<ol>
	<li>Xiao, Q., Bai, X., Zhang, C., He, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advanced+high-throughput+plant+phenotyping+techniques+for+genome-wide+association+studies:+A+review.">Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.</a> J Adv Res. 35, 215-230 (2022).</li><li>Mutka, A. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=image-based+phenotyping+methods+provide+insight+into+spatial+and+temporal+dimensions+of+plant+disease.">image-based phenotyping methods provide insight into spatial and temporal dimensions of plant disease.</a> Plant Physiol. 172 (2), 650-660 (2016).</li><li>Mutka, A. M., Bart, R. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image-based+phenotyping+of+plant+disease+symptoms.">Image-based phenotyping of plant disease symptoms.</a> Front Plant Sci. 5, 734 (2014).</li><li>Fahlgren, N., Gehan, M. A., Baxter, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lights,+camera,+action:+High-throughput+plant+phenotyping+is+ready+for+a+close-up.">Lights, camera, action: High-throughput plant phenotyping is ready for a close-up.</a> Curr Opin Plant Biol. 24 (2), 93-99 (2015).</li><li>Das Choudhury, S., Samal, A., Awada, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Leveraging+image+analysis+for+high-throughput+plant+phenotyping.">Leveraging image analysis for high-throughput plant phenotyping.</a> Front Plant Sci. 10, 508 (2019).</li><li>Yang, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Crop+phenomics+and+high-throughput+phenotyping:+Past+decades,+current+challenges,+and+future+perspectives.">Crop phenomics and high-throughput phenotyping: Past decades, current challenges, and future perspectives.</a> Mol Plant. 13 (2), 187-214 (2020).</li><li>Omari, M. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+image-based+plant+phenotyping:+A+review.">Digital image-based plant phenotyping: A review.</a> Korean J Agric Sci. 47 (1), 119-130 (2020).</li><li>Schindelin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fiji:+An+open-source+platform+for+biolFiogical-image+analysis.">Fiji: An open-source platform for biolFiogical-image analysis.</a> Nat Methods. 9 (7), 676-682 (2012).</li><li>Gehan, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PlantCV+v2:+Image+analysis+software+for+high-throughput+plant+phenotyping.">PlantCV v2: Image analysis software for high-throughput plant phenotyping.</a> PeerJ. 5, e4088 (2017).</li><li>Murphy, K. M., Ludwig, E., Gutierrez, J., Gehan, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+learning+in+image-based+plant+phenotyping.">Deep learning in image-based plant phenotyping.</a> Annu Rev Plant Biol. 75 (1), 771-795 (2024).</li><li>Fahlgren, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+versatile+phenotyping+system+and+analytics+platform+reveals+diverse+temporal+responses+to+water+availability+in+Setaria.">A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria.</a> Mol Plant. 8 (3), 1520-1535 (2015).</li><li>Yu, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+a+mobile,+high-throughput,+and+low-cost+image-based+plant+growth+phenotyping+system.">Development of a mobile, high-throughput, and low-cost image-based plant growth phenotyping system.</a> bioRxiv. , (2023).</li><li>Atefi, A., Ge, Y., Pitla, S., Schnable, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robotic+technologies+for+high-throughput+plant+phenotyping:+Contemporary+reviews+and+future+perspectives.">Robotic technologies for high-throughput plant phenotyping: Contemporary reviews and future perspectives.</a> Front Plant Sci. 12, 611940 (2021).</li><li>Berry, J. C., Fahlgren, N., Pokorny, A. A., Bart, R. S., Veley, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+automated,+high-throughput+method+for+standardizing+image+color+profiles+to+improve+image-based+plant+phenotyping.">An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping.</a> PeerJ. 6, e5727 (2018).</li><li>Tovar, J. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Raspberry+Pi-powered+imaging+for+plant+phenotyping.">Raspberry Pi-powered imaging for plant phenotyping.</a> Appl Plant Sci. 6, e1031 (2018).</li><li>Jolles, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Broad-scale+applications+of+the+Raspberry+Pi:+A+review+and+guide+for+biologists.">Broad-scale applications of the Raspberry Pi: A review and guide for biologists.</a> Methods Ecol Evol. 12 (5), 1562-1579 (2021).</li><li>Watt, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phenotyping:+New+windows+into+the+plant+for+breeders.">Phenotyping: New windows into the plant for breeders.</a> Annu Rev Plant Biol. 71 (1), 689-712 (2020).</li><li>Xie, C., Yang, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+review+on+plant+high-throughput+phenotyping+traits+using+UAV-based+sensors.">A review on plant high-throughput phenotyping traits using UAV-based sensors.</a> Comput Electron Agric. 178, 105731 (2020).</li><li>Czedik-Eysenberg, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+&amp;#34;PhenoBox&amp;#34;,+a+flexible,+automated,+open-source+plant+phenotyping+solution.">The &amp;#34;PhenoBox&amp;#34;, a flexible, automated, open-source plant phenotyping solution.</a> New Phytol. 219 (4), 808-823 (2018).</li><li>Sumner, J., Fahlgren, N., Murphy, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PCVR:+An+R+package+and+tutorials+for+guided+statistical+analysis+of+plant+phenotyping+data.">PCVR: An R package and tutorials for guided statistical analysis of plant phenotyping data.</a> Authorea Preprints. , (2023).</li><li>Schnable, P. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+B73+maize+genome:+complexity,+diversity,+and+dynamics.">The B73 maize genome: complexity, diversity, and dynamics.</a> Science. 326 (5956), 1112-1115 (2009).</li><li>Kirillov, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segment+Anything.">Segment Anything.</a> , 3992-4003 (2023).</li><li>Gabald&#243;n-Leal, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modelling+the+impact+of+heat+stress+on+maize+yield+formation.">Modelling the impact of heat stress on maize yield formation.</a> Field Crops Res. 198 (3), 226-237 (2016).</li><li>Intergovernmental Panel on Climate Change. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> SYNTHESIS REPORT OF THE IPCC SIXTH ASSESSMENT REPORT (AR6). , (2023).</li><li>Qui&#241;ones, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimized+methods+for+applying+and+assessing+heat,+drought,+and+nutrient+stress+of+maize+seedlings+in+controlled+environment+experiments.">Optimized methods for applying and assessing heat, drought, and nutrient stress of maize seedlings in controlled environment experiments.</a> Cold Spring Harb Protoc. , (2024).</li><li>Murphy, K. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Maize+abiotic+stress+treatments+in+controlled+environments.">Maize abiotic stress treatments in controlled environments.</a> Cold Spring Harb Protoc. , (2024).</li></ol>

