Your go-to blog for modern lab management

The essence of a successful and well-functioning quality control (QC) lab lies in the name itself. Achieving, maintaining, and continuously improving quality is the ultimate goal in ensuring patient safety. 

So, how can regulated labs maintain these high-quality standards and successful processes?

Many factors – such as those defined in ISO 15189:2012 or by the Clinical & Laboratory Standards Institute (CLSI) – play a role in QC lab operations. This blog focuses on managing sample and inventory processes, data, documents, and records and how digital software platforms play an essential role. 

QC Lab Requirements and Challenges

QC labs handle and process many samples, ranging from raw materials to in-process samples, drug products, and finished products. As these samples are analyzed, large amounts of data are generated, including QC test results, calibration reports, and more. 

Properly managing the sample chain of custody and associated specifications is critical for consistently high quality. And, not surprisingly, it comes with challenges.

As lab personnel processes samples and runs release testing of materials and samples, the data must be managed to ensure all information is accurate, accessible to qualified personnel, secure and traceable. 

Let’s go through some common difficulties with the samples, inventory, data, documentation, and records management process.

Sample and Inventory Management

Every step in the sample collection, handling, and testing process must be carefully controlled and tracked by QC personnel. In addition, QC lab testing methods and the overall process must be verified and validated. Inventory management is similar: the procedures for raw materials, reagents, equipment ordering, storage, and expiration must be controlled and tracked.

Many QC labs accommodate large volumes of samples daily. A significant challenge is processing, tracking, maintaining accurate records, and ensuring all samples are correctly handled.

Inventory management is another challenge in QC labs, as keeping track of supplies, equipment, and chemicals can be time-consuming and complex. Guaranteeing the required materials are in stock at the right time and stored in a way that protects integrity can be a constant difficulty. If they aren’t correctly managed, there is a risk of incorrect or expired materials being used, which can impact the quality of results. Furthermore, ineffective tracking of usage and ordering trends can lead to inefficient spending.

Data Management

Data accuracy, reliability, and timeliness are essential for QC. Accomplishing this takes rigorous attention to the evolving regulatory requirements for data management, such as electronic signatures, 21 CFR Part 11 compliance, and data backup and recovery processes.

With a combination of manual testing procedures and automated instruments, several challenges related to data management emerge. This includes assuring the security of sensitive information and avoiding data loss due to system failures or human error. Another challenge is integrating data from different sources and formats into a centralized database that supports downstream data analysis and reporting in a robust, flexible way.

Document and Record Management

On top of data management, lab standard operating procedures (SOPs), protocols, and test records must be securely managed. This requires proper storage and access controls to prevent unauthorized access, tampering, or data breaches. In addition, consistent adherence to established procedures and practical training and personnel monitoring is essential for maintaining the integrity of the testing process, demonstrating compliance with regulations, and supporting continuous improvement in QC labs.

Overcoming QC Barriers with Digital Laboratory Platforms

Digital lab platforms (DLPs) ameliorate the sample tracking and data management woes discussed above. They proved a standardized, comprehensive approach to most QC processes, reducing the risk of errors, providing a fully traceable account of lab operations, improving overall efficiency, and ensuring regulatory compliance.

Here’s how:

• Centralized and standardized QC operations: DLPs enable digital record keeping for tracking and managing all samples, inventory, data, documents, and records. It also implements a process for the consistent execution of workflows, reducing the risk of human error.
• Thorough regulatory compliance: Many DLPs offer automatable processes, full traceability, and audit-ready capabilities. Organization of the abovementioned information (e.g., samples, inventory, data, etc.) in a centralized place also helps drive compliance by maintaining accurate records, automating processes, and enabling a transparent ‘birds-eye view’ of laboratory operations.
• Streamlined reporting: A DLP can facilitate creating a transparent and reliable reporting process to communicate valuable quality information to all relevant stakeholders. Furthermore, reporting can be automated, enhancing the overall efficiency of the lab and supporting more confident decision-making.
• More secure data: DLPs provide a highly secure framework for implementing and maintaining safe processes for collecting, storing, and sharing information. Most DLPs have access control, encryption, backup, and disaster recovery capabilities.

Try eLabNext’s DLP for Your QC Needs

Digital platforms help solve typical sample tracking and data management challenges in a QC environment.

Book a personal demo today to see how eLabNext’s DLP fits into your QC lab!

Sustainability has become more important than ever as we become increasingly determined to reduce its impact on the planet and reverse climate change. If we want to maintain our current quality of life, ensure future biodiversity, and protect the health of our global ecosystem, leaders must implement more sustainable practices. 

If you read that sentence again, you’ll notice that sustainability is centred around protecting “life” – either the lives of humans or the millions of other species we share the planet with. Accordingly, sustainability has become more critical in an industry where life is part of the namesake: the life science sector. With more and more companies, universities, and government labs hiring sustainability officers and publishing Environmental, Social, and Governance (ESG) reports, it's clear that the industry has made sustainability a major priority. 

While the increase in participation is worth celebrating, there’s still a long way to go, especially regarding lab sustainability. For example, estimates suggest the world’s labs produce more than 5.5 million tons of plastic waste annually. The global pharmaceutical industry is 55% more carbon emission intensive than the automotive industry. Meanwhile, 4.4% of worldwide global greenhouse gas emissions are produced by the healthcare sector (e.g., hospitals and laboratories) alone. 

A cultural shift in the life science industry needs to occur. And what better time to discuss it than on Earth Day? 

With more sustainable lab practices and lab equipment, we can all do our part toward a healthier future. We’ll discuss how below.

Climate Change Is Affecting us All

Climate change is already impacting human health, not to mention damaging the environment and the habitats of animals around the globe. Hotter temperatures lead to more heat waves, higher cases of heat-related illnesses, increased risk of wildfires, and more drought. Storms become more frequent, including hurricanes and typhoons. Melting ice sheets cause the sea level to rise, putting millions of people at risk.

Weather changes also make it harder to herd, hunt, and fish. Heat stress can limit water sources, causing crop yield to drop. As we struggle to feed the world, we’re losing species 1,000 times faster than any other time in recorded human history.

All of these negative impacts are a direct result of human activity. We burn fossil fuels to generate power for manufacturing plants, homes, and transportation. We use fossil fuels to produce plastics, electronics, building materials, and more. We cut down forests to make space for farms and pastures. All of these elements play significant roles in producing the greenhouse gasses that warm our planet and threaten the way we live and the future of our planet.

And as activity and investment in the life sciences accelerate, our collective environmental footprint will scale accordingly.

Prioritize Sustainability in Labs: a Call-to-Action

Companies that take measures now can significantly reduce future costs and risks and simultaneously increase their value. Many businesses in the life science sector already partner with government organizations and global institutions that will ultimately set environmental regulations. 

It’s also better for the bottom line. In a review of 200 studies on sustainability in the corporate world, 88% showed that good ESG practices lead to better operational performance. 80% showed a positive correlation between stock performance and good sustainability practices.

A Digital Solution for Building More Sustainable Labs

Many companies invest in data-driven technology to improve production, R&D, and supply chain continuity. For example, AI, engineered automation innovations, and overall lab digitalisation are aiding in implementing more sustainable lab practices. Digitalisation can help minimize lost resources by decreasing the number of needlessly repeated experiments. 

Many research companies unnecessarily waste money purchasing excess or redundant reagents and materials. Digital inventory tracking trims much of this waste by giving lab personnel a continuously updated view of current stocks, making ordering more efficient. This highlights an important issue: There needs to be an adequate, efficient, and pre-existing digital infrastructure for many labs to move in a more sustainable direction. 

One of the most prodigious energy consumers in labs around the globe is the storage of samples in freezers. With sample management, we can minimise and manage the contents of freezers more efficiently, limit the number of freezers required, and cut down on energy use.

Digitalisation can also help companies organize messy data into easily accessible and searchable information. Likewise, companies can set regulations to measure and report on sustainability efforts and waste management, then provide direction for their existing personnel on how to meet these guidelines. Of course, proper funding is necessary to ensure that employees can invest in sustainable lab equipment and practices that will pay off in the long run.

Sharing is a  Sustainable Lab Practice 

Open inter- and intra-lab collaboration offer another excellent opportunity for reducing the environmental impact of R&D. Shared equipment results in lower utility loads and savings on energy by removing duplicate instrumentation that uses significant energy and takes up precious laboratory space. Additionally, sharing reduces the need to expand building ventilation and utilities to serve excess equipment.

Additionally, sharing data can reduce the number of experiments necessary, further limiting the need for resources and lowering the environmental footprint of the life science industry. Digitalisation enables the free flow of data between collaborators. For example, using electronic lab notebooks (ELNs) simplifies and automates the documentation of experiments, reducing the labour required, eliminating the need for paper lab notebooks, and making it easier to share information. 

This practice also allows us to reduce the amount of lab space used. Digitalisation allows us to access and analyze data from anywhere. In some cases, fewer staff members can keep an entire lab running safely and efficiently. The more efficient labs become, the less energy and resources we need, and the more sustainable this sector can be.

