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opencc-by-4.0Feb 2023View details →

Data segmentation and analysis: developing algorithms to virtually dissect plants

The following video describes how biological data analysis and image segmentation is conducted at different scales and informs the ROMI data pipeline. Funded by EU Grant 773875. Videos are available in: <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> Video script: (LEGRAND) I’m an engineer in a biological data analysis. So what I'm doing is to create tools and create a code helping biologists to go from these images they acquire to this labeled image from which we extract cellular features, and from these features we do statistical analysis. Okay so on this board we describe the pipeline we are trying to set up for this image analysis so we're starting with potentially five dimensional images so you have the XYZ spatial Dimension then you have channels and potentially time. So with these images they go to a reader this reader creates a specific data structure so for example if you have a multiple observation of the same object from different angle what you would like to do is to reconstruct and fuse together this multiple angle and then that gives you one big image that you may want to filter and from from these images you can then perform segmentation. For example nuclei detection or cellular segmentation for example this is a pull out transport pump being able to quantify how many pumps you have gives you an idea of the flow of protein or hormones so from that you will be able to build up models and to try and make a realistic model of flower development or phyllotaxis. If you talk about the flower arrangement around the the stem. So compared to my original work the ROMI project is for me we represent a change in scale so we're moving from the cellular scale or to tissular scale, to where we try to understand how flower or leaves are arranged around the stem to use a macroscopic scale where you see the plant in full. And you're trying to observe and also quantify also how flowers or leaves are arranged around the stem. (HÉTROY-WHEELER) So I'm a bit at the end of the pipeline so as input we take a 3D Point cloud which is a sample of the plant represented in a virtual way so we've got a points which each of them has three 3D coordinates. And only from the set of points we try to infer the geometry of the plant and from this geometry so basically to recover the shape of the plant we try then to segment the plant into its organs so the leaves the stems and in between stems and leaves the petals. So the idea is that only from geometry and maybe some colour information or other information which we try to use as less information as possible we would be able to detect the organs of the plants and then do some computation for example simple computation like computing the number of leaves but also more advanced computation like for example trying to guess what is the area of the leaves or the angles between the different stems and so on things that are useful for a biologist and also for people in Agronomy or agriculture.

opencc-by-4.0Apr 2023View details →

Modifying the expression of cysteine protease gene PCP affects pollen develop-ment, germination and plant drought tolerance in maize

Figure S1: The sequencing results of pollen grains of T2 mutants. Figure S2: The relative expression levels of PCP were analyzed via RT-qPCR in pcp mutant lines. Data represent means ± SD of three replicates. Significant differences were indicated with different letters (P < 0.05, one-way ANOVA). Figure S3: Pictures of pollen germination and growth in vitro between wild type and transgenic lines after 4 h and 6 h incubation. Scale bar: 100 μm; Figure S4: Phenotypic observation of wild-type and transgenic maize at flowering stage. Scale bar: 10 cm. Figure S5. DAB staining detection of ROS in WT, KO and OE plants under drought stress. Table S1: PCR primers used in this study.

opencc-by-4.0May 2023View details →

Development time and fecundity of Spodoptera frugiperda fed on different host plant

Data set of development time and fecundity of Spodoptera frugiperda fed on different host plant in Indonesia

Fig. 4 in Suspension cell secretome of the grain legume Lathyrus sativus (grasspea) reveals roles in plant development and defense responses

Fig. 4. Physicochemical assessment of the grasspea suspension secretome (GSS), including pI (A), molecular weight (in kDa) (B), and hydrophilicity (C), with respect to MSS, DSS and LSS (MSS corresponds to the monocot suspension secretome, DSS to the dicot suspension secretome and LSS to the lower plant suspension secretome).

Fig. 2 in Suspension cell secretome of the grain legume Lathyrus sativus (grasspea) reveals roles in plant development and defense responses

Fig. 2. Generation of grasspea calli, establishment of suspension culture and isolation of the grasspea suspension secretome (GSS). (A) Root-cut and shoot-cut embryo axes were employed for the generation of 4-week-old calli, which were bulked together in a suspension culture. (B) Microscopic examination of suspension cells and viability assessment using Evans blue (left panel) and FDA (right panel). (C) Quantitative analysis of physicochemical properties including changes in pH in the suspension culture, fresh weight (FW), dry weight (DW), soluble sugars and total protein. (D) Protein SDS-PAGE profile of the grasspea secretome. Lane 1 represents the molecular weight marker (MW). Purity evaluation of grasspea secreted fraction using (E) catalase activity and (F) western blotting with anti-RbcL (Supplementary Fig. S1). Relative catalase activities are presented as mean ± SE of triplicate experiments.

