In and out of the life sciences, the conversation about artificial intelligence (AI) is impossible to avoid. Because AI has crossed over into mainstream culture, the discussion about the pros and cons of its use is amplified. As with any new technology, there is fear and calls for an immediate half-year moratorium on research.

The flip side is the radical optimism espoused by Sam Altman of Open AI, with statements about its ability to improve the human condition. This sentiment is at the centre of the concept of human-centric AI, which we at eLabNext believe can benefit the biotech community. The following blog will discuss the basics of human-centric AI and how it can drive positive change in today’s modern biotech labs.

What is Human-Centric AI?

Human-centric AI refers to designing, developing, and deploying AI systems that prioritize the well-being, needs, and values of humans. In other words, it's the use of AI to improve the human condition.

Key Principles of Human-Centric AI 

To ensure that AI systems are developed and deployed in ways that align with human interests, some guiding principles have emerged to help those actively engaged in AI work towards improving the human condition.

1. Transparency & Explainability: Make AI systems explainable and understandable to humans, ensuring transparency in decision-making.
2. Fairness and Avoiding Bias: Mitigate biases and guarantee fair treatment of individuals from diverse backgrounds, considering factors such as gender, race, or socioeconomic status.
3. Privacy and Data Protection: Respect individuals' privacy rights and implement robust data protection measures to safeguard sensitive information.
4. User Empowerment, User Control, and Autonomy: Design AI systems that empower individuals by giving them control, autonomy, and the ability to understand and influence AI's behaviour.
5. Collaborative Interaction: Encourage human-AI collaboration and create AI systems that complement human capabilities, fostering teamwork and shared decision-making.
6. Social & Environmental Impact: Assess the broader societal consequences of AI deployment and strive to address potential negative impacts while maximizing positive outcomes.
7. Robustness and Reliability: Develop AI systems focusing on reliability, robustness, and safety, minimizing the potential for errors, biases, or unintended consequences. Adequate testing, validation, and risk assessment procedures should be in place.
8. Ethical Governance: Integrate ethical considerations into all stages of AI development, including data collection, algorithm design, deployment, and monitoring.

Applying Human-Centric AI in Biotech

AI is already being applied in healthcare, where it’s being used to directly enhance the human condition with better disease detection and prediction.

Further upstream, in the biotech discovery or drug and diagnostic development spaces, human-centric AI enables vetting drug candidates, developing fruitful pre-clinical testing strategies, and more. There have been early adopters of AI systems and those who are more cautious, waiting until the dust clears to implement AI into their workflows.

Whether you fall into one camp or another, AI implementation in a laboratory environment requires a robust digital infrastructure. For those utilizing old-school, in-house built systems or pen-and-paper record-keeping with no long-term digitalization strategy, harnessing AI's power is bound to be a multi-stage, lengthy, and costly process. The foundation for being a human-centric AI biotech company is having a robust digital foundation across the board, from day-to-day sample management to large-scale raw file data control.

Ultimately, it comes down to having a Digital Lab Strategy that can lead your organization to implement human-centric AI more seamlessly, either now or in the not-too-distant future. 

Start Your Lab’s Digital Journey Today

Are your samples and experiments digitized? Can you easily access and analyze your data? Is there a healthy collaborative culture and technical capability?

If so, then the rest is easy. Schedule a free personal demo with our digitalization specialist to get started!

The goal for anyone working in laboratory automation is to “set it and forget it.”

In a perfect world, we could set up an assay on an instrument of our choosing, start the program, and walk away to do one of the other thousands of things on our “to-do” list. 

Even though automation has gotten more powerful and precise, the amount of time you can walk away from a machine without worry – appropriately called walkaway time – requires trust and confidence in your instrument, infrastructure, and yourself. It also requires some optimization.

There are many ways to increase your walkaway time. In the following blog, we’ll talk about the top brands in laboratory automation and some strategies to increase your walkaway time. 

Top 10 Laboratory Automation Brands

Several top brands have established themselves as leaders in lab automation, offering cutting-edge solutions to streamline laboratory workflows and enhance efficiency. These brands shape the future of lab automation, enabling scientists to achieve higher productivity and reproducibility in their research endeavors.