Digitalisation is Part of a Comprehensive Solution for Lab Sustainability

Despite all the benefits of the digital sustainable lab practices highlighted above, there is a downside to consider: storage. The big data revolution is in full swing, and data storage is essential to the data lifecycle. In a digitized world, we’ll depend on servers to store and access that information. Those servers require energy and maintenance, which drives CO₂ emissions. 

Thus, we must continually investigate and monitor the CO₂ emissions of such technology in the life sciences. A recent study estimated the CO₂ emissions from a genome-wide association analysis (GWAS) analysis to be 4.7 kg of CO₂ to 17.3 kg of CO₂, depending on which software version is used. 

For context, a passenger car emits about 14.3 kg of CO₂ per 100 kilometres.

We can make servers more sustainable by using the lessons above on sharing and collaboration. Using central servers, which are operated with more energy-efficient practices than smaller local servers, and using green energy as a power source can reduce the environmental impact of data storage significantly. 

Protecting the Planet with Sustainable Labs

Sustainability improves the quality of our lives, protects our ecosystem, and preserves natural resources for future generations. While digitalisation is a challenge, it has enormous potential to aid in reducing CO₂ emissions if we can wisely deploy it. 

As more labs turn to digital inventory and data management solutions, the life science industry can share data, instruments, and servers more efficiently, reduce energy consumption by cold storage, and ensure efficient operations.  As a result, we can create less waste and produce fewer greenhouse gas emissions. 

If you’re looking for a path to digitalisation this Earth Day, eLabNext’s digital lab platform can facilitate the process. Schedule your demo today, and we’ll show you how we can turn your lab into a lean, green research machine.

Softwaresystemen als LIMS (laboratorium-informatiemanagementsysteem) en ELN (elektronisch labjournaal) verrijken de mogelijkheden om labs te digitaliseren. Die noodzaak wordt breed gevoeld in de labwereld, signaleert Margriet Mestemaker van eLabNext, inhakend op de toegevoegde waarde en actualiteiten als data-integriteit, dataveiligheid en AI ofwel kunstmatige intelligentie.

De mogelijkheden om laboratoria te digitaliseren nemen gestaag toe, met steeds meer functionaliteit in software-systemen, aldus Margriet Mestemaker, accountmanager Benelux bij eLabNext. LIMS en ELN worden beiden voor deze digitalisering gebruikt, al ziet ze wel verschillen. “Een laboratorium-informatiemanagementsysteem is vaak ‘stug’, vastgelegd voor een bepaald labproces met veel functionaliteit en weinig flexibiliteit. Een elektronisch labjournaal biedt juist veel flexibiliteit en mogelijkheden voor koppelingen met andere systemen, om een passende oplossing te customizen. Dat is volgens mij de toekomst van labdigitalisering.”

Paperless lab

Het ‘paperless lab’ is echter nog geen gemeengoed, volgens Mestemaker. “Iedereen gebruikt een smartphone en heeft geproefd van de digitale mogelijkheden. De labsector draait echter op structuur en routine en blijft daarom nog te vaak hangen bij papier. Labs die alles nog op papier doen zie ik niet veel meer, maar de huidige automatiseringsoplossingen zijn vooral hapsnap ingevoerd. Men zoekt nog naar één centrale plek waar de complete labworkflow digitaal is georganiseerd.” De labwereld wil dus één oplossing waar alles samenkomt, van inventarisbeheer, analyseplanning en dataverzameling tot kwaliteitscontrole en communicatie over de resultaten.

“De labsector blijft nog te vaak hangen bij papier”

Margriet Mestemaker van eLabNext

Overstap op LIMS of ELN

De uitdaging hierbij is dat de overstap naar een ELN- of LIMS-systeem meestal een verandering vergt. “Men zit dan nog vast aan een bepaalde werkwijze en eigen workflow: ‘We deden het altijd zo’. Daarom is het verstandig om met een open blik te kijken hoe men de workflow zo kan aanpassen dat het logisch past bij het nieuwe systeem. Ik zie vaak dat iemand in een trial met een nieuw systeem ervaart dat de bestaande manier van werken toch niet de meest praktische is.”

Witness signing

Natuurlijk is er behoefte om meer labhandelingen te automatiseren, maar zeker zo belangrijk is de compliance: zorgen dat die handelingen volgens de voorschriften worden verricht. Traceability is hier het sleutelbegrip, voor procesbeheersing, kwaliteitscontroles en toelatingsprocedures voor bijvoorbeeld een nieuw medicijn. Alles moet worden gelogd en navolgbaar zijn bij audits. Dat vraagt om vaste templates, gestructureerde workflows en borging van data-integriteit, aldus Mestemaker. Een digitaal hulpmiddel waarnaar steeds meer vraag komt is ‘witness signing’: het zetten van een digitale handtekening door een expert of toezichthouder, bijvoorbeeld voor akkoord op een protocol of afsluiting van een experiment, waarmee de data dan zijn vastgelegd. “Dit wordt tegenwoordig voor alle aspecten van het labproces gevraagd. Het beperkt bijvoorbeeld de vrijheid om af te wijken van de workflow en maakt datamassage een stuk lastiger.”

LIMS, ELN en dataveiligheid

Over data gesproken: zorgen over dataveiligheid leven breed in de labwereld, weet Mestemaker. Daarom zouden alle datacenters voor hosting van LIMS- en ELN-webapplicaties in de (publieke of private) cloud gecertificeerd moeten zijn voor informatiebeveiliging volgens ISO 27001. Gebruikers kunnen ook alles in eigen huis houden, op eigen servers, maar dat heeft niet haar voorkeur. “Die optie vind ik het minst veilig, omdat gebruikers dan zelf verantwoordelijk zijn voor cybersecurity, back-ups, enzovoort, terwijl dat niet hun core business is.”

“Een veelbelovende ontwikkeling, maar het heeft nog wel wat jaren nodig voordat het dagelijkse praktijk is”

Margriet Mestemaker van eLabNext

AI en big data

Uiteindelijk draait labdigitalisering om data, en dat worden er steeds meer. Kunstmatige intelligentie (AI) komt dan in beeld om uit big data zinvolle informatie te halen. Bijvoorbeeld uit meetresultaten correlaties tussen parameters afleiden of foto’s van celculturen snel analyseren. “Dit is een veelbelovende ontwikkeling, maar het heeft nog wel een aantal jaren nodig voordat het dagelijkse praktijk is op het lab.”

Voordelen labdigitalisering

Verandering kost tijd, weet Mestemaker, of het nu specifiek om de cloud of AI gaat of om automatisering en digitalisering in brede zin. “Dat is geen onwil, al is er wel sprake van enig conservatisme. Maar als voorlopers met fantastische resultaten komen, zal de rest snel volgen. Beschouw daarom positief-kritisch de workflows op je eigen lab, onderzoek de voordelen van een compleet digitaal labplatform en kijk vooral met een open blik naar labdigitalisering.”

________________________________________________

Flexibel platform voor ELN

Het gebrek aan automatisering en traceability in hun researchlab voor biotechnologie was in 2010 voor twee Groningse onderzoekers aanleiding om eLabNext te starten. Ze begonnen met inventarissoftware en dat groeide uit tot een platform voor labdigitalisering: elektronisch labjournaal, inventarisbeheer- en sample-trackingsysteem, labprotocolmanager en eLab Marketplace. De marktplaats bevat apps, ook van derden, voor koppeling aan de software van eLabNext om de functionaliteit verder uit te breiden. Dankzij de flexibele opzet is de software van eLabNext ook geschikt voor gebruik buiten de biotech R&D, bijvoorbeeld in een analytisch-chemisch lab. Het bedrijf is wereldwijd actief, telt bijna vijftig medewerkers en is nu onderdeel van laboratoriumleverancier Eppendorf.

Hans van Eerden

Lees op LabInsights

We are happy to share a recap of the panel discussion that took place during our new office opening in Glendale, CA, last month. The event was an excellent opportunity for attendees to network with the various booths, and we've provided highlights of each one so that you can reach out to them directly.

We'd like to take a moment to express our gratitude to SmartGate VC and Hero House for their warm hospitality and welcome. It's an honor to be part of such a vibrant AI ecosystem, and we're thrilled to be contributing our biotech expertise to it. We also extend a warm welcome to Mayor Ardy and Senator Portanito, who joined us to celebrate this exciting new chapter.

