AI + IQ-11

AI + IQ-11 = Faster 1D Gel Analysis

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1. Find the Calibration Strip

This Step is Straightforward

Our first task is to find the calibration strip. If you use a Bio Image auto-calibrating scanner, we don't need to look for the strip; acquiring the sample automatically finds it. In the image below, the thick black bar is the water-tight dam separating the sample portion of the gel tray from the calibration strip.

The sample below was acquired on a Bio Image scanner, but not an auto-calibrating one. We'll run profiles from side-to-side (or top-to-bottom, if the calibration strip is placed vertically), looking for the black end monotonically decreasing steps. Once we know the outline of the strip, we can find the steps, and quantify the pixels within each one.

Our Bio Image scanner comes with a National Institute of Standards and Technology (NIST) certified and calibrated strip, which lets you make meaningful quantitative comparisons between samples.

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2. Find the Lanes

This Step is Straightforward, Too

Your gel cassette gives us the number of lanes, and their approximate expected width and length. We’ll use these to create a template which we will pass over the sample from upper-left to lower-right, comparing the gray tone of the template to the image pixels. The best fit will be when the template is atop the lanes.

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3. Find the Bands

The Next Two Steps are the Hard Part, and Good Results Won't Come Overnight

You will build up a training set of samples, on which operators have detected bands using our algorithm and then manually added and/or deleted bands.

And then we'll incorporate any processing protocols you have. Examples of protocols might be which lanes have saturated silver-stained bands, or minimum/maximum quantitative values for specific bands, or deciding which reference or sample lanes from the basis of further calculations.

This step will likely continue for a while, as new samples are added to the training set, and potentially teach us new tweaks to where and how we find bands.

Our Algorithm

The algorithm calculates the second derivative of each pixel on a lane's densitometry profile. This measures change, which is what we're looking for; and it yields a set of peaks and valleys. All the profile between any pair of valleys is a potential band, but those must meet a darkness criterion, which is also part of our algorithm. Contact us if you’d like full explanation, complete with equations.

The algorithm did a pretty good, but not perfect, job. You might want some of the faint bands detected. And it definitely saw the saturated bands as two: one on the leading edge, and one on the trailing edge. When you look at the lane profile, it’s obvious why that happened.

Manual Editing

Here is where you take over: adding bands you think IQ-11 missed, and deleting those you think it shouldn't have found. IQ-11 records all of these actions, helping it learn what lane profile characteristics necessitated manual edits. This learning process will be on display during the validation of results as new samples are put up against the training set.

The operator deleted 9 bands, and added 1. In fact, these edits combine both differences between IQ-11's algorithm and the manual editing, but also incorporation of the sample protocol, as mentioned above. The samples have 3 Reference lanes, and 3 Sample lanes. According to the protocol, when the Reference or Study lane has saturated bands, the top 2 darkest bands are ignored in favor of the bands in the adjacent lanes.

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Like band detection, band quantitation can also be done manually: the operator marks a series of boundary points which are used in place of IQ-11's automatically detected points. In the image below, the IQ-11 points are on the left, and the manually marked points on the right.

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5. Calculate Standards Values – Molecular Weight (Mw), Iso-Electric Focal (IEF), or Concentrations

We're Back to Another Straightforward Step

Your gel cassette has specific lanes for any Mw/IEF and/or concentration/dilution standards. So once IQ-11 locates the lanes, it knows which lanes have standards values. The training set has identified what bands in those lanes have what standards value. That information gives IQ-11 a pattern to look for, as shown below.

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6. Report Results

This Step is Also Straightforward

IQ-11 has almost 60 different data items for every lane – for example, Total Volume of All Bands, or Band Detection Width – and over 30 data items for every band – for example, Band Area, Band Dilution, or Band Mw/IEF. What items, and in what order, are specified in a Report Template, letting you customize how you see the data for a sample.

But IQ-11 reports can be customized for proprietary calculations, too. This is especially helpful if you’ve been using Microsoft Excel®, have migrated to Office 365®, and want to avoid continual revalidation of your calculations every time Office 365 has an update. In the report below, the entries in red were previously done in Excel. The "Fitted Curve" and two chain columns were calculated with Excel's regression feature.

(Data items containing proprietary or identifying information have been XX’d out)

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Summary

Together, we combined IQ-11's proven algorithms with a training set of your samples to calibrate each sample, find its lanes, find and quantitate its bands, assign them standards values, and report results.

And saved you hundreds of hours. Maybe even more.

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Have any questions or need help?

Send us an email to info@bioimage.net. We are here to help you and answer any questions that you may have.

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