If you work in a laboratory, you know how important it is to effectively share equipment and resources with your colleagues. Advances in laboratory technology have given us access to remarkable analyzers and instruments for our research and diagnostic needs. But, while lab equipment can make workflows faster and more cost-effective, there are the added challenges of upfront investment costs, staying organized, continuous upkeep, and integration with other platforms. 

In the following blog, we’ll discuss these struggles in more detail and how to solve them with a simple and accessible solution.

The Problem: De-Centralized and Unconnected Organization

Whether coordinating a small or large lab, managing a suite of equipment and their associated operation is no easy feat. 

Here are a few challenges that we’ve heard over the years:

Lab equipment is spread across different rooms and floors. 

Validation and preventative maintenance schedules vary amongst instruments. 

When equipment issues arise, vital information, like a faded serial number or a lost user manual, can become unexpectedly unavailable.  

Multiple users with different schedules have continuous conflicts with equipment usage.

Different users have different experimental protocols or techniques, requiring time-consuming and error-prone setup transitions.

Many labs try to proactively circumvent these issues by implementing a shared spreadsheet or paper log. These approaches are not designed to be at the forefront of the lab workflow; they become "optional" rather than "necessary." As a result, labs still experience delays and conflicts with equipment reservation and preventative maintenance schedules. A missed re-validation may result in unusable or non-compliant data and potentially weeks of downtime due to part availability or field service engineer scheduling. 

While each scenario is distinct, the result is the same: Limited equipment availability. Ultimately, the consequences can quickly halt research, leading to lost time and money.

The Solution: A Lab-Focused Digital Approach

Without a centralized approach that lab personnel can easily access and utilize, lab efficiency will suffer.

A digital lab platform is designed with the lab's needs in mind and can help you and your colleagues manage lab equipment effectively and efficiently. By having a centralized repository for your lab equipment, you can optimize your workflow, increase productivity, and limit potential equipment downtime.

Here are the top features that can provide significant benefits to your lab:

• Reservation System - Many platforms provide centralized scheduling systems that allow users to book preferred time slots for equipment usage easily. Researchers can view equipment availability in real time with a simple calendar interface, enabling them to plan their experiments accordingly. Additionally, digital lab platforms often include automated notifications and reminders, ensuring users know their scheduled time slots and reducing the chances of equipment being idle or unused. You can also use options to block equipment reservations or change equipment status if repair or maintenance is required. The benefits of these features are fewer scheduling conflicts and higher efficiency.
• Equipment Summary - If something goes wrong or a new technician is getting trained to use a piece of equipment, do you have quick access to vital information? Digital lab platforms allow you to capture and store essential metadata such as equipment specifications, maintenance records, calibration data, and usage history. This centralized approach ensures that researchers have a reliable and up-to-date source of information about the shared equipment. Users can access detailed documentation, including user manuals, operating procedures, and troubleshooting guides, enabling them to make informed decisions and operate the equipment correctly. Furthermore, the platform's search and filtering capabilities allow researchers to quickly locate specific equipment based on parameters like availability, functionality, or compatibility with experimental requirements. 
• Equipment History - Digital lab platforms allow researchers to access a detailed record of past experiments, including experimental parameters, results, and any issues encountered. This historical data provides valuable insights into trends regarding the performance and reliability of the equipment, allowing users to make informed decisions about its suitability for specific experiments. Moreover, tracking equipment history helps identify any recurring problems or patterns of malfunction, enabling proactive maintenance and minimizing downtime.

Try eLabNext's Digital Lab Platform for Your Equipment Management Needs 

Overall, digital lab platforms help optimize the management of shared equipment by streamlining scheduling, increasing equipment uptime, and lengthening the lifetime of an instrument. Additionally, they can help promote collaboration, facilitate remote access to equipment, and “future-proof” your lab. These platforms increase lab efficiency, enable sustainability, improve communication, and enhance productivity in shared lab environments.

eLabNext is the most advanced digital lab platform that can help elevate your laboratory equipment workflow. Request a personal demo or start a free trial today to see how it can integrate seamlessly into your lab’s operations.
You can also explore the eLabMarketplace, where you can find and install add-ons and integrations that suit your specific needs.

Biotech is an industry characterized by ebbs and flows. 

Currently, we’re experiencing an exciting growth phase with the rapid increase of artificial intelligence (AI) and its potential applications. The intersection of AI and biotech holds immense promise, offering opportunities to make significant biological advances. This growth has changed the VC funding landscape in new and exciting ways and presented new challenges for biotech startups.

In this blog post, we will explore the current state of venture capital (VC) funding in the biotech sector, how you can best navigate the funding landscape, and the future of biotech.

The Promise of AI in Biotech and How its Affecting VCs

AI's ability to process vast amounts of data and identify patterns has opened new avenues for biotech innovation. With the integration of predictive and generative AI, researchers can streamline drug discovery processes, identify potential targets, and accelerate clinical trials. 