Key Takeaways

• The AI Revolution is happening as you read this, whether we like it or not, and those who prepare for it will benefit tremendously. Those that don’t will fall behind, especially in the biotech/pharma industry. This is also very closely related to the Academic and Healthcare industries.
  • Resource #1: Harvard Business Review – “The Ai Resolution is Just Beginning"
  • Resource #2: “10 Actionable Steps for Using AI in Your Research Lab”
• Erwin Seinen, Founder of eLabNext
  • The development of new technologies is opening up new possibilities,
    demonstrated by this use-case of conservation efforts that include the
    potential to bring back extinct species.
  • The use of big data analytics and machine learning is playing an ever
    an increasingly important role in advancing scientific research.
• Zareh Zurabyan, Head of eLabNext, Americas
  • Key to a successful R&D Lab is having a connected digital ecosystem and being AI-ready. Utilizing digital tools that have the capability to integrate with one another and a system that organizes all of your data and allows you to classify them and develop deep neural networks to leverage in the future.
  • Digital Lab Strategy is critical to having a successful laboratory.
  • Learn how Colossal Biosciences uses AI to bring back extinct animals like the Wooly Mamoth and the Dodo.
• Mehdi Saghafi, Bayer’s Principal Data Engineer
  • Implementing Digital Solutions is very simple; you need to have a very strategic approach to it right from the beginning, i.e. having timelines, and very specific goals of digitizing sample data, reporting data, and equipment data, and tackling them one by one, with agile project management. Learn about “Adoption Barriers and How to Overcome Them”.
  • Having an open ecosystem is necessary for a comprehensive and holistic solution for a large company like Bayer. There are many scientists, many operations, and many digital tools that are used. Having a connection between them is vital in ensuring efficiency and limiting any chance of data loss. Find out more.

• Lucy Abgaryan, Founder of GrittGene and ProoneLabs
  • There is a shift from previous generations to new ones. It is essential to train your staff accordingly in the benefits of digitising your lab and being innovative and early adopters of new technologies, like AI. If you are a PI, a Research Tech, that is about to go on a digital journey, ensuring a proper training regimen and defining digital strategy right from the beginning is vital for success. Learn more about how Moderna does this.
• Taylor Chartier, Founder of Modicus Prime
  • During a global recession, you can't afford to not invest in cost-saving technologies that will accelerate your research.  Empower your scientists with AI tools that will automate their workflows to achieve repeatable results faster.
  • Quality control over your research processes is just as important as the quality of your research product.  AI softwares make routine lab processes less burdensome and error-prone, giving scientists both structure and peace of mind as they conduct experiments that save time and resources formerly wasted on poor-quality studies.

LinkedIn Profiles

• Zareh Zurabyan, Head of eLabNext, and AI/Biotech Consultant
• Erwin Seinen, Founder of eLabNext, and Ai/Biotech Consultant
• Lucy Abgaryan, Founder of GrittGene and Proone Labs
• Mehdi Saghafi, Principal Data Engineer of Bayer Pharma
• Taylor Chartier, Modicus Prime, CEO and Founder of Modicus Prime
• Ashot Arzumanyan, Founder of SmartGateVC, Investor

Featured Booths and Contact Information

CompanyContact InformationNikon Instrument, Inc.Junya Yoshika, Senior Scientist, junya.yoshika@nikon.com
Fumiki Yanagawa, General Manager, fumiki.yanagawa@nikon.com
Henning Mann, Business Development and Partnerships, henning.mann@nikon.comEppendorfLoreline Lee, Sales Director, lee.l@eppendorf.comImplen Inc.Austin Brazzle, Product Specialist, abrazzle@implen.comOhan Cardiovascular InnovationsVahagn Ohanyan, President, vohanyan@ohcvi.comBrinter Inc.Tom Alapaattikoski, CEO, tom.a@brinter.comMicroscapeJohn Francis, CTO and Co-founder, john@microscape.xyzPurpose BioLital Gilad-Shaoulian, CEO and Founder, lital@purposebio.comModicus PrimeTaylor Chartier, Founder and CEO, taylor@modicusprime.comAmaros AIBen Toker, Co-Founder/CTO, ben@amaros.aiOkomeraSidarth Radjou, CEO, sidarth.radjou@okomera.comMetaba A.EyePhilip Sell, CEO, events@metaba.us

From biobanks with millions of biospecimens to your academic molecular biology laboratory, sample tracking and cold storage are essential for efficient and streamlined laboratory operations. And the ultra-low temperature (ULT) freezer is the foundational workhorse supporting this critical process.

Keeping biospecimens or biomolecules at stable temperatures ranging from -70℃ to -196℃, ULT freezers preserve sample integrity and quality by limiting degradation and biological activity. And doing so requires some impressive engineering that relies on high-quality insulation, powerful compressors, advanced temperature control systems, and backup systems to ensure protection against power outages or temperature fluctuations. 

This job comes at a cost: It requires significant energy. It’s estimated that a single ULT freezer uses about 20 kWh/day, approximately the same amount as a single-family home in the US. With such energy use, ULT freezers have become a central element in the growing conversation about reducing the environmental impact of life science laboratories and moving the industry in a more sustainable direction. 

ULT freezers have evolved considerably from their initial “cold rectangle” format to more refined, sleek, and energy-efficient designs. But they are only a piece of the sustainability puzzle. In the following blog, we view sustainability through a holistic lens, looking at various barriers to a more environmentally-friendly cold storage and lab sample management solution and how we envision the future of sustainability in the life sciences beyond the ULT freezer.

Improving Sample Management in Green Labs: The ULT Freezer Energy Problem

To understand the full scope of energy ULT freezers use, we need a better understanding of your typical lab's current problems and the barriers to more efficient cold storage sample management. Over the decades I’ve spent in the life sciences, I’ve seen several common problems plague those using ULT freezers.

Samples Unknown

At Eppendorf, we’ve estimated and seen firsthand that about 25% of freezers hold samples of no value to anybody. They may be missing information, totally forgotten, or last used by personnel that have left the lab for other roles. As a result, no one in the lab has even touched them in years.

So why do they remain? Many labs accrue these unknown or forgotten samples because eliminating them takes time and energy. There’s also a fear of destroying samples that are – unbeknownst to current personnel – precious and irreplaceable. 

Real Estate Problems

The accumulation of old and unknown samples makes freezer spaces disorganized and confusing for current and future personnel. In addition, these samples take up precious freezer real estate, forcing lab managers to purchase new freezers to accommodate new samples. 

Think about adding 2 to 3 new freezers a year to your lab to store new samples when there is perfectly good space taken up by useless samples. 

That’s an extra 40 to 60 kWh/day in energy used and an extra $20,000 to $40,000 a year that your lab needs to account for in its budget.

Reduced Freezer Lifetime and Sample Integrity 

How long does it take you to locate and remove your samples every time you open your ULT freezer? 

15 seconds? A minute? 

When your freezer is littered with disorganized or unknown samples, the time is bound to be longer. Here’s a snapshot of what can happen every time you open your freezer:

• Temperature Rise: When you open a freezer door, warm air enters. The warm air will cause the temperature inside the freezer to rise. The rate of temperature rise will depend on the amount of warm air that enters, which is proportional to the amount of time your freezer is open. As temperature rises, the integrity of samples can be threatened.
• Condensation: Warm, moist air that enters your freezer can condense on the cold surfaces inside the freezer, including shelves, walls, and samples.
• Frost Buildup: The warm air that enters the freezer can cause frost buildup on the evaporator coils, which can reduce the cooling efficiency of the freezer and cause further temperature fluctuations. Frost can also condense on the freezer door and, in extreme situations, prevent door closure, requiring extreme torque to close the mechanical handle for the freezer door.
• Compressor Overload: When warm air enters the freezer, the compressor must work harder to maintain the set temperature. The longer the door is open, the harder the compressor has to work. This can cause the compressor to overload, potentially leading to ULT freezer damage or failure.

The issues above only increase the longer your freezer is open. This ultimately reduces the lifetime of your freezer and the samples within. 

Enhancing Sustainability: ULT Freezer Sample Management Solution

The problems above are rooted in inefficient sample tracking and management practices. Ultimately, they lead to decreased productivity, increased operational costs, and escalating energy usage. While there’s no retrospective way to figure out what the old samples clogging up your freezers are, we can help ensure that all new samples are appropriately catalogued, tracked, and stored to avoid the perpetuation of energy-wasting lab practices.

At Eppendorf and eLabNext, we’ve developed an end-to-end cold storage solution, Sample360, that empowers sample protection, storage, tracking, and monitoring using an easy-to-use digital lab platform. Along with our barcoding system, RackScan, and GLP-compliant sample management software, eLabInventory, we are helping keep their ULT freezers organized and, therefore, more sustainable.

To see Sample360 in action, schedule a personal demo today!

Too often, folks speak about lab digitalisation as a one-time task.

You do it. It’s done. And it’s off your plate. 

On to the next task, right?

The reality is that digitalisation is more than that: It’s a process, a journey of many steps, big and small. The goal is not to reach a final destination that reads, “Your Lab is Digitalised.”

The goal is to take the path of continuous improvement over time, where you're looking for opportunities to streamline your lab’s operations further.

Making Digital Habitual

How many of you started the year with a New Year’s Resolution to exercise more? And how long did it take for you to abandon it? A month? A week? A day?

Getting up and going for a jog one morning might technically make you a runner, but that’s not really the goal of your resolution. Even completing your first 5k isn’t really the goal. On your way to completing your first 5k, you may be seeing the benefits and feeling more motivated to exercise. That’s the goal, isn’t it? To feel better about yourself? To be habitually healthy? To be active? 

