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

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.

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.

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. 

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 modern laboratory environment is pretty sophisticated: Specialized instruments can perform automated workflows, and software platforms make data collection and analysis more streamlined. Various platforms save researchers time and money, improve data accuracy and reproducibility, and make collaboration a breeze.

Yet, the number of instruments and software platforms in a lab can sometimes create challenges with data decentralization. Critical information may be stored in many different places rather than a centralized access point. Traditionally, software developers focused on creating one-dimensional software that did a single task well. In today’s lab, having everything in one place creates an advantage over some of the misperceived benefits of decentralization, such as increased security, privacy, and resilience.

With eLabNext, we can provide a cohesive Digital Lab Platform (DLP) that allows seamless integration and connectivity between your instruments, workflows, and data. This solves many issues with decentralized information that we’ve seen in many of our labs. 

In the blog below, we discuss 7 of the top issues we see with a decentralized data model. 

1) Data Integrity

With decentralized data, there is a risk of inconsistencies, duplicates, or errors. There may be a conflicting version of data stored across multiple instruments or software platforms and a breakdown in the integrity of the data. Ultimately, this can lead to inaccurate results and negatively impact the reliability or reproducibility of the laboratory's work.

2) Data Security

Decentralized data can be vulnerable to hacking or theft, especially if the data is not adequately secured or encrypted. Multiple access points for data provide multiple vulnerabilities.

3) Data Accessibility

Accessing and sharing data between different laboratory locations or with external partners can be challenging when data is decentralized. In science, collaboration is a pillar of progress, necessary for pushing the boundaries of what’s possible. Barriers to collaboration, such as decentralized data, can slow down partnerships and limit data analysis and interpretation. It can be difficult to access and share data between different laboratory locations or with external partners when data is decentralized.

4) Data Standardization

Data standardization refers to establishing common formats, structures, and protocols for data to ensure consistency and interoperability. With decentralized data, there is a risk of using different data formats or standards, making it challenging to integrate data from different sources for analysis and interpretation.

5) Data Management

Decentralized data poses a major problem for data organization. Managing consistency and integrity across multiple data locations is difficult, leading to challenges in finding, tracking, and using the data effectively.

6) Regulatory Compliance

Because of some of the risks discussed above, decentralized data may need to meet the regulatory requirements for data storage, access, and use. Regulatory agencies are mainly concerned with protecting the personal information of clinical trial participants and patients. If it’s not fully covered due to decentralization, regulatory agencies may require a centralized approach.

7) Data Backup and Recovery

Decentralized data can be vulnerable to data loss or corruption, and it can be challenging to implement a robust backup and recovery strategy to ensure the availability of the data in case of system failures or other issues.

Get Centralized with eLabNext

When going on a digital transformation journey, it is vital to limit data decentralization and consider how your software platforms and instruments can communicate.

As you review your past purchasing decisions and those of the future, look at API and SDK tools available that can help you create a flexible, cohesive system that centralizes and secures your data.

Contact us today if you are interested in our API and SDK capabilities as part of the eLabNext platform.

Whether or not it’s Halloween, here at eLabNext, we like to tell spooky life science stories. Here’s one that got us all pretty frightened:

The year is 2023.

You’re a process scientist working in Big Pharma, and it’s your first chance to lead a commercial drug manufacturing campaign that could result in millions of dollars of profit for your company. You’ve done months, maybe years, of prep work and scale processing to prove yourself and your campaign, hoping it translates as smoothly as possible to the manufacturing floor.

The stakes are already high, with your job and reputation on the line. Then comes more stress: The FDA has decided to make a surprise visit.

Dun, dun, duuuun!

The audit process starts with a tour of the manufacturing suite: Auditors make several observations about the untidy and disorganized work environment and technicians not following SOPs or batch records.

Then, the FDA auditors bring into question the validity and origin of a particular piece of paperwork or file.

You know you have the original file somewhere. Or, rather, someone somewhere knows where the original file is. The question is, where is it, and who is that person?

Was it scanned into a computer somewhere, living in a data silo?

Was it filed away years ago and sent over to your records building?

Quality Assurance is now hounding you for proof of this one measly piece of paper, and you can’t do anything but hope and pray it turns up somewhere.

You search,

and search,

and search

and all you find is…

nothing.

Storytime’s Over. Don’t Let This Audit Agony Happen to You!

Are you sweating yet? Because I am!

