CellMek SPS System: Precision Performance

Workflow

Sample Preparation

CellMek SPS Sample Preparation Station main unit

CellMek SPS

Sample Preparation System

DURACartridges with the packaging

DURACartridges

10-Color dry reagent cocktail

Sample Acquisition

Navios EX Flow Cytometer

Navios Flow Cytometer

Sample Analysis

Kaluza C Analysis Software 10 Color Data

Kaluza C

Flow Cytometry Analysis Software

Introduction

The CellMek SPS is an automated sample preparation system intended for in vitro diagnostic use that can be programmed by the user to perform a variety of liquid handing operations, including sample preparation for flow cytometry. It is designed to automate staining, lysing, incubating, and washing of different biological specimen types, which enhances productivity by minimizing resource allocation for repetitive work in the clinical laboratory. The purpose of this study is to demonstrate the precision performance of CellMek SPS (repeatability and reproducibility) using a representative sample preparation workflow that utilizes the Cell Wash Module (CWM) as well as the Dry Reagent Module, which supports the DURACartridge custom dry reagent format.

Table 1. DURACartridge Custom Dry 10C Panel.

Methods

The test cases in this study utilized a Wash-Stain-Lyse/Fix-Wash workflow with a 10-Color (10C) cocktail in DURACartridge dry format and the IOTest 3 +0.25% fixative lysing solution. The 10C cocktail included Kappa-FITC, Lambda-PE, CD10-ECD, CD5-PC5.5, CD200-PC7, CD34-APC, CD38-AA700, CD20-AA750, CD19-PB, and CD45-KrO. Data from samples prepared by the CellMek SPS were acquired on a Navios flow cytometer and analyzed using Kaluza C software. Standard Deviation (SD) and Coefficient of Variations (%CV) were calculated for each marker by each instrument and across all instruments.

For repeatability testing, peripheral blood specimens obtained from normal donors were spiked with CD34+ KG1a Cells and processed by three CellMek SPS instruments (1 donor/instrument, 10 replicates/donor). A single-output-tube panel was defined to process 100 μL of specimen at a time, which was then run in replicate thus eliminating any system variability due to multi-dispense of specimen.

Analysis Strategy for Repeatability:

  • The data files of the repeatability samples were analyzed offline using Kaluza C v1.1 software.
  • Standard deviation (SD) and coefficient of variations (%CV) were calculated for each marker by each instrument (Table 3).
  • Repeatability variability was compared to the specified repeatability acceptance criteria (Table 2).

For reproducibility testing, one lot of ClearLLab Control Cells, Abnormal (part number B90003) was processed on three CellMek SPS instruments. To ensure even use of both assemblies within the CWM and capture of maximum module variability, a two-output-tube panel was defined to process 100 μL of specimen in duplicate per specimen tube run, which was then assigned to two identical specimen tubes that were run in parallel (4 output tubes total). Specimen pairs were run in duplicate (8 output tubes) at least once a day (AM and/or PM) for at least five days for a total of 80 replicates per instrument. The AM and PM runs were processed at least 2 hours apart.

Analysis Strategy for Reproducibility:

  • The data files were analyzed offline using Kaluza C v1.1 software.
  • Standard deviation (SD) and coefficient of variations (%CV) were calculated for each marker by each instrument and across all instruments (Tables 4 and 5).
  • Variability was compared to the specified reproducibility acceptance criteria (Table 2).
  • ClearLLab Control Cells Abnormal were used and assessed for % positive cell populations measured against the lot-specific assay sheet (Table 6)

Table 2. Precision Acceptance Criteria.

Results

Conclusion

Data of the repeatability and reproducibility samples were analyzed for each marker by each instrument and across all instruments. For each immune subset, the SD was less than 2 when the population was ≤ 20%, and the %CV was less than 10 when the population was > 20%.

Acknowledgements

We are grateful to the whole technical team, Beckman Coulter Blood Services, University of Miami and Beckman Coulter Bangalore Development Center. Special thanks to Xu Gang and Karen Lo for the statistical data analysis.

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