The growing optimism surrounding AI's potential to revolutionize the field has attracted attention from investors seeking to capitalize on this transformative technology.

While seed funding in biotech ventures has remained relatively stable, there’s been a decline in series A and late-stage funding. This shift suggests a more cautious approach among investors in funding companies as they progress through their development stages. 

What Investors Want

Investors are seeking companies that can achieve significant milestones with minimal resources, promoting a lean and cost-effective approach to operations. Consequently, biotech startups must adopt strategies prioritizing efficient resource allocation while pursuing breakthrough innovations.

Moreover, the investment community has become more risk-averse. Investors are exhibiting a preference for ventures that balance ambition with a solid risk management strategy. This shift underscores the need for startups to demonstrate a clear understanding of their market, addressable challenges, and potential regulatory hurdles to gain investor confidence.

Startup Challenges and Solutions

As a result of these changes in investment behavior, early-stage biotechs need to focus on capital efficiency and quickly demonstrate a unique value proposition to secure short- and long-term funding. 

But how? Most biotech startups require substantial R&D investment to generate promising data, and overspending can strain a company's resources, hindering growth. Therefore, managing liquidity and reducing volatility are critical factors if a startup wants to be around in a year.

Here are three tips for managing your money and your risk efficiently.

Tip #1: Diversify Funding Sources

The involvement of diverse investors is crucial for the growth and stability of the biotech sector. With new biotech funds being announced often, the industry is witnessing an infusion of capital from different sources. 

This diversity broadens the pool of available funding and brings a range of expertise and perspectives to the table. To ensure continued funding, startups should actively seek investment opportunities that align with their long-term goals and forge strategic partnerships to maximize their chances of success.

Tip #2: Explore Tax Benefits and Stay on Top of Shifting Regulatory Requirements

Startups should explore the Qualified Small Business Stock (QSBS) tax benefits, as these incentives can provide significant advantages in fundraising and capital management. These include tax savings, employee incentive programs, financial flexibility, and more. 

Additionally, staying informed about regulatory changes and incentives within the biotech sector can help companies leverage favorable conditions and navigate potential challenges. For example, cell and gene therapies have significant potential to revolutionize medicine. Yet, developing and producing these products requires new technologies, and regulatory agencies must evaluate and provide clear guidance for the huge group of companies looking to translate their pre-clinical candidates into the clinic. 

Tip #3: Scalable Solutions with AI

As biotech problems become increasingly complex, the demand for sophisticated technological solutions rises. Fortunately, advancements in AI and related technologies offer new solutions and insights. In the life sciences, AI is broadly applicable, from agriculture to medicine. The inherent scalability and adaptability of these solutions can help tackle the growing complexity of biological challenges, driving significant breakthroughs in the near future. AI can help startups de-risk and be more cost-efficient by creating a shorter path from data to insights.

The Future is Bright

The anticipation of an interest rate decrease announcement in 2024 signals a potential growth year for the biotech industry and a bright future that could foster innovation and more investment. However, companies should remain agile and adaptable to evolving market conditions while also being mindful of long-term sustainability.

Biotech is currently at the intersection of technological advancements and investment opportunities. With AI's increasing prominence and potential to catalyze breakthroughs, the field holds immense promise. The biotech sector is undergoing a transformative phase, fueled by advancements in AI and the possibility for innovation. Biotech startups can position themselves for success by efficiently navigating the funding landscape, managing risks, and embracing technological solutions. 

To find out how you can harness the power of AI at your startup, book a demo of eLabNext’s digital lab platform today.

In 1950, medical knowledge was on pace to double every fifty years. 

By 1980, the doubling time was seven years. 

By 2010, it was cut to three and a half years. 

And the rate of data growth continues to increase. There were 153 exabytes of global healthcare data generated in 2013 alone, which rose to an estimated 2,314 exabytes generated in 2020.

This acceleration is incredible, yet it’s happening irrespective of how all that information is used. In this blog, we’ll review the innovation that led to our current golden age of laboratory automation and how data management can be further improved in the life sciences.

Innovation Begets Innovation: Historical Examples in the Life Sciences

When I initially read about the data doubling time over the past few decades, I wondered what caused such a rapid increase in these timelines. In the 1950s, the Nobel Prize was awarded to John Enders, Thomas Weller, and Frederick Robbins for growing poliovirus in culture, paving the way for large-scale vaccine production, and contributing to the development of the measles, mumps, rubella, and chickenpox vaccines. 

Before this advancement, the first electrically driven centrifuges were introduced in 1910, and in the late 1940s, the first subcellular components were isolated using centrifugation. Shortly after these techniques proved helpful, the abovementioned breakthroughs by Enders, Weller, and Robbins happened. 

Was it the sole reason? 

Almost certainly not. However, the continued innovation revolutionised Enders and colleagues’ knowledge of intracellular components' structure, composition, and function. Also, it demonstrated the incredible potential of centrifugation for biomedical research.

