1
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Jia Y, Ma P, Yao Q. CellMarkerPipe: cell marker identification and evaluation pipeline in single cell transcriptomes. Sci Rep 2024; 14:13151. [PMID: 38849445 PMCID: PMC11161599 DOI: 10.1038/s41598-024-63492-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 05/29/2024] [Indexed: 06/09/2024] Open
Abstract
Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe ( https://github.com/yao-laboratory/cellMarkerPipe ), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker's overall reliable performance in single marker gene selection, with COSG showing commendable speed and comparable efficacy. Furthermore, we demonstrate the pivotal role of our approach in real-world medical datasets. This general and opensource pipeline stands as a significant advancement in streamlining cell marker gene identification and evaluation, fitting broad applications in the field of cellular biology and medical research.
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Affiliation(s)
- Yinglu Jia
- School of Computing, University of Nebraska Lincoln, 256 Avery Hall, Lincoln, NE, 68588, USA
- Department of Chemistry, University of Nebraska Lincoln, Hamilton Hall, Lincoln, NE, 68588, USA
| | - Pengchong Ma
- School of Computing, University of Nebraska Lincoln, 256 Avery Hall, Lincoln, NE, 68588, USA
| | - Qiuming Yao
- School of Computing, University of Nebraska Lincoln, 256 Avery Hall, Lincoln, NE, 68588, USA.
- Nebraska Center for the Prevention of Obesity Diseases, 316C Leverton Hall, Lincoln, NE, 68583, USA.
- Nebraska Center for Virology, University of Nebraska, 4240 Fair St., Lincoln, NE, 68583, USA.
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2
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García-Gómez ML, Ten Tusscher K. Multi-scale mechanisms driving root regeneration: From regeneration competence to tissue repatterning. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024. [PMID: 38824611 DOI: 10.1111/tpj.16860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 05/15/2024] [Accepted: 05/20/2024] [Indexed: 06/03/2024]
Abstract
Plants possess an outstanding capacity to regenerate enabling them to repair damages caused by suboptimal environmental conditions, biotic attacks, or mechanical damages impacting the survival of these sessile organisms. Although the extent of regeneration varies greatly between localized cell damage and whole organ recovery, the process of regeneration can be subdivided into a similar sequence of interlinked regulatory processes. That is, competence to regenerate, cell fate reprogramming, and the repatterning of the tissue. Here, using root tip regeneration as a paradigm system to study plant regeneration, we provide a synthesis of the molecular responses that underlie both regeneration competence and the repatterning of the root stump. Regarding regeneration competence, we discuss the role of wound signaling, hormone responses and synthesis, and rapid changes in gene expression observed in the cells close to the cut. Then, we consider how this rapid response is followed by the tissue repatterning phase, where cells experience cell fate changes in a spatial and temporal order to recreate the lost stem cell niche and columella. Lastly, we argue that a multi-scale modeling approach is fundamental to uncovering the mechanisms underlying root regeneration, as it allows to integrate knowledge of cell-level gene expression, cell-to-cell transport of hormones and transcription factors, and tissue-level growth dynamics to reveal how the bi-directional feedbacks between these processes enable self-organized repatterning of the root apex.
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Affiliation(s)
- Monica L García-Gómez
- Computational Developmental Biology Group, Department of Biology, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
- Experimental and Computational Plant Development Group, Department of Biology, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
- CropXR Institute, Utrecht, The Netherlands
- Translational Plant Biology Group, Department of Biology, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
| | - Kirsten Ten Tusscher
- Computational Developmental Biology Group, Department of Biology, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
- Experimental and Computational Plant Development Group, Department of Biology, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
- CropXR Institute, Utrecht, The Netherlands
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3
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Tansley C, Patron NJ, Guiziou S. Engineering Plant Cell Fates and Functions for Agriculture and Industry. ACS Synth Biol 2024; 13:998-1005. [PMID: 38573786 PMCID: PMC11036505 DOI: 10.1021/acssynbio.4c00047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/06/2024]
Abstract
Many plant species are grown to enable access to specific organs or tissues, such as seeds, fruits, or stems. In some cases, a value is associated with a molecule that accumulates in a single type of cell. Domestication and subsequent breeding have often increased the yields of these target products by increasing the size, number, and quality of harvested organs and tissues but also via changes to overall plant growth architecture to suit large-scale cultivation. Many of the mutations that underlie these changes have been identified in key regulators of cellular identity and function. As key determinants of yield, these regulators are key targets for synthetic biology approaches to engineer new forms and functions. However, our understanding of many plant developmental programs and cell-type specific functions is still incomplete. In this Perspective, we discuss how advances in cellular genomics together with synthetic biology tools such as biosensors and DNA-recording devices are advancing our understanding of cell-specific programs and cell fates. We then discuss advances and emerging opportunities for cell-type-specific engineering to optimize plant morphology, responses to the environment, and the production of valuable compounds.
