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.

Antibodies are critical components of past, current, and future biomedical research. They have truly revolutionized our understanding of biology and the development of modern medicine. Both monoclonal and polyclonal antibodies aid in the detection, isolation, and quantification of proteins and different cell types as they are vital reagents for laboratory techniques such as enzyme-linked immunosorbent assay (ELISA), western blot, immunohistochemistry (IHC), flow cytometry.

As essential reagents in most laboratories, their management, quality, and organization are paramount. In the following blog, we’ll provide you with a primer on the top providers of antibodies in the biological R&D space, their primary applications in research, and best practices for managing a collection of antibodies.

Here’s what we’ll cover:

• The Top 10 Global Antibody Providers
• The Most Popular Antibodies
• 3 Research Fields where Antibodies are Indispensable
• Best Practices for Antibody Library Tracking
• Best Practices for Antibody Library Storage
• Conclusion

Top 10 Antibody Companies

Many companies provide antibodies, but the "top" antibody companies depend on a few personal factors, such as your specific research needs and your labs’ budget. 

Here are ten companies that are among the largest and most well-known providers of antibodies in the United States:

• Thermo Fisher Scientific (formerly Life Technologies)
• Abcam
• Santa Cruz Biotechnology
• Bio-Rad Laboratories
• Cell Signaling Technology
• BD Biosciences
• R&D Systems
• Sigma-Aldrich (now part of Merck KGaA)
• EMD Millipore (now part of Merck KGaA)
• Agilent Technologies

A cautionary note: This is by no means an exhaustive list. Many other reputable companies provide antibodies. It is important to carefully evaluate the quality and specificity of any antibodies before purchasing them for use in experiments.

The Most Popular Antibody Products

The most used antibodies can vary over time and across different research fields or trends, as the popularity of different targets and applications can shift over time. 

Here are a few examples of some of the most commonly used and sold antibodies in research:

• Anti-GAPDH (Glyceraldehyde-3-phosphate dehydrogenase) antibody: GAPDH is a ubiquitous enzyme that plays a key role in glycolysis and is often used as a loading control in western blotting experiments.
• Anti-beta-actin antibody: Beta-actin is a widely expressed cytoskeletal protein that is also often used as a loading control in Western blotting experiments.
• Anti-FLAG tag antibody: The FLAG tag is a small peptide tag often used to label and purify recombinant proteins in molecular biology experiments.
• Anti-GFP (Green Fluorescent Protein) antibody: GFP is a widely used fluorescent protein that is often used as a reporter in live-cell imaging experiments.
• Anti-CD3 antibody: CD3 is a cell surface protein found on T cells, and antibodies against CD3 are widely used to study T-cell function in immunology research.
• Anti-CD4 antibody: CD4 is another cell surface protein found on T cells, and antibodies against CD4 are widely used in immunology research to label and study various T-cell subsets.

These antibodies are popular because they are widely used across many large research fields, are relatively easy to work with, and have been validated by many research studies. Additionally, many of these antibodies have been on the market for a long time, so they have had time to become well-established and trusted by researchers.

3 Research Fields Where Antibodies Applications are Indispensable

Antibody libraries can be useful in various research fields, as they provide a ready source of diverse antibodies that can be used for various antibodies applications. 

Here are some of the best practices for antibodies tracking and naming in a library:

1. Immunology: The study of the immune system and its function often involves the use of antibodies to label and isolate different immune cell types, as well as to detect various cytokines, chemokines, and other immune molecules. Antibody libraries are used to generate and screen large numbers of antibodies against different targets, which can help identify new therapeutic targets or biomarkers.
2. Cancer research: Antibodies are widely used in cancer research to detect and target specific tumor cell biomarkers. In particular, monoclonal antibodies that target specific proteins on the surface of cancer cells are used as therapeutics in several contexts. Antibody libraries can help identify new protein targets or to generate and screen new monoclonal antibodies for cancer treatment.
3. Neuroscience: Antibodies are used in neuroscience research to label and detect specific proteins and cellular structures in the brain, such as neurotransmitter receptors, ion channels, and synapses. Antibody collections can be used to generate and screen antibodies against different neural targets, which can help identify new therapeutic targets for neurological disorders or improve our understanding of the brain and its function.

