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An open-source experimental framework for automation of cell biology experiments

Pavel Katunin, Ashley Cadby, Anton Nikolaev

Preprint posted on 3 July 2020 https://www.biorxiv.org/content/10.1101/2020.07.02.185454v1

Expanding the open source toolkit for cell biology

Selected by Mariana De Niz

Categories: cell biology

Background

Deep learning and artificial intelligence-based methods have been explored over the last decade, and have proved to be extremely useful for high throughput image analysis, optimization tasks, and robotics. Moreover, platforms have been designed with the purpose of providing a user-friendly interface for users with little or no experience using deep learning and artificial intelligence-based methods. While in terms of image analysis these advances have been revolutionary, large training datasets need to be generated, and this is often not feasible in biological experiments, as they often take a long time to conduct. Therefore, there is a demand for automated systems that would allow hundreds or thousands of experiments to be performed simultaneously. Various currently available systems have various limitations, the main ones being that they are cost-prohibitive, non-flexible in terms of setup, or lack integrated solutions (for example image acquisition and simultaneous analysis). To address this gap, Katunin et al  (1) present here a novel open-source hardware and software that allows for automatic high-throughput generation of large amounts of cell-biological data.

Figure 1. Various components of a novel open-source experimental framework for automation of cell biology experiments (From Ref. 1).

Key findings and developments

System design

The system consists of a hardware part, based on a 3D-printed modular design. The modules include a X-Y stage capable of moving a multiwell plate horizontally; a perfusion manifold, capable of applying up to 8 solution into individual wells; and a small autofocusing epifluorescent microscope. The authors make available the 3D printing files at https://github.com/frescolabs/FrescoM

The perfusion system is based on syringe pumps, and allows solution addition to individual wells, as well as solution changes. The microscope is a standard inverted microscope system including a non-infinity corrected objective, a 100mm camera lens, and no-tube lens; blue and green Throlabs filters, and a dichroic mirror. The objective moves separately in a Z-stage.

The authors also share details of the automatic control and electric circuitry; and assembly instructions for the hardware (XY platform, the perfusion manifold, the Z-stage, and a mini-microscope).

A further advantage of this design, is that it is very cheap (between 300 and 2500 GBP based on the components required); it is customizable towards user needs; and it is fully 3D printable, which further reduces costs of repairs if needed.

Software for operation

The software for operation is written in Python 3 and allows key functions including starting live fluorescence imaging without recording; moving the platform in X and Y directions (forward, backward, left and right), moving the application manifold up and down, returning to zero position, setting top-right and bottom-right positions for a multiwell plate; moving the pump forwards and backwards; and starting an experiment. The software is available for download at https://github.com/frescolabs/FrescoM/blob/master/software

Proof of principle and applicability.

As proof of principle, the authors imaged calcium dynamics in response to ATP, and show that the resulting images were of sufficient quality to allow identification of individual cells and their response to calcium. The system allows imaging of fluorescent reporters in live cells, or fixed samples labeled with antibodies. Altogether, this experimental framework would make generation of large scale biological data quicker and more accurate.

The authors discuss the potential applications of their design, including high-throughput screening experiments allowing exploring chemicals affecting a range of biological functions; optimization of experiments in an automatic manner for a large range of conditions, with the output being fluorescence-based; large-scale characterization of cells derived from individual patients; and routine cell biology experiments.

What I like about this preprint

I firmly believe in the value of open-science and this work is based on this principle. Beyond the uses in general labs, I think this will allow performing the experiments discussed in this work, in remote areas too.

References

  1. Katunin P, et al, An open-source experimental framework for automation of cell biology experiments, bioRxiv, 2020.

 

Posted on: 15 July 2020

doi: https://doi.org/10.1242/prelights.23132

Read preprint (1 votes)

Author's response

Anton Nikolaev shared

Open Questions

1.Congratulations on your work. You mention in your discussion, the idea of including a temperature block to perform PCR. Does the current setup include a temperature and gas chamber that would allow long-term in vivo experiments? If not, is it easily adaptable?

It doesn’t but the whole thing is small enough to be put in a CO2 incubator. The key thing that is yet absent is a proper cover. Because we would like to do experiments one after another for hours, we will need to have only one well accessible while others are covered, so that culture media does not evaporate.  Solution we are currently working on is to use a large plate (double the size of a 96 well plate in each dimension) firmly attached to the application manifold. In theory this should work.

2.As I understood from your design, exploration of a full plate is possible- but not of many plates. Using further robotics, would it be possible for your system to enable switching plates as well?

It is not going to be easy because we have an application system on top and microscope on the bottom. So, you will need to find a way to slide new plates into the system. Another possibility is to use longer rails and put several 384-well plates next to each other. Finally, and I believe this is a perfect solution for many experiments, is to design a plate with a much smaller well size. If a field of view is all you need, you can fit 10000 1mm x 1mm wells in a standard plate.

3.For further portability, can the microscope be powered by batteries?

Yes, it can. I have tried to use a standard 9V battery and I could get the LED to be bright for some 30-40 min non-stop. In this case you will need design your own power supply system. Don’t forget, though, that you still need to power 4 motors + whatever number of pumps you need. It would be difficult to feed all this with one battery.

4.One of the last points you touch upon is very interesting: have you used already an algorithm that allows selecting individual cells for clone selection? This could also be done on the basis of fluorescence- measuring transfection efficiency automatically, and selecting and passaging the clones to new wells, and monitoring them. Is this something you will do?

We haven’t done it yet but we certainly will try to design a cloning system.

5.You discussed in your proof of concept, the use of fluorescence as an experimental output. Does your system allow measuring with relatively high resolution other parameters such as cell morphology, etc based on bright field characteristics too?

In our experiments we plan to do simple calcium imaging and therefore we will use 10x or 20x objectives. If you want to use higher magnification objectives, you may need to use a better camera and make sure there is always oil or water on the objective. You can also put the entire system on top of a big microscope (confocal or 2-photon) but fix the objective onto our autofocusing system. In this case you will need to find a way to communicate with the microscope. Some commercial microscopes will allow this, others won’t.

6.Based on your discussion in “further improvements”, you propose the idea of having other platforms. Is it your hope to create a multi-modular setup capable not only of imaging-based experiments but a larger scale of techniques including various molecular and cellular methods?

There are many ways to change the system. For example, you can adapt the application manifold to use standard pipetman to combine a liquid handling robot with a microscope. In this case you will improve the precision. You can also substitute the manifold with an injector and generate fish lines.

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