This horror story is obviously a worst-case scenario, but I’m sure many people reading this (myself included) have been in a similar situation. And unfortunately, this common occurrence is most likely due to the absence of proper tools that keep your data and workspace digitized, organized, and easily retrievable.

If you’re new to GMP, you may ask yourself, “Isn’t it 2023? Doesn’t big pharma and similar GMP industries have this figured out already?”

I’m sorry to report that most do not!

That’s not to say they’re incapable of following the necessary FDA guidelines for producing safe and effective products; they just tend to do it the old-fashioned way. As in, “we use excel to keep track of everything” old-fashioned way.

Sound familiar?

If this resonates with you, I have excellent news! There’s a solution to all of this:

A digital lab platform (DLP)!

4 Reasons Your GMP Facility Needs a Digital Lab Platform

Imagine all of your lab work – experimental data, sample information, lineage tracing, protocols, and inventory management tools – living on one cohesive platform. What if you never had to worry about finding that long-lost file from 15 years ago or had reports readily available for when the FDA shows up at your door? Sounds great, right!?

With digital lab platforms, you’ll have all the tools necessary to remain organized and compliant in any GMP facility.

There are a plethora of reasons to switch over to a DLP, but they ultimately boil down to 4 key points as to why your GMP facility should “go digital”:

Reason #1: GMP Environment Centrality

Remember those data silos I talked about above?

The files within folders within folders within folders on someone’s laptop?

If you and your colleagues deal with this daily, you know how frustrating it is to find a specific Excel sheet containing raw or structured data through this computational rat’s nest.

Or maybe you have an inventory of thousands of samples, distributed across 20 different -80℃ freezers, and you’re struggling to find the location of a certain aliquot of a CHO cell line from last year’s campaign. Or maybe, you’re utilizing various modalities – digital and paper – to keep track of everything, and you’re struggling to juggle it all.

Sounds stressful, right?

DLPs serve as a central location for your entire workflow, offering a better way to manage your information and data.

From keeping track of projects, experiments, assays, and test results to maintaining a proper and structured inventory of samples, DLPs can be a one-stop shop to keep everything under one roof. No more data silos, no more questioning where a sample is living, and no more juggling multiple organizational tools! On top of this, many DLPs are also cloud-based, meaning you can access your work anywhere at any time.

Reason #2: GMP Compliance

Compliance has been a big part of my manufacturing career, as I’m sure it has with yours. And while you may think you have a decent handle on maintaining compliance, you can consistently implement tools to make GMP compliance more straightforward and certain. Audit trails and traceability of status tags, log sheets, batch records, process changes, etc., are among the most highly scrutinized aspects of an FDA audit. And unfortunately, many facilities cannot produce fully traceable change logs quickly or efficiently.

So, instead of trying to scrounge up this information yourself, let a DLP do it for you!

Many DLPs on the market offer automated audit trail capabilities, so you don’t have to worry about updating paper records.

Even better, “forgetting” to update these particular documents is a thing of the past.

Need to find who did what at a specific date and time? No problem, just search through your DLP – using simple keywords and defined search parameters – to find this exact information in seconds.

And let’s not forget about adherence to SOPs or batch records! Many compliance issues stem from technicians going rogue or working too fast and missing critical steps of a process. DLPs can be set up with digital workflows integrating SOPs and batch records. With performer and witness signatures, they can also be structured to ensure every step of an SOP is followed.

Reason #3: Security in Your GMP Environment

One thing that often goes hand in hand with compliance is security. But what does it mean to be secure in a GMP environment? I’m, of course, talking about the security of your data. If you’re in big pharma, your company likely has a very robust security system developed internally by your IT department. Therefore, security might not be too much of a concern for you.

But what if you don’t have these measures in place? Who’s protecting your information?

Jim, your colleague who took a coding class a few years ago, might have set up your firewall, but that won’t cut it (sorry, Jim!). When thinking about your data and protected GMP processing techniques, you should consider how this information will be kept securely. Luckily many DLP providers have done the hard work for you already!

Whether your DLP is cloud-based or not, you’ll want to be sure some or all of the following security measures are in place: a dedicated security team, third-party penetration tests, servers hosted at a site with on-site security, multiple data centres with encrypted backup servers, disaster recovery features, single sign-on, two-factor authentication, user roles and permission settings, and IP restrictions.