Many additional research fields, such as infectious disease research, plant biology, and others, use antibody collections. The specific research needs of a laboratory will determine the usefulness of an antibody library in a field or laboratory.

Best Practices for Antibody Library Tracking

Antibody tracking and establishing consistent naming conventions for antibody collections is critical to ensure the quality and accuracy, and reliability of these key reagents. If one antibody is mislabeled or misplaced, experimental results could be misconstrued, and the pace of research could be impeded. 

Here are some of the best practices for tracking and naming antibodies in a library:

1. Assign a unique identifier: Each antibody in the library should be assigned a unique identifier, such as a number or a combination of letters and numbers. This identifier should be used consistently across all documentation and tracking systems.
2. Document antibody information: In addition to the identifier, information about the antibody should be documented, such as the antigen it targets, the host species it was raised in, and the specific epitope it recognizes.
3. Use a tracking system: A tracking system, such as an electronic database or a laboratory information management system (LIMS), can help track the location and usage of each antibody in the library.
4. Standardize naming conventions: Consistent naming conventions can help avoid confusion and ensure accuracy. For example, naming conventions could include the antibody identifier, followed by the target antigen, and then the host species, such as "Ab1234-CD3-mouse".
5. Use barcoding or RFID technology: Barcoding or RFID (Radio Frequency Identification) technology can be used to track and locate individual antibodies within the library. Each antibody can be labeled with a unique barcode or RFID tag, which can be scanned or read to quickly identify and find the antibody.
6. Regularly update and review your library: It is important to regularly update and review the tracking and naming conventions to ensure they remain accurate and effective, especially as new antibodies are added to the library or experiments are conducted. 

Best Practices for Antibody Library Storage

Proper storage of antibodies in freezers is another crucial aspect for maintaining the stability and activity of a collection over time. 

Best practices for storing antibodies in freezers include:

1. Monitor freezer temperature: Use a thermometer to regularly monitor the temperature inside the freezer. It is recommended to use a thermometer with a calibrated probe that can be placed near the antibody storage area. The temperature should be maintained at -80°C for long-term storage.
2. Use freezer alarms: Set up an alarm system that alerts lab personnel in case of a freezer malfunction or temperature deviation. Many freezers come with built-in alarms, or you can use external alarms that are connected to the freezer.
3. Minimize freezer opening and closing: Minimize the frequency and duration of door openings to reduce the risk of temperature fluctuations. Encourage lab personnel to take out all the needed materials in one visit and avoid leaving the freezer door open for prolonged periods of time.
4. Maintain freezer organization: Ensure the freezer is organized and the antibody storage area is easily accessible. Use freezer racks or boxes that are clearly labeled and organized by antibody type or experiment to facilitate quick and easy retrieval.
5. Employ backup storage: Consider using a backup storage freezer or off-site storage for critical antibody samples to protect against potential freezer malfunctions or power outages.
6. Regular maintenance: Perform routine maintenance and cleaning of the freezer to ensure it functions properly. Clean and defrost the freezer as needed, and check for signs of wear and tear, such as damaged seals, that could affect its performance.

Conclusion

Managing an antibody library in the lab involves keeping track of many reagents, ensuring their quality, and organizing them to facilitate their use. By following the best practices above, you can help ensure that your antibody library is adequately stored and maintained, which will help ensure the quality and reliability of your research.

On top of these best practices, you can facilitate easy access to the antibody collection by implementing lab inventory management software, such as those offered by eLabNext.

To learn more about how our platform can enable efficient and effective management of your antibody collection, contact us for a personal demo. 

eLabNext has incorporated DMPTool, a free online platform for creating data management plans (DMPs), into its library of digital lab platform add-ons. With the addition of DMPTool, research labs and their affiliated institutions can generate DMPs for a wide range of funding organizations – including the National Institute of Health (NIH) – and review or download them directly from eLabNext’s software, enabling more effortless collaboration, grant drafting, proposal submission, and continued compliance.

What is DMP (Data Management Plan)?

A Data Management Plan (DMP) is a structured document that outlines how data will be handled both during a research project and after its completion. It details the types of data to be collected, methodologies for data collection and analysis, plans for sharing and preserving data, and strategies for ensuring data security and privacy. The DMP is essential for maintaining data integrity and ensuring that the data can be effectively used for future research, audits, or replication of the study. Funding agencies, research institutions, and published journals often require its usage to ensure good research practices and compliance with ethical guidelines.

