsis root (Birnbaum et al., 2003). These observations show that gene expression patterns traverse traditional anatomical boundaries and show a series of hormonal response. In another study, to understand previously unknown cellular functions and to identify cell-specific transcriptional signatures, a high-resolution comprehensive map at various developmental time points within a single Arabidopsis root was developed by Brady et al. (2007). These high-resolution gene expression maps showed no correlation with previous anatomical and histological studies on Arabidopsis root. Gene expression at the single-cell level has been widely studied in Arabidopsis thaliana (Lieckfeldt et al., 2008) and other crops like barley (Lu et al., 2002), maize (Nakazono et al., 2003), and soybean (Qiao et al., 2017). On the continuing journey of single-cell omics studies, Qiao et al. (2017) hypothesized that several root hair regulatory elements might be conserved between plant species, so they compared root hair transcriptome and genome of Arabidopsis thaliana and

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FIG. 14.2 Schematic representation of single-cell genomics.

Glycine max, which revealed a detailed picture of expression of paralogous and orthologous genes in root hairs. Considering the importance in pollination, intensive studies were carried out in pollen morphology and biochemistry (Mascarenhas, 1975); several efforts have been made to unravel the genetic basis of pollen development, resulting in identification of some of the key genes that are expressed during the development of pollen (Franklin-Tong, 1999; Hepler et al., 2001; Lord and Russell, 2002; Twell, 2002). Twell (2002) extensively worked on several species and identified 150 pollen expressed genes having importance in pollen development. Becker et al. (2003) identified 1584 genes in Arabidopsis pollen, which significantly increased the current knowledge on the male gametophyte development. The overall simplified process of single-cell genomics is diagrammatically illustrated in Fig. 14.2.

14.4

SINGLE-CELL TRANSCRIPTOMIC IN PLANTS

Plant growth and development is a complex system regulated by numerous genes, proteins, and metabolites. All the cells of an organism have the same genotype and contain the same genes, but their expression varies from cell type to cell type. Different cell types have different transcriptomes, which need to be

14.4

Single-Cell Transcriptomic in Plants

unraveled to the single-cell resolution level to determine the gene’s regulatory network underlying various physiological functions, finally deciding the phenotype of the organism. Advances in next-generation sequencing technologies and single-cell isolation techniques have ensured researcher curiosity about the explicit and higher magnified targeted single-cell transcriptome. Several pathways play a central role at the cellular level, as plants are immensely exposed to various biotic and abiotic signals during their development. Many scientists have explained the need of omics studies at the single-cell level (Wang and Bodovitz, 2010). First, the single-cell transcriptomic was carried out using in vitro cDNA amplification from individual hemopoietic cells (Fink et al., 1990), which later articulated the foundation for single-cell cDNA microarray and RNA-Seq analysis. Limitations of the microarray have been overcome by using RNA-Seq of mRNA. Recent advances in whole genome and transcriptome studies have allowed researchers to develop abundant data points that represent a biased analysis. Understanding transcriptomic data at the single-cell level increases the sensitivity and response to environmental conditions. With advances in homogeneous cell isolation techniques, targeted single-cell transcriptome analysis has led to identification of complex gene regulatory networks, which wouldn’t be possible by analyzing the transcriptome at the whole plant, organ, or tissue (bulk) level (Rogers et al., 2012). In order to identify candidate genes in Arabidopsis, many different cell types were evaluated at the transcriptomic level, i.e., root (Birnbaum et al., 2003; Brady et al., 2007; Gifford et al., 2008; Dinneny et al., 2008), shoot apical meristem (Yadav et al., 2009), leaf guard cells and mesophyll (Leonhardt et al., 2004), and trichomes ( Jakoby et al., 2008). Similarly, in other plant species such as maize (Nakazono et al., 2003; Woll et al., 2005), rice (Takehisa et al., 2012), tobacco (Cui et al., 2011; Harada et al., 2010), and tomato (Schmidt et al., 2011), cell-specific transcriptome analysis has been carried out to identify various genes and regulatory networks. In soybean root hair cells, Libault et al. (2010) demonstrated complete transcriptome changes in response to rhizobia infection. A simplified schematic representation of steps involved in single-cell transcriptome analysis is depicted in Fig. 14.3. Roots are an essential part of a plant system, as roots take up water and minerals from the soil and transfer them to the shoot of the plant, making it essential to study the molecular mechanisms of root development. Extensive progress has been made in understanding these mechanisms in the model plant Arabidopsis thaliana (Itoh et al., 2007) as well as in other crop plants like rice (Takehisa et al., 2012). A whole genome transcriptome analysis was carried out by Takehisa et al., (2012) for comprehensive understanding of rice root development. Takehisa et al. (2012) performed transcriptome analysis of the crown root into eight different developmental stages, combining laser microdissection and microarray. They have identified 22,297 genes as

