If you've ever worked in a laboratory, or even if you've just seen them on TV, then you're probably familiar with the important but monotonous task of pipetting. To make it easier and less prone to errors, a team at the MIT-affiliated Whitehead Institute has created iPipet, an app that allows an iPad to visually guide "pipetters" in their work.

Putting it simply, pipetting involves using an eye dropper-like glass pipette to transfer precise measurements of liquid samples between sets of vial-like wells contained within trays known as well plates. Given its often mind-numbingly repetitive nature, however, mistakes can be made – if a sample accidentally goes from a "source" well into a "destination" well that contains one reagent as opposed to another, for instance, then the results of the experiment can be heavily skewed.

Robotic pipetting systems certainly do exist, although they're expensive, require trained personnel to set up, and are not entirely infallible themselves.

iPipet keeps human lab techs in the picture, but uses the iPad screen to help them keep track of what they're doing. They start by utilizing a spreadsheet interface to enter the protocol of their specific task – which samples need to go into which wells, that sort of thing.

That information is uploaded to the iPipet website, where it's used to generate two grid-style displays that are sent back to the iPad. The source and destination well plates are then placed over top of those displays. Individual glowing dots within the grids correspond to individual wells, illuminating them from below, showing users on a step-by-step basis which samples should go to which destination wells.

To keep the well plates aligned with the grids, users can download a file that will allow them to 3D-print a plastic adapter that fits over the tablet screen.

In a test of the system, humans using iPipet were able to perform almost 3,000 fixed-volume pipetting steps in about seven hours – by contrast, a robotic system was reportedly able to manage only half that number within the same amount of time.

iPipet was created in the lab of Yaniv Erlich, and can be downloaded from the project website. A paper on the research was recently published in the journal Nature Methods.

The program can be seen in use in the following video.

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