Here are the top 10 lab automation brands:

1. Eppendorf: Eppendorf is known for its wide range of laboratory equipment, including the epMotion^® series of automated liquid handlers that improve pipetting accuracy and precision for reproducible results.
2. Promega: Promega offers automation solutions for various applications, including nucleic acid extraction, DNA quantification, and genotyping. Their Maxwell series of instruments is well-regarded in the field.
3. Hamilton Robotics: Hamilton Robotics specializes in advanced robotic systems for liquid handling, sample preparation, and plate handling. Their Microlab series and STAR workstations are widely used in biotech labs. Check out this case study to see how these instruments were used to build an automated COVID-19 testing facility at Boston University.
4. Beckman Coulter: Known for their Biomek series of liquid handling robots, Beckman Coulter offers versatile and reliable automation solutions for various laboratory workflows.
5. Tecan: Tecan is a leading provider of laboratory automation solutions, including liquid handling systems, plate handlers, and integrated workstations such as the Fluent and Freedom EVO platforms.
6. Thermo Fisher Scientific: Thermo Fisher Scientific offers a comprehensive range of laboratory automation solutions, including plate handlers, liquid handling systems, and integrated workstations like the Cytomat and Orbitor series.
7. QIAGEN: QIAGEN provides various automated nucleic acid extraction and purification solutions, such as the QIAcube and QIAxtractor systems, broadly used in molecular biology and genomics research.
8. Agilent Technologies: Agilent Technologies offers a diverse portfolio of laboratory automation solutions, including liquid chromatography systems, sample preparation platforms, and robotic workstations like the Bravo and VWorks series.
9. PerkinElmer: PerkinElmer provides various automation solutions for high-throughput screening, imaging, and data analysis. Their Janus and Opera systems are used in drug discovery and genomics research.
10. Illumina: Illumina, a leader in next-generation sequencing (NGS) technologies, offers automation solutions for library preparation and sequencing, including the NovaSeq Prep System and the iSeq library prep kits.

Personally, I like GenieLife and OpenTrons. They are newer players in the lab automation game, but I highly recommend looking into them.

6 Strategies For Increasing Your Walkaway Time

Everyone could do for a few more minutes in their day. By increasing your walkaway time, you and your team can reclaim some of your precious time to take care of important tasks.

Here are some ways to optimize your lab’s workflows and increase your walkaway time.

Process in Batches

Optimize the use of robotics by performing tasks in batches. Group similar experiments or assays together and schedule them to run sequentially or in parallel, allowing the robot to perform multiple tasks in a single run. This approach can minimize downtime and maximize the use of resources, increasing your walkaway time.

Optimize Resources

Ensure that all necessary resources, such as consumables, reagents, and samples, are readily available before initiating automated processes. Stock up on commonly used items to minimize interruptions. Implement automated inventory management systems to track and replenish supplies as needed, reducing the need for manual intervention.

Monitor and Maintain

Regularly monitor the performance of your robotic systems and automation equipment to identify potential issues or errors. Implement preventive maintenance schedules to keep the equipment in optimal condition. This proactive approach can reduce unexpected downtimes.

Create Redundant and Failover System

Implement redundant systems and failover mechanisms where possible to minimize the impact of equipment failures or malfunctions. Having backup robotic systems or spare parts readily available can help ensure continuous operation and increase the walkaway time.

Manage Data Efficiently

Implement a robust data management and analysis system to handle the large volumes of data generated during automated processes. Utilize bioinformatics pipelines and software tools to automate data processing, analysis, and reporting. This reduces the need for manual data handling.

Ensure that your digital lab platform of choice has an Open API and SDK to allow you to connect your robots and software systems. This enables you to perform complicated tasks in a multi-dimensional ecosystem and utilize AI/ML to access the data for analysis.

Get Digital…

…but do it strategically. Implementing a digital lab strategy is essential to keep your software and instruments in a connected web that drives efficient experimentation, data analysis, collaboration, and accessibility. All of this enhances the walkaway time for your instruments and facilitates the removal of manual activities like exporting data, processing raw data, and more.

Conclusion

What’s your walkaway time? If you’d like to increase it and be more efficient, try some of the steps above to make your lab more streamlined. And don’t forget about other important aspects of lab operations, including quality and precision. After all, even if your walkaway time is high, you're just wasting time and money if you’re generating low-quality, inaccurate data.

To explore how you can increase and optimize your walkaway time, schedule a free personal demo today!

Cyber attacks have become a significant concern for life science businesses and research organisations in today's digital world. Recently, a cyber attack on a research institution, The Kaiserslautern University of Applied Sciences in Germany, led to a complete shutdown of their IT network.

And they are not the only life science organisation to suffer such a blow to their operations: The University of Zurich had a severe cyber attack in early 2023, and many others have endured similar issues.

The problem exists across all industries, with cyber attacks increasing since 2019 – more than 300% from 2019 to 2020 – driven primarily by the pandemic and new adjustments to remote work. However, in the life sciences, where laboratories play a crucial role in scientific progress, researchers need to adopt robust security measures.

