Gel-based Quantification

Sources of variation between gels Internal variation between gels Same loading amount? Same gel condition? Same staining condition? External variation after gel developed Unwanted spots (dye or reagent deposit) Dirty spots (hair, dust) Its just like this slide, too much information to deal with 1/3 of all genes have a known function?

Image analysis workflow Manipulation of image/ normalization -Separation of overlapping spots, removing lines and speckles Spot detection/ quantification -Background subtraction, spot segmentation, land-marking ,spot matching Gel comparison -Matching of gels (e.g. normal, diseased, treated),alignment Data analysis -Defining changes in expression Data representation -Annotation of spots, linking of data: spots -intensity - MS data

Background subtraction and quantification Background subtraction method: No background Mode of non-spot Manual background Lowest boundary Average boundary

Normalization General normalization method: Total spot volume Single spot Total volume ratio

Expression analysis Comparison of individual experimental gels to master gels. Identification of variant spots

Expression Analysis Spot detection Spot matching Normalization of spot intensities PTM? Downregulation?

Expression comparison Changes in expression are thought to be significant if they are greater than experimental variation Its just like this slide, too much information to deal with 1/3 of all genes have a known function?

DIGE DIGE

Traditional 2-DE analysis: Sources of variation Gel to gel variation Presence - Absences Volume variation Resolution Issues Software Variation added during spot detection, background subtraction, matching etc Biological variation Animal to animal Culture to culture

Replicate gels - problems At least 3 times as many gels as you should need. Makes analysis more complex. Averages out gel to gel variation, instead of removing it. There is no ‘Gold Standard’ of 2D to see how effective replicate gels are, what part of the system contributes more variation, how many replicates are required to overcome variation

Principles of 2-D DIGE methodology Conventional 2-D Are spot differences due to induced biological changes or differences in the way the gels have been cast/run/stained? gel 1 gel 2 Silver stain control treated differences 2-D DIGE control treated differences Dual scan Dye 1 Dye 2 Separate two samples on 1 gel In conventional 1 color 2-D one sample is separated per 2-D gel. Gels are visualized using a stain e.g. silver. The spot patterns on each gel are compared to find differences in protein abundance between samples. However, differences between gels can also be caused by differences in gel casting, run and stained which makes it difficult to tell whether the difference s are really due to changes induced by the treatment e.g. drug/disease state/life cycle. Using 2-D DIGE, protein samples are covalently labelled using fluorescent dyes. Different samples can be separated on the same gel so any proteins showing change are due to differences between the samples NOT differences due to physical experimental differences between gels. Achieving accurate quantitative data

Overlay of two images This shows two images obtained from a single gel when two different samples have been separated. The purple spots represent proteins that are present in both samples. Those in blue are only present in the control and those in red in the treated sample. A predominance of one or the other represent a change in expression. A small area has been enlarged to show this more clearly.

Fluorescence Emission Filters 488 nm 532 nm 633 nm Cy2 Cy3 Cy5 520 BP 40 580 BP 30 670 BP 30 Bandpass to distinguish multiple fluors An example of resolving 2 different fluorochromes, both excited with the green laser. Two band pass filters are used that collect the bulk of emission from the 2 fluors. 400 500 600

Volumes expressed as ratios relative to pooled internal standard Workflow Outline for ‘pooled’ approach Pooled internal standard Label with Cy2 Protein extract 2 Label with Cy5 Protein extract 1 Label with Cy3 Mix labelled samples Separate by 2-D PAGE Cy2 excitation wavelength Cy5 excitation Cy3 excitation Volumes expressed as ratios relative to pooled internal standard Create the standard by pooling aliquots of all biological samples in the experiment Label standard with one CyDye DIGE fluor Run standard with individual samples: Cy2 Cy3 Cy5 Gel 1 Std sample 1 sample 2 Gel 2 Std sample 3 sample N Samples are in-gel linked to a common standard, Quantification should be accurate Separates gel to gel from biological variation Goes a long way to removing gel to gel variation altogether

Linking samples Conventional 1-sample-per-gel 2-D No intrinsic link between samples Internal Std sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 GEL A GEL B GEL C 2-D DIGE Internal standard links samples between gels Multiplexing links samples within each gel sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 This slide shows an example of the conventional way a differential 2-D experiment is set up 1-sample-per-gel. Gel-to-gel differences can arise from many sources including: Differences in the amount of protein entering into the strip or transferring from strip to 2-D gel. Differences in IEF running conditions e.g. differences in current between strips run on different IPGphors, or differences in temperature/current between gels run in different positions in the DALT tank. When protein spots are compared between gels in 1-sample-per-gel experiments, it is impossible to tell whether a spot has changed intensity as a result of an induced biological change or because of this gel-to-gel variation. Achieving accurate quantitative data

