Measurement

Measurement

Introduction

Accurate measurement was essential in our project to evaluate how the NRAS^G12D mutation influences cell viability, gene expression, and migration. We established a series of quantitative methods that were reproducible, well-documented, and broadly applicable to other iGEM teams. Each measurement included appropriate controls and calibration steps, ensuring that the results are reliable and comparable.

Beyond validating our own results, our measurement strategy was designed with transferability in mind. By documenting formulas, workflows, and control conditions in detail, we created protocols that can be easily adopted by other teams investigating cell-based models, drug responses, or 3D culture systems. This emphasis on standardization and reproducibility not only strengthens our findings but also contributes to the broader iGEM community by offering practical methods that can be applied across diverse projects.

<1. Cell Counting and Viability>

  • Target: Determine viable cell number before seeding experiments.
  • Method: Trypan Blue exclusion + Hemocytometer counting.
  • Formula:
Viability (%) = Live cells / Total Cells × 100
  • Controls: Triplicate counts, blank grid checks.
  • Contribution: Provided consistent seeding density across assays.
Figure 1-1
Figure 1-1(Korea-CX member manually loading stained cell suspension into the hemocytometer)
Figure 1-2
Figure 1-2 (Microscopic image of hemocytometer grid used to distinguish live and dead cells for triplicate counts)

<2. Transfection Efficiency>

  • Target: Delivery success of NRAS^WT and NRAS^G12D plasmids.
  • Method: Lipofectamine 3000 transfection; mCherry used as a fluorescent reporter.
  • Readout: Quantification of mCherry fluorescence intensity at 48 h post-transfection using a microplate reader.
  • Controls: Untransfected cells as negative control.
  • Standardization:
    • Background subtraction using wells without cells.
    • All samples measured under identical reader settings.
  • Result Representation:
    • Data presented as mean fluorescence intensity (±SD).
    • See Table 1 for quantitative values.
Table 1
Table 1. Quantification of transfection efficiency using mCherry fluorescence. Mean fluorescence intensity was measured at 48 h post-transfection with NRAS^WT or NRAS^G12D plasmids. Values are normalized to the untransfected control (CON) and presented as mean ± SD. Statistical significance versus control was determined by Student’s t-test (p < 0.05, *p < 0.01).

<3. Drug Response Measurement (CCK-8 with U0126)>

  • Target: Quantify viability after MEK inhibition.
  • Method: CCK-8 assay; absorbance at 450 nm.
  • Formula:
Cell viability %( ) = Abs.sample / Abs.control × 100
  • Controls: Blank wells, DMSO vehicle control.
  • Standardization: Triplicate wells, dose–response curve fitting to calculate IC₅₀.
Figure 2
Figure 2. Cell viability after MEK inhibition with U0126. Cell viability was measured by CCK-8 assay at 450 nm in control (CON), NRAS^WT, and NRAS^G12D cells with or without U0126 treatment. Data are presented as mean ± SD from triplicate wells.

<4. Migration Quantification in LOC>

  • Target: Track migration in a 3D bone marrow–like microenvironment.
  • Method: Matrigel-filled LOC; imaging at 0–48 h.
  • Readout: Migration distance (µm) and migration speed (µm/h), quantified using ImageJ.
  • Controls: Untransfected vs. WT vs. G12D.
  • Workflow refinement:
    • 2D scratch assay → LOC-based 3D migration (stepwise accuracy improvement).
Figure 3-1
Figure 3-1 (Cell migration analysis by wound-healing and quantification assays. Representative scratch assay images of control (CON), NRAS^WT, and NRAS^G12D cells at 0, 24, 48, and 72 h, showing progressive wound closure (yellow outlines))
Figure 3-2
Figure 3-2 (Quantification of wound closure (%) over time, presented as mean ± SD from triplicate wells. NRAS^WT and NRAS^G12D cells demonstrated accelerated closure compared with control)

<5. Hardware Validation>

  • Target: Ensure LOC device is reliable for biological assays.
  • Method: Flow test with PBS/Ethanol; leakage and gradient stability check.
  • Readout: Visual confirmation of uniform flow.
Figure 4-1
Figure 4-1, 4-2. Validation of lab-on-a-chip (LOC) hardware. Representative images of the fabricated LOC device during flow testing with PBS/ethanol. Visual inspection confirmed uniform flow distribution and absence of leakage, supporting device reliability for downstream biological assays.
Figure 4-2
Figure 4-1, 4-2. Validation of lab-on-a-chip (LOC) hardware. Representative images of the fabricated LOC device during flow testing with PBS/ethanol. Visual inspection confirmed uniform flow distribution and absence of leakage, supporting device reliability for downstream biological assays.

Discussion & Impact

  • Reproducibility
    All of our measurements were performed with both biological and technical replicates, ensuring consistency across experiments. Step-by-step protocols, including reagent concentrations and formulas, were documented so that other iGEM teams can easily reproduce our results.
  • Controls & Calibration
    Each experiment included appropriate controls: blank wells and vehicle controls for viability assays, GAPDH normalization for expression analysis, and untransfected cells for transfection. For imaging and migration assays, exposure times and pixel-to-micron calibration were standardized to reduce bias and allow direct comparison across samples.
  • Usefulness
    The measurement methods we established are broadly applicable. For example, our CCK-8 viability assay with proper controls can be adopted by teams testing different drugs, and our RT-PCR + gel workflow provides a low-cost option for expression validation. The LOC-based migration assay, though used here for NRAS studies, can be adapted by other teams modeling 3D environments such as microbial migration or immune cell tracking.
  • Advancement
    Our measurement pipeline progressed from simple assays (cell counting, viability) to more advanced functional studies (drug response, 3D migration). This stepwise approach shows how basic tools can be expanded into complex, physiologically relevant measurements, providing a workflow that other iGEM teams can follow.

Reference

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  2. [2] Felgner, P. L., et al., Proceedings of the National Academy of Sciences (1987). Lipofection: a highly efficient, lipid-mediated DNA-transfection procedure. 84(21), 7413–7417. https://doi.org/10.1073/pnas.84.21.7413
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