Lab heads and managers can help protect their operations by choosing software platforms that take data security into account. For those who work in digitised labs, the electronic lab notebook (ELN) software providers offer different possibilities to ensure data security. In this blog post, we will discuss the essential features to look for, the pros and cons of a cloud-based and on-premise hosting solution,  and what to consider regarding cyber security. 

Security Infrastructure and Dynamic Security Measures

A cloud-based hosting solution offers the advantage of scalable and dynamic security measures and a robust security infrastructure provided by the cloud service provider. As cyber threats evolve, cloud providers can quickly implement and update security protocols to address new vulnerabilities. This includes deploying patches, updates, and enhanced security features across their entire infrastructure, benefiting all users of the cloud-based ELN. These providers invest heavily in state-of-the-art security measures, including advanced firewalls, intrusion detection systems, and encryption protocols, which result in a comprehensive and resilient infrastructure.

With an on-premise installation, customers have more control and customization over the security infrastructure. Organisations can implement specific security protocols and access, which might be regulatory requirements when working with sensitive data. Should you decide to go for an on-premise installation, investing in and maintaining your organisation's security infrastructure is crucial. This includes regularly implementing and updating security measures, which usually require significant resources and expertise. 

Enhanced Resilience and Disaster Recovery

Another essential point to remember when choosing a hosting solution is what happens in the event of a cyber attack. How fast can you be back on your feet to continue working?

A cloud-based solution usually offers the advantage of resilience and disaster recovery capabilities. Cloud providers operate in multiple data centres across various geographic locations, which minimises the impact of a single point of failure. This ensures that even if one data centre is compromised, operations can seamlessly transition to another location, minimising service disruption.  Furthermore, cloud providers backup data automatically and regularly, allowing for easy recovery in case of data loss or system failures. Additionally, providers have dedicated disaster recovery plans and infrastructure, ensuring that services can be quickly restored after significant incidents. This relieves the organisation from managing its disaster recovery infrastructure and simplifies the data restoration process.

Given that in an on-premise solution, the customer has direct control over its hardware and infrastructure, the level of resilience and disaster recovery strategy will depend on the organisation. It is crucial for customers with an on-premise installation to implement redundant systems, backup power supplies, and failover mechanisms to ensure continued operations in case of a cyber attack. Additionally, these organisations need to have a disaster recovery strategy, which includes performing regular data backups, rigorous testing, and maintaining off-site backup facilities. 

Expert Security Monitoring and Response

Cloud-based ELN software has the benefit of security monitoring and response experts. These providers usually have a dedicated security team equipped with advanced security tools and technologies to monitor the cloud infrastructure for potential threats. This allows them to proactively identify and respond to security incidents, leveraging their experience with a wide range of clients and attack patterns. Cloud providers have also established incident response protocols to swiftly and efficiently handle cyber attack threats. In a security incident, they can quickly contain the threat, investigate the root cause, and implement necessary remediation measures.

In contrast, on-premise solutions require the organisation to establish and maintain its expert security monitoring team. This team is responsible for continuously monitoring the network, system logs, and user activities to detect suspicious or anomalous behaviour. In the event of a cyber-attack threat or breach, the on-premise security team takes immediate action to contain the threat and mitigate the damage. Since the response time and effectiveness heavily rely on the expertise and experience of the in-house team, it is important the organisation invests in hiring and training cybersecurity experts. 

A Final Word on Vetting a Cloud-Based vs On-Premise Hosted ELN

Cloud-based ELNs offer many advantages, but you and your team are responsible for carefully and meticulously investigating the security measures offered by a cloud provider and ensuring that they align with their specific security requirements and the compliance standards you need. 

One way to ensure that a cloud provider follows international standards for quality security and data protection is to check for their ISO Certifications. The most relevant ISO Certification is ISO 27001, which focuses on information security management systems (ISMS) and ensures the provider can effectively manage and protect sensitive data.

On-premise solutions offer greater control over security measures and allow you to keep sensitive data within the organisation's boundaries. Still, they pressure your organisation to build and maintain your security monitoring and response capabilities. Implementing all these measures can cost significant time and money.

Ultimately, choosing between an on-premise Installation and a cloud-based solution will depend on factors like an organisation's resources, security expertise, data sensitivity, and regulatory requirements. While on-premises solutions offer more direct control over general security measures, they also require higher resources and in-house management. On the contrary, cloud-based solutions provide convenience and potential benefits from specialised expertise but require trust in the cloud provider's security practices.

Contact us today to talk to eLabNext about your ELN and data security needs!

Companies in the life sciences often discuss Business Strategy and R&D Strategy, focusing primarily on creating value and gaining an edge over their competition. 

