Mahni Ghorashi

Lab robots crucial for end-to-end food safety data systems

Mahni Ghorashi — Thu, 14 Dec 2017 05:00:22 +0000

Next generation sequencing is beginning to replace traditional DNA methods in food safety testing. As this trend continues, sequencing will no longer be the time intensive process it once was. Laboratories will be limited by how quickly they can prepare samples, not how quickly they can sequence them. Automation will, therefore, play an increasingly critical role in the evolution of laboratory processes.

Critical stages of the NGS (next generation sequencing) workflow are already being automated by a variety of hardware and software innovations. Robotic solutions, for example, play an important role in addressing the substantial bottlenecks created by humans preparing lab samples.

In the next two to three years, automation of a different kind will help us to fully leverage the precision and speed of NGS technologies. The effective automation of bioinformatic workflows will dramatically increase our ability to analyze enormous bodies of data and identify macro-level trends across large volumes of data.

By leveraging NGS technologies and the technologies that automate NGS workflows — from library preparation to sequencing, analysis, and interpretation — the food industry at large will finally have the kinds of tools and information it needs to proactively identify threats and prevent outbreaks from occurring.

Sequencing secrets
Next generation sequencing is on its own an automated DNA sequencing technology. The technology has revolutionized the study of genomics and is quickly making inroads in food safety applications. NGS has distinct advantages over traditional sampling methods like Polymerase Chain Reaction (PCR) or antigen-based methods.

First, NGS generates and analyzes millions of sequences per run, allowing researchers to sequence, re-sequence and compare data at a rate previously not possible. Second, NGS testing is universal, while PCR testing methods are targeted. With PCR you have to know what you expect to find in order to test for it. What’s more, each target requires a separate testing run. This is costly and doesn’t scale.

By contrast, a single NGS test exposes the precise ingredients and all potential threats — both expected — in any given sample. In the not too distant future, the cost and speed of NGS will meet and then surpass legacy technologies.

Adopting NGS technologies, however, doesn’t necessarily mean you’ve solved a number of bottlenecks in workflow. Robotics is playing a key role in automating critical stages of the NGS workflow.

Challenges to food analysis automation
Preparing sample materials for food testing workflows requires a coordinated series of molecular biology reactions. Library prep can take up to 60 percent of a lab technician’s time and directly impacts the quality of resulting analyses. This process, known as sample preparation or library preparation, can be performed manually or automatically and can vary according to application and throughput.

In clinical and pharmaceutical settings, for example, robotic systems automate numerous operations, including liquid handling, creating efficiencies and helping to generate the highest quality data possible.

Adopting existing robotics systems for food testing is not as straightforward as it might seem, though. In doing so, the food testing industry has had to overcome unique challenges. In contrast to the materials being analyzed in clinical and pharmaceutical settings, food comes in a wide variety of forms: environmental samples, packaged foods, dairy, meat, and produce in solid, liquid, powdered, frozen, cooked, raw and concentrated forms. All of these require different methods of preparation before they can be analyzed.

Another challenge inherent to food analysis is that the compounds in natural products are variably distributed throughout unprepared samples. Complex food items further complicate this issue, as dozens of ingredients can be heterogeneously distributed throughout any given product.

Cryogenic mills or grinders blend samples at low temperatures and are often used to prepare samples of fruits and vegetables being analyzed for volatile pesticides. These mills and grinders produce homogenous samples of small sizes that can be held in a relatively small volumes of solution. This is important because it increases the efficiency of liquid-handling applications, those most commonly automated in today’s food testing labs.

Robotic liquid handling
Having long been deployed in clinical settings, robotic liquid handlers are a mature and reliable technology. The same handler a food lab has used to prepare samples for PCR can be deployed to prepare samples for NGS.

Robotic liquid handlers are XYZ robotic systems that dispense selected quantities of samples, reagent, or other solutions into the appropriate containers for any given application. Some examples on the market include: BioRad’s iQ-Check Prep System, a robotics platform that performs DNA extraction and PCR plate set-up, as well as Illumina’s automated solutions that are specific to NGS sample prep.

Food safety’s next frontier
Beyond robotics, another target for automation in the near term time is in artificial intelligence and machine learning to amplify our existing bioinformatic workflows. NGS provides an enormous amount of data, much of which goes unused in food safety applications today. The next revolution in food safety is in automating data-science operations that can leverage this data.