To improve yourself!

Sure, you can hang the success or failure of a goal on a discrete endpoint, but don’t let it cloud the significance of the journey you took to get there or stand in the way of long-term fitness.

But this isn’t a blog post about running, so let’s get back on track and away from analogies (for now…).

Digitalising your lab is just like your intent to exercise: It only happens when you accept the process and make it habitual. It’s a habit you form and maintain through incremental improvement over time.

If sample tracking is your primary area of focused improvement and you’re still keeping track using paper records, then try transitioning to a digital system, like an Excel file or Google Sheet, as a first step. 

Once you’ve done that, don’t stop! A digital spreadsheet is better than paper but still has significant drawbacks. Find a GxP-compliant online sample management platform that offers barcode integration and a collaborative interface. 

Boom! You just ran a 10k.

What if your lab notebooks are the current source of your stress? Switch from paper notebooks to digital documentation like OneNote or Google Docs. 

Just like in our first examples, that’s better but still has a few drawbacks. Once you’re comfortable with this digital step forward, keep improving. Next, find a cloud-based electronic lab notebook (ELN) that offers encryption, backups, and 21 CFR part 11 compliance.

Next-Level Digitalization: Data Integration

“But Jim,” you say, “isn’t this blog supposed to be about data integration?” 

Yes! 

And anyone who’s stuck with our New Year’s resolution analogy might grasp this next step: Once you’ve done a 5k, you may find yourself taking the next step in living an active lifestyle.

Maybe you head to the pool to swim laps, pick up a road bike at a yard sale, or start working with a personal trainer. What was, until this point, just running is now an integrated habit of fitness. You are pulling multiple pieces of the exercise puzzle together for the larger goal of whole-body fitness.

Scientists should take the same outlook with lab digitalisation. Pull all of your digital solutions together so that all of your data and information is integrated. Together, this will help you work towards your goal of whole-lab digital fitness.

Make sure the pieces all work together. Running, swimming, and biking are great on their own. But when you put them together, you can compete in an Iron Man. This is your goal with an integrated lab digitalisation process. Have all the pieces in place, but also ensure they all work together in a complementary way.

Be an Iron Man of your lab’s digital journey. 

Digitalisation, Integration and More, All in One Platform

A platform such as eLabJournal gives you that integration. All the digital pieces of your lab work together in concert to accelerate your efficiency gains.

So what comes after that? How are you going to continue to push the boundaries of fitness and lab digitalisation tomorrow?

If we’re talking about the digital lab, it’s artificial intelligence or “lab of things” (LoT) instrument integration. The specifics don’t matter. If you have built a solid and integrated foundation, you’re ready for new challenges. You don’t start back on the couch when trying a new sport. You integrate that activity into your fitness routine faster and at a higher level of performance. 

eLabJournal is an excellent example of this in the digital lab space. The open development tools (API & SDK) and Marketplace allow the platform to grow with you and meet every future need. You don’t buy new, separate software (start back on the couch). Your digital platform grows and expands to integrate new technology with ease. 

Get a personal demo today and see how eLabNext and our lab digitalisation experts can help you navigate the journey to full lab digitalisation, data integration, and more.

Bringing back lost species will take a pioneering scientific effort — and the tools to leverage vast swathes of genomic data.

Wildlife conservation has traditionally focused on protecting species before they disappear, but advances in genome editing technology are prompting previously unimaginable questions. Foremost among these: is there a way back from extinction? And if so, could struggling ecosystems be ‘rewilded’ with long-extinct animals?

In 2021, entrepreneur Ben Lamm and world-renowned Harvard geneticist George Church founded Colossal Biosciences with the audacious plan of creating animals very similar to woolly mammoths using Church’s groundbreaking genetic engineering techniques. By January 2023, Colossal had attracted $225 million in venture capital and expanded its mission to include bringing back the thylacine — commonly known as the Tasmanian tiger — and the dodo.

“Our goal is to build an end-to-end scientific pipeline for de-extinction,” says Eriona Hysolli, who heads Colossal’s biology division and leads its woolly mammoth project. “People are beginning to see how valuable genetic technologies can be for the conservation toolkit.”

Mammoth undertakings

The concept behind Colossal, first outlined publicly by Church in a 2013 TEDx talk, revolves around rewriting the genes of the mammoth’s closest genetic relative, the Asian elephant, to incorporate critical elements gleaned from analysis of ancient mammoth DNA — fat deposits, shaggy hair, small ears, circadian biology and other features related to cold-weather hardiness, for instance. The new hybrid species could be reintroduced to tundra ecosystems, where researchers believe their heavy footprints would improve cold penetration into permafrost to prevent it from melting, as well as supporting the change from a slow-cycling tundra to a fast-cycling grassland ecosystem.

Initially, funding agencies showed little enthusiasm for the de-extinction research taking place in Church’s lab. One person who did take an interest was Hysolli, a stem cell expert who joined the lab in 2015 as a post-doc.

“At the time I was reading Neanderthal Man by Svante Pääbo and was fascinated by the journey it took to sequence ancient DNA,” she recalls. “George is mentioned in that book because he was thinking beyond just sequencing a species, but also how its return can restore a whole ecosystem.”

After successes including improved multiplex base editing of mammalian cells — a technique that uses engineered enzymes, such as CRISPR-Cas systems, to recode multiple parts of a genome simultaneously — Hysolli leapt at the opportunity to join Colossal as its first biologist.

“We do such groundbreaking research and our workflows are very unique, so it still feels like a lab,” she says. “De-extinction encompasses many areas where you have to develop expertise and new technologies, so there’s still that basic research feel at Colossal.”

Move fast and (don’t) break things

Immediately after joining the start-up, Hysolli was faced with the challenges of assembling a team and developing protocols for the woolly mammoth project. While she was accustomed to traditional pen-and-paper methods of record-keeping in the Church lab, this new venture required a digital approach.

“If you want to build a team fast, you have to be able to share experimental data immediately,” says Hysolli. “One of the first things we did was to partner with an electronic lab notebook provider. It enables knowledge flow, not just within my team but across teams. It's easy to look at the experiment and download the result.”

With multiple near-complete genomes of the woolly mammoth sequenced in 2015 and 2021, Hysolli and her colleagues have turned much of their attention toward big-data analytics of the Asian elephant. In July 2022, Colossal and the Vertebrate Genomes Project announced they had successfully sequenced and assembled the Asian elephant genome at reference-genome level, the first of its kind for elephants.

“Labs are creating data lakes,” says Zareh Zurabyan, lab digital strategy specialist and head of eLabNext America, whose technology manages Colossal’s data and workflows. “There are countless sets of data from multiple instruments, experiments, many forms of file attachments and samples with thousands of meta-data fields. This is the perfect ecosystem for using machine and deep learning and AI, not only for deep data analysis, but to define the research and business strategy of the company, allowing you to refocus work in real time.”

eLabNext co-founder Erwin Seinen sees a trend toward multi-disciplinary companies that seamlessly blend cutting-edge AI/ML techniques with traditional wet-lab work. “This approach is becoming the norm for biotech startups and established companies alike,” he says. “Colossal exemplifies the synergy between these two areas. The result will be a new era of scientific discovery, where the power of machine learning and data analytics is harnessed to drive innovation in the life sciences.”

Form follows function

Hysolli notes that the value proposition for Colossal investors lies not just in de-extinction, but in the broader development of new tools for biologists, from cellular engineering and reprogramming to gestational technology. “We're really pushing the limits for mammalian, marsupial and avian biology, and these technologies extend beyond de-extinction,” she says.

In September 2022, Colossal announced its first spin-off, a computational biology platform called Form Bio, which the firm developed to manage its de-extinction pipelines. With $30 million in venture funding, the newly independent software company aims to replace cumbersome, code-heavy processes with an accessible interface that enables scientists to easily perform bioinformatics.

“Form Bio does custom genomics analysis for us, especially as it pertains to DNA and trait relationships,” explains Hysolli. “It serves as our ancient DNA database. We also use it for computing power and storage, and if we want to run our own analysis, many workflows have built-in AI capabilities.

“With our data results centralized through eLabNext’s platform, they’re easily accessible by the AI and machine learning teams. We generate so much data, and it’s all untapped potential.”

Protect and preserve

Hysolli highlights Colossal’s continuing work to advance elephant conservation efforts, including development of novel treatments and a vaccine to prevent elephant endotheliotropic herpes virus. It also plans to build reference genomes of the African savanna elephant and forest elephant.

“What if these elephants disappeared in a few years, but you hadn’t started building the embryology and assisted reproductive technologies to bring them back — the same tools needed for our de-extinction work?” asks Hysolli. “We have the tools to create biodiversity in a dish, but with even more samples sequenced and preserved you can restore entire populations rather than individuals.”

Remaining open with the public about the goals — and data — of de-extinction is critical to Colossal’s outlook, emphasizes Hysolli.

“We’re trying to scale our workflows to easily enable species preservation,” she says. “We’re committed to restoring our natural heritage and engaging with stakeholders because when you’re building models for rewilding ecosystems, it has to be done transparently and ethically.”