But we rarely discuss a newer type of strategy: Digital Lab Strategy, which has become a foundational pillar for a successful organisation. The number of software and instruments that deal with raw data analysis, collaboration, and accessibility is now massive and an immediate need of day-to-day laboratory operations. 

So, if developing a Digital Lab Strategy is at the bottom of your to-do list, you may be setting yourself up for failure.

Digital Lab Strategy has revolutionised the industry by allowing labs and research facilities (and most likely your competitors) to drive innovation and digitalisation. In this blog, we will discuss why labs need a comprehensive Digital Lab Strategy and how you can implement it to accelerate performance and achieve better results. 

Digital Lab Strategy: A Multi-Faceted Solution for the Life Science Industry

Whether navigating the road to FDA approval, applying for grants, and/or publishing research papers, there is generally a rough strategy that will help you achieve your goal. This may include hiring the right people, choosing the suitable therapeutic modality or target, developing the proper internal team hierarchy, identifying partners from other organisations, outsourcing animal studies to skilled collaborators, and attracting investors or grants to give you the money to achieve all of the above. 

Previously, digital solutions were just some of the many tools used to achieve these goals.

Nowadays, however, they define the strategies, set the pace and timelines, and serve as a unique selling point for collaborators and investors.  

For example, an un-digitised biotech start-up may appear not to be keeping up with times or keen on moving forward by a potential investor, regardless of how revolutionary their IP might be.  

But, Digitalisation is Difficult…

Countless barriers stand in the way of organisations developing and implementing a Digital Lab Strategy.

For Big Pharma companies, the problem is being “too digital.” One of the biggest problems is having decentralised data and using many digital tools. This leads to a loss of data and longer data analysis periods.

In Academia, the problem is a bit different. Labs and PIs are rushing to get grants and churning out publications in an environment with a rapid churn of personnel. This makes it difficult to formulate a sustainable digital foundation and leads to repeating old experiments, losing samples, and a slower research pace.

In healthcare, the lack of digital lab strategy is primarily due to using ancient, in-house systems. For example, an older Laboratory Information Management System (LIMS) can be inconsistent and not very user-friendly, and it can experience issues with data updates. Taken together, this makes scientists apprehensive about using it. Decentralising data in different digital tools and creating a sustainable ecosystem becomes a headache for scientists working in the industry.

Your Digital Lab Strategy Checklist

To prevent these issues and inconsistencies, having a Digital Lab Strategy is integral for all labs and research facilities. Digitaliasation is multi-faceted, and there are a lot of different parts of lab operations where it can be integrated. To help you prepare a comprehensive digital lab strategy, we have provided a checklist for further guidance:

General Sample Strategy

• Make a list of all the sample types that you are working with
•  Develop a suitable naming convention, and determine if you will be able to scale using your current system
•  Make a plan regarding storing, tracking, accessing, and analysing samples
•  Conduct temperature monitoring; check if you have reliable sensors for your incubators and freezers
•  Label and secure your prepared samples. Check if your labelling needs can be easily digitalised into your current system

General Inventory Strategy

• Check the equipment you are currently using, and see if you are keeping track of their calibration/validation schedules
•  Determine how you are tracking the equipment usage
•  Analyse the supply and ordering management you are currently using. Make a note if there are any persistent issues or concerns due to backorder
•  Barcode your inventory
•  Ensure that you have an automated workflow

SOP Tracking and Development Strategy

• Control all your protocols and procedures 
•  Develop clear ownership of protocols, and create proper collaboration tactics
•  Check if there is an approval process involved in the audit trail
•  Determine if your protocol development integrates and positively influences your sample and experimental design management

Data Reporting and Experimental Design Strategy

• Check if a digital project management strategy is in place, such as program coding, naming conventions, collaboration hierarchies, etc.
•  See what tools you use to manage your general projects/tasks, and specify your experiments and lab reports 
•  Clearly define lab report lengths and the format in which they will be completed (e.g., how are results written for easy access and translation)
•  Ensure that everything is standardised and that everyone is developing their own result structure
•  Implement a proper handoff system in place between colleagues and departments
•  Maintain proper correspondence about data transfer and management with the automation team

Automation Strategy

• Utilise instruments and software that can be integrated with other systems
•  Optimise your walkaway time

Customization and Integration Strategy

• Check if the systems you are using are capable of integration using an open API
•  Check if the system has a Developer Hub
•  See if you have an easily accessible Software Developer Kit (SDK) to make your own customisations
•  Assess if you can integrate the system with your robots and other instruments
•  Check if you have all the desired software and if integrating with them is a possibility

General IT and Digital Security Compliance

• Decide if you want to outsource the IT services or hire an in-house team
•  Ensure you have the expertise and training to manage the servers internally
•  Check your internal security standards