Differential Analysis using the pool GEL A Sample 3 Sample 4 Internal Std GEL B Sample 5 Sample 6 Internal Std GEL C DIA In-gel co-detection DIA DIA Sample 2 Sample 1 Master BVA Cross-gel matching Internal Std This slide summarizes the whole analytical process in DeCyder. IN DIA: Each image is co-detected with its internal standard, producing two image pairs per gel. The ratio of standard:sample is calculated for each protein in each image. IN BVA: One of the internal standard images is selected as the master image and all internal standard images matched to this. Sample:standard spot ratios for each protein in each sample are then compared, giving T-test values, fold changes and ANOVA values for each protein. Proteins of interest are filtered out to generate a pick-list. Protein difference ratios Achieving accurate quantitative data

DeCyder Software Structure Differential In-gel Analysis (DIA) Co-detection of image pairs and triplets Automated detection, background subtraction, Automatic quantification, normalization and first level (In-gel) matching Low user interaction, high throughput Biological Variation Analysis (BVA) Automated gel to gel matching Statistical/Trend analysis Batch Processor Processing of 500 gel image pairs and triplets without user interaction XML Toolbox For the purposes of explanation I will divide the software into four parts. Firstly the batch processor; up to 500 image pairs can be selected for analysis at one time. This reduces user hands-on time significantly. Secondly the Differential In-Gel analysis. This part of the software automatically co-detects the image pairs, subtracts background, quantitates, normalises and matches the detected spots. It has been designed to minimise user interaction which in turn ensures that subjective editing is removed reducing user to user variation. The only parameters required to input is approximate spot number and a filter number set to automatically remove dust particles that might otherwise thought to be proteins spots. This is very simple and the user becomes familiar very quickly. It is important to remember that ratio measurements are always from within gel and never obtained from two different gels. A standard should be used in each gel increasing the accuracy of the result. This makes the process analogous to an assay. The third part is the biological variation analysis which matches together the spots from different gels. The matching process is made easier because the internal standard can be used to match which ensures that the same complement of proteins are represented in each gel. Again this is only possible with our novel and proprietary DIGE multiplexing technology. I will come back to that in a moment The fourth part is the XML toolbox, designed for easy extraction of data from your analysis in two output forms, web-based or table-based.

DIA (Difference In-gel Analysis) Results Number of Spots Detected: About 1500 in each gel image

BVA (Biological Variation Analysis) Results

Using an Internal Standard Sample Pooled standard has many benefits: Every protein in the population should appear on each gel Decreases gel to gel variation Each sample is compared internally to the same standard Enables fully automated accurate spot statistics Makes gel to gel matching easier

Traditional 2DE analysis: Types of variation Present only in (a) Stronger in (b) than (a) or (c) Sample 1 a Sample 1 b Sample 1 c Present in (a) and (c) not (b) Is this gel to gel variation or biological variation? 29-Apr-04

Internal standard - theoretical benefits Cy5 Cy3 Gel 1 Gel 2 Cy2 Standard Sample 3 Sample 4 Gel to gel variation – since spot absent from standard in gel 2

Biological variation: In both standards but absent in sample 1 Internal standard - theoretical benefits Standard Cy2 Cy5 Cy3 Gel 1 Gel 2 Sample 2 Sample 1 Sample 3 Sample 4 Biological variation: In both standards but absent in sample 1

Biological decrease, not increase, also gel to gel variation Internal standard - theoretical benefits Cy5 Cy3 Gel 1 Standard Cy2 Gel 2 Sample 2 Sample 1 Sample 3 Sample 4 Biological decrease, not increase, also gel to gel variation

Normalisation of spots between gels Normalised, but not to standard Normalised to standard This slide highlights the importance of normalisation. The major source of error occuring when obtaining ratio measurements from two gels is gel to gel variation. In this example after normalisation it is clear that these two data groups are showing a significant difference. In this example it is an average change of 1.79-fold with greater than 99.9% t-test confidence. Now going to the non-normalised information if you view the paired results of the standard and group 1 or standard and group 2 joined by the broken lines it is obvious that there is an upward trend for group 1 compared to the standard and a downward trend for group 2 compared to the standard. Now consider the ringed group in blue and the ringed group in red only, this is what you would have if single gels had been prepared using conventional post-labelling.It would be very difficult to make the same conclusion that DIGE allows you to make. Key message DIGE allows accurate assessment of differences that are not possible when ratio measurements are obtained from different gels