Creating Laboratory Workflows That
Grow With Your Research

Research rarely follows a fixed path. New scientific questions can change study design, require different methods or experimental endpoints, introduce new sample or analytical requirements, and create opportunities to incorporate new technologies.

Supporting changing research requirements requires laboratory workflows built around both consistency and flexibility. Consistency establishes reliable processes for handling samples, performing procedures, maintaining quality, and generating reproducible data. Flexibility allows those processes to accommodate new requirements without unnecessarily disrupting the work already in place.

Understanding where consistency is essential and where flexibility is needed to accommodate change is key to maintaining research continuity and keeping projects moving forward.

Build Consistency Across The Laboratory Workflow  
A laboratory workflow is only as consistent as the conditions surrounding each step and the connections between those steps. What consistency requires will differ depending on the laboratory and the research being conducted.

Standard operating procedures (SOPs), for example, can establish consistent methods for activities such as sample preparation, reagent handling, equipment use, and data recording. Routine calibration and maintenance, standardized sample handling, lot tracking, and defined quality checks help maintain consistency, minimize operational errors, and support data accuracy.

In addition, effective documentation can reinforce established practices, preserve laboratory knowledge, and help maintain consistency as work moves between people or stages of the research process.¹

Consistency does not mean that every part of the workflow must remain unchanged. Establishing control where it matters most provides a reliable foundation for adapting to changing research requirements.

Build Flexibility Into The Laboratory Workflow
A flexible laboratory workflow allows researchers to accommodate changing research requirements without unnecessarily disrupting established processes. Over the course of a research program, experimental methods may evolve, analytical requirements may change, instrumentation may need to be upgraded, and capacity may need to expand.

Building flexibility into the workflow can help researchers adapt while saving time and keeping research moving forward. It can also make it easier to modify or expand individual parts of the research process without requiring broader changes across the laboratory. Flexibility does not always require large-scale changes to laboratory operations. Targeted upgrades can expand what researchers are able to do while allowing established processes to remain in place.

A recent BSL-2 laboratory upgrade at Lambton College provides a practical example. Rather than undertaking a major renovation, the college built on its existing containment infrastructure with targeted equipment upgrades and functional zoning to support more advanced research. The laboratory also selected “plug-and-play” equipment that could be installed within existing electrical, plumbing, and spatial constraints, allowing ongoing research to continue with minimal disruption.²

This type of flexibility can help researchers expand capabilities, make better use of existing laboratory resources, and advance their work without the time and disruption associated with large-scale changes.

Integrate New Capabilities Without Rebuilding The Workflow
New instrumentation, automation, digital technologies, and AI are expanding what researchers can do across the laboratory. These capabilities are also becoming increasingly connected, linking experimental processes with imaging, data generation, analysis, and research decision-making. As a result, integrating a new technology requires considering how it will interact with the processes, instrumentation, data requirements, and analytics.

Automation can reduce repetitive manual steps and increase processing capacity, while digital technologies and AI can accelerate analysis and support more quantitative approaches to evaluating data. Emerging AI systems are extending this integration further by supporting activities such as hypothesis generation, experimental design, and data analysis, while advances in laboratory automation are creating tighter connections between experimental execution and analysis.³

Planning for these integrations can help researchers introduce new capabilities efficiently, make better use of existing resources and data, and expand what they can accomplish without rebuilding the entire workflow.

Build Adaptability Into The Laboratory Workflow
Researchers cannot anticipate every direction their work may take, but they can build adaptable laboratory workflows to support change and innovation. When research priorities shift or new capabilities are needed, adaptability can help laboratories respond without extensive changes that interrupt ongoing work.

HistoSpring offers 25,000 square feet of flexible wet lab and BSL-2 space designed to support modern translational research. With open bench and tissue culture labs, advanced instrumentation, and integrated histology and digital pathology services, researchers have the resources needed to advance their projects.

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