The automated prep station step for NanoString nCounter runs typically takes between 2 hours 40 minutes and 3 hours 10 minutes, depending on the sensitivity setting.
Understanding NanoString Run Times
The duration of a NanoString experiment can vary based on the specific steps involved, but a core automated phase, the prep station, has defined timings. This information focuses on the time required for automated sample processing on the NanoString nCounter prep station.
Prep Station Processing Times
The NanoString nCounter system's prep station is essential for preparing samples for subsequent data collection. The time it takes for this step primarily depends on the chosen sensitivity mode, which influences the throughput and the required processing.
- Normal Sensitivity Run: For a typical run involving 12 samples, the prep station completes its process in approximately 2 hours and 40 minutes after initiation. This setting is generally used for standard applications where the target abundance is moderate to high, offering a balance between speed and detection capabilities.
- High Sensitivity Run: When your experiment requires detecting targets at lower abundance, the high sensitivity mode is utilized. This mode extends the prep station time by about 30 minutes compared to the normal sensitivity run. Consequently, a high sensitivity run will take approximately 3 hours and 10 minutes (2 hours 40 minutes + 30 minutes).
Here's a quick overview of the prep station timings for common scenarios:
| Run Type | Approximate Duration (Prep Station) | Notes |
|---|---|---|
| Normal Sensitivity | 2 hours 40 minutes | For runs of up to 12 samples, standard detection. |
| High Sensitivity | 3 hours 10 minutes | Adds ~30 minutes for enhanced detection of low-abundance targets. |
What These Times Mean for Your Workflow
The times provided above specifically refer to the automated processing phase on the NanoString prep station. It's important to consider other parts of the overall workflow when planning your experiments:
- Upstream Sample Preparation: This includes critical steps like nucleic acid extraction, quantification, and quality control. The duration of these steps varies significantly based on sample type and lab protocols.
- System Setup and Loading: Time taken to load samples and reagents onto the prep station, and to configure the instrument settings before starting a run.
- Data Collection (Reporter Reading): After the prep station completes its work, the processed samples are moved to the Digital Analyzer for signal readout and data collection. The time for this step is not included in the prep station times but typically proceeds quickly per sample.
- Data Analysis: Once raw data is collected, subsequent bioinformatic analysis, quality control, and interpretation of results are also integral parts of the experimental timeline, often requiring significant time.
Optimizing Your NanoString Workflow
To ensure efficient use of your NanoString system and minimize overall experiment time, consider these practical insights:
- Batching Samples Effectively: While the prep station times are often given for specific sample counts (e.g., 12 samples), planning your experiments to maximize batching can optimize instrument usage and reduce idle time between runs.
- Thorough Pre-preparation: Have all your samples, necessary reagents, and consumables ready and organized before initiating the prep station run to prevent any delays.
- Understand Your Experimental Needs: Choose between normal and high sensitivity modes based on your target abundance and specific experimental goals. This ensures you obtain the required data without unnecessary extended run times or insufficient detection.
- Familiarize with the NanoString nCounter Systems: A comprehensive understanding of the different modules and their functionalities can help you streamline your process and troubleshoot efficiently.
By understanding these key timings and workflow considerations, you can effectively plan and execute your NanoString experiments, from initial sample preparation to final data acquisition.