Fig. 3 in Suspension cell secretome of the grain legume Lathyrus sativus (grasspea) reveals roles in plant development and defense responses

Fig. 3. Overview of total grasspea suspension secreted (GSS) proteins and prediction of mode of secretion and (A) localization using multiple tools (B). Comparison of shared and distinct GSS proteins, first (C) with respect to total in vitro secretome (IVS) and in planta secretome (IPS) and second (D) compared to the in vitro suspension culture secretome reported in monocots, dicots, and lower plants, abbreviated as MSS, DSS and LSS, respectively (MSS corresponds to monocot suspension secretome, DSS to dicot suspension secretome and LSS to lower plant suspension secretome).

Fig. 1 in Suspension cell secretome of the grain legume Lathyrus sativus (grasspea) reveals roles in plant development and defense responses

Fig. 1. Schematic representation of the experimental design and workflow of the establishment of the grasspea suspension secretome (GSS). Proteomic profiling was accomplished by generating suspension culture and sequential assessment of physicochemical properties and protein identification.

Fig. 6 in Suspension cell secretome of the grain legume Lathyrus sativus (grasspea) reveals roles in plant development and defense responses

Fig. 6. Localization validation of endochitinase (S597) and G-type lectin S-receptor-like serine threonine kinase (S718). The panels include (A) expression of YFPtagged S597 in onion epidermal cells, (B) plasmolysis of S597-transformed onion peel (C), expression of YFP-tagged S718 in onion peel cells and (D) plasmolyzed onion peel cells expressing YFP-tagged S718. A pSITE3CA empty vector control was also monitored besides the target genes (E).

Fig. 6 in Variation in production of cyanogenic glucosides during early plant development: A comparison of wild and domesticated sorghum

Fig. 6. Proportion of nitrogen allocated to dhurrin and nitrate (NO3) in dried, finely ground tissues of S. bicolor, S. brachypodum and S. macrospermum plants at 35 d post-germination. A) Dhurrin allocation; B) Nitrate allocation; C) C:N ratio. Graphs show mean ± 1 standard error (n = 3). Columns with different letters within each tissue are significantly different (p <0.05).

Fig. 3 in Variation in production of cyanogenic glucosides during early plant development: A comparison of wild and domesticated sorghum

Fig. 3. Leaf characteristics of S. bicolor, S. brachypodum and S. macrospermum plants at six harvest points during the first 35 d post-germination. A) Total leaf number; B) Total leaf area (TLA); C) Specific leaf area (SLA); D) Leaf area ratio (LAR). Graphs show mean ± 1 standard error (n = 5), with statistically significant differences indicated at each time point: *p <0.05.

Fig. 5 in Variation in production of cyanogenic glucosides during early plant development: A comparison of wild and domesticated sorghum

Fig. 5. Tissue-specific hydrogen cyanide potential (HCNp, mg HCN per g dw 1) and morphology of individual A) S. bicolor, B) S. brachypodum and C) S. macrospermum plants at six time points during seedling development. The HCNp of a section of the sheath, roots, and each individual leaf was measured at 3, 7, 14, 21, 28 and 35 days (D) post-germination. Colour scale indicates HCNp, used as a proxy for dhurrin concentration (green = low; red = high). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 in Variation in production of cyanogenic glucosides during early plant development: A comparison of wild and domesticated sorghum

Fig. 4. Hydrogen cyanide potential (HCNp, mg HCN per g dw 1) and concentration of nitrate (NO) in dried, finely ground tissues of S. bicolor, S. brachypodum and 3 S. macrospermum plants at six harvest points during the first 35 d post-germination (at 35 dpg only for NO3). A) Leaf HCNp; B) Sheath HCNp; C) Root HCNp; D) Total NO3. Graphs show mean ± 1 standard error (n = 5), with statistically significant differences indicated at each time point: *p <0.05. Columns with different letters within each tissue are significantly different (p <0.05). Data for leaf HCNp at 3 days post-germination not shown as a true leaf had not emerged at this stage.

Fig. 1 in Variation in production of cyanogenic glucosides during early plant development: A comparison of wild and domesticated sorghum

Fig. 1. Known geographic distribution of the two wild Sorghum species S. brachypodum and S. macrospermum and the site of collection of the accessions examined in the current study. Seeds were obtained from the Australian Grains Genebank (AGG), Horsham, Victoria. Occurrence records of S. brachypodum and S. macrospermum were obtained from the Atlas of Living Australia (ALA), htt p://www.ala.org.au. Each blue circle represents an occurrence record of S. brachypodum and each orange circle represents S. macrospermum (circled). Collection localities of individual accessions examined here are marked by darker coloured circles. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)