You’ll also want to look for the following certifications and compliances: ISO 27001 certification (this includes the system itself, not just the cloud hosting provider), 21 CFR Part 11 compliance, GDPR compliance, GxP compliance, HIPAA compliance, and FedRAMP compliance/certification. Your data is only as secure as the system it’s hosted on, so be sure to do your homework and make this a priority!

Reason #4: GMP Environment Efficiency

While centrality, compliance, and security are the backbones of any great DLP, one of the most beneficial impacts of instituting a DLP in your GMP environment is increasing efficiency in your day-to-day workflow. Aren’t you sick of wasting time looking for something that is unfindable? Don’t you wish you could automate basic tasks?

Think about everything you do throughout your average workday: Searching for reports, files, test results, or samples, checking inventory, stressing about compliance, organizing the manufacturing floor, and so on.

Does that take up most of your day? My guess is probably, at least, that’s how I felt!

When you lack the proper tools to do your job efficiently, much of that burden falls back on you.

Great DLPs make your life and the lives of your colleagues better! And I mean your actual life, not just your “work-life”. After all, if your “work-life” is more efficient and better organized, the rest of your actual life will be that much better.

Taking a Step Towards Digital a GMP-Compliant Facility

I could go on and on about the benefits of DLPs and how they can transform the operations of your GMP-compliant facility. Still, the fact of the matter is the digitization of your organization is not up to me.

The only way to make your daily routine more organized, compliant, secure, and efficient is to take the initiative!

So, what are you waiting for?

It’s 2023. Go digital and try a 30-day trial of eLabNext’s DLP today!

Across biological and chemical R&D labs, referring to “sample management” can mean many different things.

A “sample” could be a live mouse within a large colony. 

Or a cell line in a cryotube that’s on its 20th passage. 

Or a newly synthesized chemical. 

Or a recently constructed plasmid.

“Management” is similarly ambiguous: It can refer to what type of biospecimen or chemical a sample is, where it’s stored, who used it last, and when, or how much of it is stored. To manage all of this sample information along with the sample itself, labs need to carefully plan and pay attention to these details.

Fortunately, there are software solutions for this. Traditionally, scientists used Laboratory Inventory Management Systems (LIMS) for efficient and effective sample management, but more recently, scientists have relied on Digital Sample Management Software (DSM) platforms. 

There are some subtle differences between a LIMS, DSM platform, and many others, but regardless of what you call it (we’ll use LIMS in the following blog), there are key sample management features that all labs require to be successful. 

In this blog, I’ve highlighted 5 critical traits for any digital sample management platform.

Sample Traceability

Sample tracking is essential for locating the exact location of a sample in the lab and where it’s stored. This is particularly important when multiple researchers and technicians rely on or regularly use the same samples.

Audit Trail

An audit trail provides a chronological record of all sample collection, handling, storage, analysis, and disposal activities. Think of it as a historical account of a sample's who, what, when (down to the date and time), where, why, and how.

Audit trails are essential in ensuring the integrity and traceability of a sample. It can be used to identify any deviations from standard operating procedures or potential errors in sample handling and provide a record of any changes made to the sample or its associated data.

Sample Updates

Like we update our social media status, samples get status updates. An example would be when you do something to the sample (e.g., check for contamination and now need to update the sample to “contamination: pass”). Because this is such a frequently performed activity, any LIMS must support rapid and easy features for doing this. 

Biology- or Chemistry-Specific Capabilities

There are LIMS out there that are simple or list-based data management platforms. But what happens if you have to store specialized data, such as simplified molecular-input line-entry system (SMILES) chemical notation strings or integrate with other digital platforms, such as rendering a plasmid from GenBank?

Many labs make the mistake of buying 4 or 5 software platforms to satisfy those needs without realizing that there are comprehensive solutions that combine a variety of necessary sample management tasks together. 

Check out our article, “The digital lab: in search of leaner, greener operations” in Nature which talks more about this.

Sample Lineage

Whether you’re tracking chemical derivatives or the parent-child relationship of your biospecimens, ensure your system can track sample lineages and relationships between samples in your collection. This feature becomes particularly important if you’re involved in biobanking. Features like these can sometimes be overlooked or assumed to be a part of LIMS.

Find The Best Way to Manage Your Samples

The best software platforms have all the features embedded into the system. If a software platform is not capable of combining these capabilities into an easy-to-use, customizable interface or creates more work for you, then it may not be the most efficient or effective solution for your sample management.