Why are Data Management Plans Important?

Proper data management and sharing ensure that all scientific data (and associated metadata) is findable, accessible, interoperable, and reusable to the present and future scientific community. Following current guidelines from funding agencies guarantees that discoveries are attributed to the right scientists and empowers future researchers to reuse data for additional scientific advances.

The NIH, a major funding source for R&D life science labs, has prioritized data management and sharing. They expect “...researchers to maximize the appropriate sharing of scientific data, taking into account factors such as legal, ethical, or technical issues that may limit the extent of data sharing and preservation.” Accordingly, the NIH has published extensive resources and policy documents for all NIH grant awardees to implement in their operations, with a recent update to the policy in early-2023.

But writing and submitting a data management and sharing plan – now required by many other public and private funding organizations – is challenging, requiring in-depth descriptions of data types, analysis methods, standards that will be followed, timelines for data preservation and access, potential roadblocks, and how compliance will be checked and ensured. In addition, different funding agencies have unique requirements which are continuously being updated, putting pressure on individual researchers and their academic, non-profit, government, or industrial organisations to perform pre-submission quality control checks to ensure adherence with each funding agency’s current guidelines. Finally, after grants are awarded, it can be difficult for all laboratory personnel to access and understand DMPs, leading to non-compliant data management practices and, potentially, data loss.

What Is DMPTool and How Does It Work

DMPTool, an open-source, free, web-based platform, enables researchers to draft data management and share plans that comply with funding agencies by providing a simple agency-specific DMP template. The writing wizard streamlines writing by asking a user about each element of their DMP and providing sample responses in an easy-to-use interface. By breaking down the required elements, DMPTool brings ease and simplicity to grant submissions.

In addition, more than 380 institutions and organizations have implemented DMPTool as an integral part of their grant review process, enabling affiliated users to access organization-specific templates and resources, suggested text and answers, and additional support to further facilitate internal review and approval. DMPTool also directly links to funding organisations websites to ensure that the platform is up-to-date with the latest requirements and best practices.

These benefits have led to the widespread adoption of DMPTool, with over 96,000 researchers using the online application to submit more than 92,000 DMPs.

Efficient Proposal Review, Submission and Data Management Plan Implementation with eLabNext Integration

eLabNext provides a flexible, multi-dimensional software solution for the ever-evolving needs of the life science lab. One defining characteristic of the platform is its ability to expand functionality. The addition of the DMPTool to our eLabMarketplace library of add-ons is the most recent example of this and one that was requested by Harvard Medical School (HMS) users of both platforms.

The eLabNext integration of DMPTool will enable users at HMS and elsewhere to pull DMPs from DMPTool and present plan summaries within eLabNext, along with a link to download the complete plan. Therefore, any eLabNext user can access the DMP and reference as they perform research. This benefits researchers by helping maintain compliance and facilitating full DMP life cycle management from the grant drafting process through the post-award period.

Try DMPTool in a free trial

About DMPTool

DMPTool is a free, open, online platform designed to assist researchers in creating and managing data management and sharing plans. It provides a collection of templates and resources, step-by-step guidance, and comprehensive examples to guide researchers through the process of developing effective DMPs that align with funder requirements and best practices.

Please scroll down for the English translation

Do caderno de anotações ao software de gerenciamento, a migração do papel ao digital é global, e acontece em todas as áreas.  O investimento em digitização de laboratórios de pesquisa nas universidades, P&D, biotechs ou pequenas empresas possui grande potencial crescimento, no entanto, ainda se encontram em fase inicial. Por que a digitalização está demorando tanto para acontecer na América Latina?

Primeiro, precisamos voltar um pouco no tempo. Em 2019, a pandemia escancarou que muitos ramos da biotecnologia precisavam acelerar a transformação digital, para chegar perto da taxa de desenvolvimento, por exemplo, da indústria de diagnóstico ou farmacêutica.  Além disso, a perspectiva socioeconômica herdada pós-covid não era das melhores. Pequenas empresas foram as mais afetadas e vivenciamos um cenário acentuado e complexo devido as debilidades estruturais existentes na região, reforçando a necessidade de explorar cada vez mais a transformação digital para fortalecer as instituições¹.