Read on Nature

When you ask biotech professionals where the top biotech hubs in the U.S. are, Boston is at the top of most lists. But the Massachusetts biotech scene is much more than just Kendall Square and the Greater Boston metropolitan area. 

Far from the long shadow cast by Boston, Central Massachusetts, particularly the city of Worcester, has grown into a robust and vibrant biotech hub of its own.

“If Worcester were in any other state, it would be the powerhouse cluster of biotech companies, workforce, and lab space,” exclaims Melina Reid, Operations Associate at Massachusetts Biomedical Initiatives (MBI), whose goal is to build up Worcester and the Central Massachusetts region into an energetic and unique centre for biotech startups. “Because we’re so close to Boston,” she continues, “We are sometimes dwarfed by its reputation and size.”

In innovative fields like biotech and biopharma, bigger isn’t always better. Over the past few decades, Boston has become a hotbed of competition for lab space, skilled personnel, and attention where only later-stage companies and global corporations can engage. For these larger companies, being in Boston is essential. As a result, early-stage startups with tighter budgets and “outside-the-box” ideas start at a significant disadvantage, overshadowed by established behemoths with heaps of money and resources to maintain and expand their footprint.

Establishing Infrastructure and a Thriving Ecosystem

The MBI is focused on making Central Massachusetts a welcoming home for creative startups with solid ideas. To help them get their footing in the industry, the MBI provides cost-effective, high-quality laboratory space and support services. Assistance goes beyond the “seed stage,” as MBI doesn’t limit how long a company can spend in its incubator space. Furthermore, they offer a graduation space to support startup growth further as they advance toward commercialisation.

“Our approach has been successful,” Melina observes. “As the Commonwealth’s longest-running non-profit startup incubator, MBI has supported over 175 companies through graduation from their space, with more than 14 companies going on to IPO or getting acquired by companies such as Pfizer, Perkin Elmer, Vertex Pharmaceuticals, and Charles River.” 

Over the past few years, the MBI has expanded its capabilities and initiatives to fill the many needs of biotech startups. They were pivotal in bringing the Reactory – a high-quality, cost-effective, custom biomanufacturing facility – to the Worcester biotech community. They are currently building a pilot Biomanufacturing Center that will provide lab space for companies to go from “concept to clinical trials.”

The MBI has also launched initiatives to establish a skilled and excited workforce, with partners like AbbVie, to support Central Massachusets’s growing life science community. “We’re heavily involved in increasing diversity in STEM through partnerships with local middle and high schools and community and state colleges,” explains Melina. “For example, we’ve helped Quinsigamond Community College establish their Biomanufacturing Technician program for adults looking to break into the biotech field. By encouraging the next generation of young minds to pursue science careers, we are doing our part to create a solid workforce for the continued growth of Central Massachusetts biotech.” 

Accordingly, Worcester was chosen as #15 on the Top 25 Life Sciences Research Talent Clusters list, just below mega-metropolitan areas such as Houston (#13) and Atlanta (#14).

Fostering More Efficient R&D for MBI’s Startup Community

While the MBI is constructing a framework in Worcester and Central Massachusetts to support community growth, the infrastructure inside the lab needs to be solid to enable efficient and effective management of a startup’s most important asset: its data. 

To this end, the MBI has partnered with eLabNext – which provides digital data management platforms to laboratories – so that startups and later-stage companies can fully digitise their operations. 

“We’re excited to be a preferred vendor for MBI,” says the Head of eLabNext in the Americas, Zareh Zurabyan. “Our Digital Lab Platform (DLP) helps labs of all sizes improve the efficiency of their workflows, quality of their data, and security utilising LIMS/ELN features and even AI/ML tools for data science in the day-to-day. Ultimately, we see that defining the lab’s digital strategy right from the beginning, through lab digitalisation, accelerates timelines and drives progress for the many startups making Central Massachusetts their biotech home.”

The eLabNext platform serves various life science and chemistry laboratories in government, academia, and industry, making it a perfect fit for MBI’s startup environment, which includes companies in cell and gene therapy, chemistry, and other scientific specialities. 

Through this partnership and the ongoing efforts of the MBI, Central Massachusetts is positioned to continue its expansion as a vibrant ecosystem for biotech startups. 

To learn more about the unique environment that the MBI has built and the biotech community in Central Massachusetts, please visit massbiomed.org. 

Sometimes buzzwords like "artificial intelligence" or "neural network" can take on their own life. Just look at the explosion and success of ChatGPT, which we've used to generate inspiration for our blog "10 Reasons You Should Digitise Your Lab Operations." The blog below outlines the actionable steps to wielding the power of big data, machine learning, and more in the life sciences. 

Moving Beyond Buzzwords: A Few Definitions

But before we dive in, let's get some clear definitions down:

• Artificial Intelligence (AI): Refers to the simulation of human intelligence in machines to think like humans and mimic their actions. The goals of AI include learning, reasoning, and perception without human input or intervention.
• Machine Learning (ML): A subfield of AI focusing on supervised, unsupervised, or reinforced learning that enables computers to perform pattern recognition, predictions, data classification, and more without explicit programming
• Deep Learning: A subfield of ML that uses neural networks (see below for definition) to learn how to recognise images and speech or natural language processing from large amounts of data.
• Neural Network: A computational model (inspired by the architecture and function of the human brain) that consists of layers of interconnected nodes that process and transmit information. Through analysis of input data, these models can find complex relationships in data.
• Big Data: LARGE structured and unstructured data volumes that are difficult for scientists, teams, and organisations to manage or analyse using traditional techniques. 

AI in Life Science Research Lab

AI, its subfields, and big data have made inroads into many aspects of biological and biomedical science, including drug discovery and development, precision medicine, genomics, transcriptomics, and more. 

And the results are pretty impressive: Look at what AlphaFold has done for 3D protein structure prediction.

While powerful, it's still early days for AI's widespread and cavalier adoption across all areas of research and medicine. ML and DL algorithms can be subject to data bias based on the training dataset, difficulties interpreting predictions, and an overall lack of clear guidance or standardisation. 

Yes, AI's application in the life sciences feels like the "wild west," with researchers and the field needing actionable guidance.

Implementation of Artificial Intelligence in Labs: 10 Steps

As more and more labs and organisations dip their toes into AI algorithm implementation, ensuring clear documentation, reporting, and analysis is critical. Bioinformatics and data science teams need to be integrally involved as their experience with coding, IT, API, and SDK is invaluable for this task.

Another essential factor is using digital platforms for transparent and secure data management and easy integration with other computational tools, such as AI, ML, or DL programs.

At eLabNext, we live for the digitisation of all labs. And as the AI field has grown, we've seen what works and doesn't. 

Below we've synthesised ten steps to implement AI tools in your lab.

Step #1: Identify the problem or question

What are you trying to solve with AI or ML? With the problems these algorithms have been applied to, there are a growing number of off-the-shelf AI/ML solutions for data analysis and visualisation. 

For example, programs such as Modicus Prime or PipSqueak Pro can be used for image analysis; Biomage can be used for single-cell analysis; and Immunomind can be used for AI-driven multi-omics.

Step #2: Research available AI/ML software models or tools

We mentioned a few tools above, but consider accuracy, speed, and ease of use before choosing a solution. It's also essential to research the level of support, resources (such as tutorials and forums for troubleshooting), and proof-of-concept data available for the tool. 

And if there's no off-the-shelf solution, you may be forced to develop a custom model tailored to your problem.

Step #3: Evaluate your data and determine if it is suitable

Consider your data's quality, quantity, structure, and possible biases or limitations. You may need to collect additional data or clean and pre-process existing data to make it suitable for analysis. Standardisation is also crucial for this step, as it helps to ensure that the data is consistent and comparable across different sources and samples.

Step #4: Develop a testing plan to validate accuracy and reliability

Validation in the life sciences is vital for relying on a technique to generate accurate results. With AI/ML tools, you can divide your data into training and testing sets to evaluate performance. Other ways exist to test the AI/ML tool or model. Just be sure to have a plan for testing and ensure it includes testing data outliers to assess the vulnerabilities of the model or device you are implementing.

Step #5: Train your AI/ML model using the data you have prepared

If you've built an AI/ML model from the ground up, teaching it to recognise patterns or perform other tasks is the next step. The goal is to find the optimal parameters that best fit the data, minimise error, and perform well on test data.

Step #6: Test and validate your AI/ML model

Testing on a separate dataset from the one used for training is the next step in vetting an AI/ML model. This helps determine model accuracy, precision, and recall. The validation phase involves tuning the model's parameters and evaluating its performance to avoid overfitting, where the model performs well on the training data but poorly on test data.

Step #7: Integrate the AI/ML tool into your laboratory workflow

Consider how you will use the AI/ML analysis results in your pre-existing laboratory processes. The tool must be compatible with your existing infrastructure and software in the lab, particularly with any digital platforms used for information management. 

