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Related Concept Videos

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues11:26

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues

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We report a method for introduction, tracking and quantitative analysis of GFP expression in plant cells. This method utilizes a custom-designed robotics system for semi-continuous image collection from large numbers of samples, over time. We also demonstrate the use of ImageJ and ImageReady for analysis of image...
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Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells10:34

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What is Gene Expression?01:42

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
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Presented here is a protocol for laser-capture microdissection (LCM) of plant tissues. LCM is a microscopic technique for isolating areas of tissue in a contamination-free manner. The procedure includes tissue fixation, paraffin embedding, sectioning, LCM and RNA extraction. RNA is used in the downstream tissue-specific, temporally resolved analysis of...
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Related Experiment Video

Updated: Jan 20, 2026

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues
11:26

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues

Published on: May 5, 2010

12.9K

Gene Expression Maps in Plants: Current State and Prospects.

Anna V Klepikova1,2, Aleksey A Penin3

  • 1Institute for Information Transmission Problems of the Russian Academy of Sciences, Moscow 127051, Russia. annklepikova@gmail.com.

Plants (Basel, Switzerland)
|August 31, 2019
PubMed
Summary
This summary is machine-generated.

Recent omics advancements offer high-resolution gene function identification. This review explores plant transcriptome maps, addressing data integration challenges for broader research applications.

Keywords:
RNA-seqdatabasegene expression atlasmicroarrayplanttranscriptome map

More Related Videos

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Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants
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Related Experiment Videos

Last Updated: Jan 20, 2026

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues
11:26

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues

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Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
10:34

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells

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Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants
08:33

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Published on: August 5, 2020

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Area of Science:

  • Plant Science
  • Genomics
  • Bioinformatics

Background:

  • Classical genetics has long been used for gene function identification.
  • Omics technologies like transcriptomics, proteomics, and metabolomics provide high-resolution, indirect methods.
  • Transcriptome maps offer valuable functional insights in plants, aiding gene regulatory network inference and evolutionary studies.

Purpose of the Study:

  • To review the current status of plant transcriptome maps.
  • To analyze challenges in combining large-scale data from diverse studies.
  • To propose solutions for improving data usability and analysis.

Main Methods:

  • Review of existing literature on plant transcriptome maps.
  • Analysis of data integration issues in omics databases.
  • Discussion of computational and bioinformatic approaches for data analysis.

Main Results:

  • Transcriptome maps are powerful resources but often lack integration into accessible databases.
  • Combining data from various studies requires extensive preprocessing, limiting usability.
  • Limited integration hinders comprehensive analysis and application of plant gene function data.

Conclusions:

  • Improved data integration and standardization are crucial for plant transcriptome map utilization.
  • Developing robust analytical frameworks can overcome challenges in multi-study data analysis.
  • Enhanced accessibility and analysis of transcriptome data will accelerate plant science research.