Team:UC Davis/Measurement


Measurement Overview

The behavior of transiently expressed engineered genetic systems in mammalian cells depends heavily on transfection efficiency. By extension, accurately measuring quantitative changes in regulated gene expression (mRNA levels) in a population of engineered mammalian cells presents unique challenges due to the innate variation of transfection efficiency in the population. To enable comparisons between experiments, it is therefore vital to optimize transfection efficiency and to adjust for differences in transfection efficiency when making measurements. We developed an easy-to-use protocol for optimizing transfection efficiency in three mammalian cell lines and also developed protocols for measuring transfection efficiency that rely on different technologies (fluorescence microscopy and flow cytometry).

Additionally, we developed a method that adjusts for plasmid copy number through qPCR analysis. Together with our new mathematical models, these measurement methods allow us to quantifiably characterize the output of systems in mammalian cells by controlling for factors uniquely associated with transient transfection.

Optimizing Mammalian Transfection

In order to properly characterize the Light Activated CRISPR-dCas9 effector (LACE) system within multiple mammalian cell lines, we needed to maximize the transfected ratio of the total cell population. Compared to Prokaryotic transformations, Eukaryotic transfection is more complicated. Variations between cell lines, transfection technique, reagent choice and plasmid quantity can drastically influence the success of your transfection. Due to this, we recommend conducting a series of preliminary tests to experimentally determine the optimal conditions for your experiment, measured in terms of percent transfection efficiency. We devised a series of tests with detailed protocols for optimizing this process within adherent cell lines to improve system effectiveness for both our LACE system tests and other future aspiring mammalian iGEM teams.

We transfected NIH-3T3, CHO-DG44, and AML-12 lines with single plasmid and multi-plasmid systems to observe variations over different experimental conditions. See our Experiments Page for more information.

Following our main project objective of increasing the accessibility level of mammalian synthetic biology within iGEM, we decided to utilize multiple experimental tests to verify each of our experiments.

  1. Flow Cytometry was used with single plasmid and multi-plasmid fluorescent protein producing systems as well as CY3 and CY5 plasmid dye on the transfected vectors themselves for optimization of transfection efficiency and the study of general system effects.
  2. Fluorescence imaging was used to analyze single and multi-plasmid transfections within three adherent mammalian cell lines. ImageJ software was designed to count brightfield and fluorescence images as an alternative and more commonly accessible form of calculating transfection efficiency.
  3. qPCR was used to track mRNA production as a measure of endogenous gene LACE system effectiveness. These values were normalized against housekeeping endogenous genes, plasmid reference genes, and qPCRs of untransfected samples to establish a baseline.

Flow Cytometry

Initial testing with the LACE system was done with the three plasmid control in addition to Mirus Label IT tracker dye, which was applied to both the CRY2 (dyed with CY3) and PX330A-CDC (dyed with CY5) plasmids. Flow Cytometry was then used to document transfection efficiency, differences in plasmid ratios, and system expression in the form of GFP expression.

This method was also used to establish transfection efficiency (TE) for each plate of dose response curve tests to normalize data and enable cross-experiment comparisons.


Flow Controls

  • pMAX : Confirmed transfection was sucessful.
  • Dyed 2 Plasmid System : Calculate the tranfection efficiency of the two plamid system.
  • Cells Only | Blue Light : Determine the cell count when exposed to light.
  • Cells Only | Dark : Determine the cell count without light or transfection


qPCR

For qPCR we did both DNA and mRNA extractions to track upregulation of the endogenous target genes in comparison to resting-state without LACE activation. The DNA extraction allowed us to determine cell population numbers through reference endogenous genes (see design page for more information on the genes selection) while the mRNA levels revealed system effectiveness in addition to potential intracellular stress in the form of fluctuating endogenous reference genes.

qPCR Controls

  • Cells Only | Dark : Determine basal expression of target gene and refrence gene.
  • Cells Only | Blue Light : Determine if blue light can effect basal gene expression.
  • Cells Only | Red Light : Determine if red light can effect basal gene expression.
  • Tranfected Cells | no sgRNA : Determine the effect of transfection protocol on gene expresion.
  • Transfected cells | pPCR for refrence gene : Standardize gene expression against a reference gene.
  • Transfected Cells | Red Light: Determine if the system is responcive non-specific (is activated by other wavelengths).

Data analysis

qPCR Analysis

To analyze raw qPCR data, the cycle numbers must be modified so that each qPCR well is normalized and reported as a relative difference to another well. This controls for different cDNA concentrations.

  1. Averaging Ct’s :The Ct’s of technical duplicates of the gene of interest are first averaged and the standard deviation of the duplicates is found. Additionally, the technical duplicates of the reference gene are averaged and the standard deviation is found.
  2. ΔCt: The ΔCt of the gene of interest is calculated by subtracting the average Ct of the reference gene from the average Ct of the gene of interest. The error of ΔCt is determined via the following equation:
  3. Averaging ΔCt’s: The ΔCt’s of biological duplicates are averaged and the error is determined via the equation above.
  4. Calculating ΔΔCt: ΔΔCt’s are calculated by subtracting the Average ΔCt of calibrator (cells only or transfected cells in dark) from the Average ΔCt of the condition of interest. Error is determined using the equation above.
  5. Calculating Fold Difference: Fold difference of gene of interest over calibrator expression is calculated via the following equation:
  6. Fold Difference Error: Fold difference error is calculated via the following equation:
  7. Analysis is completed by graphing fold difference and using fold difference error as error bars