Step #8: Monitor and evaluate ongoing performance

While your AI/ML model may initially provide relevant and high-quality analysis, performance can drift, and lab priorities can change. Continuous monitoring and model updating is necessary to ensure performance metrics are met and the model is still relevant to the laboratory's evolving needs. 

Step #9: Update and fine-tune the AI/ML model

Improving performance is a crucial step in the lifecycle of an AI/ML tool or model. This can involve testing with new data, retraining with new data, and revalidating performance. You can also adjust the parameters or architectures of the models to fine-tune performance. 

Step #10: Ensure compliance

AI and ML are still new tools in the life sciences and other industries. To protect your data, adhere to regulations like GDPR and HIPAA. There are also ethical implications due to decision bias in unvalidated or inaccurate AI/ML models. To avoid these, implement a QC process involving regular performance reviews and key stakeholders.

Conclusion

AI, Ml, DL, and "big data" are here to stay in the life sciences. 

The steps above can help you and your team move toward AI implementation to answer your research questions. Off-the-shelf solutions for common research questions may exist. However, you may need to work with computational biologists and bioinformaticians to develop a new model. We recognise that training, validating, and testing a new model is no small feat: It requires focus, patience, and state-of-the-art infrastructure. For additional reading on the technical application AI/ML tools in your lab, read the comprehensive guidance from Lee et al.

At eLabNext, lab digitisation is the future and is dedicated to helping researchers, labs, and organisations implement AI solutions for deeper insights into their big data.

If you're interested in how your AI/ML models can interface with your other digital lab platforms, contact our experts at eLabNext. 

We live in a time when digital is taking over our lives and labs. Now more than ever, life scientists use digital tools to speed up timelines, work in a decentralised way, and keep data secure. The application of artificial intelligence (AI) and machine learning (ML) algorithms to diverse biological datasets is also increasing, generating deeper insights and answers to challenging biological questions.

Yet, there is still a large group of “digital holdouts” in the life sciences. Take paper notebooks, for example, which have been a traditional record-keeping format since the dawn of science. With the rise of “omics” and bioinformatics, many life scientists live a hybrid existence, keeping a paper notebook out of habit and tradition and using a digital platform for data generation, storage, sharing, and management. As a result, they find haphazard and ineffective ways to integrate the paper-digital worlds.

If you live in this world, then you know there are drawbacks to this approach. So, instead of laying out each one, we’ll use this time to sing the praises of an “all-digital” method provided by digital lab platforms, which offers numerous benefits to many lab tasks, such as protocol, inventory, and data management.

Here are ten of our favourite benefits.

#1: Increased Efficiency

Science has many inefficient tasks: Repetitive workflows, difficulty sharing large data files, errors in data transcription, and more. Digital lab operations can streamline processes and reduce the time and resources required to complete experiments. They provide a centralised repository for data and information, enabling personnel to share data and protocols in real-time, integrate with instruments, and automate traditionally manual tasks.

#2: Improved Accuracy

Data loss or inaccuracies during manual transfer are a common problem in science, particularly if data is stored in several locations or on several instruments. Digital lab platforms reduce the risks of these errors by integrating with various tools and devices and allowing the tracking and storage of laboratory information. These capabilities increase the security of data and the reproducibility of experimental results. 

#3: Enhanced Data Management

The centralised organisation of data in a digital platform is another significant benefit. Information is located in one place, accessible, and easily shared. Some platforms also allow integration with other data analysis and visualisation tools, effectively keeping raw, processed, and analysed data. This helps manage data across the entire organisation or research group from creation to destruction and every step in between. 

#4: Improved Collaboration

Collaboration is integral to science, leading to deeper understanding and insight into complex biological questions. Yet, operating in a world where paper lab notebooks must be photocopied or photographed to facilitate multi-institutional collaborations is inefficient and downright primitive. Digital lab platforms enable rapid and simple sharing of data, protocols, and samples and easy management of assigned permissions, regardless of location, just like a Google Doc. 

#5: Enhanced Security

Because permissions can be easily managed and authentication, encryption, and network security can all be implemented and monitored on digital platforms, all information within a digital system is more secure. This reduces the risk of data breaches and unauthorised access. 

#6: Increased Transparency

A downstream benefit of the easy sharing and management of scientific data and results is allowing for peer review and replication of experiments. In addition, every action in a digital lab notebook can be tracked, providing a fully auditable record of every change made to data, protocols, or samples. These capabilities reduce the likelihood of data manipulation and increase the possibility of identifying errors before they become a problem.

#7: Reduced Costs

Operating costs drop when personnel, processes, and workflows are faster and manual tasks get automated. Take sample storage, for example. Digital lab platforms can integrate with barcode generators and scanners to streamline this process, reducing the man and woman hours required to freeze many biospecimens.

#8: Improved Regulatory Compliance

Digital systems can help you meet regulatory requirements more efficiently by providing clear documentation and records. Audit trails and traceability are important features of regulatory compliance. Many digital lab platforms comply with essential regulations such as ISO 27001:2013, GDPR, HIPAA, and 21 CFR 11.

#9: Increased Mobility

Digital tools can allow you to access your data and systems from anywhere. For organisations adopting more flexible work models or bioinformatics researchers who need access to data and computational capabilities, digital lab platforms can facilitate remote working without sacrificing collaboration or security.

#10: Future-Proofing

As technology evolves, AI/ML models become more sophisticated, and digital platforms will become necessary for the scope of “big data” balloons. Adopting digital solutions can help your lab stay competitive and prepare for the future as technology evolves.

Integrating Computational Biology with Digital Platforms: The Future of Research

Advanced computational biology is increasingly used to answer fundamental biological questions and design, develop, and produce cell and gene therapies. ML, for instance, can make an even more significant impact than it already has, influencing how we analyse data and navigate the entire R&D process. 

Here are a few ways it might do so:

• Automation - ML algorithms can be trained to analyse large amounts of data quickly and accurately, freeing researchers to focus on other tasks.
• Improved accuracy - ML algorithms often outperform humans at tasks like image or data analysis, leading to more accurate results.
• New insights - Patterns in data that humans may not observe can be uncovered by ML algorithms, resulting in fresh perspectives and findings.
• Predictive modelling - ML algorithms enable the creation of models that can forecast results or offer suggestions based on prior data.

When you stack these capabilities with those described above with digital lab platforms, the pace of research and the applications to everyday problems in a broad range of industries has the potential to reach breakneck speed. 

Sign up today for a free demo of eLabNext’s platform, the most intuitive, customer-centric, reliable, and secure digital lab solution.

The biotech industry has undergone a significant transformation in recent years, with digitalisation emerging as a critical tool for streamlining the R&D process. Traditional manual methods of tracking, managing, and analysing data and information are becoming obsolete, and laboratories must adapt to compete in today's fast-paced and competitive environment. 

The blog below gives an overview of the full scope of digitalisation in the biotech industry, its advantages, and how life science hubs on the West Coast, particularly Southern California, are leading the charge.

The Current Era of Digitization in Biotech R&D

Digital technology has permeated all aspects of the biotech pipeline, from uncovering foundational insights during R&D to optimising manufacturing and logistical operations. With the current challenges facing companies in the space – a competitive marketplace, complex regulatory requirements, and high research costs – digitalisation is no longer a "nice have." It's necessary for survival and continued innovation.

So, what exactly does digitalisation look like for modern-day biotech companies? 

No matter your company's size, the chatter about artificial intelligence (AI) and machine learning (ML) has likely piqued your interest. News about the work of AlphaFold and Meta AI has erupted into the mainstream, as it provided a promising solution to protein folding, which opens up a multitude of R&D avenues for synthetic biology and biopharma companies. 

Other biotech digital solutions emerged from biology's ability to generate "big data" through next-generation sequencing (NGS) and other technologies. To manage, analyse, and visualise the sheer volume of NGS data, computational approaches for cleaning raw sequencing data, aligning reads to a reference genome, detecting and calling variants, and performing downstream analyses such as functional annotation, pathway analysis, and statistical testing became a necessity.

Furthermore, AI- and ML-driven solutions have recently been deployed to identify patterns and make predictions from the vast public data available. Until recently, these bioinformatics tools and pipelines were only accessible to those with computational skills. However, that tide has shifted recently, and these sophisticated analyses are being democratised with easy-to-use interfaces, a simple user experience, and no coding experience required. 

Initiatives like CELLxGENE, published as an open-source software tool so biologists can easily access and analyse their single-cell RNA sequencing data, are a perfect example. Companies, such as Form Bio, have taken this to the next level, launching a commercial platform that puts the power of bioinformatics workflows in the hands of wet lab biologists and cell and gene therapy developers. 

Benefits of Digital Transformation in Life Science and Biotech Research

The examples above have one clear, direct benefit of the utmost importance for biotech R&D: Unprecedented insight that's simply unavailable if digital tools were not in use. 

But these tools offer additional benefits too. Many computational platforms provide increased efficiency, improved collaboration and communication, data security, and intellectual property protection through their use in the digital space.