Skip ahead to the ’70s and ’80s when Walter Fiers became the first to sequence the DNA of a complete gene (the gene encoding the coat protein of a bacteriophage MS2). Next, Fredrick Sanger introduced the dideoxy chain-terminating method for sequencing DNA molecules, which became the most widely used for over 30 years. 

However, Sanger sequencing lacked automation and was very time-consuming. In 1987, Leroy Hood and Michael Hunkapiller succeeded in automating Sanger sequencing by bringing two major improvements to the method. DNA fragments were labelled with fluorescent dyes instead of radioactive molecules, and the data acquisition and analysis were made possible on the computer. The creation of the AB370A in 1986 was a huge step in increasing the throughput of this revolutionary technique, leading to the sequencing of 96 samples simultaneously.

Thus, “first-generation sequencing” was born. 

Next on the Horizon: Liquid Handling and Automation

The way automation helped advance DNA sequencing served as a landmark for further laboratory automation. The first automated liquid handler was built when the first complete gene was sequenced. As mentioned above, its development occurred in discrete steps. 

In the ‘70s, companies added a motor to pipettes to control aspiration and dispensing. 

In the ‘80s, we saw full workstations able to complete complex protocols. 

And in the ‘90s, high-throughput screening was developed, 

Followed in the early 2000s with next-generation sequencing (NGS). 

Soon after, the advancement of the computer and user-friendly software from companies like Eppendorf launched liquid handling into the mainstream.

Liquid handling is one of the most variable tasks in a lab and undoubtedly the most time-consuming. The development of automated workstations, combined with the modern-day computer, has certainly contributed to the increase in scientific knowledge. 

But, the cost of automated instrumentation has long prohibited widespread implementation. Remember, back in the ‘80s and ‘90s, automation was available but only to the labs/companies who were willing to shell out a pretty penny for the workstations. The companies producing these units required dedicated software programmers; some still require that speciality! 

It wasn’t until the early 2000s that automation became more attainable due to lower costs and increased ease of use. It wasn’t just the pharmaceutical companies and well-funded biotechs that had access anymore. With the release of liquid handlers from Eppendorf, like the first automated pipetting system, the EpMotion, every lab could see a dramatic reduction in their pipetting error, increased throughput, and better compliance with strict regulatory requirements. Automated workflows now drive huge innovations and breakthroughs. Below, we delve into why automated liquid handlers, specifically Eppendorf’s EpMotion, are indispensable in a research lab and their numerous benefits:

1. Precision and Accuracy: One of the key features of the Eppendorf EpMotion liquid handler is its exceptional precision and accuracy. With advanced pipetting technologies, innovative liquid level detection, and intelligent software algorithms, the EpMotion system ensures precise and reproducible pipetting of samples, reagents, and buffers. This level of accuracy minimises human error, enhances experimental reliability, and significantly improves data quality.
2. Flexibility and Scalability: The Eppendorf EpMotion series offers a wide range of liquid handling platforms to meet the diverse needs of laboratories, from small-scale research projects to high-throughput applications. Whether you require a compact benchtop system or a fully automated robotic workstation, Eppendorf provides a solution that can be tailored to your specific requirements. 
3. Intuitive Software and User-Friendly Interface: Eppendorf understands the importance of user experience and has developed a user-friendly software interface for the EpMotion liquid handler. The intuitive software allows for easy programming of pipetting protocols, sample tracking, and data management. The graphical user interface (GUI) provides step-by-step guidance, making it simple for experienced researchers and newcomers to operate the system efficiently. Additionally, the software can seamlessly integrate with laboratory information management systems (LIMS) for streamlined data transfer and analysis.
4. Versatility Across Applications: The Eppendorf EpMotion liquid handler is suitable for various applications, including genomics, proteomics, drug discovery, assay development, and more. Its flexible pipetting capabilities enable precise handling of different sample types, volumes, and formats, including microplates, tubes, and reservoirs. Whether you need to perform PCR setup, nucleic acid purification, serial dilutions, sample transfers, or NGS library prep, the EpMotion system can streamline your workflow and save valuable time.
5. Eppendorf Quality and Support: Eppendorf is renowned for its commitment to quality and customer support. The EpMotion liquid handler is built with high-quality materials and undergoes rigorous testing to ensure reliability and long-term performance. Eppendorf's worldwide network of service and support teams provides timely assistance, troubleshooting, and maintenance, ensuring the uninterrupted operation of your liquid handling system.

These benefits and EpMotion’s robust history in launching and driving laboratory automation have empowered the life science industry to continue innovating.

Data Management on Paper: A Problem Ripe for Innovation

We’ve used technology to advance and accelerate sequencing and liquid handling, yet other things we do in labs have remained woefully archaic.

I’m still puzzled when I work with researchers and labs on automating their methods, and most lab members are still carrying around huge notebooks filled with their protocols, notes, results, tweaks, etc. 