Let’s chat about Digital Transformation, AI, and the world of biotech! Contact us here.

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. 

Leaders can come from anywhere within an organisation in the life sciences, where innovation and adaptation are essential. The newest research technician hired last week can be as effective at enacting widespread change through high-quality leadership as the 25-year industry veteran in the C-suite. In fact, change is often most efficiently implemented from the ground up rather than the top down. After all, the end user who has to use a new product or implement a new process daily is ultimately the best advocate for change.

So, what qualities does it take for an excellent leader to enact lasting change? 

In my experience, bringing the eLabNext digital lab platform to life science organisations big and small, I can tell you it’s no one thing. Good leadership stems from several shared attributes. Effective communication, inspiration, and others are all important, but it’s more than that. 

Here are 7 leadership qualities I’ve seen have a hugely positive impact when labs, big and small, are shifting to eLabNext’s digital platform.

Set Timelines Or Else Time Will Run Out! 

For any organisation, short- and long-term goals are critical. They provide a direction and focus for the months and years ahead and can fill lab personnel with a sense of purpose. 

To implement a new software platform (or any other change), focus on the 1-month, 3-month, 6-month, and 1-year milestones. The more specific and actionable your goals are, the better. With them, you may find yourself, your team, and your organisation more robust, with an idea of when and where to start or what success should look like. 

Here are some examples of what these goals might look like if you were adopting eLabNext’s platform:

• Month 1: Get all physical items in the lab, including storage units, instruments, equipment, samples, and supplies, digitised.
• Month 3: Digitise all protocols and projects and ensure everyone in the lab is comfortable using the new system. If they’re not, create a training plan to resolve this.
• Month 6: Everyone in the company will utilise the new platform’s features to their full potential.
• End of Year 1: Management has implemented protocols for reviewing data and analytics. The company has standardised and grandfathered in all workflows. If applicable, several automation features have been used to save time.

Of course, if you’re leading the charge on a different type of change, your goals will differ, but just be sure to set actionable, specific goals and timing associated with each.

Take Baby Steps, Get a Big Leap

One month is four weeks. That’s an average of 30 days or 720 hours or 43,200 minutes. Sometimes it doesn’t feel like it, but when you plan it, you can easily designate a few hours a week for taking the “baby steps” of setting a basic foundation and infrastructure for your new change. 

If we take our first month’s goal from above, here’s what each baby step might look like for an eLabNext implementation plan:

• Week 1: Set up all freezers and other storage units.
• Week 2: Set up all equipment and supplies.
• Week 3: Set up all sample types.
• Week 4: Import all of your legacy samples into eLabNext.

Divide and Conquer!

You can’t do everything. No leader can. 

And you don’t have to. 

Together, as a team, you have a whole arsenal of strengths. And with those, you can divide and conquer the tasks ahead of you. 

Teamwork makes the dream work, and in the case of eLabNext, the dream is to digitise your lab. 

You can divide the tasks between the people in the team, and each person can take ownership of different portions of the project, depending on their strengths. 

Felicia can do the freezers, while Steve can set up the sample types. All while Emmanuel does the equipment. 

This way, you allow many perspectives, encourage discussion and brainstorming between folks, build team camaraderie, strengthen the digital foundation, and set yourself up to be a digitally healthy and sustainable lab for years to come. 

Lead by Example

As you’re dividing and conquering, lead by example. Pick one of the weekly “baby steps” and do it flawlessly within the timeline provided. 

And if you don’t, own up to your team and find a collective solution.

This will set the tone for everyone, inspire and encourage, and solidify your group’s learnings as tribal knowledge to be passed down to all new hires. Practising what you preach and vouching for what you know can benefit the whole lab. 

Don’t Be Afraid to Ask For Help

If you’re confused or overwhelmed, going to someone for support or guidance can help you solve a problem or accomplish a task without wasting time. Asking others for help can sometimes feel weak, but all good leaders “know what they don’t know.” To continue with the example of implementing eLabNext’s platform, you can always request help from our experienced technical support (which prides itself on its expertise and customer success) or search through our resource library. 

Incentivize Key Users

Who doesn’t love a free lunch? At the 1-month mark, once all goals have been completed, you might consider rewarding key personnel that have helped you drive change. You could order food for the entire team or use the vendor (if applicable to your change) to help. 