Como observamos o mercado mais desenvolvido na digitalização é o mercado diagnóstico. Um exemplo é uma das maiores empresas na America Latina, o DASA - Diagnósticos da América S.A. – que investiu milhões para a transformação digital para melhor atendimento ao paciente e redução de custo de operação². Essa mesma lógica pode se aplicar aos laboratórios de pesquisa, biotechs e statups no Brasil, que também tem sido uma tendência crescente nos últimos anos, com a adoção de tecnologias digitais para aprimorar a coleta, análise, armazenamento e compartilhamento de dados. Hoje, revistas cientificas de relevância exigem o compartilhamento de dados brutos para publicação³ e imagine você conseguir compartilhar com apenas um clique? Ou acessar dados do lab em qualquer lugar do mundo.

Não podemos negar que com a realidade da região sempre teremos que contar com as instabilidades socioeconômica e política gerando inseguranças sobre os investimentos que serão injetados nas universidades e startups. Investir ou planejar o seu projeto considerando soluções de software é uma ação que torna esse ambiente mais sustentável e é essencial para a saúde e manutenção do lab. E que o investimento - conseguido com muito suor - seja aplicado de forma otimizada trazendo maior produtividade e inteligência na utilização de recursos e pessoas e garantindo que os dados e amostras sejam protegidas e armazenadas com segurança.

Você está preparado para abandonar o seu caderno e viver uma nova era?

Referências bibliográficas:

1. Perspectivas Económicas de América Latina 2020: transformación digital para una mejor reconstrucción
2. 2022: as contribuições da Dasa para entregar mais saúde aos brasileiros
3. Nature: Data sharing is the future

How Digitization Can Optimize Laboratories in Latin America

From notebooks to management software, the migration from paper to digital is global, and happening in all areas.  Investment in digitizing research labs in universities, R&D, biotechs or small companies has great growth potential, but is still in its early stages. Why is digitization taking so long to happen in Latin America?

First, we need to go back in time a bit. In 2019, the pandemic made it clear that many branches of biotechnology needed to accelerate their digital transformation, to get close to the development rate of, for example, the diagnostic or pharmaceutical industry.  In addition, the socioeconomic outlook inherited post-covid was not the best. Small businesses were the most affected and we experienced a sharp and complex scenario due to the existing structural weaknesses in the region, reinforcing the need to increasingly exploit digital transformation to strengthen institutions¹.

As we have observed the most developed market in digitalization is the diagnostic market. An example is one of the largest companies in Latin America, DASA - Diagnósticos da América S.A. - that has invested millions in digital transformation to improve patient care and reduce operating costs². This same logic can be applied to research laboratories, biotechs and statups in Brazil, which has also been a growing trend in recent years, with the adoption of digital technologies to improve data collection, analysis, storage and sharing. Today, relevant scientific journals require the sharing of raw data for publication³ and imagine being able to share with just one click? Or access lab data from anywhere in the world.

We cannot deny that with the reality of the region we will always have to reckon with socioeconomic and political instabilities generating insecurity about the investments that will be injected into universities and startups. Investing or planning your project considering software solutions is an action that makes this environment more sustainable and is essential for the health and maintenance of the lab. And that the investment - made with a lot of sweat - is applied in an optimized way, bringing more productivity and intelligence in the use of resources and people, and ensuring that data and samples are protected and stored safely.

Are you ready to abandon your notebook and live a new era?

Every scientist knows the frustration of digging through countless Excel spreadsheets and paper notebooks in desperate search of crucial data, forgotten experimental details, and critical reagent locations. As we’ve discussed before, digitizing your lab is how to get around these troubles. 

However, sifting through the available options and difficult-to-decode acronyms can be overwhelming.

You may have noticed that most digital platforms for the life sciences are classified as a Laboratory Information Management System (LIMS) or Electronic Lab Notebook (ELN). On paper, they sound the same, but there are some critical distinctions between them. In this blog post, we’ll explore the differences between an ELN and LIMS, discuss their advantages, and provide valuable tips to help you choose the right solution for your lab.

ELN vs LIMS: Everything You Need to Know

Let’s start by breaking down just what an ELN and LIMS are and their benefits.