The same process was used back in 1950 when Enders, Weller, and Robbins were culturing the poliovirus in search of a vaccine. Yet, as I said at the beginning of this blog, the amount of data generated by lab scientists has exploded! How can the life science industry expect to manage it using only paper?

It’s Time for Next-Generation Lab Notebooks

eLabNext is critical in the next step of our advancement in the scientific industry: It provides a digital platform for tracking your samples, integrating with automated liquid handlers, mapping and visualising your workflow, keeping your data secure, managing your inventory, and easy collaboration. eLabNext has a way of organising and thus prioritising useful and actionable data.

At Eppendorf and eLabNext, we have an end-to-end solution for the modern laboratory: Sample tracking from the sample inception to cold storage, processing on your EpMotion, and beyond. 

And now that AI is making even more inroads into the life sciences, integration with digital platforms is the next exciting innovation on the horizon! Read 10 Actionable Steps for Using AI in Your Research Lab to learn more.

In research, as in life, there are setbacks, tragedies, and mishaps.

Unforeseen electrical problems, accidental or purposeful human intervention, or extreme weather can all have lasting consequences for your lab’s samples, inventory, data, records, and, ultimately, the pace at which you recover and progress in your research.

Take, for instance, a recent story from Rensselaer Polytechnic Institute (RPI), where a custodial worker, annoyed by an alarm from an ultra low-temperature (ULT) freezer, allegedly flipped a circuit breaker, causing the freezer to heat up to -32℃ from its normal temperature at -80℃. 

The consequences were devastating: The destruction of samples collected over 25 years of research and at least $1 million in damages.

Over the past two decades, extreme weather events have also caused massive destruction to research laboratories. During Hurricane Katrina, many ULT freezers lost power, warming to room temperature. At Louisiana State University (LSU), 100% of animals housed in animal facilities were lost. Similar animal deaths were seen at NYU Langone Medical Center, an unfortunate consequence of Hurricane Sandy hitting New York City.

Lab Safety Procedures: Building Resilience Through Digitalization

Nothing can reverse the impact of these painful and sad situations. 

And while we may never be able to control the weather, there are ways to minimise the impact of the unforeseen events mentioned above. 

Future-proofing your lab against disaster relies on digitalisation of lab operations. Here are three considerations for moving your lab towards an “all digital” strategy.

Implement a Digital Lab Platform in Your Workflow

Rebuilding after losing samples, animal models, or data will likely require you and your team to regenerate samples or models, repeat experiments, and replicate and re-analyze data. Doing this requires rapid and unfettered access to protocol, sample, and experimental data.

Digital platforms and databases enable efficient organisation and storage of experimental data, making it easier to locate and retrieve archived information when needed. Furthermore, digitalisation promotes collaboration and knowledge sharing among researchers, fostering the exchange of ideas and accelerating the recovery and replication of lost samples, models, and data.

Many digital platforms utilise cloud computing and storage technologies, allowing for easy access to lab information anywhere in the world. So, if you need to evacuate your lab due to a natural disaster, accessing your data is as easy as logging into the platform once you get to safety.

Manage and Track Samples

If a freezer fails, as it might in the real-world situations described above, you’ll need to relocate samples to functional freezers rapidly and prioritise your most important samples. If you lose samples, you’ll need to access any related metadata about those samples so that you can repeat experiments and re-generate them.

Digital platforms provide centralised databases with sample information, including location, storage conditions, and related data, which can be recorded and easily accessed. Barcode or RFID-based tracking systems enable efficient sample identification, reducing the risk of errors and misplacements. Researchers can track samples throughout their lifecycle, from collection to storage, analysis, and disposal, ensuring proper handling. So, in the event of a freezer mishap, you can rapidly locate your most essential samples and get them back to optimal storage conditions.

Train Lab Personnel for Digitalisation

To safeguard your laboratory against unforeseen threats, everyone from lab technicians to lab directors must be trained and feel comfortable on your digital lab platform. By doing this, your team can tap into the true benefits of digitalisation, such as improved communication and collaboration, enhanced data integrity and security, and increased productivity. 

This type of shift in strategy doesn't happen overnight, though. It requires training, leadership, and a steady transition toward digitalisation. We’ve overseen so many labs going through the process of making this transition that we know the common pitfalls and have developed a process for mitigating them. When everyone is armed with a digital lab platform and the knowledge of how to use it, everyday efficiency increases, and you provide your lab with comprehensive preparation for dealing with unforeseen samples or data loss.

Embrace Lab Safety & Secure Your Digital Journey

Unforeseen events and disasters can devastate your lab work, causing samples, data, and research progress loss. While we cannot see the future, there are steps we can take to protect our labs and minimise the impact of such unpredictable incidents. 

Future-proofing your lab against loss requires a full embrace of digitalisation. By implementing a digital lab notebook, you can efficiently store and retrieve experimental data, facilitate collaboration, and accelerate the recovery and replication of lost samples and data.

If you want to learn more about how eLabNext or Sample360 can help streamline and protect your lab operations from unforeseen circumstances, schedule a personal demo today!

