Lab automation offers many benefits for research laboratories. It allows researchers to perform repetitive and hazardous tasks more safely and efficiently. Automated systems can handle tasks that would be impractical, dangerous or impossible for humans to perform directly. This frees up researchers to focus on more advanced tasks that require human expertise, creativity and decision making.
Automation also improves accuracy and precision. Robotic systems are very precise and consistent in their movements and can perform minute repetitive motions without tiring or making errors. This improves reproducibility of experiments and quality of results. Automated liquid handling and analysis systems virtually eliminate pipetting errors and human mistakes that can compromise experimental data.
In addition, automation enables higher throughput by allowing multiple tasks and experiments to run simultaneously 24/7 without supervision. Researchers can program automated systems to work overnight and through weekends to maximize use of expensive reagents, chemicals and laboratory space. This accelerates the pace of research.
Applications of Lab Automation
Lab automation is used across various areas of life science research including drug discovery, bioproduction, genetics, diagnostics and more. Here are some common applications:
Liquid Handling
Automated liquid handlers are indispensable for high-throughput screening in drug discovery. They can dispense, mix and transfer tiny volumes of liquids with extreme accuracy and speed needed for large-scale assay work. This allows screening of thousands of potential drug candidates in parallel.
Sample Preparation
Preparing biological samples often requires tedious repetitive tasks like centrifugation, homogenization, and nucleic acid/protein extractions. Automated sample prep systems consolidate these multiple steps and process large sample queues unsupervised. This improves consistency and throughput.
Assay Plate Handling
Automated plate handlers are used to transport assay microplates between liquid handlers, readers, incubators, and storage units. They use robotics to efficiently retrieve and deliver plates as needed to coordinate multi-step high-throughput screening workflows.
Detection and Analysis
Imaging systems, PCR thermocyclers, microplate readers and other analytical instruments have built-in robotics to automatically load and unload assay plates, cuvettes or slides for quantitative analysis with high speed and precision. This maximizes useful instrument time.
Laboratory Information Management
LIMS software integrates and tracks instruments, robots, experimental data and samples all through their lifecycles. It orchestrates automated lab processes and acts as the central nervous system for digital labs. LIMS also supports compliance with data management regulations.
Challenges of Lab Automation
While lab automation delivers clear advantages, certain challenges still exist that researchers and vendors continuously work to overcome:
Cost and Complexity
Even basic automated workflow systems require substantial upfront investments and technical skillsets for installation and maintenance that smaller labs may lack access to. Standardizing protocols, minimizing custom steps and achieving scalability are key to making automation practical for more labs.
Sample Tracking
Proper tracking of samples as they move between different robotic systems remains difficult. Advanced sample ID technologies like RFID tags, 2D barcodes and lab blockchains still require wider adoption to realize the full potential of automated digital sample tracking from collection to reporting.
Interfacing Multiple Systems
Integrating instruments and robots from different vendors is still challenging due to lack of standardized interfaces and data formats. Open architecture standards are helping but more work is needed for true plug-and-play capability between lab bots from any supplier.
Software Glitches
Even minor software bugs or connectivity issues in complex automated systems can disrupt workflows and run entire batches of experiments. Continuous software testing, updates and robust error recovery are important to maintain smooth operations.
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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it