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FIG. 14.3 Schematic representation of single-cell transcriptome analysis.

transcriptome signatures corresponding to different developmental stages and tissues, and they also unraveled the gene network for root cap function and lateral root formation. Abiotic stress affects crop productivity, especially during reproductive stages; to mitigate the crop productivity, one should have knowledge of the key genes involved in stress tolerance. Dinneny et al. (2008) carried out transcriptome analysis in different cell layers and developmental stages of the Arabidopsis salt-stressed root, using microarray, and characterized the response to salinity and iron deprivation. They identified 244 cell-specific genes whose expression pattern does not significantly change with either stress, and they concluded that salt-responsive genes also responded to iron deprivation. Apart from this, several scientific studies evidenced that trichomes of tobacco leaves play crucial roles in the defense against biotic and abiotic stresses (Harada et al., 2010). To understand the role of the trichome as stress-responsive tissue, Harada et al. (2010) constructed cDNA libraries from control and cadmium (Cd)-treated leaf trichomes and identified stress-responsive genes involved in primary metabolism, suggesting that trichomes could be a source of genes for abiotic and biotic stress tolerance. Targeted single-cells differ in their transcriptomic profiles, so it is always better to isolate single-cells from a group of cells and study their response to the environmental stimuli. Gifford et al. (2008) studied cellular profiling in five cell types of Arabidopsis roots, to understand the cellular response to influx of nitrogen, resulting in several transcripts that were poorly understood so far. They also identified microRNA167, which facilitates in lateral root growth, response to nitrogen, and coordinates the response of various cell types during development and to external stimulus.

14.5

14.5

Single-Cell Proteomics in Plants

SINGLE-CELL PROTEOMICS IN PLANTS

Proteomics is the study of proteins and their function. Studying the protein functions helps in understanding the physiological mechanism and the biochemical pathways involved in different processes. Different approaches have been used so far to study proteins at the single-cell level; some of them use twodimensional gel electrophoresis (2DE) followed by liquid chromatography with tandem mass spectrometry (LC-MS/MS), matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOFMS), isobaric tags for relative and absolute quantitation (iTRAQ), isotope coded affinity tags (ICAT), etc. There are a limited number of single-cell proteomics studies due to the difficulties involved in single-cell protein acquisition in large quantities. Plant organs, like pollen cells of A. thaliana (Grobei et al., 2009), Oryza sativa (Dai and Chen, 2012), egg cell (Okamoto et al., 2004), guard cell (Zhao et al., 2008), and root hairs of Zea mays L. (Brechenmacher et al., 2009; Nestler et al., 2011), have been studied extensively to identify the distinctive protein profiles. However, by using cell suspension cultures, a total of 1107 proteins have been € identified in Arabidopsis thaliana (BOhmer and Schroeder, 2011). Grobei et al. (2009) presented the proteomics data on Arabidopsis pollen development and identified 3876 unique proteins using an LC-MS/MS approach. Similarly, several other scientific groups also worked on the pollen of Arabidopsis thaliana using MALDI-TOF MS and LC-MS/MS and identified 189 (Zou et al., 2009a, b), 135 (Holmes-Davis et al., 2005), and 121 (Noir et al., 2005) proteins. Mature pollen and pollen coat from Oryza sativa were used to identify 401 novel proteins associated with pollen germination and pollen tube growth (Dai and Chen, 2012). Recent advances in functional proteomics can lead to understanding the signaling pathways, as Zhao et al. (2008) represented new stomatal signaling pathways while studying the functional proteomics of Arabidopsis thaliana guard cells. Similarly, Sheoran et al. (2007) has performed an experimental analysis to understand the proteome data of tomato pollen and identified 133 distinct proteins involved in defense mechanisms, energy conversion, pollen germination, and pollen tube growth. Okamoto et al. (2004) worked on the identification of proteins in egg cells of maize and identified three enzymes of the glycolytic pathway and two mitochondrial proteins with high expression in egg cell. The guard cells of stomata control transpiration and play an important role in stress tolerance. Stomata, which are formed of guard cells, control gas exchange and water loss in the plant. The influx and efflux of K+, Cl
ions ascribe the guard cells shrinking and swelling. Zhu et al. (2012) identified 84 proteins in response to methyl jasmonate from guard cells of B. napus using the isobaric tags for relative and absolute quantitation (iTRAQ) approach. Zhu et al. (2010) further analyzed abscisic acid responsive protein in Brassica napus guard cells using multiplexed isobaric tagging. Cellspecific proteins involved in sulfur metabolism and detoxification were identified from trichomes of Arabidopsis using specific cell sampling and followed

