Along these lines, nascent single-cell metabolomics, proteomics, and imaging technologies show great promise in helping to address this unmet need. One could potentially envision integrated workflows in the future that combine multiple technologies on a single device to monitor several features from the same cell. There has also been recent progress in developing mass spectrometry-based metabolite imaging for spatially profiling metabolite quantities in plants Further development of this technology will nicely complement advances in other single-cell methods.
As the technological capabilities to profile plants at the single-cell level improves, we believe that three specific areas, in particular, should be of immediate interest. These include a detailed understanding of how plants respond to biotic and abiotic environmental factors, opportunities for improved functional annotation of genomes, and applications for the production of bioproducts and biomaterials.
To support these goals, technological and analytical challenges must be overcome, which will require significant investment, including a centralized resource to facilitate the sharing of single-cell data and identification of potential funding avenues for single-cell science in energy and the environment. Emerging single-cell technologies are expected to enable impactful discoveries in studies of plant responses to their environment.
Examples of interactions with particularly high relevance include pathogenic infections and mutualistic associations with nitrogen-fixing bacteria, as well as abiotic environmental conditions such as drought, heat, or limited nutrient availability. Both pathogenic and commensal microorganisms typically interact with very specific subpopulations of cells in a host plant, with many or most plant cells not in direct contact with or infected by specific microbes.
For example, arbuscular mycorrhizal fungi specifically target only a subset of cortical cells of the plant root. Current methods of performing RNA-seq on bulk tissue or cell populations isolated by fluorescence-activated cell sorting FACS of reporter-labeled plant lines massively dilute any signal originating from affected cells in the plant Fig.
Microfluidic-based single-cell RNA-seq, in combination with emerging spatial transcriptomics methods, holds great promise for elucidating cell-specific responses to pathogenic infections or other perturbations. Of particular importance for this research area is the development of methods that are capable of capturing RNA molecules from both eukaryotic and prokaryotic organisms in the same experiment, since current methods are limited to eukaryotic cells that have mRNA polyadenylation.
Drought is another high-priority focus area in this domain. In addition to the long-term goal of understanding the biological effects of decreased water availability caused by changing environments, single-cell methods could be used in the short term to better understand which experimental systems that are currently used to simulate drought in the lab are the most biologically relevant. With resource investments in developing tissue preparation methods and new technologies, along with the study of targeted scientific questions, single-cell technologies have the potential to revolutionize plant environmental science.
Relatively few plant cells interact directly with most pathogens. However, these local interactions often determine disease severity. Thus, understanding gene expression in these few cells could be valuable for enhancing resistance. Unfortunately, bulk tissue RNA-seq greatly dilutes the signal from interacting cells, and signals from genes upregulated throughout the leaf in response to pathogens can mask expression changes in the interacting cells.
While methods like microdissection can improve the signal-to-noise ratio to a degree, they are labor-intensive and not universally applicable to all pathogens. Thus, the increased cellular resolution promised by single-cell RNA-seq could revolutionize our understanding of plant-microbe interactions. A second major scientific focus area where single-cell technologies could have a substantial impact is in the functional annotation of genes from plants, fungi, and algae.
Newly sequenced and assembled genomes are run through standardized annotation pipelines, which include using DNA sequence homology to genes in well-studied model species e.
However, due to the ubiquity of large gene families with similar sequences in plants, identification of exactly homologous gene pairs between species is often challenging. Further complicating this challenge, functional understanding for most genes, even in well-studied species, is lacking.
This can be mitigated by the use of gene expression information, in addition to sequence homology. With scRNA-seq data, it will be possible to perform such analyses across dozens of cell types, thereby increasing the accuracy of the resulting annotations and inferred gene functions Fig. Indeed, a recent study leveraging Arabidopsis single-cell data found extreme cell type-specific expression bias among pairs of homologous genes gene duplicates Using this information could result in substantial improvements to plant functional gene annotations across species.
Expression profiles across multiple cell types derived from single-cell transcriptome data of tissues from different plant species left , in combination with sequence homology-based comparison of protein sequences top right , can be used to identify functionally homologous genes across different plant species bottom right , thereby substantially enhancing the ability to assign functional knowledge from deeply annotated model species correctly to other species that are of interest to bioenergy and biomaterial production.