Compliance with Different Regulatory Environments

• GxP
•  HIPPA
•  GDPR
•  21CFR Part11
•  CLIA 

Data Science Strategy

• See if you will be using AI and ML solutions, and if so, what are your guidelines 
•  Check which analytical techniques (e.g., multi-omics, image, flow cytometry, etc.) you will base your research strategy on 
•  Decide if you have plans to scale the business 
•  See if you have plans to participate in continuous data analysis or do you plan to shift direction 

Overall Digital Strategy

• Determine your 3-year plan. For example, how many robots you’d like to integrate, what other integrations you’d like to have, and with which systems
•  Pay attention to your long-term strategy. Decide how you will mine and analyse all the data that you have gathered over 5 to 10 years
•  Think about the hiring trajectory and whether you have resources to train your staff, promote a culture of innovation, and continuously grow in the current space 

Conclusion

In conclusion, Digital Lab Strategy is now “in the DNA” of all labs and trickles down to the research and business strategy rather than the other way around. The sooner an organisation embraces digitalisation, the quicker it can pivot in the right direction. It is anticipated that labs that uphold strict and standardised digital protocols and adopt AI and ML will be leaps ahead of their competition. This pattern can already be observed with the current customers. 

If you are ready to strategise about your digital lab journey, get in touch with us today!

Biofoundries are a relatively new player in biotechnology, but they’ve rapidly become hubs of innovation and scientific advancement. These multidisciplinary centres combine concepts from computer sciences, engineering, and biology to transform basic research findings into widespread societal change. With biofoundries positioned to solve global issues, including pandemic preparedness, we’ve put together the following guide on the keys to building a successful biofoundry.

Let’s start with the basics.

What is a Biofoundry?

A biofoundry is an integrated facility that combines biological, chemical biology, and engineering systems with tools like automation, high-throughput measurement, integrated data analysis, and artificial intelligence (AI) to enable feedback loops that facilitate iterative end-to-end cycles of design, build, test, and learn.

7 Guidelines for Successful Biofoundry Operations

In a multidisciplinary setting like this, you can imagine many difficulties that hinder progress can arise. Chief among them is data connectivity and ensuring that all instruments, personnel, and software communicate effectively. 

So, how do you navigate those challenges and ensure that your bio-foundry operations continue smoothly?

Often, labs throw a lot of money into purchasing the latest toys and assume that everything can be automated. But automation starts with a strong foundation of standardised practices.  Biofoundries must have an overall workflow schema that is tested and optimised before the fancy toys even enter the lab.

Here are 7 steps to building a connected, successful biofoundry.

Build an Infrastructure

Biofoundry success starts with a framework that supports all personnel, equipment, and software. When creating your infrastructure, consider the following:

• Standardisation: As simple as developing naming conventions for samples, services, equipment, projects, and programs is, it can go a long way in setting the stage for your biofoundry to grow into a functional ecosystem. Standardisation plays into how you manage your data, accessibility, and collaboration between departments and teams. 
• Scalability: You’re not just planning for now but five years from now. What will the new liquid handling robots look like, and what new functionalities will they have? How can AI and ML be used for data analysis based on how you collect your data? More importantly, what do your organisation's needs look like now and in the future? If one robot breaks and a new one comes in, or one lab tech leaves and another one comes in, how short will your downtime be, and how fast will you be able to get back to full speed? Think about whether or not you’ll be able to expand your workflows and instruments as you grow.
• Interdisciplinary expertise: The future is collaborative. And so is the present. Traditionally, chemists and biologists worked separately. It is the same for IT folks, computational biologists, and bioinformaticians. Each individual must have interdisciplinary expertise to work with people from different research backgrounds. Personnel must also have exceptional project management skills to ensure no data loss and full ownership of projects. 

Spend Strategically

Fancy robots cost a lot of money. Be sure to evaluate your biofoundry’s values. Do you need to pay for that liquid handler that is accurate to within 0.000001 mL? If so, make it a strategic purchase, as part of your business plan. 

If not, there are many other reliable and less expensive robots out there that can get the job done for you. 

Other budget considerations include:

• Training and education: There will be a constant need to train and educate. A strong budget for these initiatives is essential for your organisation's and personnel's continued growth.
• External services and collaborations: Developing relationships with business partners like DNA sequencing companies or custom manufacturers will enable your biofoundry to expand capabilities and increase efficiency.
• Office and lab space: Using Boston as an example, lab and office space in Kendall Square is pretty saturated, and the cost of office space is extremely high. It is important to consider the location for biofoundry positioning, client generation, staff travel, and more. In Boston specifically, Watertown, Woburn, and Natick have emerging biotech scenes with 40% less associated cost, so considering other areas outside of the “limelight” may be in your budget’s best interest.