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by shotgun peptide sequencing (nano LC/MS/MS) (Wienkoop et al., 2004). Trichome plays a major role in plant defense by secreting secondary metabolites. Van Cutsem et al. (2011) have identified the abiotic stress responsive protein involved in secondary metabolite production from trichomes of Nicotiana tabacum. Schilmiller et al. (2010) used shotgun proteomics analysis to identify and quantify 1973 proteins from tomato trichome. Root hairs are made of epidermal cells; to understand the protein profiles in unicellular specialized epidermal cell of maize root hairs, Nestler et al. (2011) used a shotgun proteomic approach along with 1DE and nano LC-MS/MS and identified 2573 proteins abundantly expressed in these cells.

14.6

SINGLE-CELL METABOLOMICS IN PLANTS

As genomics, transcriptomics, and proteomics are studies of genomes, mRNA, and protein respectively, the study of metabolites on a large scale is known as metabolomics. The study of metabolites is essential, as it represents the biochemical profiles of an organism. The major disadvantage of metabolic profiling at the single-cell level is the extremely low concentration of metabolites at this level (Matsuda et al., 2009). Extensive work needs to be done to understand metabolomics at the single-cell level. Essentially, proteins are required for the production of metabolites, as metabolites are the product of several processes and pathways, which makes them more complex molecules; hence, they have been only partially understood. Being a model plant, the genes and their functional annotation in Arabidopsis have been well understood over the period of the past few decades, but knowledge of the metabolite and its regulating network will aid in redefining the existing network structure and help in understanding new gene networks. Recent advances in single-cell isolation and analytical tools have aided in carrying out extensive research for studying plant’s single-cell metabolites. Epidermal trichrome or pavement cells of A. thaliana were analyzed for metabolic profiling using GC-TOF-MS and resulted in identification of several classes of metabolites, including organic acids, polyols, sugar conjugates, and alkanes (Ebert et al., 2010). Similarly, by using LC-(MRM)-MS/MS, phenolic flavonoids and amino acids were detected in the guard and mesophyll cell protoplast of A. thaliana ( Jin et al., 2013). To profile the metabolites in the epidermal cell layer, palisade mesophyll cells, vascular bundle, columella, cortex, stele, etc. in A. thaliana, MALDI-TOF-MS (Obel et al., 2009), GFP-FACS, and UPLC-QToF-MS (Moussaieff et al., 2013) have been used and various cell-specific metabolites have been reported. Metabolic profiling has been carried out not only at the single-cell level or single-cell-type level, but also at the subcellular level. Tohge et al. (2011) using GC-MS, UPLC-FT-MS have identified 259 metabolites from vacuoles (mesophyll cell protoplast) of Hordeum vulgare. Even