Automating bioinformatic workflows will dramatically increase our ability to analyze enormous bodies of data and identify macro-level trends. Imagine the insights we could gain when we combine trillions of genomic data points from each phase in the food safety testing process — from routine pathogen testing to environmental monitoring to serotyping.

The first step is to unify all of the various tests performed at each stage of the food safety workflow into a single, universal test. Just having the all this data in a single place represents enormous opportunity.

We could begin to query the data for questions you’d typically ask of your food safety systems:

What is the risk profile of a given sample?
Does it contain harmful pathogens?
What strains are present?
Are the pathogens from a supplier or the environment in which it was manufactured?

A single algorithm could reveal such information in a single test run.

This is just the first layer of automation — which on its own we expect to drive down the costs of testing, increase scale, and reduce errors that emerge in today’s data silos.

Where it gets interesting is when you add in a secondary layer of automation in the form of machine learning and artificial intelligence. That’s how our industry will truly get our data to work harder for us. Imagine what’s possible when we can automate insights that are impossible to identify today without a team of bioinformaticians working with reams of data over long periods of time?

With the addition of machine learning, we can conduct analyses across bioinformatics runs. We can associate genomic data with data gathered across the supply chain. This will dramatically enhance our ability to identify emerging pathogen threats, locate problematic suppliers, build rapid response consumer-alert systems, model for long-term threats of climate change, and even draw conclusions about consumer preferences.

What’s next?
The holy grail of food safety is to have end-to-end systems in place for gathering supply chain and sequencing data, which can then be appended to and stored on immutable decentralized block chains.

We’re not too far off from that. Recent advances in IoT are making it easier than ever before to gather data at each stage of the supply chain, from farm to table.

Block chain technology is already being tested for food industry use cases in large pilots with top global brands. We’re even starting to see the first miniaturized sequencers becoming commercially available, which is the first step toward in-line sequencing.

The essential technological components exist. In order to build the end-to-end system of the food safety future, we will have to fuse these components and

their operation together. That is an enormous technological, infrastructural, and cultural challenge. I’m confident we’ll get there. The opportunities are too great to imagine otherwise.

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Even if GMOs are safe, mandatory labeling is a good idea