Beyond using quantitative expression information for identification of functional gene homologs between species, nascent technologies for capturing full-length transcripts from single cells e.
The initial panel should include a diverse set of species, including both better studied models e. Alternatively, the initial panel might be selected based on more pragmatic criteria, like the availability of tissue preparation methods. Once established, this program could then be scaled to include a much wider diversity of species. Development of better cell culture transformation systems for environmental species would complement this effort.
Such methods will be essential for performing high-throughput gene functional characterization in environmental species using Perturb-seq 44 screens, or conceptually similar methods such as CROP-seq 53 or CRISP-seq These methods could potentially be adapted to plants using a source of relatively homogeneous cells e.
While these cells may behave differently than they would in planta , the high-throughput gene expression manipulation afforded by Perturb-seq and related methods would greatly accelerate gene function prediction and serve as a powerful hypothesis generation tool. Collectively, single-cell technologies performed on a diverse panel of plant species and tissues, along with the application of high-throughput functional screens, could substantially improve our understanding of gene function.
In addition to elucidating a foundational understanding of metabolism in plants and microbes, single-cell data will be important for both discovering natural product pathways and for successfully leveraging genome engineering and synthetic biology methods to produce biomaterials efficiently.
Single-cell techniques could aid in predicting and refactoring biosynthetic pathways, optimizing bioproduction, and generating predictive metabolic models.
One important application for single-cell technologies will be in the area of biosynthetic pathway discovery. Some bioproducts produced by plants are synthesized predominantly in one or a few specific cell types e.
While many types of enzymes can be predicted from genome information based on sequence similarity to related proteins, this information generally is insufficient to understand which genes work as part of a common pathway in vivo. High-throughput single-cell metabolomics and proteomics methods would be invaluable for systematically mapping where naturally occurring bioproducts are produced in plant tissues.
For those products restricted to specific cellular populations, cell type-specific expression profiling could be used to narrow down components of a common biochemical pathway by identifying sets of enzymatic genes that are co-expressed in the same cell type Fig.
Additionally, single-cell expression information has the potential to improve bioengineering processes. Single-cell technologies applied to diverse plant tissue types are widely expected to aid identification of cell types that are best for making a product. More importantly, these approaches can also identify promoters or other regulatory elements that can direct expression to those cell types with high specificity, thereby providing crucial building blocks for biosynthetic engineering Fig.
Finally, single-cell transcriptome profiling can be coupled with single-cell proteomics, antibody labeling or high-throughput microfluidic phenotyping systems using plant protoplasts or unicellular eukaryotes, such as algae.
By combining single-cell gene expression and phenotyping information, it will be possible to correlate transcript abundance with cellular measurements, enabling a rapid assessment of thousands of genetic manipulations for their phenotypic impact. Example applications for this approach include the search for genes and pathways that increase production of a biomaterial of interest in a given species.
Top panel: single-cell resolution data can be used to find genes in biosynthesis pathways by identifying co-expressed genes in individual cells or cell types. Middle panel: single-cell expression data can identify cell-specific and condition-specific building blocks, as genes that co-vary across clusters of cells are likely regulated by common components e.
This can be exploited to identify promoters useful for bioengineering applications where production in a specific cell type is desired. High-throughput analyses of mutant strains or libraries containing engineered biosynthetic clusters could be used to identify or verify which genes and pathways are necessary for the production of specific products and to optimize for higher production yield.
Adapting single-cell technologies transcriptome, proteome, and metabolome to fungal and algal species, in addition to plant cell suspension systems, will be particularly important for improving bioproduct and biomaterial production. These methods could additionally provide a foundational understanding of culture population diversity, facilitate pathway optimization through parallelization, elucidate synthesis dynamics, and reveal whether heterogeneous populations are important for synthesis.
For instance, the synthesis of some bioproducts may require a combination of cell types and a mechanism for transport of metabolites between cell types. The development of methods that allow sampling of multiple different molecule types in parallel e. Plant, algal, and fungal species, in contrast to animals, have complex polysaccharide cell walls that must be removed or permeabilized for single-cell characterization.