14.7

Applications of Single-Cell Omics in Plants

metabolomics profiling has been carried out on the cells of petals and leaves and glandular trichomes from Arabidopsis thaliana, Hypericum perforatum, and Hypericum reflexum, to understand the metabolite profiles at a single-cell level (H€ olscher et al., 2009). Shrestha and Vertes (2009) used AP-ESI-MS as an analytical tool and epidermal single-cells of bulbs from plant material of Allium cepa, Narcissus pseudonarcissus to dissect out 32 distinct specialized metabolites and oligosaccharides. About 600 metabolites have been detected in root hairs of Glycine max using GC-MS and UPLC-QToF-MS as an analytical tool. The detected compound includes flavonoids, organic acids, phenolics, glucosinolates, saponins, and alkaloids (Brechenmacher et al., 2009). Harada et al. (2010) identified stress responsive genes and metabolic pathways specific to leaf trichome in tobacco. In Catharanthus, Murata et al. (2008) identified a novel protein from leaf epidermal cells involved in metabolite production. While some researchers have worked on the identification of the biosynthetic pathway, Rios-Estepa et al. (2008) identified a monoterpene biosynthesis pathway in leaf trichome of peppermint. Knoch et al. (2017) has carried out genetic dissection of metabolic variation in Arabidopsis seed and identified 311 polar primary metabolites and further identified 786 metabolic QTLs distributed across the genome. Pollen cells are unicellular haploid cells of male gametophytes, having great importance in fertilization. Obermeyer et al. (2013) utilized GCMS to understand the metabolic profile of pollen cells and pollen tubes of Lilium longiflorum, and hence detected 250 metabolites. Similarly, several scientific communities have worked on the length and strength of cotton fiber, as it has great importance and determines the quality of fabrics. Gou et al. (2007) studied gene expression and metabolite profiles of cotton fibers during their elongation and identified several metabolites and seven metabolic pathways involved during fiber development. In another study, Naoumkina et al. (2013) identified the process of cotton fiber elongation by utilizing mutation in Li2 (Ligon lintless-2) and compared the GCMS data of a Li2 mutant fiber sample as well as a wild type and reported that the mutation in Ligon lintless-2 alters the metabolic profile in the mutant as compared with the wild type. They also reported that the metabolites like glutamine, arginine threonine, tryptophan, and several others were upregulated significantly in the wild type, whereas shikimate, valine, and p-Coumaric acid were upregulated in Li2 mutant. Single-cell metabolomics is a promising method for understanding cellular biochemistry and physiology as well as biological mechanisms of multicellular organisms.

14.7

APPLICATIONS OF SINGLE-CELL OMICS IN PLANTS

Before the invention of single-cell isolation technologies, most of the omics studies were applied at the organ or organism level, which provided average

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information of the heterogeneous cell’s population. However, recent studies have addressed the limitations of whole tissue, organ, and population omics through single-cell omics. Single-cell or cell type omics provides an unbiased, detailed understanding about a single-cell at the DNA, transcript, protein, and metabolomics levels. Hence single-cell omics should be the method of choice to understand the complexity of gene interaction and gene expression and to identify novel, promising proteins and metabolites. Single-cell isolation is the essential step in single-cell omics studies. As mentioned earlier, several techniques have been invented and implemented for both animal and plant cells, which includes FACS (Bonner et al., 1972; Gifford et al., 2008; Long, 2011), MACS (Miltenyi et al., 1990), and LCM (Nakazono et al., 2003; Rogers et al., 2012) etc. These techniques have been applied by various researchers to understand cell development, cell signaling, and plant cell and environment interactions (Fig. 14.1; Table 14.2). Arabidopsis thaliana (Betancur et al., 2010; Brady et al., 2007; Lieckfeldt et al., 2008) and barley (Fricke et al., 1994) have served as model plant systems for single-cell studies in which specific cell types from root hairs (Kwasniewski et al., 2010; Lan et al., 2013; Libault et al., 2010), trichomes (H€ ulskamp, 2004; Betancur et al., 2010), and sclerenchyma cells (Betancur et al., 2010) were utilized as the model cell types to understand single-cell genome and transcriptome. Similarly, single-cell omics studies were carried out in pollen (Twell, 2002), leaf guard cells, mesophyll (Leonhardt et al., 2004), egg cells (Okamoto et al., 2004), guard cells (Zhu et al., 2010), etc., to understand cell-specific molecular networks. Advances in nextgeneration sequencing techniques and genome editing offer gene function prediction by mutation screening (Cong et al., 2013; Hsu et al., 2014). Approaches like CRISPR-seq ( Jaitin et al., 2016) and CROP-seq (Datlinger et al., 2017) enable detection of trancriptional effects of gene disruption at a single-cell level. Single-cell omics further helps in understanding harmonal signaling and biotic/abiotic stress signaling pathways and could possibly correlate biological pathway information by identifying unknown regulators and genes, all factors that will further help in understanding the complex plant system at the cellular level.