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Affiliation(s)
- Connor Tansley
- Engineering
Biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ United Kingdom
- Department
of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA United
Kingdom
| | - Nicola J. Patron
- Engineering
Biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ United Kingdom
- Department
of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA United
Kingdom
| | - Sarah Guiziou
- Engineering
Biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ United Kingdom
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4
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Islam MT, Liu Y, Hassan MM, Abraham PE, Merlet J, Townsend A, Jacobson D, Buell CR, Tuskan GA, Yang X. Advances in the Application of Single-Cell Transcriptomics in Plant Systems and Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0029. [PMID: 38435807 PMCID: PMC10905259 DOI: 10.34133/bdr.0029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/28/2024] [Indexed: 03/05/2024] Open
Abstract
Plants are complex systems hierarchically organized and composed of various cell types. To understand the molecular underpinnings of complex plant systems, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for revealing high resolution of gene expression patterns at the cellular level and investigating the cell-type heterogeneity. Furthermore, scRNA-seq analysis of plant biosystems has great potential for generating new knowledge to inform plant biosystems design and synthetic biology, which aims to modify plants genetically/epigenetically through genome editing, engineering, or re-writing based on rational design for increasing crop yield and quality, promoting the bioeconomy and enhancing environmental sustainability. In particular, data from scRNA-seq studies can be utilized to facilitate the development of high-precision Build-Design-Test-Learn capabilities for maximizing the targeted performance of engineered plant biosystems while minimizing unintended side effects. To date, scRNA-seq has been demonstrated in a limited number of plant species, including model plants (e.g., Arabidopsis thaliana), agricultural crops (e.g., Oryza sativa), and bioenergy crops (e.g., Populus spp.). It is expected that future technical advancements will reduce the cost of scRNA-seq and consequently accelerate the application of this emerging technology in plants. In this review, we summarize current technical advancements in plant scRNA-seq, including sample preparation, sequencing, and data analysis, to provide guidance on how to choose the appropriate scRNA-seq methods for different types of plant samples. We then highlight various applications of scRNA-seq in both plant systems biology and plant synthetic biology research. Finally, we discuss the challenges and opportunities for the application of scRNA-seq in plants.
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Affiliation(s)
- Md Torikul Islam
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Yang Liu
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Md Mahmudul Hassan
- Department of Genetics and Plant Breeding,
Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh
| | - Paul E. Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Jean Merlet
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education,
University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Alice Townsend
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education,
University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - C. Robin Buell
- Center for Applied Genetic Technologies,
University of Georgia, Athens, GA 30602, USA
- Department of Crop and Soil Sciences,
University of Georgia, Athens, GA 30602, USA
- Institute of Plant Breeding, Genetics, and Genomics,
University of Georgia, Athens, GA 30602, USA
| | - Gerald A. Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Xiaohan Yang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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5
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Yao Q, Jia Y, Ma P. cellMarkerPipe: Cell Marker Identification and Evaluation Pipeline in Single Cell Transcriptomes. RESEARCH SQUARE 2024:rs.3.rs-3844718. [PMID: 38313296 PMCID: PMC10836098 DOI: 10.21203/rs.3.rs-3844718/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe (https://github.com/yao-laboratory/cellMarkerPipe), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker's overall reliable performance in single marker gene selection, with COSG showing commendable speed and comparable efficacy. Furthermore, we demonstrate the pivotal role of our approach in real-world medical datasets. This general and opensource pipeline stands as a significant advancement in streamlining cell marker gene identification and evaluation, fitting broad applications in the field of cellular biology and medical research.
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6
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van Wijk KJ, Leppert T, Sun Z, Kearly A, Li M, Mendoza L, Guzchenko I, Debley E, Sauermann G, Routray P, Malhotra S, Nelson A, Sun Q, Deutsch EW. Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas. J Proteome Res 2024; 23:185-214. [PMID: 38104260 DOI: 10.1021/acs.jproteome.3c00536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.