In the realm of life sciences, plasmids, self-sufficient double-stranded DNA molecules, are invaluable tools used extensively in laboratories for genetic engineering, recombinant protein synthesis, vaccine and therapy development, and gene function analysis. Owing to their ability to carry specific genes and regulate their expression, plasmids serve as crucial elements for developing gene therapies and vaccines, offering unparalleled control and selectivity.

However, managing an expanding plasmid library can be challenging, given that minute changes in their sequence can transpire during cloning, passaging, or optimizing for increased expression and efficiency. Additionally, their quality may degrade over time due to improper storage or contamination. The key to navigating these complexities is rigorous record-keeping and storage protocols involving unique identifiers, frequent quality checks, and the use of digital databases such as Microsoft Excel trackers, dedicated Laboratory Information Management System (LIMS) or Electronic Lab Notebooks (ELN). It’s crucial to exercise extreme caution when using these systems, as any inaccuracies in the plasmid backbone, antibiotic resistance, selection marker, or optimal bacterial cells to transform into can create confusion, errors, and an unnecessary drain on time and resources.

In this blog, we’ll introduce some of the common plasmids used in the life science space and provide some best practices for building, maintaining, managing, and storing a plasmid library.

The Most Widely Used Plasmids in R&D

Akin to choosing the right tool for a job, constructing a suitable plasmid library tailored to your research needs is vital. Researchers commonly have a variety of base plasmids and their derivatives in their repertoire, ready for use based on the type of experiment planned. For instance, to understand a gene's role in a disease model, you might construct a plasmid library consisting of various functional domains of the gene or variants missing specific domains and carrying targeted mutations. Maintaining organized information about each plasmid, including the backbone, cloning strategy, and purification strategy, is crucial for achieving reliable and reproducible results.

Numerous plasmid variants are extensively utilized in research and development, with some of the most popular ones being pUC19 vectors, pET vectors, pGEX vectors, pBABE vectors, and lentiviral vectors. pUC19 vectors have been pivotal in DNA sequencing, recombinant protein production, genetic engineering of crops, and bacterial genetics study. pET vectors, known for high-level protein expression in E. coli, are renowned for their T7 promoter, selection markers, multiple cloning sites, fusion tags, and inducible expression. pGEX vectors, on the other hand, are used to express and purify recombinant proteins fused with glutathione S-transferase (GST) in E. coli. pBABE vectors enable retroviral gene transfer and stable gene expression in mammalian cells. Lastly, lentiviral vectors are preferred for gene transfer and gene therapy in mammalian cells, providing efficient gene delivery, gene editing, and potential uses in cancer therapy and vaccine development.

Molecular Biology Techniques for Working with Plasmids

A plethora of molecular biology techniques are employed in wet labs for the creation and upkeep of plasmid libraries, each tailored to the project's specific requirements. Some commonly utilized techniques include PCR amplification, restriction enzyme digestion, and ligation, which aid in gene or gene fragment amplification, isolation, and insertion into plasmids. Transformation is a fundamental procedure involving the introduction of plasmids into bacterial cells for replication and maintenance.

Post-transformation, antibiotic or fluorescence-based selection plays a crucial role in maintaining cells with plasmids. Sequencing aids in determining the DNA sequence of plasmids or libraries, thus facilitating the identification of specific genes or DNA fragments. DNA extraction and purification, encompassing processes like alkaline lysis, precipitation, and column-based or bead-based purification, are necessary for isolating DNA from bacterial cells. Innovative cloning techniques like Gibson assembly or Golden Gate assembly can also be employed for plasmid synthesis. Choosing the most suitable techniques for plasmid library construction and maintenance hinges on several project-specific factors, such as the library's size, the type of plasmids utilized, and the intended downstream applications.

Time to Take Your Plasmid Library to the Next Level

Building, managing, and analyzing a plasmid library can be complex, but with the right tools and strategies, you can create a sustainable resource that drives your research forward. Knowing how to maintain, store, and manage your plasmid library effectively is crucial to ensure consistent, reliable results in your work.

Luckily, we have curated an in-depth guide titled "The Ultimate Guide to Building, Managing, and Analyzing Your Plasmid Library". This guide provides comprehensive insights into the following:

• Creating a sustainable plasmid library
• Best practices for maintaining a plasmid library
• Best Practices for storing your plasmid library
• Utilizing software tools for In Silico Plasmid Library and Sequence Management

By utilizing this guide, you can optimize your strategies, streamline your processes, and keep your research at the cutting edge of scientific discovery.

Digitalisation is taking over our personal and professional lives. 

Now more than ever, life science organisations are digitising their lab tools and research operations to increase efficiency, enhance data management, foster collaboration, and ensure data security.

The application of artificial intelligence (AI) and machine learning (ML) has also become widespread, thereby generating deeper insights and answers to the grand (yet challenging) biological questions we face today.