Mahni Ghorashi — Thu, 24 Mar 2016 05:00:59 +0000

In November 2015 the FDA approved the first genetically engineered salmon as fit for human consumption, paving the way for genetically modified organisms to become a regular part of the American diet. The “super salmon” designed by AquaBounty Technologies produces growth hormones year round rather than just during the summer and reaches adult size in 18 months instead of three years. Not surprisingly, consumer and environmental groups have loudly opposed the FDA’s decision – there’s widespread unease when it comes to GMOs. Regardless whether you think GMOs are safe or not, the United States needs a mandatory GMO labeling law. Consumers have the right to make purchasing decisions with confidence. If we want to support mandatory-labeling legislation, we’ve got to forge some some alliances that span the aisle of the GMO debate. Proponents of mandatory labeling need to be clear that they support transparency, not the outright banning of all GMOs. People have they right to know what they eat, but they also have a responsibility to understand how economic interests manipulate GMO politics. If we center the conversation on transparency and education instead of prescriptive policies, we put consumers in charge, not politics. To move forward, we have to confront the fact that the GMO debate has been tainted, not only by politics and economic self-interest, but also by a pervasive cultural fear that technology threatens the perceived sanctity of nature. The costs and benefits of GMOs Proponents of GMO crops, including many large farmers and producers of them, are fighting against organic farmers, specialty retailers and major organic brands. They see mandatory labeling laws as an undue economic burden with little to no scientific bearing. Meanwhile, the pro-labeling stance is held by the fast-growing organic food industry, which sees labeling as a competitive advantage. Proponents of GMOs contend that not a single conclusive test, credible report or any scientific data points to GMO crops being harmful to humans. They also point to the good that GMOs have done, reducing by a factor of 10 the amount of insecticides used on some crops, while simultaneously reducing food costs and decreasing CO2 emissions. With nine billion people forecasted to populate the planet by 2050, the world will have to grow 70% more food by 2050 to keep pace with population growth. Opponents of so-called Frankenfoods are equally militant. And they speak with their dollars. Non-GMO is one of fastest growing label trends in U.S. food packages, with sales of items growing 28% last year to about $3 billion, according to a report in the Wall Street Journal. Many claim GMO crops are just a ploy by Monsanto, Bayer and Dupont to sell more herbicides, dominate the supply chain, and leave farmers solely dependent on high-priced transgenic seeds. They believe that inserting foreign genes into crops can make food dangerous or allergenic. The truth lies somewhere in between. A (very) brief history of genetic engineering Defenders of genetic engineering remind us that humans have been genetically modifying food for thousands of years via artificial selection, with large swaths of genes being swapped or altered in the breeding process, often unpredictably. Farmers were crossbreeding plants and animals to select for desirable characteristics long before the German monk Gregor Mendel began conducting his pea-plant experiments in the mid-19th century. Today’s genetic engineers can target single genes, making them perhaps the most efficient breeders the world has ever known. You’ll find many them walking the halls of CalTech, Harvard and MIT, but genetic engineers congregate in far larger numbers in the waiting rooms of doctor’s offices, elementary-school classrooms and subway cars – viruses, the world’s first genetic engineers, have been inserting their DNA into genomes of crops and humans since the dawn of evolution, cross-pollinating the genes of other species. In fact, the humane genome is filled with genetic sequences originating in viruses. The lesson here is that to determine whether a food item is “natural” or not, we rely on value judgements that are mostly personal. We ignore the fact that humans have been cross breeding plants and animals for millennia while we object to more precise, and most likely safer, forms of genetic engineering. Nature is never as pure as we wish it to be. What we can learn from science-fiction In referring to genetically modified food like super salmon as “Frankenfoods,” opponents of GMOs explicitly allude to the world’s first science-fiction novel, Mary Shelley’s Frankenstein. They also betray the fact that the contentious GMO debate is largely motivated by two emotions: fascination and fear. Calling GMOs “Frankenfoods” is simply a catchy way of labeling them monstrous, unnatural things. Monsters, as any child knows, are both fascinating and frightful. Even more interesting is the fact that the term “Frankenfood” most accurately alludes to Shelley’s character Victor Frankenstein, the scientist, not the unnamed monster he pieces together in a lab. Composed in the throes of the Industrial Revolution, Shelley’s novel is a cautionary tale: Victor Frankenstein is a brilliant, lonely technologist who upsets the natural order in attempting to engineer a new species. Frankenstein attempts to play God, and he is punished for his pride. Like Frankenstein and his “monster,” genetic engineers and GMOs inspire intense negative emotions because they challenge our perception of what is natural. This is why GMO discourse so often devolves into a struggle between technology and nature, a false opposition that has been with us since before the Industrial Revolution and remains unresolved. When we pit technological innovation against the perceived sanctity of nature, we hijack proactive, constructive debate. Mandatory labeling A New York Times survey found that 93% of Americans believe foods containing GMO ingredients should be labeled to reflect that. Increasingly, consumers are demanding assurance that food products are safe, for real-time information on purchases, and for product origination insight. Opponents of GMO labeling say it would be expensive and raise costs for consumers, but the reality is that 64 other countries have GMO labeling laws and food prices haven’t increased. Consumers haven’t stopped eating GMO foods. They simply have more information about what is in their food and how it’s produced, which is how it should be. More transparent labeling is also good for the top line – clear labels are a major factor in influencing consumer-purchase decisions when shopping for food or beverage products. The food industry is beginning to catch on. Campbell, General Mills, and Mars recently announced that they’ll be labeling all GMO ingredients voluntarily. Vermont became the first U.S. state to require mandatory labeling for foods containing GMOs starting in July of this year. Maine and Connecticut have also passed labeling laws, but those depend on neighboring states taking similar steps. In July 2015, Congress banned states from requiring mandatory GMO labeling. Perhaps unsurprisingly, the bill, called the Safe and Accurate Food Labeling Act of 2015, was backed by industry groups like Monsanto. Ultimately, a national standard for labeling laws is preferable to state standards. Consumers need consistent, transparent, and recognizable labels. Some of us see distorted, dangerous monsters where others perceive deliciously plump salmon. When it comes to food, most of our purchasing decisions are rooted in emotions, personal values, and perception. Innovative new technologies are now dramatically illuminating product origin and quality insights. It’s time we give consumers the information they deserve to make the best personal decisions, free of political or business influences. Note on contributor: Mahni Ghorashi, co-founder of Clear Labs, leads commercial activities at the company, including strategy, marketing and corporate development. After graduating from MIT and getting his MBA, Mahni was head of marketing at Bina Technologies. Mahni is also a concert pianist in his off hours and a tireless supporter of the arts.