The High-Throughput Bottleneck - From Manual Mapping to Automated Plates

As R&D labs scale, the transition from experimental design to plate execution often becomes a manual bottleneck, where errors in complex planning can lead to costly mistakes. What many labs find is that hours spent manually mapping out dozens of samples into plates are impossible when you shift from a few 96-well plates to over 50 384-well plates in a single run. 

While Benchling offers significant native flexibility, high-throughput automation requires a strategic approach to data architecture. At Karchem Consulting, when scientists approach trying to solve the struggles of high-throughput plating and maintaining accurate plate records in their ELN, we start with a fundamental question: Is the pattern repeatable?

From Pain Point to Pattern Recognition

In mid 2025, scientists at a biotech client reached out, facing a growing challenge: their qPCR workflows were scaling rapidly, with plans to run upwards of 50, 384-well plates per month. Manual plate mapping was consuming hours of scientists' time and introduced risk at every step; a single misplaced well could compromise an entire experimental run.

Our discovery process began with understanding their specific pain points. Through initial conversations, we learned that while their plating strategies varied across different assay types, each followed consistent, repeatable patterns within its category. The challenge wasn't the logic itself; it was the manual execution at scale.

We conducted detailed requirements-gathering sessions to map their primary workflows, documenting the specific stamping patterns, dilution series, and sample-to-well mappings that define each assay type. The requirements process revealed something crucial: despite the apparent complexity, each workflow had clear, programmable logic.

At smaller scales, many labs rely on 96-well plates with predefined layouts. These layouts typically follow a consistent pattern, and where consistency exists, automation opportunities follow. The specific plate configurations: duplicates, triplicates, and reagent wells, and strategic empty wells, may differ from group to group, but once defined for a given process, we can build. 

Example 96-WP w/ Pattern

We’ve seen previously how consultants Angel Kleiman and Caitlin Tormey demonstrated the use of unstructured and inventory tables to create preset plate maps that include formulas to streamline setup. Creating a template for plate mapping allows users to spend less time manually assigning wells and more time focusing on experimental outcomes. However, as throughput increases, maintaining these structures manually becomes increasingly difficult. 

Building the Solution

With a baseline mapping system established and clear requirements in hand, we leveraged Benchling Connect and AWS infrastructure to automate their unique plating strategies. While our implementation supported multiple complex workflow types, the underlying principle remained simple: define the pattern once, execute it repeatedly.

For a straightforward expansion example, we built a run that takes a received 96-well plate and expands it to 384-well format. The process starts by registering the 384-well plate from the input plate that scientists want to expand.

Once the new plate is inserted into the run, Connect creates an input CSV file based on the provided expansion logic that can then be used to expand the 96-well plate into a 384-well plate.

After processing, each source well is automatically expanded into multiple wells, while still preserving the original layout or pattern. Whether you're working with duplicates, triplicates, quadruplicates, or even more complex designs, a few simple clicks can handle the entire expansion for you. No manual mapping required.

Critically, this automated registration creates the foundation for downstream data integration, and each well is properly tracked and ready to receive assay results, maintaining complete traceability from sample to data point.

From Implementation to Impact

During implementation, we conducted user acceptance testing and training sessions to ensure scientists could confidently execute these automated workflows. The question that often blocks the shift from manual to automated processes is not “Where should these samples go” but often “What defines how these samples move” - if there is structured logic, labs can:

• Generate plate maps programmatically

• Register output plates and fill automatically

• Maintain complete sample traceability across plate expansions

• Run many 384-well plates simultaneously without scaling manual effort

Whether a lab is running a handful of 96-well plates or orchestrating dozens of 384-well plates per run, the underlying challenge is the same. As complexity increases, the need to maintain speed, accuracy, and traceability increases. 

At Karchem Consulting, we work with teams across discovery, screening, and high-throughput environments to design Benchling workflows that grow with you, not against you.

If high-throughput plating or scaling sample tracking is becoming a bottleneck in your lab, we'd love to help. Get in touch with #TeamKC today.