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imaging and analysis for quantifying maize (zea mays) abiotic stress phenotypes

Imagem e análise para quantificar fenótipos de estresse abiótico de milho (Zea mays)

Measuring quantitative plant phenotypes, or traits, is essential for understanding plant responses to environmental factors, including biotic and abiotic ...

imaging-analysis-for-quantifying-maize-zea-mays-abiotic-stress

Universidad San Sebastian

Research

JoVE Journal

Biology

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

563 visualizações.  Donald Danforth Plant Science Center. Um método é apresentado para imagens de baixo custo de plantas para medir fenótipos, juntamente com as melhores práticas para captura de imagens e um pipeline de análise de imagens para quantificar as características das plantas. Esses métodos foram aplicados para medir os fenótipos do milho (Zea mays) sob condições de calor, seca e estresse abiótico combinado.

Vídeo: Imagem e análise para quantificar fenótipos de estresse abiótico de milho (Zea mays)

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

A Neuronal e astrocitária Ensaio Co-Cultura de Análise de Conteúdo de Alta de neurotoxicidade

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

Biologia

Quantifying Mixing using Magnetic Resonance Imaging

Mixing is a unit operation that combines two or more components into a homogeneous mixture. This work involves mixing two viscous liquid streams using an in-line static mixer. The mixer is a split-and-recombine design that employs shear and extensional flow to increase the interfacial contact between the components. A prototype split-and-recombine (SAR) mixer was constructed by aligning a series of thin laser-cut Poly (methyl methacrylate) (PMMA) plates held in place in a PVC pipe. Mixing in this device is illustrated in the photograph in Fig. 1. Red dye was added to a portion of the test fluid and used as the minor component being mixed into the major (undyed) component. At the inlet of the mixer, the injected layer of tracer fluid is split into two layers as it flows through the mixing section. On each subsequent mixing section, the number of horizontal layers is duplicated. Ultimately, the single stream of dye is uniformly dispersed throughout the cross section of the device.
 Using a non-Newtonian test fluid of 0.2% Carbopol and a doped tracer fluid of similar composition, mixing in the unit is visualized using magnetic resonance imaging (MRI). MRI is a very powerful experimental probe of molecular chemical and physical environment as well as sample structure on the length scales from microns to centimeters. This sensitivity has resulted in broad application of these techniques to characterize physical, chemical and/or biological properties of materials ranging from humans to foods to porous media 1, 2. The equipment and conditions used here are suitable for imaging liquids containing substantial amounts of NMR mobile 1H such as ordinary water and organic liquids including oils. Traditionally MRI has utilized super conducting magnets which are not suitable for industrial environments and not portable within a laboratory (Fig. 2). Recent advances in magnet technology have permitted the construction of large volume industrially compatible magnets suitable for imaging process flows. Here, MRI provides spatially resolved component concentrations at different axial locations during the mixing process. This work documents real-time mixing of highly viscous fluids via distributive mixing with an application to personal care products.