Follow Regulatory Guidelines

GxP or 21CFR Part11 compliance might not be necessary for your lab; however, ensuring you don’t step out of compliance will bring more trust and accuracy to your workflows.

Compliance with specific regulations can also open inroads to fruitful collaborations. Consider GDPR, CLIA, and HIPAA compliance to attract partnerships with hospitals and other healthcare companies. 

Forge a Tribe with a Culture of Collaboration

As remote work has taken over, prioritising company culture has fallen by the wayside. People are getting fired on Zoom and sending passive-aggressive emails about a minor conflict when a face-to-face conversation over coffee will do. Thoughtful and conscious communication has disappeared. But in a multidisciplinary environment, like a biofoundry, openness to transparent communication must be in the organisation's DNA. 

Establishing stand-up meetings, consistent training regimens, and facilitating a culture of incentivized ideas and suggestions will go a long way. There are always great ideas for optimization that frequently get lost in the mix due to poor communication or fear of rejection. 

Communication about operations can also be streamlined and clarified through digital platforms, where workflows and protocols can be accessed, tracked, and updated to maintain and optimise biofoundry performance.

Adjust to New Tech and Evaluate

We frequently mistake looking at a period of plateau in performance as a “good enough” result when, in reality, it is just short-term stability. In the age of AI/ML, where software and robotics are updated a minimum of 4 times a year, training, educating, and motivating staff to stay updated with the latest technologies is essential. This encourages a culture of constant improvement and experimentation. But, be sure to evaluate how these adjustments affect your performance: If a new tool isn’t suiting you, move on and look for another solution.

Optimise for Walkaway Time

One of the most underestimated tools in biofoundries is automation. Your robot may be fancy, but if you have to tend it every 5 minutes or don’t trust its performance, then you’re not using automation to its full potential. Measure the success of your automation based on the time you can use your instrument with confidence that a protocol will run precisely as you intend. 

Develop a Data Strategy

The most important aspect of biofoundry automation is developing a digital lab and data strategy. In short, that means how you will digitise your biofoundry’s operations.

Defining a strong sample strategy (i.e., A systematic plan for handling, processing, and analysing samples) right from the beginning is imperative to successful lab operations. Next, consider the data that will be generated from sample analysis.

Key questions include: 

• What instruments will be used to generate data? Are they ISO certified, secure, and compliant?
• Does your digital platform (e.g., ELN, LIMS, etc.) have an open API/SDK for integrations and data analysis? Can it integrate with your favourite robots and other software you use in the lab?
• Is your platform future-proof?  Will you outgrow it once you scale operations?

Conclusion

In an article in Nature about Flagship Pioneering (an early backer of Moderna), the consensus is clear: Biotech innovators derive numerous benefits from software that connects their operations. Digital lab platforms that enhance collaboration between chemists, biologists, bioinformaticians, and other biofoundry personnel are becoming the norm in our post-pandemic world.

And the benefits are tangible. Flagship Pioneering’s Aram Adourian says, “In our groups, experiments are often planned between wet lab teams and computational teams, enabling a sort of iterative, back-and-forth optimization process to address the biological questions at hand.” 

In a biofoundry, this is the fabric that holds everything together! 

To find out more and get a detailed free consultation on managing your biofoundry better and defining its Digital Lab Strategy, schedule a personal demo today!

Labs and organizations never skimp on developing a solid, well-researched business strategy. 

However, the starting point for data, intellectual property, and scientific publications – biological and/or chemical samples – are often ignored during strategic planning meetings,  falling by the wayside as a byproduct of overall lab operations. 

With the introduction of the Sample Strategy, this is changing. Here, we present to you a new perspective on lab operations, where Sample Management becomes the foundational fabric of the lab, enabling the future-proofing of operations. 

Let’s start with what a sample strategy is.

What is a Sample Strategy?

In a laboratory setting, a sample strategy refers to a systematic plan for handling, processing, and analyzing samples. It involves defining a study's objectives, determining the type and number of samples required, and establishing the appropriate collection methods, storage conditions, and handling protocols. A well-designed sample strategy ensures the reliability and reproducibility of experimental results, minimizes potential biases, and maximizes the efficiency of laboratory resources.

To help you formulate your sample strategy, let’s focus on some key questions to consider throughout the lifecycle of a sample.

Sample Selection

This involves determining the appropriate type and number of samples to be collected. It considers factors such as the purpose of the study, the environmental, biological, or chemistry specimen being sampled, and any specific criteria or guidelines that need to be followed.

