A guide to sample management for modern R&D

Your manager has tasked you with the formidable job of digitally and physically organizing your lab’s samples and inventory into an intuitive and easy to maintain system of organization. Easy, right?

But as you attempt to excavate the ice encrusted graveyard of samples of coworkers long gone in your lab’s ancient sample freezer and sift through expired inventory that may have been purchased at the turn of the century, you wonder if modern sample management may be harder than you think.

In this guide, we break down sample management into bite-sized tasks, share key principles for effective sample management, and outline what modern solutions exist so that you can spend less time organizing and more time on your next experiment.

What is sample management?

Sample management is a vital part of scientific R&D, that if done right, has the potential to improve product quality and patient health. It encompasses the processes and practices involved in handling samples of materials, products, or substances throughout their lifecycle.

While these practices may vary from company to company, common sample management tasks include:

• Sample collection: This initial step establishes standardized procedures for collecting and documenting samples, including origin, collection method, and any relevant environmental conditions. 

• Labeling and identification: Using labels to assign identifying information, as well as the use of barcodes, ensures that each unique sample can be tracked in the lab and you’re not spending precious minutes trying to decipher half-smudged Sharpie.

• Storage: Storing samples under appropriate conditions preserves integrity, while optimizing storage location means you’re not digging through the lab freezer on a regular basis.

• Tracking and analysis: Consistent documentation of the handling of samples provides a clear record of sample movement. When analyzing samples, it’s important to follow protocols to drive consistency with results and insights so that you don’t have to repeat experiments when data is somehow misplaced.

• Quality control and compliance: It’s a prerequisite for effective management that samples maintain standards for quality, commonly achieved through regular audits. Regulatory and industry standards such as Good Laboratory Practice (GLP) provide guidance for creating such conditions. 

• Disposal: Safe and compliant disposal, including records of disposed samples, ensures visibility and traceability for all parties and prevents you from having to sift through expired samples and reagents. 

Why you need effective sample management in the new R&D era 

Effective sample management practices are foundational to successful R&D operations, supporting accuracy, compliance, and innovation. But as the number of compounds being handled in a lab grows, managing samples can become more challenging. In this new era, R&D is driven by increasingly complex experiments, calling for sample management practices that are built for such intricacy. 

As the number of variables and samples involved in experiments rises, so does the volume of data. In our 2024 State of Tech in Biotech report, 87% of biotech leaders surveyed expected at least a 2x growth in R&D data within their teams alone this year. And because these teams are embracing new approaches to discover and develop drugs, not only are they generating exponentially more scientific data, but that data is increasingly fragmented as well. Managing samples effectively allows for better organization and integration of such, helping researchers make sense of large datasets and derive meaningful insights.

The new era of science calls for more: more people, more data, and more technology. In our report, 53% of large-company respondents said they collaborate and share data with over 20 colleagues, so integrating data and teams becomes essential. Following the principles for effective sample management can help ensure that data is accurate, well-organized, and accessible, making it easier to correlate, share, and analyze results across experiments and studies.

Principles for effective sample management

It’s no question that managing samples effectively is critical for accurate, reliable experimental results. By sticking to the following principles, teams can ensure that their sample management practices support the integrity of their R&D activities — and avoid a dreaded experimental redo from a bad or lost sample.

1. Accurate labels: Ensuring consistent, clear labels for all samples eliminates potential errors, while aiding in tracking and retrieval downstream. Best practice for identifying information includes criteria such as sample ID, date, time and any other relevant descriptors. 

2. Standard operating procedures (SOPs): Establishing and adhering to standardized processes for each phase of sample management helps to further avoid inaccuracies. 

3. Inventory and data management: Integrating sample data with other data sources allows for more robust analysis. And by using inventory management systems to keep track of sample quantities, locations, and usage, the chances of sample loss or redundancy is greatly reduced. 

4. Collaboration and communication: Building the avenues for effective communication and collaboration across and within teams strengthens coordination for improved innovation.

5. Continuous improvement: In order to scale your sample management practices, continuous review must occur. By auditing existing practices, opportunities arise to incorporate new technologies, address issues, and improve efficiency. Listening to your team, and seeking feedback from regular users, will contribute to the overall management process.

To put this all into action, you need more than a crumpled printout taped to a fridge or an Excel sheet that rarely gets opened, let alone updated. You need a solution that serves as a digital window into your physical lab. In other words, a LIMS platform. 

LIMS for sample management

A modern LIMS serves as a central productivity hub to manage lab data, workflows and teams, and increase lab operational efficiency, productivity and effectiveness in delivering results. A LIMS is a prime solution for sample management due to its ability to streamline and enhance many elements of laboratory operations. Serving as a centralized platform for data management, it allows teams to access and manage sample-related data in a single place. By streamlining these processes, a LIMS can enhance productivity, saving valuable time for high-value work. 

Here are a few specific ways that a LIMS can move the needle for managing samples:

• Efficient sample tracking: LIMS provides real-time tracking of samples, providing insight into status, location, and handling history. 

• Customizable workflows: The flexibility LIMS affords ensures that workflows can fit the specific criteria of analyses and sample handling. 

• Data security: Compliant with industry security standards, LIMS offers robust security features that keep sensitive data protected.  

• Data integration: LIMS has interoperability with other laboratory systems, ensuring seamless data transfer and reducing the need for time-consuming manual data entry. 

Why companies love Benchling for sample management

While there are many software applications that serve as point solutions, Benchling’s sample management solution gives you lab inventory management that’s intelligently connected with your entire R&D data– meaning you can see the entire historical context of a sample with a few clicks. With Benchling, R&D teams can optimize space usage, connect physical samples to their experimental context, and automate location tracking to ensure data integrity

Benchling offers custom inventory management, chemical inventory, barcode integration, permissions and location tracking, so you don’t have to spend time hunting for reagents or samples. By interconnecting your samples, experimental results, and metadata, workflows become streamlined, allowing scientists to submit assay requests directly from input samples. And, by integrating instruments such as plate readers, samples are automatically updated in Benchling in real time. 

“It's an enormous benefit transitioning to Benchling because of the wins in operational efficiency. We're seeing end to end times across our screening funnel that are substantially faster, and we're seeing significant improvements on accuracy and precision.” - Jonathan Eads, SVP, Informatics 

Here are a few of the features and functionality that our customers enjoy by choosing Benchling for sample management:

• Sample tracking: Track the locations of your samples in custom containers, plus configure custom storage types, locations, and box dimensions. 

• Labels and barcodes: Utilize zebra printer integration for one-click printing from Benchling, pull up any sample’s full experimental history with our barcode scanner integration, and generate barcode labels automatically. 

• Data entry integrations: Develop integrations for automated data entry, including plate readers and FACS machines, qPCR machines, materials testing machines, and barcode scanners and printers. 

• Link to Registry: From an entity in Registry, see all downstream samples and results that were produced, and use this information to monitor inventory items, tie it to order management and procurement.

Achieving the processes that support better outcomes, quality products, and improved patient health — plus brownie points with your boss — is possible with an intelligent, connected solution.

Want to learn more about getting started with Benchling for sample management? Reach out today.