This challenge has substantially hindered the application of these methods to such species. Methods of using enzyme cocktails to remove cell walls i. However, the benefits of successful protoplast isolation are that the whole cellular complement of biomolecules can be potentially sampled, which may prove important, especially for low-abundance transcripts or quantification of proteins that are outside of the nucleus.
Isolation of nuclei, rather than whole cells, and cellular fixation methods e. However, there would be great value in revisiting and reviving historical methods of cell isolation and tissue preparation 55 , 56 , 57 , 58 , Overall, developing better tissue, cellular, and nuclear preparation methods for plants, fungi, and algae is an immediate focus area that would broadly enable the application of single-cell methods to environmental and energy science.
One of the more exciting new areas in single-cell characterization is the development of technologies beyond the commonly used microfluidic scRNA-seq methods.
One limitation of the microfluidic methods is that they require tissue dissociation, and spatial information about where a specific cell came from within the tissue is lost.
Several such methods e. Additionally, methods such as MERFISH 10 , 60 can reveal gene expression down to specific sub-regions within cells but currently require substantial investments and specialized expertise in microscopy equipment.
There is a great need for the development of high-throughput single-cell transcriptomics methods that could capture information for both eukaryotic and non-eukaryotic organisms at the same time since current widely used methods are restricted to reading RNA transcripts that have polyadenylation signals. This prohibits their use for profiling bacteria or archaea that are interacting with plants, an essential element for fully characterizing complex soil communities.
One potential solution lies in adapting the chemistry of single-cell reagents to not rely on pre-existing polyA sequences, as has been recently demonstrated for high-throughput plate-based barcoding assays of bacteria 61 , If this translates well to spatial transcriptomics assays, it could also solve the more difficult challenge of how to quantify the transcript abundance of both plant and prokaryotic cells in a symbiotic system while preserving their spatial context.
For instance, bacteria are often non-uniformly distributed across multiple planes when colonizing plants. Recently, methods incorporating polyadenylation enzymes that target mRNA from bacteria have been demonstrated to overcome limitations in prokaryotic transcriptome capture 62 , Still, other technologies have shown the possibility of describing transcriptomic changes in 3-dimensional space e. Further application and integration of such methods would substantially benefit the study of plant-microbe interactions in the environment.
Currently, many single-cell or spatially resolved transcriptomics methods result in data that is restricted to specific regions of genes e. Beyond gene expression, there is a strong need for high-throughput single-cell proteomics and metabolomics methods. Such methods are in development but have throughputs that currently lag substantially behind transcriptomics methods or require specialized antibodies, limiting their application to specific panels of proteins 67 , Emerging methods such as CITE-seq 69 combine scRNA-seq with antibody labeling to interrogate gene expression and the repertoire of cell surface proteins for individual cells in the same experiment.
In addition to single-cell profiling methods, there is a need for better methods to validate single-cell results, including improved in situ hybridization protocols such as single-molecule FISH 70 for plants, as well as faster and more efficient ways to generate reporter lines.
Additionally, the application of technologies like the 10x Genomics Visium platform could also serve as a powerful validation and discovery tool With the exception of an early, low resolution incarnation of spatial transcriptomics used to profile plant shoot tissue 39 , 40 , most of these exciting new technologies have been exclusively applied to animal systems. However, there is great promise that more modalities of biomolecule profiling will soon advance our understanding of plants. Complementary to novel microfluidics methods and advanced molecular biology reagents and protocols, innovative computational methods employing statistical tools rooted in machine learning have been the third technology pillar that has enabled breakthrough advances in single-cell approaches in recent years.
While many of the tools already developed for analysis of single-cell data from mammalian tissues will be applicable to analysis of plant data sets, and indeed some tools were specifically developed for the analysis of plant single-cell data e. For example, as research moves from Arabidopsis roots in the first wave of studies to new species and tissues, how do we know if cell clusters represent true cell types if no high-quality cell type-specific markers are already known?