This blog post will explore the increasing data management challenges academic, industry, government, and non-profit research organisations face in our rapidly evolving era of AI, automation, and multi-omics.

The Need for a Digital Solution for “Everything” in the Life Science Lab

The need for a comprehensive digital lab solution has become more evident as research data becomes more dispersed across various data analysis and information management systems. In today’s dynamic landscape, organisations seek a more centralised platform to oversee “everything” in a life science lab: Data, samples, protocols, notebook entries, reagents, inventories, instruments, and more. 

Moreover, the demand for interoperability and seamless integration with other systems is rapidly growing, along with the need to comply with ever-changing research governance, ethics, data security, and educational requirements.

To address these challenges effectively, the transition from traditional paper lab notebooks to electronic lab notebooks (ELNs) began over two decades ago and is now accelerating and growing globally. Adopting an ELN offers a range of benefits, including user-friendly interfaces, enhanced security measures, and compatibility with other systems.

By digitising laboratory processes, scientific progress and publications are expected to scale, regulatory compliance will improve, and job satisfaction and student learning experiences will be enhanced.

It is important to note that the success and impact of lab digitalisation depend on internal change management practices, process standardisation, and robust end-user training and support structures.

With these elements in place, life science organisations can fully leverage the potential of digital lab solutions and navigate the transformative journey toward a more efficient research environment.

How Do Digital Lab Platforms Help Research Operations and Management? 

There are many ways that digital lab platforms can benefit life science labs. Here, we review a few key publications that offer reliable data to support the advantages of using digital lab platforms.

Faster and FAIRer Data Quality Output

When utilised effectively, ELNs significantly increase the speed of data collection, analysis, and collaboration. 

Researchers who have successfully implemented ELNs have reported faster completion of research experiments compared to traditional paper notebooks. This is partly because modern research equipment generates digital data, allowing for seamless integration with ELNs. 

A 2022 Nature article highlighted that using ELNs frees up more time for actual research by reducing the time required for data collection, analysis, and manuscript preparation. Can you imagine how much time you could save if you didn’t have to print data on paper, trim the excess with scissors, and glue or tape it into a paper lab notebook? Moreover, the digitalisation of laboratory processes facilitates the standardisation of data collection and analysis, promoting transparency and reproducibility of experiments.

Another challenge scientists and researchers face is facilitating knowledge discovery of scientific data and its associated workflows and algorithms by machines and humans. FAIR data practices outline principles to make data Findable, Accessible, Interoperable, and Reusable, thus facilitating the uninhibited data flow to the broader scientific community. With ELNs, you can document all device setups, plan experiments, save digital experiment data, and add human or analogue observations, enabling researchers to comply with FAIR data practices seamlessly.

In addition to these benefits, certain ELN providers offer Application Programming Interfaces (APIs) and Software Development Kits (SDKs) that enable users to connect their ELN with other research software platforms and systems, such as Microsoft365, GraphPad Prism, and other third-party software.

These integrations streamline workflows, minimise errors and duplications, and enable easy data transfer or sharing between platforms.

Lab digitalisation enhances research output and future-proofs your processes by facilitating further integration and adapting to evolving inter-operational requirements. By embracing ELNs, researchers can experience accelerated research progress while establishing a robust foundation for their ongoing scientific endeavours.

Increased Regulatory Compliance

Beyond the obvious benefits like protecting sensitive data, intellectual property, and patents, an excellent digital lab platform ensures compliance with legal and cybersecurity standards; ELNs can also reinforce compliance with bio-risk and hazardous materials management regulations.

For example, ELNs can include features that facilitate proper handling, storage, and disposal of biological and hazardous materials. They provide audit trails and generate reports, simplifying compliance demonstrations during inspections or audits. In addition, ELNs enable project- and user-based organisation, rather than just the rigid and traditional user-based organisation seen in paper lab notebooks. Thus, the protocols, samples, and data from multiple individuals working on a specific project can be accessed from a single place within the ELN. This enables more accurate tracking of operations, as there may be personnel turnover throughout the course of a project or preparation of a manuscript.

In a review published in the Journal of Biosafety and Biosecurity, Sun et al. recommend using digital lab platforms to ensure safety, efficiency, and compliance with bio-risk management regulations in biosafety laboratories (BSLs).

These digital solutions streamline data collection, track the movement of biological and chemical samples, and maintain up-to-date Standard Operating Procedures. ELNs offer simple interfaces and customisable features for dealing with challenges, such as genetically-modified (GM) specimens, radioactive samples, or cytotoxic materials. 

By embracing ELNs and other digital lab solutions, researchers can enhance compliance with bio-risk management regulations, improve data traceability, and streamline processes related to handling hazardous materials. 

Enhanced Lab Personnel & Student Experience

ELNs offer a reliable and efficient way to maintain up-to-date records of experiments and research data. Equipped with digital features, these solutions enable scientists to collect, organise, templatise, and analyse data with improved efficiency. This saves time and ensures that information is readily accessible whenever needed.

