More and more, the software is getting integrated into the hardware… Yesterday’s software is today’s hardware. Those two things are merging. And the line between hardware and software is going to get finer and finer and finer.Steve Jobs, 1980
Jobs, almost 40 years back, predicted the merger of the machine and software. He went on to prove his idea when he built the Macintosh and the iPhone, where one could not separate the software from the underlying machine. Even though this proof of concept has led transformations in many other industries, the drug discovery industry lags in utilizing this ideology.
Generation of Omics data such as metabolomics, transcriptomics, genomics et al, still happens on software made by instrument manufacturers which are often clunky and not easy to use. The advent of better analytical algorithms and methods which use machine learning and artificial intelligence to enable hypothesis generation and validation in target discovery has further deepened the divide between software and machine. The software which does the kind of interpretation as mentioned previously requires very different interfaces and user experience. These interfaces cannot be handled by the present software made by instrument manufacturers. At the same time, this next-generation interpretation software and algorithms require the machine to automate. This eases the user experience of interpreting biological data.
The above-mentioned challenge is why Elucidata collaborated with Waters. Elucidata is the maker of omics data interpretation and processing tools. Waters is one of the world’s leading mass spec instrument manufacturers. These instruments are used to find different compounds such as metabolites in a cell and measure them. The theme of this collaboration was to bridge the gap between machine and software to enable high throughput target discovery. Which ultimately helps in finding new drugs for disease and conditions.
Symphony Polly Integration
To bridge the machine and software gap Elucidata and Waters decided to integrate Symphony and Polly. Symphony Data Pipeline is specifically designed to form custom chains of data processing steps right from when data has been outputted from a mass spec instrument, converting and storing it in the desired format and location and finally pushing it through external software such as Polly. And Polly is a platform which contains a suite of workflows for processing, analyzing and interpreting Omics data.
For this integration, we focused on automating metabolic flux analysis using Symphony and PollyPhi, a relative flux workflow on Polly. We were trying to solve the challenge that data generated for relative metabolic flux analysis is complex. It often consists of multiple experimental conditions and time points along with relevant isotopologue/isotopomer information for each metabolite of interest. Processing these data files and plotting of the data to visualize incorporation of the isotope label is a time-consuming task. It often requires multiple manual data processing steps and the utilization of various software.
The integration had three critical components. These were
- Mass spec instrument for generating flux data (Water’s Xevo G2-XS QToF)
- PollyPhi for relative flux analysis
- Link the two to Symphony
A short diagram of the integration is shown below:
The integration took 2 months to build The productivity benefits and time savings of automation through the use of Symphony™, was approximated at an estimated of 60% increase in efficiency, combined this with PollyPhi the productivity was even higher. The flux analysis which typically took weeks could now be done in less than 30 mins.
The outcome of this collaboration has been multiple technical notes and talks at ASMS and CPSA. But most importantly, the Symphony Polly integration is already being used in labs to generate flux insights swiftly. And it has been less than 6 months that it was developed. An example of this is its use at Georgetown University where with the help of the Symphony Polly integration scientist were able to figure out how cancer cells behave differently when incubated in monolayer and spherical cell cultures. Scientists were able to quickly assess that glycolysis is significantly unregulated in spheroid cell cultures as opposed to monolayers and that a higher contribution of glucose goes to the TCA cycle via acetyl CoA in spheroid cell culture as opposed to monolayer cell culture.
The machine, software and interpretation gap is solved in Symphony-Polly integration. It’s just one of the many ways Elucidata as a company is thinking about the challenges faced in target discovery. And we plan to continue striving to bridge such gaps to bring faster and better interpretation to scientists working towards finding new drug targets.