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 series.
The following methodology outlines a protocol for image analysis of gene expression using an automated image collection system. For ease of explanation, the overall approach was broken down into four steps: 1) seed preparation, 2) gene introduction using particle bombardment, 3) robotic image collection, and 4) image analysis. Although this general methodology can be used for a broad-range of other applications, the present sets of protocols are based on the use of the green fluorescent protein (gfp) gene, which is a useful reporter gene for tracking gene expression in the same piece of living tissue over time.
I. Seed Preparation
II. Gene Introduction using Particle Bombardment
III. Automated Image Collection
IV. Image Analysis
V. Representative Results
The robotic image collection and analysis procedure (Fig. 1) reported here allows the acquisition of a large amount of quantitative data on gene expression in a short time period. For plant promoter characterization using the gfp gene, this methodology is not only useful to create transient expression profiles (Fig. 2) but also to track in a detailed-manner GFP expression in transiently- and stably-transformed plant tissues5,6. Short time-lapse animations generated with the collected sequential images are a valuable tool for in-depth analyses of gene expression over time in plant tissues. This methodology also has great application to evaluate factors that directly affect gene expression. For example, transient GFP expression under the presence of different suppressors of silencing of viral origin has been successfully studied using our automated image collection and analysis system7,8.
Figure 1. Image analysis procedure consists of four main steps. (A) Acquisition of image series using the robotic image collection system, (B) separation of images into red, blue and green channels, (C) segmentation of the expressing pixels by adjusting the threshold levels, and (D) obtaining the output results containing the grayscale values and the GFP-expressing focus counting.
Figure 2. Graph showing different transient expression profiles driven by plant promoters fused to gfp. Data was collected using our robotic image collection system and analyzed with ImageReady and ImageJ software.
The use of robotics has enormous applications in different aspects of human life; specifically, robots have been effectively used to perform activities in dangerous environments, to automate tedious and complex activities, and to carry out tasks in a more precise way. In molecular biology, and specifically gene expression analyses, robots can help track not only features of genes but also tissue growth and development over time. Many biological phenomena occur dynamically, which may be difficult to follow using single time point observations.
The use of the gfp gene for expression studies brings additional advantages for observing tissue response and growth. For our experimental procedures, GFP allows us to follow gene expression in the same piece of tissue over time as GFP detection is a non-destructive. Furthermore, our version of the GFP protein is sufficiently stable to allow detection but also shows some turnover to minimize accumulation in plant tissues, allowing us to follow both the rise and fall of gene expression.
We have already utilized our robotic image collection and analysis system for a wide range of applications. We foresee high potential for many biological applications where a dynamic understanding of a phenomenon is desired. For example, growth and development of plant tissues can be tracked using our system giving valuable insight/information on these processes. Also, the dynamics of protein transport using reporter genes like gfp, can be easily visualized using time-lapse animations. The methodology described in this report is technically complex but conceptually simple. Our results are robust and new applications are continually being discovered.
Salaries and research support were provided by the United Soybean Board, and by State and Federal funds appropriated to The Ohio State University/Ohio Agricultural Research and Development Center. This research was also partially supported by a fellowship from CONACYT, Mexico, to CMHG. Mention of trademark or proprietary products does not constitute a guarantee or warranty of the product by OSU/OARDC and also does not imply approval to the exclusion of other products that may also be suitable. Journal Article No HCS 09-17.
Material Name | Typ | Company | Catalogue Number | Comment |
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ImageJ | Software | U. S. National Institutes of Health | http://rsbweb.nih.gov/ij/ |