Furthermore, how will typical mapping pipelines for scRNA-seq perform when aligning to transcripts from poorly annotated genomes? To enable robust and valid data analysis, we will need to adapt or develop tools for cross-species comparison that do not require a one-to-one gene mapping between organisms In considering the funding landscape for such efforts with a focus on species relevant to bioenergy applications, DOE, with its strong history of driving plant and microbial computational tool development, would be particularly well-positioned to support such efforts.
In contrast, research communities that have organized around different plant species often have vastly different data-sharing practices, and journal publication requirements alone have proven inadequate to compel consistent data sharing. A vision is currently emerging for a Plant Cell Atlas 84 that would include not only single-cell transcriptomics data but multi-scale imaging, proteomics, and other data types, as well.
A unified portal for single-cell and other data from the plant community would greatly facilitate the widespread movement toward FAIR data principles. There was universal agreement that such a platform, like the Human Cell Atlas, should have international support and accessibility and not be wholly funded by a single country or funding agency.
In addition to enabling data sharing, such a resource would have the added benefit of establishing high standards for data quality, enable consistency in data analysis, and provide innovative and powerful data visualization tools. Having a unified platform supporting multiple plant research communities would facilitate solutions to emerging problems, such as how to define analogous cell types between different species.
It could also serve as a platform to support the sharing of information beyond results, such as tissue dissociation or preparation protocols.
An overarching question is whether the effort to establish a Plant Cell Atlas should focus exclusively on very deep characterization of a single plant species or generation and curation of data from a wide variety of species. Given the existing research and database infrastructure, a single-species effort would almost certainly focus on A.
Pragmatically, it makes inherent sense if resources are limited to commit to completing a deep, multimodal characterization of a single species. Such a dataset would have the best potential for being able to integrate different types of data with machine learning and similar strategies to construct accurate systems-level models of an entire plant.
This approach would provide critical baseline information about a variety of plant species important for the environment, energy, biosynthesis, and food production. It would establish a centralized open data resource for the research communities working on these species and inform downstream experimental studies, genome annotation, and genetic engineering of these organisms. Ideally, both wide and deep efforts would not be mutually exclusive and would work together to coordinate data production and release through a centralized portal that would broadly serve the plant biology community.
Over the past several years, an astounding array of new single-cell technologies has driven unprecedented advances in the biomedical sciences. New methods that leverage advanced experimental and computational tools provide single-cell resolution transcriptome and epigenome information and are complemented by nascent methods for proteome, metabolome, and spatially resolved transcriptomics at the single-cell level.
Within the past few years, we have begun to see some of these same approaches demonstrated in plants, fungi, and algae. While significant technical challenges still need to be overcome before these techniques can be broadly applied to the wide array of species that are of interest to energy and environmental studies, this initial wave of published studies is only a harbinger of the powerful discovery opportunities these methods will enable. Thus, the time is ripe for focused investments into the development and adoption of single-cell methods to drive the next wave of biological innovation for energy and environmental science.
Single-cell molecular profiling methods are expected to have the same paradigm-shifting potential for plant and environmental biology as they have already had in the biomedical sciences. Applying single cell methods to microbial and fungal species, in addition to plants, would enable greater understanding of how plants and microbes interact in commensal, competitive, and pathogenic relationships. In addition, fundamental insights into cell state properties of eukaryotic microbes could be used to improve bioreactor-based production.
Lastly, single-cell measurements of individuals across a population can capture properties such as life cycle, measure population heterogeneity, distinguish between stochastic and regulated processes, and guide how desired cell states can be selected through engineering.
This new frontier for single-cell science is likely to face unique challenges, but these issues could be addressed through targeted investments in technology development and a data-sharing platform. Advances in single-cell technologies will have exciting and far-reaching impacts when widely applied to plants, fungi, and microbes, and will be transformative for both our understanding of environmental biology and for trait engineering for bioenergy and biomaterials.
Insight into plant cell type function : Nearly all biological functions a plant executes in vivo occur through the interplay of many different cell types with highly specialized functional profiles. Resolving the molecular blueprint transcriptome, proteome, metabolome, etc.