Key Questions

• What are your sample types?
  • Cell lines? 
  • Plasmids?
  • Antibodies?
  • Biospecimens? 
  • Chemicals?
  • Live animals or plants?
• What are the metadata fields that you keep track of?
  • Date of the collection? 
  • Sequence
  • Passage number
  • SMILES code
  • GeneBank file
• What files, images, and references must be linked to your sample? 

Sample Collection

This step involves physically obtaining the samples according to pre-determined protocols. It may include techniques such as sampling from a larger batch, using specialized equipment or instruments, or following specific procedures to ensure consistency and accuracy.

Key Questions

• Are you using automation-like scanners, QR Codes, mobile apps, and/or pre-barcoded tubes for collection? 
• Do you plan on labeling your samples after the fact? If so, which printers are you using?
• Can you easily collect, move, and update your samples? 

Sample Storage

Proper storage of samples ensures long-term stability and maintenance of sample integrity. It can involve sample dilution in a new buffer, cryoprotectant, or lyophilization before storage. In addition, the temperature and storage container are both considerations for your sample strategy.

Key Questions

• Where are you physically storing your samples (e.g., fridge, freezer, cryotank, etc.)
• Where are you storing digital information associated with your samples?
• Do you monitor the temperature and the viability of your samples? 

Sample Handling

Proper handling of samples is also crucial to maintain their integrity and prevent contamination or degradation. This may include labeling, preservation, storage conditions (e.g., temperature, humidity), and transportation considerations. Adhering to standard operating procedures (SOPs) is important to maintain the quality of the samples.

Key Questions

• Are you using liquid handling instruments like epMotion, Hamilton, GenieLife, or Opentrons?

Sample Preparation and Protocol Management

Depending on the analysis required, samples may undergo certain preparation procedures before testing. This could involve sample grinding, dilution, extraction, filtration, or other techniques to make the samples suitable for analysis.

Key Questions

• What SOPs are you using to prepare and process your samples?
• Is there a clear version control of your protocols and an approval process?
• Are your prep assays standardized and auditable? 
• What instruments will you be using, what will be the output files of your samples, and how do you plan on analyzing that data? 

Documentation and Recordkeeping

A comprehensive sample strategy includes proper documentation and recordkeeping throughout the entire process. This includes recording sample information, collection dates, handling procedures, deviations or incidents, and other relevant data.

Key Questions

Do you have a proper Electronic Lab Notebook (ELN) to reference the sample-related experimental design and data analysis? 

Analysis Plan

The sample strategy also encompasses an analysis plan outlining the methods, techniques, and instruments to analyze the samples. It may include specific testing protocols, quality control measures, and data analysis approaches to ensure accurate and meaningful results.

Key Questions

• What software do you use to analyze your large CSV files?
• Do you use AI and ML for your sample data analysis? 
• Do you have a long-term data analysis plan?

Conclusion

Samples go through a predictable life cycle and have a lifespan – just like our cars, lab equipment, and bodies! You must oil your car, calibrate your instruments, and have a healthy diet and exercise regimen to maintain everything properly! 

Samples are no different. A successful lab must have a short- and long-term strategy for its samples, from collection to analysis and beyond. 

We are here to assist you with that. If you’d like to find the best answers to the questions above, schedule a free personal demo today!

Note: Consider looking into Sample360 – this will help you to define a Sample Strategy while incorporating your lab’s instruments into the mix!

Whether in a small academic lab or part of a large team in a big pharma lab, managing and storing hazardous or potentially infectious substances are crucial for personal safety and environmental protection.

Whilst improper chemical storage can lead to serious incidents like fires, chemical burns, or even glass vessel ruptures, recent events, such as the fear surrounding the possible lab origin of SARS-CoV-19, provides a stark reminder of the importance of the safe and effective storage of viruses to prevent any potential risks to public health and safety.

In this blog, we'll explore best practices for safely and effectively handling chemicals and viruses in the laboratory. Equipping yourself with this knowledge can create a safer, more organised, and more secure working environment. By no means are the laboratory practices listed here a comprehensive list, so please consult with your EH&S supervisor to ensure that your lab fully complies with your organisation's safety regulations within the country you operate in. If you operate in multiple countries, consider adopting the highest standards from each to create a global standard that can be used in every country.

Best Practices for Chemical Management

Chemicals are widely used in various life science and pharmaceutical applications for research, product development, and production. However, improper storage of chemicals can lead to serious accidents such as explosions, fires, and toxic gas releases. Therefore, it is essential to store chemicals safely and efficiently to prevent accidents and ensure the safety of your lab personnel.

Here are some best practices and tips for safely and efficiently storing chemicals.