Another notable advantage of ELNs is their positive impact on student learning experiences. Research from Riley et al. has shown that ELNs facilitate learning in laboratory settings. Students benefit more from quickly searching and retrieving information, streamlining their workflow, and feeling more engaged and motivated in their work. ELNs also support team-based learning, fostering collaboration and knowledge sharing among students.

Besides supporting student learning, ELNs enhance interdisciplinary collaboration and knowledge sharing among researchers. They enable scientists to collaborate more effectively with external partners, facilitating the transfer of knowledge and expertise and improving productivity and efficiency in the laboratory. 

By automating routine tasks such as data entry, calculations, and report generation, scientists can allocate more time to high-value research activities. Notably, ELNs that are interoperable with other systems are expected to add value to the everyday work of laboratory personnel, as they will further streamline workflows.

While adapting to change can be challenging for end-users, the benefits of a digital lab environment, backed with appropriate training and support, will undoubtedly have a positive and long-lasting impact on the experience of research staff and students working in laboratories.

Conclusion

In conclusion, electronic lab notebooks benefit organisations regarding research management and operations. While the use of ELNs and lab digitalisation is dependent on the internal rollout and support structure for these systemic changes, the evidence suggests that they can: 

1. Contribute to more efficient and collaborative research processes, which can ultimately lead to faster publication times.
2.  Facilitate compliance through improved tracking, documentation, and auditing.
3.  Improve the laboratory experience of students and lab personnel by reducing their administrative workload and freeing up time for their high-value work (i.e., performing research and data analysis and preparing manuscripts).

Our product, eLabJournal, is more than just an ELN. It is an all-in-one comprehensive Digital Lab Platform (DLP) for managing your research data, protocols, and inventory as well as having the capacity to integrate with existing research systems. 

Contact us for your free 30-day trial and/or a demonstration to see for yourself!

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.

If you’re reading this, you’re likely on a desktop computer, tablet, or phone. 

We often take the complex inner workings of these devices for granted, but what they do is incredible, managing input and output from a wide range of software and hardware. 

And at the center of it all is the operating system (OS), an essential piece of software that communicates with the central processing unit (CPU), hard drive, memory, and other software, integrating them so your device can operate correctly. It also enables you as a user to communicate with your computer, tablet, or phone and perform tasks through a simple visual interface without knowing how to speak your device’s language. 

While the basic function is the same, not all OSs are created equally: Apple’s OS provides a visually stunning interface with an emphasis on simplicity and integration. In the case of Microsoft’s OS, high performance, security, and usability are the priorities. 

For the past few years, my team and I have envisioned a world where an OS could exist in a life science lab. Instead of using a different program for each instrument, all instruments and equipment could be accessed and controlled using one software interface without prior knowledge about the specifics of their inner workings, bringing lab automation to a new level. This possibility would make experimentation accessible to personnel of all experience levels and save massive amounts of time on a lab-, department-, and organization-wide scale.

In the following blog, we’ll dive deeper into lab automation, the current limitations of automated instrumentations, and how our mission – building a “Lab OS” – can bring about the next generation of life science research. 

The Basics and Benefits of Lab Automation

Over the past few decades, the number of sophisticated automated liquid handling and analytical instruments has increased, arming scientists with powerful tools for advancing our understanding of the world around us.

There are 3 core components of lab automation that make it possible:

• Robotic systems: Robotic systems can perform a wide range of routine laboratory tasks, including liquid handling, sample preparation, plate handling, and assay processing. These automated systems are equipped with precise mechanisms and sensors that enable them to manipulate small volumes of liquid, accurately dispense reagents, and carry out repetitive pipetting steps with high precision. They can work around the clock, with minimal hands-on time, accelerating the pace of experimentation and increasing productivity.
• Instrument software: Robotics hardware is essential but is useless without software to tell it what to do and provide a user with a portal for controlling it. Automation software allows for the control and coordination of various instruments and devices in the laboratory. It enables the design and execution of complex experimental protocols, the scheduling of tasks, and the monitoring of instrument performance. 
• Data management and analysis systems: Data management and analysis systems facilitate the storage, retrieval, and analysis of experimental data generated from some instruments, making it easier for scientists to manage and interpret large volumes of information. Depending on the platform, a data management system may be a simple “one trick pony” or an end-to-end solution for the entire data lifecycle. 

