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Vong GYW, McCarthy K, Claydon W, Davis SJ, Redmond EJ, Ezer D. AraLeTA: An Arabidopsis leaf expression atlas across diurnal and developmental scales. PLANT PHYSIOLOGY 2024; 195:1941-1953. [PMID: 38428997 PMCID: PMC11213249 DOI: 10.1093/plphys/kiae117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 03/03/2024]
Abstract
Mature plant leaves are a composite of distinct cell types, including epidermal, mesophyll, and vascular cells. Notably, the proportion of these cells and the relative transcript concentrations within different cell types may change over time. While gene expression data at a single-cell level can provide cell-type-specific expression values, it is often too expensive to obtain these data for high-resolution time series. Although bulk RNA-seq can be performed in a high-resolution time series, RNA-seq using whole leaves measures average gene expression values across all cell types in each sample. In this study, we combined single-cell RNA-seq data with time-series data from whole leaves to assemble an atlas of cell-type-specific changes in gene expression over time for Arabidopsis (Arabidopsis thaliana). We inferred how the relative transcript concentrations of different cell types vary across diurnal and developmental timescales. Importantly, this analysis revealed 3 subgroups of mesophyll cells with distinct temporal profiles of expression. Finally, we developed tissue-specific gene networks that form a community resource: an Arabidopsis Leaf Time-dependent Atlas (AraLeTa). This allows users to extract gene networks that are confirmed by transcription factor-binding data and specific to certain cell types at certain times of day and at certain developmental stages. AraLeTa is available at https://regulatorynet.shinyapps.io/araleta/.
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Affiliation(s)
- Gina Y W Vong
- Department of Biology, University of York, York YO10 5DD, UK
| | - Kayla McCarthy
- Department of Biology, University of York, York YO10 5DD, UK
| | - Will Claydon
- Department of Biology, University of York, York YO10 5DD, UK
| | - Seth J Davis
- Department of Biology, University of York, York YO10 5DD, UK
| | - Ethan J Redmond
- Department of Biology, University of York, York YO10 5DD, UK
| | - Daphne Ezer
- Department of Biology, University of York, York YO10 5DD, UK
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2
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Danciu DP, Hooli J, Martin-Villalba A, Marciniak-Czochra A. Mathematics of neural stem cells: Linking data and processes. Cells Dev 2023; 174:203849. [PMID: 37179018 DOI: 10.1016/j.cdev.2023.203849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/29/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023]
Abstract
Adult stem cells are described as a discrete population of cells that stand at the top of a hierarchy of progressively differentiating cells. Through their unique ability to self-renew and differentiate, they regulate the number of end-differentiated cells that contribute to tissue physiology. The question of how discrete, continuous, or reversible the transitions through these hierarchies are and the precise parameters that determine the ultimate performance of stem cells in adulthood are the subject of intense research. In this review, we explain how mathematical modelling has improved the mechanistic understanding of stem cell dynamics in the adult brain. We also discuss how single-cell sequencing has influenced the understanding of cell states or cell types. Finally, we discuss how the combination of single-cell sequencing technologies and mathematical modelling provides a unique opportunity to answer some burning questions in the field of stem cell biology.
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Affiliation(s)
- Diana-Patricia Danciu
- Heidelberg University, Institute of Mathematics (IMA), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Jooa Hooli
- Heidelberg University, Institute of Mathematics (IMA), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Heidelberg University, Faculty of Biosciences, Im Neuenheimer Feld 234, 69120 Heidelberg, Germany; German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Ana Martin-Villalba
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Anna Marciniak-Czochra
- Heidelberg University, Institute of Mathematics (IMA), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany.