Magnetic resonance imaging (MRI) provides a powerful tool to evaluate the effectiveness of process equipment during operation. We discuss the use of MRI to visualize mixing in a static mixer. The application is relevant to personal care products, but can be applied to a broad range of food, chemical, biomass and biological fluids.

Quantificar mistura usando Ressonância Magnética

Mixing is a unit operation that combines two or more components into a homogeneous mixture.  This work involves mixing two viscous liquid streams using an ...

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.
Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.

Análise rápida e Exploração de microscopia de fluorescência Images

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized ...

Analysis of Oxidative Stress in Zebrafish Embryos

High levels of reactive oxygen species (ROS) may cause a change of cellular redox state towards oxidative stress condition. This situation causes oxidation of molecules (lipid, DNA, protein) and leads to cell death. Oxidative stress also impacts the progression of several pathological conditions such as diabetes, retinopathies, neurodegeneration, and cancer. Thus, it is important to define tools to investigate oxidative stress conditions not only at the level of single cells but also in the context of whole organisms. Here, we consider the zebrafish embryo as a useful in vivo system to perform such studies and present a protocol to measure in vivo oxidative stress. Taking advantage of fluorescent ROS probes and zebrafish transgenic fluorescent lines, we develop two different methods to measure oxidative stress in vivo: i) a &#8220;whole embryo ROS-detection method&#8221; for qualitative measurement of oxidative stress and ii) a &#8220;single-cell&#160;ROS detection method&#8221; for quantitative measurements of oxidative stress. Herein, we demonstrate the efficacy of these procedures by increasing oxidative stress in tissues by oxidant agents and physiological or genetic methods. This protocol is amenable for forward genetic screens and it will help address cause-effect relationships of ROS in animal models of oxidative stress-related pathologies such as neurological disorders and cancer.

Here we report a protocol to measure oxidative stress in living zebrafish embryos. This procedure allows reactive oxygen species (ROS) detection in both whole embryo tissues and single-cell populations. This protocol will accomplish both qualitative and quantitative analyses.

Análise do estresse oxidativo em embriões Zebrafish

High levels of reactive oxygen species (ROS) may cause a change of cellular redox state towards oxidative stress condition. This situation causes ...

Lateral Root Inducible System in Arabidopsis and Maize

Lateral root development contributes significantly to the root system, and hence is crucial for plant growth. The study of lateral root initiation is however tedious, because it occurs only in a few cells inside the root and in an unpredictable manner. To circumvent this problem, a Lateral Root Inducible System (LRIS) has been developed. By treating seedlings consecutively with an auxin transport inhibitor and a synthetic auxin, highly controlled lateral root initiation occurs synchronously in the primary root, allowing abundant sampling of a desired developmental stage. The LRIS has first been developed for Arabidopsis thaliana, but can be applied to other plants as well. Accordingly, it has been adapted for use in maize (Zea mays). A detailed overview of the different steps of the LRIS in both plants is given. The combination of this system with comparative transcriptomics made it possible to identify functional homologs of Arabidopsis lateral root initiation genes in other species as illustrated here for the CYCLIN B1;1 (CYCB1;1) cell cycle gene in maize. Finally, the principles that need to be taken into account when an LRIS is developed for other plant species are discussed.

Lateral Root Inducible System in Arabidopsis and Maize

The Lateral Root Inducible System (LRIS) allows for synchronous induction of lateral roots and is presented for Arabidopsis thaliana and maize.

Sistema induzível de raiz lateral em Arabidopsis e milho

Lateral root development contributes significantly to the root system, and hence is crucial for plant growth. The study of lateral root initiation is ...

