Maximize Return on Your Lab Automation Investment Through Digitalization
June 12, 2023 Sascha Fischer
Have you recently implemented automation hardware in your laboratory? This was probably a big investment in terms of equipment and time, but a worthwhile one, because it should ultimately allow you to obtain results more efficiently, with less labor, from more or larger experiments. However, your automated setup outputs only the raw data—what about getting from there to those desired results? Have you also examined your data workflows?
To fully leverage your lab automation investment, you must also digitalize. Digitalization includes (but is not limited to) hands-off data capture and transfer, full automation of data processing and analysis, and data-driven decision-making. To implement these, digitalization partners like Genedata can help. In a series of upcoming blog posts, we’ll examine the aspects of digitalization that are key to achieving truly end-to-end automation—meaning automation of both experimental execution and data workflows. We start off in this post by considering automated data transfer.
Missing Files and Costly Delays: A Story of Digital Gridlock
Picture getting stuck in a congested highway exit: you’ve cruised down the road until now, but you’re suddenly hitting the brakes, inching towards your destination. Frustrating, right? Perhaps, as an R&D scientist, you also struggle with the same feeling: you’ve automated the laboratory and everything is running smoothly, but then you hit a roadblock because you haven’t automated data transfer.
Let us illustrate this with a situation based on many we’ve observed. A large pharmaceutical company decided to automate some of their drug discovery assays. They purchased expensive, state-of-the-art laboratory automation hardware—a constellation of robotics, liquid handlers, analytical instruments, and more—that could prepare and execute experiments, start to finish in an unattended fashion. Automation engineers worked with lab scientists to optimize every motion to the millimeter and minimize dead volumes to save precious samples and reagents. With their setup, they could generate data on hundreds of thousands test molecules, without any of the repetitive and time-consuming manual labor formerly required.
In stark contrast to this smoothly running hardware setup, when it came time to process the data, a lab technician would inform the project scientist that the experiment was done. The technician would then manually hand over all the raw and meta-data files to the scientist, by dragging-and-dropping potentially hundreds of files from the analysis instrument onto a shared folder, copying them onto a USB-stick, or even attaching them to an email.
Provided that the scientist did not miss or forget these files, he or she would download the data and manually load it into an analysis software. The scientist would then copy-paste the results, including important quality control metrics, into a spreadsheet—an error-prone process that could result in misnamed, incomplete, or duplicate files—and share this with the technician by email, so that they could together plan the next experiment.
How to Automate Data Transfer: Clearing the Traffic
The company realized that this manual way of transferring data created a time bottleneck and source of error, hampering their overall laboratory automation effort. Fortunately, with the help of Genedata Screener®, they solved their problem by automating data loading from the instruments. Now, every morning at seven o’clock, all data collected in the previous 24 hours are programmatically loaded into Screener, without a single click. Following data loading, Screener automatically analyzes the data and reports results to the data warehouse, notifying the whole project team that analysis is complete and results are available. Team members receive an email containing a link to their results in Screener, where they can review, backtrack to the raw data, or adjust the original processing steps as needed.
This kind of automated data ingestion and transfer is the first step towards truly end-to-end automation. And even if you don’t have a fancy laboratory automation system or conduct high-throughput screens, you can still use digitalization to save time and protect data integrity: you can also automate data ingestion from standalone instruments, for applications like high-content imaging and lower throughput assays (see the case from Roche below), or for technologies like mass spectrometry.
On the flip side, for high-throughput screens with hundreds of plates or screens involving very precious reagents or samples, it’s beneficial to have an automation solution that not only automatically loads the raw data on a per plate basis as soon as the plate has been processed on the analysis instrument: ideally, the solution also analyzes the data, so that experimental quality can be constantly monitored in real-time. As one example, with their highly automated platform, Evotec is running screening campaigns at a throughput of >1,000,000 wells in a single day. There could be a time lag of several hours between data analysis and, if a problem was identified, taking action to fix it on the lab automation system. For a screen of this scale, this led to loss of time, wasting costly reagents/consumables and samples. Now, Genedata Screener automatically uploads raw data as soon as available, analyzes the data in real-time based on pre-defined QC criteria and scientists are immediately notified if an issue arises or can monitor the results on a dashboard. Evotec scientists no longer need to be physically present in the lab to monitor their HTS runs–providing them peace of mind in not wasting precious resources and giving their team more flexibility and time to focus on other tasks.
As another example, during the development of robust biotherapeutic manufacturing processes, bioreactors collect high volume data on culture conditions such as titer, viability, and pH. Frequently, the time required to transfer the data into an analysis program creates a major bottleneck. A solution like Genedata Bioprocess® alleviates this bottleneck via integrations with bioreactors (such as the Ambr® systems), chromatography control systems, and offline analysis instruments, through which it automatically imports these data; it then analyzes and stores all information, including both raw and processed data, such that it can be evaluated in context at any time. In addition to automatic data capture, Genedata Bioprocess supports barcode-based sample management.
When automating data transfer, there are important aspects you should look for in your software provider of choice. First, they should minimize overhead for your informatics team by offering enterprise-suitable, out-of-the-box solutions that reduce effort to deploy and maintain. They should equip your informatics team with the proper tools to integrate with your data infrastructure and accommodate unique needs. Ideally, their software should have user-friendly interfaces and visualizations, so that you can not only set up automated experiment runs, but monitor the resulting data, in real time via a central observer dashboard if needed.
Second, if your software provider has partnered with cutting edge instrument and automation hardware vendors, this can smoothen the integration process. Genedata is hardware-agnostic and has partnered with many vendors of cutting-edge laboratory instruments, automation hardware, and complementary control, scheduling, and sample registration software. Genedata partners include Biosero, Sciex, BMG LABTECH, Titian, and HighRes Biosolutions®. The latter partnership benefited Sygnature Discovery, when they sought to fully automate high throughput screening, using HighRes lab automation hardware and scheduling software, Titian Mosaic for sample registration, and Genedata Screener for automated data loading and analysis. They were up and running with their solution in under six months—impressive for an implementation of this scale.
By collaborating with knowledgeable and experienced digitalization providers, you can best shape the level and quality of integration with your laboratory automation systems and improve the ROI from your laboratory automation.
Look out for the next article in our series, which will address automation of data validation and analysis, especially for complex assays.
Sascha Fischer is Business Development Manager for Automation, Genedata Screener.
Notes: This post was originally published May 11, 2022 and has been updated.