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3
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Balcerowicz M, Mahjoub M, Nguyen D, Lan H, Stoeckle D, Conde S, Jaeger KE, Wigge PA, Ezer D. An early-morning gene network controlled by phytochromes and cryptochromes regulates photomorphogenesis pathways in Arabidopsis. MOLECULAR PLANT 2021; 14:983-996. [PMID: 33766657 DOI: 10.1016/j.molp.2021.03.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/04/2021] [Accepted: 03/22/2021] [Indexed: 06/12/2023]
Abstract
Light perception at dawn plays a key role in coordinating multiple molecular processes and in entraining the plant circadian clock. The Arabidopsis mutant lacking the main photoreceptors, however, still shows clock entrainment, indicating that the integration of light into the morning transcriptome is not well understood. In this study, we performed a high-resolution RNA-sequencing time-series experiment, sampling every 2 min beginning at dawn. In parallel experiments, we perturbed temperature, the circadian clock, photoreceptor signaling, and chloroplast-derived light signaling. We used these data to infer a gene network that describes the gene expression dynamics after light stimulus in the morning, and then validated key edges. By sampling time points at high density, we are able to identify three light- and temperature-sensitive bursts of transcription factor activity, one of which lasts for only about 8 min. Phytochrome and cryptochrome mutants cause a delay in the transcriptional bursts at dawn, and completely remove a burst of expression in key photomorphogenesis genes (HY5 and BBX family). Our complete network is available online (http://www-users.york.ac.uk/∼de656/dawnBurst/dawnBurst.html). Taken together, our results show that phytochrome and cryptochrome signaling is required for fine-tuning the dawn transcriptional response to light, but separate pathways can robustly activate much of the program in their absence.
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Affiliation(s)
| | - Mahiar Mahjoub
- Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Duy Nguyen
- Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Hui Lan
- Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | | | - Susana Conde
- Department of Statistics, University of Warwick, Coventry, UK; Alan Turing Institute, London, UK
| | - Katja E Jaeger
- Leibniz-Institute of Vegetable and Ornamental Crops (IGZ), 14979 Großbeeren, Germany
| | - Philip A Wigge
- Leibniz-Institute of Vegetable and Ornamental Crops (IGZ), 14979 Großbeeren, Germany; Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Daphne Ezer
- Alan Turing Institute, London, UK; Department of Biology, University of York, York YO10 5DD, UK.
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4
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Autran D, Bassel GW, Chae E, Ezer D, Ferjani A, Fleck C, Hamant O, Hartmann FP, Jiao Y, Johnston IG, Kwiatkowska D, Lim BL, Mahönen AP, Morris RJ, Mulder BM, Nakayama N, Sozzani R, Strader LC, ten Tusscher K, Ueda M, Wolf S. What is quantitative plant biology? QUANTITATIVE PLANT BIOLOGY 2021; 2:e10. [PMID: 37077212 PMCID: PMC10095877 DOI: 10.1017/qpb.2021.8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 04/07/2021] [Accepted: 04/07/2021] [Indexed: 05/03/2023]
Abstract
Quantitative plant biology is an interdisciplinary field that builds on a long history of biomathematics and biophysics. Today, thanks to high spatiotemporal resolution tools and computational modelling, it sets a new standard in plant science. Acquired data, whether molecular, geometric or mechanical, are quantified, statistically assessed and integrated at multiple scales and across fields. They feed testable predictions that, in turn, guide further experimental tests. Quantitative features such as variability, noise, robustness, delays or feedback loops are included to account for the inner dynamics of plants and their interactions with the environment. Here, we present the main features of this ongoing revolution, through new questions around signalling networks, tissue topology, shape plasticity, biomechanics, bioenergetics, ecology and engineering. In the end, quantitative plant biology allows us to question and better understand our interactions with plants. In turn, this field opens the door to transdisciplinary projects with the society, notably through citizen science.
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Affiliation(s)
- Daphné Autran
- DIADE, University of Montpellier, IRD, CIRAD, Montpellier, France
| | - George W. Bassel
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Eunyoung Chae
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore
| | - Daphne Ezer
- The Alan Turing Institute, London, United Kingdom
- Department of Statistics, University of Warwick, Coventry, United Kingdom
- Department of Biology, University of York, York, United Kingdom
| | - Ali Ferjani
- Department of Biology, Tokyo Gakugei University, Tokyo, Japan
| | - Christian Fleck
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Breisgau, Germany
| | - Olivier Hamant
- Laboratoire de Reproduction et Développement des Plantes, École normale supérieure (ENS) de Lyon, Université Claude Bernard Lyon (UCBL), Lyon, France
- Institut national de recherche pour l’agriculture, l’alimentation et l’environnement (INRAE), CNRS, Université de Lyon, Lyon, France
| | | | - Yuling Jiao
- State Key Laboratory of Plant Genomics and National Center for Plant Gene Research (Beijing), Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Dorota Kwiatkowska
- Institute of Biology, Biotechnology and Environment Protection, Faculty of Natural Sciences, University of Silesia in Katowice, Katowice, Poland
| | - Boon L. Lim
- School of Biological Sciences, University of Hong Kong, Hong Kong, China
| | - Ari Pekka Mahönen
- Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
- Viikki Plant Science Centre, University of Helsinki, Helsinki, Finland
| | - Richard J. Morris
- Computational and Systems Biology, John Innes Centre, Norwich, United Kingdom
| | - Bela M. Mulder
- Department of Living Matter, Institute AMOLF, Amsterdam, The Netherlands
| | - Naomi Nakayama
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Ross Sozzani
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, North CarolinaUSA
| | - Lucia C. Strader
- Department of Biology, Duke University, Durham, North Carolina, USA
- NSF Science and Technology Center for Engineering Mechanobiology, Department of Biology, Washington University in St. Louis, St. Louis, MissouriUSA
| | - Kirsten ten Tusscher
- Theoretical Biology, Department of Biology, Utrecht University, Utrecht, The Netherlands
| | - Minako Ueda
- Graduate School of Life Sciences, Tohoku University, Sendai, Japan
| | - Sebastian Wolf
- Centre for Organismal Studies (COS) Heidelberg, Heidelberg University, Heidelberg, Germany
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Lomeli LM, Iniguez A, Tata P, Jena N, Liu ZY, Van Etten R, Lander AD, Shahbaba B, Lowengrub JS, Minin VN. Optimal experimental design for mathematical models of haematopoiesis. J R Soc Interface 2021; 18:20200729. [PMID: 33499768 PMCID: PMC7879761 DOI: 10.1098/rsif.2020.0729] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 01/04/2021] [Indexed: 11/12/2022] Open
Abstract
The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected. We use a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. We overcome this difficulty by modelling the unobserved cellular levels as latent variables. We then use principles of Bayesian experimental design to optimally distribute time points at which the haematopoietic cells are quantified. We evaluate our approach using synthetic and real experimental data and show that an optimal design can lead to better estimates of model parameters.
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Affiliation(s)
- Luis Martinez Lomeli
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
| | - Abdon Iniguez
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
| | - Prasanthi Tata
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
| | - Nilamani Jena
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
| | - Zhong-Ying Liu
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
| | - Richard Van Etten
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Division of Hematology/Oncology, University of California Irvine, Irvine, CA, USA
- Department of Biological Chemistry, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA
| | - Arthur D. Lander
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, USA
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA
| | - Babak Shahbaba
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Department of Statistics, University of California Irvine, Irvine, CA, USA
| | - John S. Lowengrub
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA, USA
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA
- Department of Mathematics, University of California Irvine, Irvine, CA, USA
| | - Vladimir N. Minin
- Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
- Center for Cancer Systems Biology, University of California Irvine, Irvine, CA, USA
- Department of Statistics, University of California Irvine, Irvine, CA, USA
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6
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Sadygov VR, Zhang W, Sadygov RG. Timepoint Selection Strategy for In Vivo Proteome Dynamics from Heavy Water Metabolic Labeling and LC-MS. J Proteome Res 2020; 19:2105-2112. [PMID: 32183509 DOI: 10.1021/acs.jproteome.0c00023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Protein homeostasis, proteostasis, is essential for healthy cell functioning and is dysregulated in many diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled online to mass spectrometry (LC-MS) is a powerful high-throughput technique to study proteome dynamics in vivo. Longer labeling duration and dense timepoint sampling (TPS) of tissues provide accurate proteome dynamics estimations. However, the experiments are expensive, and they require animal housing and care, as well as labeling with stable isotopes. Often, the animals are sacrificed at selected timepoints to collect tissues. Therefore, it is necessary to optimize TPS for a given number of sampling points and labeling duration and target a specific tissue of study. Currently, such techniques are missing in proteomics. Here, we report on a formula-based stochastic simulation strategy for TPS for in vivo studies with heavy water metabolic labeling and LC-MS. We model the rate constant (lognormal), measurement error (Laplace), peptide length (gamma), relative abundance of the monoisotopic peak (beta regression), and the number of exchangeable hydrogens (gamma regression). The parameters of the distributions are determined using the corresponding empirical probability density functions from a large-scale dataset of murine heart proteome. The models are used in the simulations of the rate constant to minimize the root-mean-square error (rmse). The rmse for different TPSs shows structured patterns. They are analyzed to elucidate common features in the patterns.
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Affiliation(s)
- Vugar R Sadygov
- Clear Creek High School, 2305 E. Main Street, League City, Texas 77573, United States
| | - William Zhang
- Department of Computer Science, The University of Texas, 2317 Speedway, Stop D9500, Austin, Texas 78712, United States
| | - Rovshan G Sadygov
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, 301 University of Blvd, Galveston, Texas 77555, United States
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