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Affiliation(s)
- Klaas J van Wijk
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Tami Leppert
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Zhi Sun
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Alyssa Kearly
- Boyce Thompson Institute, Ithaca, New York 14853, United States
| | - Margaret Li
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Luis Mendoza
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Isabell Guzchenko
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Erica Debley
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Georgia Sauermann
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Pratyush Routray
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Sagunya Malhotra
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Andrew Nelson
- Boyce Thompson Institute, Ithaca, New York 14853, United States
| | - Qi Sun
- Computational Biology Service Unit, Cornell University, Ithaca, New York 14853, United States
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
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7
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Byrt CS, Zhang RY, Magrath I, Chan KX, De Rosa A, McGaughey S. Exploring aquaporin functions during changes in leaf water potential. FRONTIERS IN PLANT SCIENCE 2023; 14:1213454. [PMID: 37615024 PMCID: PMC10442719 DOI: 10.3389/fpls.2023.1213454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/04/2023] [Indexed: 08/25/2023]
Abstract
Maintenance of optimal leaf tissue humidity is important for plant productivity and food security. Leaf humidity is influenced by soil and atmospheric water availability, by transpiration and by the coordination of water flux across cell membranes throughout the plant. Flux of water and solutes across plant cell membranes is influenced by the function of aquaporin proteins. Plants have numerous aquaporin proteins required for a multitude of physiological roles in various plant tissues and the membrane flux contribution of each aquaporin can be regulated by changes in protein abundance, gating, localisation, post-translational modifications, protein:protein interactions and aquaporin stoichiometry. Resolving which aquaporins are candidates for influencing leaf humidity and determining how their regulation impacts changes in leaf cell solute flux and leaf cavity humidity is challenging. This challenge involves resolving the dynamics of the cell membrane aquaporin abundance, aquaporin sub-cellular localisation and location-specific post-translational regulation of aquaporins in membranes of leaf cells during plant responses to changes in water availability and determining the influence of cell signalling on aquaporin permeability to a range of relevant solutes, as well as determining aquaporin influence on cell signalling. Here we review recent developments, current challenges and suggest open opportunities for assessing the role of aquaporins in leaf substomatal cavity humidity regulation.
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8
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van Wijk KJ, Leppert T, Sun Z, Kearly A, Li M, Mendoza L, Guzchenko I, Debley E, Sauermann G, Routray P, Malhotra S, Nelson A, Sun Q, Deutsch EW. Mapping the Arabidopsis thaliana proteome in PeptideAtlas and the nature of the unobserved (dark) proteome; strategies towards a complete proteome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.01.543322. [PMID: 37333403 PMCID: PMC10274743 DOI: 10.1101/2023.06.01.543322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected PTMs, and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for building the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome - the 'dark' proteome. This dark proteome is highly enriched for certain ( e.g. CLE, CEP, IDA, PSY) but not other ( e.g. THIONIN, CAP,) signaling peptides families, E3 ligases, TFs, and other proteins with unfavorable physicochemical properties. A machine learning model trained on RNA expression data and protein properties predicts the probability for proteins to be detected. The model aids in discovery of proteins with short-half life ( e.g. SIG1,3 and ERF-VII TFs) and completing the proteome. PeptideAtlas is linked to TAIR, JBrowse, PPDB, SUBA, UniProtKB and Plant PTM Viewer.
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9
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Gouesbet G. Deciphering Macromolecular Interactions Involved in Abiotic Stress Signaling: A Review of Bioinformatics Analysis. Methods Mol Biol 2023; 2642:257-294. [PMID: 36944884 DOI: 10.1007/978-1-0716-3044-0_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Plant functioning and responses to abiotic stresses largely involve regulations at the transcriptomic level via complex interactions of signal molecules, signaling cascades, and regulators. Nevertheless, all the signaling networks involved in responses to abiotic stresses have not yet been fully established. The in-depth analysis of transcriptomes in stressed plants has become a relevant state-of-the-art methodology to study these regulations and signaling pathways that allow plants to cope with or attempt to survive abiotic stresses. The plant science and molecular biology community has developed databases about genes, proteins, protein-protein interactions, protein-DNA interactions and ontologies, which are valuable sources of knowledge for deciphering such regulatory and signaling networks. The use of these data and the development of bioinformatics tools help to make sense of transcriptomic data in specific contexts, such as that of abiotic stress signaling, using functional biological approaches. The aim of this chapter is to present and assess some of the essential online tools and resources that will allow novices in bioinformatics to decipher transcriptomic data in order to characterize the cellular processes and functions involved in abiotic stress responses and signaling. The analysis of case studies further describes how these tools can be used to conceive signaling networks on the basis of transcriptomic data. In these case studies, particular attention was paid to the characterization of abiotic stress responses and signaling related to chemical and xenobiotic stressors.
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Affiliation(s)
- Gwenola Gouesbet
- University of Rennes, CNRS, ECOBIO [(Ecosystèmes, Biodiversité, Evolution)] - UMR 6553, Rennes, France.
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10
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Wen L, Li G, Huang T, Geng W, Pei H, Yang J, Zhu M, Zhang P, Hou R, Tian G, Su W, Chen J, Zhang D, Zhu P, Zhang W, Zhang X, Zhang N, Zhao Y, Cao X, Peng G, Ren X, Jiang N, Tian C, Chen ZJ. Single-cell technologies: From research to application. Innovation (N Y) 2022; 3:100342. [PMCID: PMC9637996 DOI: 10.1016/j.xinn.2022.100342] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 10/13/2022] [Indexed: 11/09/2022] Open
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