Electronic lab notebooks (ELNs) are another prototypical example. Their ability to track experiments, record results, and manage data in a centralised platform is a significant advantage for biotech R&D personnel: They increase data accuracy and integrity while saving time and reducing errors. Repetitive task automation also frees up valuable time for scientists to focus on higher-level tasks, such as strategic planning and business development.

Digitisation Doesn't Discriminate: How Digital is Changing the Biotech Startup Scene

The benefits of digitisation are not limited to global corporations. They can be enjoyed by those in startup mode too. And with optimised processes, enhanced collaboration, improved data integrity and security, and powerful AI-driven insights, there's an added benefit: Interest from potential investors. 

California has long had a thriving biotech ecosystem, attracting top talent, future-focused investors, and hungry startups. The San Francisco Bay Area, particularly South San Francisco, has emerged as a central biotech hub, with a high concentration of companies focused on genomics, personalised medicine, and drug discovery. San Diego is also a booming biotech nucleus, concentrating on biopharmaceuticals, medical devices, and diagnostics. The majority of the life science industry in California is focused on R&D, making the environment robust and nimble, and focused on the latest technology for driving innovation. 

Los Angeles has seen significant growth in the biotech industry, particularly in biotechnology, digital health, and medical technology. LA County has driven the sector's growth to over 195,000 jobs, nearly 3,000 life science businesses, and $44.2 billion in economic activity through heavy investment in workforce development, venture capital firms, and innovation hubs.

HeroHouse: Nurturing Startups at the Intersection of AI and Biotech

Hero House, founded by SmartGateVC, is one such hub focusing on investing at the intersection of AI, healthcare, and biotech. Located in Glendale, California, the space connects science, technology, entrepreneurship, and capital and provides various services that nurture startups using AI to solve complex biological problems.

The Hero House hubs' importance to biotech innovation is underscored by the recent addition of eLabNext, a digital lab platform for life science R&D laboratories, which opened a new office. As a division of Eppendorf, eLabNext will be able to leverage the city's intense biotech scene and emerging technologies to propel further digitalisation of LA's up-and-coming industry mavericks. 

Conclusion

The digital transformation of the biotech industry has revolutionised the industry, with digitalisation being a critical tool for streamlining R&D. The many benefits of "going digital" have helped further the growth of life science hubs on the West Coast, particularly in emerging areas like Los Angeles. As we move forward, it's clear that digitisation will continue to be a driving force in biotech research.

To learn more about Hero House, eLabNext, and the growth of biotech digital solutions, contact us here.

Digitizing your lab’s information can be transformative, improving your efficiency, data quality, and security. We’ve written extensively about how to make this transition, the benefits, and the key considerations for choosing one of the many open-source or commercially available software tools. In the life sciences, these tools go by different names: There are laboratory information management systems (LIMS), electronic laboratory notebooks (ELNs), environmental monitoring systems (for freezers and incubators), documentation management systems (DMS), electronic data capture (EDC) systems, equipment management software, sample labelling systems, electronic document management system (EDMS), electronic records management system, electronic lab management system (ELMS), laboratory record book (LRB), scientific data management system (SDMS), Molecular Biology Suites, chemical drawing software, and many others. These platforms each make distinct yet interrelated laboratory tasks easier. Depending on what you want your lab to do better, faster, or more cost-effective, you may use one or several of them to enable your transformative digital journey.

It’s Time to Evolve Beyond the “One-Trick Pony” Platform

But what if there was a flexible, multi-dimensional solution that could navigate every step of the journey with you? Grow with you as your operations grow and need change.At eLabNext, we’ve created a system that does just that, integrating all of the “one-trick pony” software tools mentioned above into a single, easy-to-use platform that can expand its functionality to fit your needs.We call it the Digital Lab Platform.

What is a Digital Lab Platform?

At the core of eLabNext’s Digital Lab Platform is an ELN, inventory and sample tracking system, and protocol manager, integrated for more efficient information storage and management. But what’s truly unique about eLabNext’s platform is our eLabMarketplace, where you can incorporate proprietary, modular Add-Ons. Think of it as the App Store on your iPhone, a central hub where you can customise how you perform laboratory tasks and manage information.The eLabMarketplace contains a library of available Add-Ons. We are constantly improving our collection, updating it with the latest and greatest life science applications for biobanking, productivity, reporting, and more. You also have the option to integrate any 3rd party software or build your own Add-On, giving your Digital Lab Platform nearly limitless ways to personalise your performance.

Benefit #1: Future-Proofed Operations

With the ability to expand functionality at will, your Digital Lab Platform is future-proof, poised for innovation and growth.This feature turns the disjointed digital tools we use daily into a cohesive unit: One Digital Lab Platform to govern all others. It also enables you to harness the powerful and ever-evolving world of computational life sciences. For example, we have recently introduced several AI-powered Add-Ons for image, single-cell, and multi-omics analysis to the eLabMarkeplace platform. Scientists no longer need to utilise multiple programs to grapple with challenging datasets but instead use one cohesive workflow within a single Digital Lab Platform.

Benefit #2: Comprehensive Data Security

Being the premier digital lab platform comes with great responsibility.That’s why we have an 8-fold replication of your data across multiple geographically dispersed data centres. Our fully redundant set of servers is fault-tolerant, so even in the rare event that a complete data centre blackout occurs, it won’t stop you from accessing our digital lab platform and your valuable data.

Benefit #3: API and SDK Technical Support

Even though our API and SDK are easy to use, our team of experienced developers is ready to help you get started. Our life science and IT experts are also more than happy to assist with brainstorming about your great product or services as part of the eLabNext Digital Lab Platform.

Experience a Better Path to Lab Digitization

Digital Lab Platforms can be the foundation of your company’s humble beginnings or a pillar of your global operations. We invite everyone from well-established companies to newly minted startups to implement eLabNext’s Digital Lab Platform and define a better digital strategy. Interested in seeing if eLabNext can serve your lab? Book a personal demo or start a 30-day trial today!

Los Angeles, California – The digital transformation of the life sciences industry continues apace, with Lab Digitalization at the top of the priority list. With the opening of eLabNext's new office in Glendale, CA, the area is ideally placed to emerge as a hub for innovation and entrepreneurship.

Eppendorf's eLabNext division was founded in 2010 with the goal of streamlining life science R&D by digitizing laboratory processes. The company offers a full-service experience with a team of experts who help clients along their digitization journey. eLabNext solutions have been used in a variety of research areas, including cancer research, sustainable food production, and the development of the COVID-19 vaccine.

"We are thrilled to open our new office in Glendale and join the vibrant community at Hero House," said Alisha Simmons, Key Account Manager at eLabNext, Americas, division of Eppendorf. "This move represents a major step forward in our mission to streamline life science R&D through digitization and make a positive impact in the industry."

A thriving community of startups and innovation leaders surrounds the new office at Hero House. SmartGateVC, a Los Angeles-based pre-seed and seed venture capital firm investing at the intersection of AI, Healthcare, and Biotech, founded Hero House as a startup and innovation hub.

"As we continue to expand globally, we are excited to open our new office in Glendale and become part of Los Angeles' thriving life sciences community," said Erwin Seinen, Founder and Managing Director at eLabNext.

Hero House provides the infrastructure and resources needed to power the growth of new ventures through its programs, global mentor network, angel investor group, and technology transfer support. 

"At Hero House, we are committed to cultivating a vibrant community of innovation and entrepreneurship in the life sciences industry. The arrival of eLabNext to our tech entrepreneurship hub opens up a wealth of opportunities for SoCal startups and labs and strengthens our ecosystem. Their commitment to digitizing laboratory processes aligns with our mission, and we look forward to assisting eLabNext and their clients as they continue to drive progress in this exciting field." Ashot Arzumanyan, Partner, SmartGateVC

The benefits of digitization are becoming more apparent as life science labs continue to adopt new technologies. Modern labs are streamlining their operations and allowing scientists to focus on their research by automating manual processes, minimizing data errors and improving data storage, AI-optimized processes, and more. The life sciences industry's future appears bright, with many promising players emerging in SoCal.

The opening of eLabNext's new office at Hero House demonstrates the growing importance of digitization in the life sciences and the promising future of Los Angeles' biotech scene. The area is poised to become a hub for life science R&D and biotechnology, with a thriving community of startups, innovation leaders, and an increasing number of key players entering the market.

eLabNext contact

Alisha Simmons, Key Account Manager at eLabNext, division of Eppendorf, 508-851-7747, a.simmons@elabnext

About SmartGateVC and Hero House

​SmartGateVC is a SoCal--and Armenia--based pre-seed and seed venture capital firm investing at the intersection of AI with healthcare, biotech, security and IoT across Southern California, the wider U.S., and Armenia. SmartGateVC provides startups with the resources and support they need to succeed, thanks to a team of experienced investment professionals and a global mentor network.