Ultimately, combining these three components into an automated instrument setting that can perform everything from sample preparation to analysis, leads to significant benefits for many laboratories, including:

• Enhanced reproducibility: The reproducibility crisis in the sciences and the contributing factors have long been a boon to the advancement of research. Robotic systems combat several of these issues by performing tasks with high accuracy, reducing the risk of human error (though not eliminating it), and improving data quality. Automated processes also facilitate the replication of experiments, enabling researchers to obtain reliable and reproducible results, essential for scientific advancements and regulatory compliance.
• Long-term cost efficiency: While laboratory automation requires a relatively large initial investment, it can lead to significant long-term cost savings. By increasing throughput and productivity, automation optimizes resource utilization, reducing labor costs and minimizing the need for reagents and consumables. Additionally, automation reduces the risk of costly errors and rework, enhancing operational efficiency and cost-effectiveness.
• Safety and risk mitigation: By minimizing exposure to hazardous materials and repetitive strain injuries associated with manual handling, laboratory automation helps mitigate safety risks to personnel. Automated systems can handle potentially dangerous substances and perform tasks in controlled environments, reducing the risk of accidents and ensuring a safer working environment.
• Accelerated discovery: Automation expedites the R&D process, enabling scientists to conduct experiments faster. With the ability to process large numbers of samples and perform high-throughput experimentation, automation facilitates rapid data generation and analysis. This accelerated workflow promotes faster scientific discoveries, enhances innovation, and expedites the translation of research findings into practical applications.
• Standardization and compliance: Automation helps establish standardized protocols and procedures, ensuring consistency across experiments and laboratories. This standardization is crucial in regulated environments, where compliance with strict quality standards and regulatory requirements is necessary. Automation enables precise control over experimental parameters, data collection, and documentation, simplifying regulatory compliance and audit processes.
• Improved data management: Automation integrates with sophisticated software systems to seamlessly capture, analyze, and store data. This eliminates manual data entry, reduces transcription errors, and enhances data integrity. Automated data management enables real-time monitoring and tracking of experimental progress, ensuring efficient data organization and retrieval and facilitating data-driven decision-making.

Limitations to the Current Lab Automation Ecosystem

While the benefits of automation are clear, there are still limitations that remain.

Limitation #1: Scientific Experience and Instrument-Specific Training Requirements

Working with current automated laboratory instruments and equipment requires a thorough understanding of how manual life science protocols are designed and implemented. In addition, experience with the instruments' operation, functionality, and associated software is necessary, and training by or consultation with a technical expert is usually required before operating an instrument. This knowledge and training enable laboratory personnel to make informed decisions, troubleshoot issues, and optimize the performance of automated systems.

Each automated laboratory instrument has unique features, protocols, and software interfaces. Users must receive specific training on the instrument they will be working with to understand its capabilities, constraints, and maintenance requirements. Training programs provided by instrument manufacturers or third-party organizations familiar with the technology can help users gain expertise in operating the specific instrument effectively. However, this is not a long-term solution: Trainees will forget their training over time and make mistakes.

Limitation #2: Workflow Integration

Many workflows and protocols require multiple automated instruments with unique features, protocols, and software platforms. To create a fully-automated, cohesive workflow, lab personnel must understand each instrument’s role, requiring additional training. In addition, because there are multiple platforms at play and no unifying system that interfaces with them, manual communication and processing are needed to ensure a smooth integration, data transfer, and analysis. 

Limitation #3: Human Error

Automated instruments eliminate many aspects of human mistakes in the research process, yet there are several steps that are error-prone. Most systems require specific input parameters or configurations to perform tasks accurately. If errors are made during protocol setup, an instrument may inadvertently execute the wrong steps at a much larger scale than would be done if executed manually. This can result in erroneous data, unsuccessful experiments, and a massive waste of resources, reagents, and consumables. 

Automated instruments also require regular calibration and maintenance to ensure accurate performance. Failure to properly calibrate or maintain the equipment can lead to downstream complications, and (as above) if an error goes unnoticed, it may result in inaccurate results, necessitating retesting and wasting resources.

Lab OS: Launching the Next-Generation in Automation 

At the beginning of this blog, I asked you to imagine a fully connected lab controlled by a Lab OS. 

As you can see by the limitations outlined above, there is a need for the modernization of current laboratory automation. The current automated systems, with their robotics, software, and data management systems, are unnecessarily complex.

Furthermore, the “automation” of these instruments is a misnomer. Current instrumentation has reduced hands-on time significantly compared to manual protocols. Yet, trained personnel are still needed to tend to them to handle errors and ensure protocols are executed as intended. 

To bring about the next phase in laboratory automation, my team and I at Genie Life Sciences have created a unifying Lab OS called Genie LabOS, enabling the full realization of your current automation stack without purchasing a whole new fleet of instruments.

The OS is instrument-agnostic, enabling scientists and automation engineers to design protocols across all connected instruments and accessories without needing training on instrument-specific software or hardware. Genie makes lab automation approachable by filling in the tiresome details for your deck layout, tips, and liquid class settings for clean and efficient liquid handling.

In doing so, laboratory personnel at all skill levels have access to the capabilities of their automated instruments. Building protocols can be done with simple, drag-and-drop ease. In addition, virtual dry runs capture the majority of a researcher’s intent, eliminate errors without having to do trial-and-error wet runs and enable users to publish protocols for better sharing and oversight. 

Schedule a demo today to see how you can unleash the next generation of your laboratory’s automation capabilities.

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!

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?

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

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.

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.

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

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.