Rapid Analysis of Circadian Phenotypes in Arabidopsis Protoplasts Transfected with a Luminescent Clock Reporter

The plant circadian clock allows the anticipation of daily changes to the environment. This anticipation aids the responses to temporally predictable biotic and abiotic stress. Conversely, disruption of circadian timekeeping severely compromises plant health and reduces agricultural crop yields. It is therefore imperative that we understand the intricate regulation of circadian rhythms in plants, including the factors that affect motion of the transcriptional clockwork itself.
Testing circadian defects in the model plant Arabidopsis thaliana (Arabidopsis) traditionally involves crossing specific mutant lines to a line rhythmically expressing firefly luciferase from a circadian clock gene promoter. This approach is laborious, time-consuming, and could be fruitless if a mutant has no circadian phenotype. The methodology presented here allows a rapid initial assessment of circadian phenotypes. Protoplasts derived from mutant and wild-type Arabidopsis are isolated, transfected with a rhythmically expressed luminescent reporter, and imaged under constant light conditions for 5 days. Luminescent traces will directly reveal whether the free-running period of mutant plants is different from wild-type plants. The advantage of the method is that any Arabidopsis line can efficiently be screened, without the need for generating a stably transgenic luminescent clock marker line in that mutant background.

The circadian clock regulates about a third of the Arabidopsis transcriptome, but the percentage of genes that feed back into timekeeping remains unknown. Here we visualize a method to rapidly assess circadian phenotypes in any mutant line of Arabidopsis using luminescent imaging of a circadian reporter transiently expressed in protoplasts.

Rápida análise de fenótipos circadianos em Arabidopsis Protoplastos transfectadas com um relógio Reporter luminescentes

The plant circadian clock allows the anticipation of daily changes to the environment. This anticipation aids the responses to temporally predictable ...

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves

Growth analyses are often used in plant science to investigate contrasting genotypes and the effect of environmental conditions. The cellular aspect of these analyses is of crucial importance, because growth is driven by cell division and cell elongation. Kinematic analysis represents a methodology to quantify these two processes. Moreover, this technique is easy to use in non-specialized laboratories. Here, we present a protocol for performing a kinematic analysis in monocotyledonous maize (Zea mays) leaves. Two aspects are presented: (1) the quantification of cell division and expansion parameters, and (2) the determination of the location of the developmental zones. This could serve as a basis for sampling design and/or could be useful for data interpretation of biochemical and molecular measurements with high spatial resolution in the leaf growth zone. The growth zone of maize leaves is harvested during steady-state growth. Individual leaves are used for meristem length determination using a DAPI stain and cell-length profiles using DIC microscopy. The protocol is suited for emerged monocotyledonous leaves harvested during steady-state growth, with growth zones spanning at least several centimeters. To improve the understanding of plant growth regulation, data on growth and molecular studies must be combined. Therefore, an important advantage of kinematic analysis is the possibility to correlate changes at the molecular level to well-defined stages of cellular development. Furthermore, it allows for a more focused sampling of specified developmental stages, which is useful in case of limited budget or time.

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves

Quantifying cell division and expansion is of crucial importance to the understanding of whole-plant growth. Here, we present a protocol to calculate cellular parameters determining maize leaf growth rates and highlight the use of these data for investigating molecular growth regulatory mechanisms by directing developmental stage-specific sampling strategies.

Análise cinemática da Divisão Celular e Expansão: Quantificar a base celular de crescimento e de amostragem de desenvolvimento zonas em<em&gt; Zea mays</em&gt; Folhas

Growth analyses are often used in plant science to investigate contrasting genotypes and the effect of environmental conditions. The cellular aspect of ...

Using Microfluidic Devices to Measure Lifespan and Cellular Phenotypes in Single Budding Yeast Cells

Budding yeast Saccharomyces cerevisiae is an important model organism in aging research. Genetic studies have revealed many genes with conserved effects on the lifespan across species. However, the molecular causes of aging and death remain elusive. To gain a systematic understanding of the molecular mechanisms underlying yeast aging, we need high-throughput methods to measure lifespan and to quantify various cellular and molecular phenotypes in single cells. Previously, we developed microfluidic devices to track budding yeast mother cells throughout their lifespan while flushing away newborn daughter cells. This article presents a method for preparing microfluidic chips and for setting up microfluidic experiments. Multiple channels can be used to simultaneously track cells under different conditions or from different yeast strains. A typical setup can track hundreds of cells per channel and allow for high-resolution microscope imaging throughout the lifespan of the cells. Our method also allows detailed characterization of the lifespan, molecular markers, cell morphology, and the cell cycle dynamics of single cells. In addition, our microfluidic device is able to trap a significant amount of fresh mother cells that can be identified by downstream image analysis, making it possible to measure the lifespan with higher accuracy.