​​Hero House by SmartGateVC is a startup and innovation hub in Glendale, CA, where SmartGateVC works with scientists, founders, executives, and co-investors to turn research and technology into various disciplines into industry-defining companies. It connects science, technology, entrepreneurship, and capital, fostering the creation and advancement of new ventures.

smartgate.vc and herohouse.io   

Liana Karapetyan, Associate at SmartGateVC, Director of Hero House Angels, liana@smartgate.vc 

The healthcare industry has recently seen a significant shift toward electronic patient records. One key protocol to facilitate this shift is HL7, which stands for Health Level 7, created by a non-profit organization called Health Level Seven International. 

The set of standards simplifies how electronic data is shared between different digital healthcare platforms and makes it easier and more efficient for healthcare providers to share patient data.

What is HL7?

HL7 sets international standards for exchanging, integrating, sharing, and retrieving electronic health information. 

Data formatted using the HL7 standard capture patient data in a unified and easily transmissible format. This digital message can be quickly sent between different software programs, enabling communication between platforms in a friendly, error-free, autonomous fashion. HL7 encompasses several essential standards, including:

• HL7 v2: This widely used standard defines the structure and content of messages exchanged between healthcare systems, facilitating interoperability and data exchange.
• HL7 v3: Designed for more complex healthcare scenarios, HL7 v3 provides a framework for creating detailed clinical and administrative models to improve interoperability across different systems.
• HL7 FHIR (Fast Healthcare Interoperability Resources): It is a modern and rapidly evolving standard that focuses on simplicity and web-based integration, enabling seamless exchange of healthcare data across diverse systems and platforms.
• HL7 CDA (Clinical Document Architecture): It specifies the structure and semantics of clinical documents, allowing healthcare information to be exchanged in a standardized format that supports interoperability and meaningful use.
• HL7 CCD (Continuity of Care Document): It is an HL7-compliant standard that provides a snapshot of a patient's health information, facilitating the exchange of relevant data for continuing care and transitioning between healthcare settings.

These HL7 standards play a crucial role in achieving seamless interoperability and efficient exchange of health information in the digital healthcare ecosystem.

What Types of Labs Use HL7 Messages?

The use of HL7 in healthcare is widespread, and any lab that exchanges patient information will need to send and receive HL7 messages using digital platforms.

Here are several types of labs that use HL7 messages:

• Clinical testing labs: Clinical labs test biospecimens collected from patients to diagnose or monitor medical conditions or the effectiveness of treatments. In this context, HL7 communicates test results and patient information between a clinical lab and other healthcare systems.
• Pathology labs: Similar to clinical testing labs, pathology labs perform tests on tissues or other biospecimens to diagnose disease. HL7 helps exchange test results with other healthcare systems.
• Blood banks: Information about blood donors, blood collection, and blood testing is exchanged using HL7 to communicate the results of blood tests or other patient information to ordering systems.

HL7 may also be used to exchange data with research (and many other types of) labs performing studies on patients.  

How is HL7 Used in the Healthcare Industry?

HL7 provides a standardized and interoperable way for labs to exchange information with other healthcare systems, improving the accuracy, efficiency, and quality of patient care.

Here are some ways HL7 is used in the healthcare industry:

• Interoperability: HL7 enables interoperability by providing a common language and framework for different healthcare systems to communicate with each other. It ensures that data can be exchanged accurately and consistently across diverse systems, including electronic health record (EHR) systems, laboratory information systems, radiology systems, pharmacy systems, and more.
• Patient Data Exchange: HL7 allows for the exchange of patient data between healthcare providers, hospitals, clinics, and other entities involved in patient care. This includes essential information such as patient demographics (name, age, gender, address), medical history, allergies, medications, and clinical observations.
• Clinical Messaging: HL7 defines a messaging standard that enables the transmission of clinical information, such as laboratory test results, radiology reports, and other diagnostic findings. This helps healthcare providers to access and review patient information efficiently, supporting timely decision-making and providing better quality care.
• Integration with Electronic Health Records (EHRs): HL7 plays a vital role in integrating various healthcare applications with EHR systems. It enables the seamless flow of data between different systems, ensuring that information from laboratory tests, procedures, and other sources is accurately captured and stored in the patient's electronic health record.

How the eLabNext Platform Receives HL7 Messages

eLabNext, a digital lab platform used by a wide array of laboratories that allows tracking of sample information and test results, can receive HL7 data messages within a user’s digital lab space and translate this into a sample record for processing.

This capability allows your lab to seamlessly receive physician testing orders, complete with a unique barcode identifier. The automated process reduces data loss and errors as the lab processes samples.

Any laboratory personnel can use eLabNext to track sample processing and continuously update it with testing results. Using this intuitive digital lab platform, you can easily associate your results with specific patients. The lab can send this back to the ordering system as another HL7 message when the results are complete. Full traceability enables a comprehensive audit trail.

We have established this automated loop with Point & Click Solutions and Enterprise Health’s electronic health record (EHR) systems to track and manage patient COVID-19 testing. This integration tracked a high volume of daily patient samples while managing test results and routing them back to these EHR systems. 

eLabNext also used similar capabilities to support Boston University’s in-house COVID-19 testing workflow, processing up to 9,000 samples daily.

The Details for IT Folks…

We use a REST API POST message to enable connections between platforms. The message header contains the mapping instructions for translating the HL7 fields into a sample. This allows for a very nuanced setup precisely tailored to each lab.  

Here’s what an example header looks like:

{

"sampleTypeID": 12485,

"storageLayerID": 0, /* Optional */

"position": 0, /* Optional */

"name": {

"segment": "MSH",

"field": 10

},

"description": { /* Optional */

"segment": "MSH",

"field": 9,

"component": 3

},

"altBarcode": { /* Optional: Alternative barcode information. */

"segment": "OBR",

"field": 31

},

"sampleTypeMetaIDMapping": [ /* Optional: Array of mappings for the sampleTypeMetaID to the respective segment in the HL7 message */

{

"sampleTypeMetaID": 85318,

"segment": "OBX",

"field": 5

},

{

"sampleTypeMetaID": 85317,

"segment": "ORC",

"field": 2

}

]

}

And if You’re Not Technically Inclined, No Worries

The above is JavaScript code that represents a configuration for a sample type in HL7 messaging. But if you’re not an IT professional, all you need to know is:

• HL7 simplifies the sharing of patient data between different digital platforms, making it more efficient and error-free for the life science industry. 
• HL7 is widely used in diagnostic testing labs and donor banks to exchange patient information and/or test results. 
• The eLabNext platform receives HL7 messages, allowing laboratories to process samples automatically with unique barcode identifiers. 

Overall, HL7 is crucial for digital laboratory environments.

If you’re interested in learning more about eLabNext’s platform and HL7 messaging, schedule a personal demo to see how it works.

The NanoPhotometer family are microvolume spectrophotometers designed to measure single or multiple liquid samples of small volumes with high accuracy and precision. With the ability to measure as little as 0.3-2 µl of samples, researchers can save time and precious samples while ensuring accurate results.

Seamless data flow 

Mistakes can easily happen when manually copying and pasting data, especially when dealing with large amounts of information. Automating this process can help eliminate the risk of human error and ensure data is accurately transferred to your Digital Lab Platform (DLP). The Implen NanoPhotometer add-on allows users to automatically store all measurement data from their connected NanoPhotometer(s) directly in eLabJournal. This add-on reduces procedural errors and increases consistency and traceability across multiple users and samples.  

The Implen NanoPhotometer add-on streamlines user workflows, making it easier to manage and analyse data. By having measurement data automatically transferred to eLabJournal, users can easily track and organise data over time. This can be especially beneficial for researchers and laboratory technicians who need to manage large amounts of data and track changes.  

By saving time, reducing the risk of errors, and providing a streamlined workflow, this add-on can help users efficiently manage and analyse data, ultimately leading to more accurate and reliable research results. 

What makes Implen NanoPhotometers unique 

The unique family of instruments offer a wide range of pre-programmed apps for scientists in research, education, development and quality control applications within universities, research institutions, biotech and pharma companies. 

They scan from 200 – 900 nm in less than three seconds, covering 1 – 16,500 ng/µl dsDNA concentrations or 0.03 – 478 mg/ml BSA. 

Automatic detection of contaminated samples ensures accurate results. Intuitive touchscreen operation, integrated vortex, simple pipette-measure-wipe-repeat workflow, small footprint and network integration for convenient lab bench operation. Recalibration-free patented technology--made in Germany. 

The Implen NanoPhotometer N120 scans up to 12 samples in just 20 seconds. Quantifying DNA, RNA, and proteins have never been faster. Increase your sample throughput and measure a 96-well plate in just 5 min. Less pipetting means fewer errors. 

The new Implen NanoPhotometer add-on is now available and free to install from the eLab Marketplace. Schedule a personal demo with the Implen team to test the add-on, or visit the Implen website to learn more about the technology. 

About Implen 

Implen is a privately held corporation leading supplier of spectroscopy instruments and consumables for the non-destructive analysis of ultra-low volume samples. The company focuses on biological, chemical, and pharmaceutical laboratories in industry and research. Implen strongly focuses on the customer, taking pride in providing quality products and high customer service to achieve total customer satisfaction. 

implen.com

For any questions, please contact Soeren Rowold at leads@implen.de.