What is an ELN?

An ELN is a software platform designed to record and manage data, observations, sample information, and experimental methods that one would conventionally scribble into a paper lab notebook. ELNs are an excellent solution for keeping up with growing regulatory pressures to maintain data integrity and security. Moreover, they allow you to easily collaborate with team members, record experimental observations, integrate with instruments, create detailed reports, and search using simple keyword queries.

Benefits of using an ELN

• Searchability - Given their digital nature, entries into ELNs are easily searchable, which makes them very time-efficient.
• Easy collaboration - ELNs allow labs to share data, notes, and images with colleagues, making it an excellent solution for working on projects and experiments with a team.
• Security - ELNs allow for digital signatures, so sign-off on projects and experiments can be done easily and securely.
• Traceability - ELNs provide a comprehensive audit trail of all actions taken within the system, making it easy to track who has done what and when. ELNs also include inventory and equipment management, making tracking and managing consumables and lab equipment easy.
• Standardization - ELNs can include a protocol module, enabling you to set up individual or group working templates, making it easy to standardize processes and workflows.

What is a LIMS?

In contrast to an ELN, LIMS is software designed to manage and automate laboratory workflows and operations. It is ideal for running repetitive testing or working in a quality assurance or biobanking lab since it minimizes the probability of human errors. Moreover, they allow you to track samples (and associated metadata), attach instrument records to samples, create basic analytical reports, and manage lab tasks and inventory.

Benefits of using a LIMS

• Consistency - LIMS can help labs maintain consistency by closely following predetermined workflows or templates and ensuring precise and reproducible results. 
• Standardization - LIMS help run repetitive testing or work in QC/QA or clinical labs since they are designed to streamline processes and provide easy access to essential data.
• Automation - LIMS can help automate certain procedures, such as report generation, sample management, or inventory tracking. 
• Traceability - LIMS can help you easily track samples, protocols, experiments, and results, saving time and effort. 

What are the Differences Between ELNs and LIMS?

While ELNs and LIMS are digital software platforms for laboratory data management, the two have some significant differences. ELNs are designed for many of the same functions as traditional paper notebooks, such as recording experimental protocols with the added benefits of searchability, data organization, and collaboration tools. LIMS functions focus on streamlining repetitive tasks and workflows from sample tracking to data analysis and reporting. They are typically used by large laboratories that manage lots of samples and data.

Choosing Between an ELN or LIMS: Which System is Right for You?

Now that you know the main features, benefits, and differences between ELNs and LIMS, it is time to decide which solution is right for you. 

In short, choosing a software solution that fits your and your labs’ needs is best. 

But what are those needs? The first thing is to meet with everyone who will use the ELN or LIMS software and better understand what they will be using it for. Are you looking to track samples from routine and well-defined tests? Or are you looking to organize notes, protocols, and data from experiments? If team collaboration is essential to your organization, an ELN may be the way to go. 

Next, consider the industry you work in. For instance, biotech and pharma companies doing drug discovery or early-stage development testing may find an ELN a more suitable solution. In other laboratory environments, like a QC or QA facility, a LIMS may be better suited for your tasks.

Moreover, consider the regulatory environment your lab is operating in. If you work in a standardized environment where workflow is predetermined and not very flexible, a LIMS is likely a better option.

Lastly, ELNs and LIMS come with very different price tags. If budget is a concern, research beforehand and get an accurate quote to get the most value for your money. 

ELN or LIMS: Webinars

The webinar will provide an outline of the differences between LIMS and ELNs, and how you to decide which one is more suitable for your lab.

You will learn:

• What is the difference between LIMS and ELNs?
• How to choose which one best suits your lab? 
• What are the advantages of ELNs?

Let's wrap up!

Ultimately, the choice between a LIMS and ELN will largely depend on what you're trying to accomplish, your primary lab needs, your work and regulatory environment, and your budget. Understanding what each system does can drastically help guide your decision. And as the next generation of holistic digital lab software and AI-driven solutions enter the life science market, the problems that can be solved using these platforms will evolve and change, further streamlining laboratory operations.

If you want to learn more about how eLabNext’s digital lab solutions accelerate progress in the life sciences industry, schedule a personal demo today.

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!

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.