Insight into plant development : Understanding the development of plants is critical for improving traits such as biomass yield. Understanding plant responses to environmental factors : Many factors affecting the response of plants to environmental factors, such as pathogens, drought, nutrients, climate, or soil are likely driven by very specific processes taking place only in subsets of their cell types.
Single-cell methods will make it possible to deconvolute these responses and assign specific aspects of the organismal response to the cell types they occur in. Functional annotation of plant genes and gene families : Plants tend to have large gene families and complex, polyploid genomes, which creates major challenges in correctly identifying functional gene homologs across related plant species. Single-cell technologies provide high-resolution gene expression data that can be used to enable correct assignment of functional orthologs across species, making it possible to correctly extrapolate gene function from deeply annotated model species to crops of interest.
Methods combining single cell technologies with high-throughput genome engineering could elucidate the function of genes that have not been previously characterized. Identification of targets for bioenergy crop improvement : As scientists and breeders strive for predictive engineering of plant traits, a detailed understanding of how gene networks are composed and regulated in response to environmental input at a cell type level will substantially accelerate progress towards the creation of more productive and sustainable energy crops.
Varoquaux, N. Transcriptomic analysis of field-droughted sorghum from seedling to maturity reveals biotic and metabolic responses. Spindel, J. Association mapping by aerial drone reveals genetic associations for Sorghum bicolor biomass traits under drought. BMC Genom. Taylor-Teeples, M. An Arabidopsis gene regulatory network for secondary cell wall synthesis. Nature , — Han, X. Construction of a human cell landscape at single-cell level. Macosko, E. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Cell , — This study was among the first to demonstrate single-cell transcriptomics on a massively parallel scale, and launched the modern era of single-cell science. Birnbaum, K. Power in numbers: single-cell RNA-Seq strategies to dissect complex tissues. This study explores the conept of plant cell types and how single-cell transcriptomics strategies can be leveraged to identify and characterize them.
Rodriques, S. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science , — Vickovic, S. High-definition spatial transcriptomics for in situ tissue profiling. Methods 16 , — Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.
Science , 78—82 Moreover, C. In this issue of PLOS Genetics , we describe an intracellular transcriptomic atlas of gene expression within the giant-celled species C. Predominant patterns of gene expression progress in a basal-apical direction, from the holdfast and stolon to the frond apex. Remarkably, the genes associated with these expression patterns track the progression of the gene expression process. Thus DNA polymerase II transcripts are highly expressed in the holdfast, and transcripts associated with nuclear activities, such as DNA replication, recombination, and repair, chromatin metabolism, and RNAi pathways are almost exclusively restricted to the stolon and basal frond region.
Moving apically, translation-related transcripts are found in the frond rachis and pinnules, and at the frond apex, transcripts related to vesicular trafficking are enriched. Perhaps the most striking finding is that conserved groups of genes are co-expressed between organs of a land plant tomato and the pseudo-organs of C. For example, the stolon of C. He asserted that cellular phenomenon had less to do with plant morphology than processes occurring at an organismal level.
He even went so far as to argue that land plants are siphonous, exhibiting properties similar to giant coenocytes like Caulerpa , namely because of their symplasm and plasmodesmata. Caulerpa is the manifestation of organismal theory, and our work shows how the repercussions of organismal theory are borne out at a molecular level, where cellular compartments correspond to pseudo-organs, and gene expression patterns are conserved between morphological structures within a cell Caulerpa and between the cells comprising tissues tomato and other land plants.
In fact, higher plant cells are connected to each other by means of channels called plasmodesmata, and it has been argued that multicellular land plants exhibit properties similar to single-celled organisms like Caulerpa. What if we could really think of higher plants, like tomato, as one cell instead of multitudes? This idea of thinking of multicellular land plants, like tomato, and giant single-celled algae, like Caulerpa, similarly is supported by our results that demonstrate a shared pattern of RNA accumulation.
Frankly, our results have caused us to think about plant structure from an entirely different perspective, which is the most important outcome from this research. Note: Content may be edited for style and length.
Science News. Townsley, Yasunori Ichihashi, Neelima R. Sinha, Daniel H. ScienceDaily, 29 January Donald Danforth Plant Science Center. Structure of world's largest single cell is reflected at the molecular level.
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