When we’ve transitioned labs to our eLabNext platform, sponsoring a lunch and learn helps us build relationships and enables more effective communication. It also incentivises key users, which trickles downhill to inspire and motivate the rest of the team.

Review, Report, and Reap the Benefits

Review your overall progress at each milestone and report to the team. It is essential to see the change you’ve envisioned come to fruition! When we get buried in our tasks, we have difficulty stopping and smelling the roses. 

With eLabNext, the roses are your digital representation of your physical lab. Celebrate the first 100 experiments recorded. Or the first 1,000 samples created. These rewards can make it fun for people in the lab to stay encouraged and excited to keep on with everything they’re doing. 

Ready to lead the journey to digital transformation? Schedule a personal demo of our digital lab platform today!

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!

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.

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.

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 

ASCENSCIA is your voice assistant in the lab, specifically designed for scientists by scientists. It essentially functions as your companion, providing an easy way to access information and record data while working in the lab, freeing up your hands and allowing you to focus on your experiment.

The digitisation of scientific laboratories is ongoing and often challenging. One hurdle scientists face is staying connected with their electronic lab notebooks (ELNs) from the bench. With multiple experiments to perform and data to record, multitasking can lead to wasted time and valuable research information being lost. However, the emergence of voice assistant technology developed by ASCENSCIA for scientists offers a solution. The ASCENSCIA voice assistant allows for the hands-free recording of notes and data directly into the eLabNext platform, streamlining the research process and aiding in the complete digitisation of labs. By simplifying daily tasks and eliminating manual note-taking, scientists can focus on their experiments and make the most out of their time in the lab. This technology revolutionises traditional methods, leading to more efficient and accurate scientific research.

Inefficiencies that lead to wasted resources and slow down lab digitisation

Working closely with research labs in academia and industry, we gained insight into the hidden inefficiencies that lead to wasted resources and slow digitisation. Scientists often feel frustrated by their inability to easily access or transfer data, particularly from paper notes to electronic lab notebooks (ELNs). These small obstacles can add up, hindering overall productivity in the lab. Our collaboration with research labs allowed us to identify and quantify these inefficient processes, ultimately leading to the development of the ASCENSCIA voice assistant. For example, addressing disconnected access to electronic lab notebooks and streamlining data transfer from paper notes can save significant amounts of time and improve data quality. In fact, our efforts have increased productivity by 40%, saved researchers up to 30% of the time, and reduced reproducibility issues by 70%.

I believe that this is a very interesting time for the scientific research field, moving towards lab digitization. It is very exciting to work together with partners like eLabNext to accelerate this transformation process.

Ahmed Khalil, Founder & CEO at ASCENSCIA

A fruitful partnership

At ASCENSCIA, we carefully select our partners to ensure that we all work towards the same goals and values. That’s why we chose to align with eLabNext – their mission to digitise scientific laboratories aligns perfectly with ours. Not only that, but their products and services make it easy for us to integrate our voice technology solution into labs. We were thrilled by the enthusiasm and support of the eLabNext team during our partnership explorations – all signs pointing to a successful collaboration. We are excited to see what the future holds for our partnership with eLabNext.

At eLabNext, our mission is to revolutionise life sciences research by building an expansive marketplace of customisable digital lab tools. That's why we're thrilled to announce our partnership with ASCENSCIA, a company dedicated to creating groundbreaking voice assistant technology designed specifically for scientists. By integrating this technology into our platform, we can offer even more options for researchers to streamline their workflows and make thier day-to-day lab tasks easier than ever before. We're excited about the potential this partnership brings and can't wait to see how researchers take advantage of this innovative new tool in their labs.

Lara Matthews, Business Development Manager at eLabNext

About ASCENSCIA

ASCENSCIA is a highly specialised voice assistant for scientific labs. ASCENSCIA can integrate seamlessly with existing databases, systems and machines in the lab, making them smarter by creating voice-enabled labs. Accordingly, scientists can collect data accurately, automate experimental workflows and stay connected to their databases from the lab just by the power of their voice.

We are a team of scientists who know the struggles, challenges and costs of bringing drug therapeutics to the market. Our mission is to shift scientific research towards a more data-driven era. We accelerate early-stage drug discovery research into a more efficient, economical, and sustainable process. Simply, solving the small day-to-day challenges in the lab collectively contributes to the tremendous waste of lab resources. Ultimately, we aim to transform scientific research labs into more data-centric and data-driven.

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