14.8

CONCLUSIONS AND FUTURE PROSPECTS

The omics studies at the organ or organism level provide cumulative information about the organ or the organism and mask information on the individual cell; hence, to eradicate this limitation and signify the roles of various cell types in different biochemical as well as physiological processes, it is imperative to carry out omics studies at the single-cell-type level. The studies of plant single-cell /single-cell types can help in understanding biological processes at an unprecedented level of detail. The separation and purification of

14.8

Conclusions and Future Prospects

Table 14.2 Application of Single-Cell Omics in Plants Plant Species

Tissue/Cell Type

Method Used

Key Findings

Reference

Identified trichome-specific metabolic pathways and biotic/ abiotic stress response genes Identified genes related to different root developmental stages High-resolution gene expression analyses in single-cells of maize

Harada et al. (2010)

Gene expression map of A. thaliana root Identified 1584 genes expressed in pollen Identified 1309 genes distinctively expressed in guard cell

Birnbaum et al. (2003) Becker et al. (2003) Leonhardt et al. (2004)

Identification of six major proteins in maize egg cells Manual cell sorting followed Identified abscisic acid by multiplexed isobaric tagging responsive proteins Manual cell sorting followed by Identified 84 methyl methyl jasmonate responsive proteins iTRAQ-based quantitative proteomics ICAT followed by ESI-based Identified 3876 unique proteins LC-MS/MS involved in pollen development and function 2DE followed by MALDI-TOF/ Comparative proteome profile of TOFMS mature and germinated pollen Anthocyanins and other Laser-microsampling and metabolites nanoflow liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS) LC-TOF-MS Glycosides, acylated molecules and flavonoids along with 32 other metabolites were identified GC-MS, UPLC-QTOF-MS Metabolites such as flavonoids, organic acids, nucleosides, phenolics, glucosinolates, saponins, alkaloids were found Enzyme digestion for isolation Phytohormones, signaling molecules, phenolics, guard cell protoplast supplemented with LC-(MRM)- flavonoids, amino acids MS/MS

Okamoto et al. (2004) Zhu et al. (2010) Zhu et al. (2012)

Nicotiana tabacum

Leaf trichomes

Manual cell isolation followed by semiquantitative RT-PCR

Oryza sativa L.

Crown roots

Zea mays

Laser capture microdissection coupled with microarray analysis LCM with microarray

Epidermal cells, vascular bundles, and bundle sheath cells maize Root cell FACS with microarray

Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana

Pollen grains

FACS with microarray

Guard cells and mesophyll cell protoplast

Physical isolation and enzymatic digestion of guard cell followed by microarray analysis 2-DE followed by LC-MS

Zea mays L.

Egg cell

Brassica napus

Guard cells

Brassica napus

Guard cells

Arabidopsis thaliana

Pollen

Arabidopsis thaliana Torenia hybrida

Mature and germinated pollen Petal cell

Solanum lycopersicum L.

Glandular trichomes

Glycine Max L

Root hairs

Arabidopsis thaliana

Guard cell and mesophyll cell protoplasts

Takehisa et al. (2012) Nakazono et al. (2003)

Grobei et al. (2009) Zou et al. (2009a,b) Kajiyama et al. (2006)

Schilmiller et al. (2010)

Libault et al. (2010)

Jin et al. (2013)

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homogeneous cells from a heterogeneous cell population is an essential step in single-cell omics studies. Several techniques of cell discrimination and fractionation from a complex cell population have been developed and utilized, making single-cell omics studies feasible. The advances in candidate gene identification, isolation, and characterization with genomic, proteomic, and metabolomic methodologies, along with bioinformatics analysis, will pinpoint the role of genes in gene networks operating at the cellular level, which will further give a better understanding of phenotype development.

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