Choose the Right Storage Location

The location of the chemical storage area is critical to ensure the safety of the workers and the environment. The storage area should be located away from ignition sources, such as flames, sparks, and electrical equipment. It should also be located away from direct sunlight, moisture, and extreme temperatures.

The storage area should also have adequate ventilation to prevent the accumulation of toxic fumes or gases. In addition, the area should be well-lit and have clear labels indicating the type of chemicals stored and their hazards.

Use Appropriate Containers

Chemicals should be stored in appropriate containers compatible with the chemical being stored. For example, acids should be stored in acid-resistant containers, while flammable liquids should be stored in grounded, explosion-proof containers. Chemicals should never be stored in food or drink containers or unmarked containers.

It is also essential to label all containers with the name of the chemical, its hazard class, and any other relevant information, such as the date of purchase, date of opening, and expiration date.

Segregate Chemicals

Chemicals should be segregated based on their compatibility to prevent accidental reactions. For example, acids should be stored separately from bases, and oxidising agents should be held separately from flammable substances.

Store Chemicals According to Hazard Class

Chemicals should be stored according to their hazard class. The four main hazard classes are flammable, corrosive, toxic, and oxidising. Flammable liquids should be stored in a cool, dry, well-ventilated area away from ignition sources. Corrosive chemicals should be stored in a dedicated storage area with a spill containment system.

Toxic chemicals should be stored in a secure area with limited access, and oxidising agents should be held separately from flammable materials.

Train Employees on Safe Chemical Handling

All employees who handle chemicals should be trained in safe chemical handling practices. This includes proper handling and storage procedures, personal protective equipment (PPE) requirements, and emergency response procedures. In addition, employees should be trained to read and interpret chemical labels and safety data sheets (SDS).

Implement a Chemical Inventory System

A chemical inventory system should be implemented to keep track of all chemicals in storage. The inventory system should include the name of the chemical, quantity, location, hazard class, and expiration date. The system should also have a method for safely disposing of expired or unwanted chemicals. eLabInventory is an example of an inventory management system that can be employed as a chemical inventory system (or similar). We should be aware that we currently cannot provide hazardous labelling in the system.

Best Practices for Virus Management

If you're in a laboratory that deals with viruses, then being aware of the proper safety and containment procedures is incredibly important. This reduces the risk of lab personnel being accidentally infected or spreading the infection outside the lab. Here are some commonly used methods to effectively manage the risks of working with viral pathogens.

Storage

Viruses can be stored frozen at extremely low temperatures, typically -80°C or colder, using cryoprotective agents to prevent damage from ice formation. This method is commonly used for long-term storage and can preserve virus viability for decades. Another storage method, lyophilization (also known as freeze-drying), involves removing water from the virus, leaving behind a stable, dry product. The virus is frozen, and a vacuum is applied to remove the water, preserving the virus for an extended period. This method is often used for short-term storage and transportation.

Containment Measures and Equipment

Prioritise containment measures to minimise exposure and infection risks. Utilise primary barriers, such as biosafety cabinets (BSCs) and enclosed containers. This will help prevent the release of infectious aerosols during manipulative procedures.

Design laboratory facilities with secondary barriers to protect personnel and the environment. Regularly maintain and inspect laboratory equipment to prevent malfunctioning that could lead to accidental virus release. Emphasise the importance of good microbiological techniques and specialised safety practices in handling emerging viruses safely.

Personal Protective Equipment (PPE)

Enforce the proper use of Personal Protective Equipment (PPE) when working with viruses. Ensure laboratory personnel wear appropriate gloves, gowns, face shields, and respirators, depending on the specific tasks and potential exposure risks.

Provide training on how to don and doff PPE correctly to minimise the risk of contamination. It is essential to fit-test all respirators to ensure a proper fit and consider vaccination as an additional precaution to enhance personal protection.

Biosecurity Measures

Implement robust biosecurity plans to prevent emerging viruses' unauthorised release and misuse. Conduct risk assessments and identify potential threats, vulnerabilities, and countermeasures specific to the laboratory facility.

It may also be necessary to involve specialised working groups comprising scientists, administrators, security staff, and law enforcement when necessary. Focus on physical security, personnel security, material control, transport security, and information security to safeguard against bioterrorism threats.

Conclusion

The safe and efficient management of chemicals and viruses in laboratory settings is paramount to ensure the well-being of laboratory personnel and protect the environment. Improper chemical storage can lead to hazardous incidents, while mishandling viruses can pose severe risks to public health. Part of adequate chemical inventory and virus sample management is tracking what’s in stock, where samples are, and all associated metadata.

The eLabNext digital lab platform can provide a simple, secure, and safe solution for your chemical and virus management needs.

Sign up for a personal demo of our platform today!

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.