This article presents a protocol optimized for the production of microfluidic chips and the setup of microfluidic experiments to measure the lifespan and cellular phenotypes of single yeast cells.

Usando dispositivos microfluídicos medir Longevidade e Celular fenótipos em células individuais levedura de brotamento

Budding yeast Saccharomyces cerevisiae is an important model organism in aging research. Genetic studies have revealed many genes with conserved effects ...

In Vitro Analysis of E3 Ubiquitin Ligase Function

The covalent attachment of ubiquitin (Ub) to internal lysine residue(s) of a substrate protein, a process termed ubiquitylation, represents one of the most important post-translational modifications in eukaryotic organisms. Ubiquitylation is mediated by a sequential cascade of three enzyme classes including ubiquitin-activating enzymes (E1 enzymes), ubiquitin-conjugating enzymes (E2 enzymes), and ubiquitin ligases (E3 enzymes), and sometimes, ubiquitin-chain elongation factors (E4 enzymes). Here, in vitro protocols for ubiquitylation assays are provided, which allow the assessment of E3 ubiquitin ligase activity, the cooperation between E2-E3 pairs, and substrate selection. Cooperating E2-E3 pairs can be screened by monitoring the generation of free poly-ubiquitin chains and/or auto-ubiquitylation of the E3 ligase. Substrate ubiquitylation is defined by selective binding of the E3 ligase and can be detected by western blotting of the in vitro reaction. Furthermore, an E2~Ub discharge assay is described, which is a useful tool for the direct assessment of functional E2-E3 cooperation. Here, the E3-dependent transfer of ubiquitin is followed from the corresponding E2 enzyme onto free lysine amino acids (mimicking substrate ubiquitylation) or internal lysines of the E3 ligase itself (auto-ubiquitylation). In conclusion, three different in vitro protocols are provided that are fast and easy to perform to address E3 ligase catalytic functionality.

In Vitro Analysis of E3 Ubiquitin Ligase Function

The present study provides detailed in vitro ubiquitylation assay protocols for the analysis of E3 ubiquitin ligase catalytic activity. Recombinant proteins were expressed using prokaryotic systems such as Escherichia coli culture.

In vitro Análise da Função Ligase E3 Ubiquitin

The covalent attachment of ubiquitin (Ub) to internal lysine residue(s) of a substrate protein, a process termed ubiquitylation, represents one of the ...

Mycorrhizal Maps as a Tool to Explore Colonization Patterns and Fungal Strategies in the Roots of Festuca rubra and Zea mays

Arbuscular mycorrhizal fungi are symbionts in the roots of plants. Their role is to sustain host development and maintain the nutritional equilibrium in the ecosystems. The colonization process is dependent on several factors like soil ecology, the genetic diversity of the fungi and host, and agronomic practices. Their synchronized action leads to the development of a complex hyphal network and leads to the secondary development of vesicles and arbuscules in the root cells. The aim of this research was to analyze the efficiency of the mycorrhizal patterns (MycoPatt) method for the positioning of fungal structures in the roots of Festuca rubra and Zea mays. Another objective was to explore the fungal colonization strategy as revealed by mycorrhizal maps of each species. The acquisition and assemblage of multiple microscopic images allow mycorrhizal colonization assessment in both corn and red fescue plants to provide information on the realistic position of the developed structures. The observed mycorrhizal patterns highlight the variable efficiency of each plant in terms of developing connections with soil symbiotic fungi, caused by applied treatments and growth stage. Mycorrhizal detailed maps obtained through the MycoPatt method are useful for the early detection of plant efficiency in symbiotic acquisition from the soil.

Mycorrhizal Maps as a Tool to Explore Colonization Patterns and Fungal Strategies in the Roots of Festuca rubra and Zea mays

The protocol here describes the methods for the assessment of the arbuscular mycorrhizal colonization patterns and strategy in roots for two species: Zea mays and Festuca rubra. The use of the MycoPatt method permits the calculation of parameters, the conversion of mycorrhizal structures into digital data, and the mapping of their real position in roots.

Mapas micorrízicos como ferramenta para explorar padrões de colonização e estratégias fúngicas nas raízes de Festuca rubra e zea mays

Arbuscular mycorrhizal fungi are symbionts in the roots of plants. Their role is to sustain host development and maintain the nutritional equilibrium in ...