High-efficiency procedure to characterize, segment, and quantify complex multicellularity in raw micrographs in plants

OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis

The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.

Manipulating microRNA miR408 enhances both biomass yield and saccharification efficiency in poplar

The conversion of lignocellulosic feedstocks to fermentable sugar for biofuel production is inefficient, and most strategies to enhance efficiency directly target lignin biosynthesis, with associated negative growth impacts. Here we demonstrate, for both laboratory- and field-grown plants, that expression of Pag-miR408 in poplar (Populus alba × P. glandulosa) significantly enhances saccharification, with no requirement for acid-pretreatment, while promoting plant growth. The overexpression plants show increased accessibility of cell walls to cellulase and scaffoldin cellulose-binding modules. Conversely, Pag-miR408 loss-of-function poplar shows decreased cell wall accessibility. Overexpression of Pag-miR408 targets three Pag-LACCASES, delays lignification, and modestly reduces lignin content, S/G ratio and degree of lignin polymerization. Meanwhile, the LACCASE loss of function mutants exhibit significantly increased growth and cell wall accessibility in xylem. Our study shows how Pag-miR408 regulates lignification and secondary growth, and suggest an effective approach towards enhancing biomass yield and saccharification efficiency in a major bioenergy crop.

Cholesterol Is a Regulator of CAV1 Localization and Cell Migration in Oral Squamous Cell Carcinoma

Cholesterol plays an important role in cancer progression, as it is utilized in membrane biogenesis and cell signaling. Cholesterol-lowering drugs have exhibited tumor-suppressive effects in oral squamous cell carcinoma (OSCC), suggesting that cholesterol is also essential in OSCC pathogenesis. However, the direct effects of cholesterol on OSCC cells remain unclear. Here, we investigated the role of cholesterol in OSCC with respect to caveolin-1 (CAV1), a cholesterol-binding protein involved in intracellular cholesterol transport. Cholesterol levels in OSCC cell lines were depleted using methyl-β-cyclodextrin and increased using the methyl-β-cyclodextrin-cholesterol complex. Functional analysis was performed using timelapse imaging, and CAV1 expression in cholesterol-manipulated cells was investigated using immunofluorescence and immunoblotting assays. CAV1 immunohistochemistry was performed on surgical OSCC samples. We observed that cholesterol addition induced polarized cell morphology, along with CAV1 localization at the trailing edge, and promoted cell migration. Moreover, CAV1 was upregulated in the lipid rafts and formed aggregates in the plasma membrane in cholesterol-added cells. High membranous CAV1 expression in tissue specimens was associated with OSCC recurrence. Therefore, cholesterol promotes the migration of OSCC cells by regulating cell polarity and CAV1 localization to the lipid raft. Furthermore, membranous CAV1 expression is a potential prognostic marker for OSCC patients.

A simple and efficient method to quantify the cell parameters of the seed coat, embryo and silique wall in rapeseed

BACKGROUND

Researchers interested in the seed size of rapeseed need to quantify the cell size and number of cells in the seed coat, embryo and silique wall. Scanning electron microscope-based methods have been demonstrated to be feasible but laborious and costly. After image preparation, the cell parameters are generally evaluated manually, which is time consuming and a major bottleneck for large-scale analysis. Recently, two machine learning-based algorithms, Trainable Weka Segmentation (TWS) and Cellpose, were released to overcome this long-standing problem. Moreover, the MorphoLibJ and LabelsToROIs plugins in Fiji provide user-friendly tools to deal with cell segmentation files. We attempted to verify the practicability and efficiency of these advanced tools for various types of cells in rapeseed.

RESULTS

We simplified the current image preparation procedure by skipping the fixation step and demonstrated the feasibility of the simplified procedure. We developed three methods to automatically process multicellular images of various tissues in rapeseed. The TWS-Fiji (TF) method combines cell detection with TWS and cell measurement with Fiji, enabling the accurate quantification of seed coat cells. The Cellpose-Fiji (CF) method, based on cell segmentation with Cellpose and quantification with Fiji, achieves good performance but exhibits systematic error. By removing border labels with MorphoLibJ and detecting regions of interest (ROIs) with LabelsToROIs, the Cellpose-MorphoLibJ-LabelsToROIs (CML) method achieves human-level performance on bright-field images of seed coat cells. Intriguingly, the CML method needs very little manual calibration, a property that makes it suitable for massive-scale image processing. Through a large-scale quantitative evaluation of seed coat cells, we demonstrated the robustness and high efficiency of the CML method at both the single-cell level and the sample level. Furthermore, we extended the application of the CML method to developing seed coat, embryo and silique wall cells and acquired highly precise and reliable results, indicating the versatility of this method for use in multiple scenarios.

CONCLUSIONS

The CML method is highly accurate and free of the need for manual correction. Hence, it can be applied for the low-cost, high-throughput quantification of diverse cell types in rapeseed with high efficiency. We envision that this method will facilitate the functional genomics and microphenomics studies of rapeseed and other crops.

The small GTPase RABA2a recruits SNARE proteins to regulate the secretory pathway in parallel with the exocyst complex in Arabidopsis

Delivery of proteins to the plasma membrane occurs via secretion, which requires tethering, docking, priming, and fusion of vesicles. In yeast and mammalian cells, an evolutionarily conserved RAB GTPase activation cascade functions together with the exocyst and SNARE proteins to coordinate vesicle transport with fusion at the plasma membrane. However, it is unclear whether this is the case in plants. In this study, we show that the small GTPase RABA2a recruits and interacts with the VAMP721/722-SYP121-SNAP33 SNARE ternary complex for membrane fusion. Through immunoprecipitation coupled with mass spectrometry analysis followed by the validatation with a series of biochemical assays, we identified the SNARE proteins VAMP721 and SYP121 as the interactors and downstream effectors of RABA2a. Further expreiments showed that RABA2a interacts with all members of the SNARE complex in its GTP-bound form and modulates the assembly of the VAMP721/722-SYP121-SNAP33 SNARE ternary complex. Intriguingly, we did not observe the interaction of the exocyst subunits with either RABA2a or theSNARE proteins in several different experiments. Neither RABA2a inactivation affects the subcellular localization or assembly of the exocystnor the exocyst subunit mutant exo84b shows the disrupted RABA2a-SNARE association or SNARE assembly, suggesting that the RABA2a-SNARE- and exocyst-mediated secretory pathways are largely independent. Consistently, our live imaging experiments reveal that the two sets of proteins follow non-overlapping trafficking routes, and genetic and cell biologyanalyses indicate that the two pathways select different cargos. Finally, we demonstrate that the plant-specific RABA2a-SNARE pathway is essential for the maintenance of potassium homeostasis in Arabisopsis seedlings. Collectively, our findings imply that higher plants might have generated different endomembrane sorting pathways during evolution and may enable the highly conserved endomembrane proteins to participate in plant-specific trafficking mechanisms for adaptation to the changing environment.

Plant multiscale networks: charting plant connectivity by multi-level analysis and imaging techniques

In multicellular and even single-celled organisms, individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation. Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes. Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project. In plant science, network analysis has similarly been applied to study the connectivity of plant components at the molecular, subcellular, cellular, organic, and organism levels. Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype. In this review, we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities. We next discussed the currently available analytic methodologies and multi-level imaging techniques used to map multiscale networks in plants. Finally, we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field.