An official website of the United States government
Messenger RNAs (mRNAs) function as mobile signals for cell-to-cell communication in multicellular organisms. The KNOTTED1 (KN1) homeodomain family transcription factors act non–cell autonomously to control stem cell maintenance in plants through cell-to-cell movement of their proteins and mRNAs through plasmodesmata; however, the mechanism of mRNA movement is largely unknown. We show that cell-to-cell movement of a KN1 mRNA requires ribosomal RNA–processing protein 44A (AtRRP44A), a subunit of the RNA exosome that processes or degrades diverse RNAs in eukaryotes. AtRRP44A can interact with plasmodesmata and mediates the cell-to-cell trafficking of KN1 mRNA, and genetic analysis indicates that AtRRP44A is required for the developmental functions of SHOOT MERISTEMLESS, an Arabidopsis KN1 homolog. Our findings suggest that AtRRP44A promotes mRNA trafficking through plasmodesmata to control stem cell–dependent processes in plants.
more »
« less
To the proteome and beyond: advances in single-cell omics profiling for plant systems
Recent advances in single-cell proteomics for animal systems could be adapted for plants to increase our understanding of plant development, response to stimuli, and cell-to-cell signaling.
more »
« less
- PAR ID:
- 10352057
- Date Published:
- Journal Name:
- Plant Physiology
- Volume:
- 188
- Issue:
- 2
- ISSN:
- 0032-0889
- Page Range / eLocation ID:
- 726 to 737
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Autologous cell therapy depends on T lymphocyte expansion efficiency and is hindered by suboptimal interactions between T cell receptors (TCR) and peptide‐MHC molecules. Various artificial antigen presenting cell systems that enhance these interactions are often labor‐intensive, fabrication costly, highly variable, and potentially unscalable toward clinical setting. Here, 3D centrifugation‐enabled priming of T cell immune‐synapse junctions is performed to generate tight T cell–Dynabead aggregates at a rate 200‐fold faster than that of conventional 24‐h bulk shaking. Furthermore, by forming T cell–Dynabead aggregates in the starting culture, two‐ to sixfold greater T cell expansion is achieved over conventional T cell expansion for cancer patient‐derived primary T cells while limiting over‐activation. Creating 3D T cell–Dynabead aggregates as the “booster” material enables highly efficient polyclonal T cell expansion without the need for complex surface modification of artificial antigen‐presenting cells (APCs). This method can be modularly adapted to existing T cell expansion processes for various applications, including adoptive cell therapies (ACTs).more » « less
-
Low C-rate charge and discharge experiments, plus complementary differential voltage or differential capacity analysis, are among the most common battery characterization methods. Here, we adapt the multi-species, multi-reaction (MSMR) half-cell thermodynamic model to low C-rate cycling of whole-cell Li-ion batteries. MSMR models for the anode and cathode are coupled through whole-cell charge balances and cell-cycling voltage constraint equations, forming the basis for model-based estimation of MSMR half-cell parameters from whole-cell experimental data. Emergent properties of the whole-cell, like slippage of the anode and cathode lithiation windows, are also computed as cells cycle and degrade. A sequential least-square optimization scheme is used for parameter estimation from low-C cycling data of Samsung 18650 NMC∣C cells. Low-error fits of the open-circuit cell voltage (e.g., under 5 mV mean absolute error for charge or discharge curves) and differential voltage curves for fresh and aged cells are achieved. We explore the features (and limitations) of using literature reference values for the MSMR half-cell thermodynamic parameters (reducing our whole-cell formulation to a 1-degree-of-freedom fit) and demonstrate the benefits of expanding the degrees of freedom by letting the MSMR parameters be tailored to the cell under test, within a constrained neighborhood of the half-cell reference values. Bootstrap analysis is performed on each dataset to show the robustness of our fitting to experimental noise and data sampling over the course of 600 cell cycles. The results show which specific MSMR insertion reactions are most responsible for capacity loss in each half-cell and the collective interactions that lead to whole-cell slippage and changes in useable capacity. Open-source software is made available to easily extend this model-based analysis to other labs and battery chemistries.more » « less
-
Monitoring and tracking of cell motion is a key component for understanding disease mechanisms and evaluating the effects of treatments. Time-lapse optical microscopy has been commonly employed for studying cell cycle phases. However, usual manual cell tracking is very time consuming and has poor reproducibility. Automated cell tracking techniques are challenged by variability of cell region intensity distributions and resolution limitations. In this work, we introduce a comprehensive cell segmentation and tracking methodology. A key contribution of this work is that it employs multi-scale space-time interest point detection and characterization for automatic scale selection and cell segmentation. Another contribution is the use of a neural network with class prototype balancing for detection of cell regions. This work also offers a structured mathematical framework that uses graphs for track generation and cell event detection. We evaluated cell segmentation, detection, and tracking performance of our method on time-lapse sequences of the Cell Tracking Challenge (CTC). We also compared our technique to top performing techniques from CTC. Performance evaluation results indicate that the proposed methodology is competitive with these techniques, and that it generalizes very well to diverse cell types and sizes, and multiple imaging techniques.more » « less
-
Synopsis Single-cell RNA sequencing (scRNAseq) is a powerful tool to describe cell types in multicellular organisms across the animal kingdom. In standard scRNAseq analysis pipelines, clusters of cells with similar transcriptional signatures are given cell type labels based on marker genes that infer specialized known characteristics. Since these analyses are designed for model organisms, such as humans and mice, problems arise when attempting to label cell types of distantly related, non-model species that have unique or divergent cell types. Consequently, this leads to limited discovery of novel species-specific cell types and potential mis-annotation of cell types in non-model species while using scRNAseq. To address this problem, we discuss recently published approaches that help annotate scRNAseq clusters for any non-model organism. We first suggest that annotating with an evolutionary context of cell lineages will aid in the discovery of novel cell types and provide a marker-free approach to compare cell types across distantly related species. Secondly, machine learning has greatly improved bioinformatic analyses, so we highlight some open-source programs that use reference-free approaches to annotate cell clusters. Lastly, we propose the use of unannotated genes as potential cell markers for non-model organisms, as many do not have fully annotated genomes and these data are often disregarded. Improving single-cell annotations will aid the discovery of novel cell types and enhance our understanding of non-model organisms at a cellular level. By unifying approaches to annotate cell types in non-model organisms, we can increase the confidence of cell annotation label transfer and the flexibility to discover novel cell types.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/plphys/kiab429
