TITLE: ECIS Data Analysis for Stimulation of Human Pulmonary Microvascular Endothelial Cells (HPMECs) with Human Serum
AUTHORS: Michael Bokoch

[DESCRIPTION]
Sera from patients in our UCSF Liver Transplant Biobank are used to stimulate human pulmonary microvascular endothelial cells (HPMECs) grown to confluency on ECIS platform. This protocol prospectively defines the plan for data analysis from these experiments.

[STEPS]
SECTION: ECIS Data Analysis for HPMEC stimulation by Liver Biobank Sera
1. Determine Difference in Area-under-the-Curve (Normalized Resistance at 4000 Hz)
To be calculated after recording full ECIS curve after stimulation with human serum from liver transplant (LT) patients.

Goal: To calculate the difference in AUC between the normalized ECIS curves generated by (a) Liver transplant serum and (b) Healthy pooled serum.

  ΔAUC = AUCLT serum - AUCHealthy pooled serum

Sign conventions:
Baseline resistance is normalized to 1
AUC of curve that drops below 1 = Negative AUC --> interpreted as an increase in EC permeability
AUC of curve that rises above 1 = Positive AUC

e.g., for a typical Liver serum sample that induces permeability:
AUCLT serum = Negative number
AUCHealthy pooled serum = Positive number
ΔAUC = Negative number - Positive number = Large negative number

e.g., for a typical Liver serum sample that does not induce much permeability:
AUCLT serum = Smaller positive number
AUCHealthy pooled serum = Larger positive number
ΔAUC = Smaller positive number - Larger positive number = Small negative number

Final AUC analysis will use data from 0-3 hrs.
SECTION: Align t=0
1.3. Duplicate X-Y tables with data from 0-6 hrs --> name duplicate "_adjustedt_0-3h"
SECTION: Supplementary details for each experiment
3. Cell line #
Passage #
Old plate (yes/no)
Seeding density (cells/well) - should be 40,000
Plateau capacitance at 64,000 Hz, average and st. dev - prior to stimulation
Plateau resistance at 4,000 Hz, average and st. dev - prior to stimulation
Time of stimulation - elapsed from seeding ECIS plate
SECTION: Prepare ECIS data for analysis
1.2. Export "Graph Data" and import into GraphPad prism.

Name data table "raw normalized_mm-dd-yy_cell line"
SECTION: Prepare ECIS data for analysis
1.1. Using ECIS software, normalize the resistance. This is done by dividing the resistance at each time point by the baseline resistance (the plateau resistance reached just prior to stimulation with serum). This will set the baseline resistance = 1.
SECTION: Determine AUC from 0-3 hrs
1.6. Analyze --> XY analyses --> Area under curve
Baseline Y = 1
Minimum Peak Height = ignore peaks < 5%
Minimum Peak Width = leave blank
Peak direction = "Also consider 'peaks' that go below the baseline."
SECTION: Align t=0
1.4. Align time = 0
Inspect normalized curves for a given experiment (day and cell line)
Determine time point (tstim) where curves begin to diverge from baseline resistance = 1
The time point prior to that time (tstim-1) will be set = 0 hrs
Calculate time shift in Excel using the follwing formula:
tadjusted = toriginal - tstim-1
Use tadjusted for determination of AUC 0-3 hrs.
SECTION: Predefined comparisons for analysis
2.4. Perform one-way ANOVA with the following pairwise comparisons:

Note: Control Column should be S1, Start of Surgery for all
    S1 vs. Healthy Serum - Q: Is liver serum at the start of surgery different from Healthy controls?
    S1 vs. S2 - Q: What is the effect of surgery (dissection phase) alone with no liver reperfusion?
    S1 vs. S3 - Q: What is the effect of liver reperfusion?
    S1 vs. {S4 or S5} - Q: If there is an effect of liver reperfusion, does it get worse with more time from reperfusion?

Note: If both S4 and S5 are present, keep whichever is "less" (i.e., keep the more negative, or less positive AUC)

S1 = Start of Surgery
S2 = End of Dissection (before liver removal)
S3 = 15 min after Portal Vein reperfusion
S4 = 60 min after Portal Vein reperfusion
S5 = 120 min after Portal Vein reperfusion (or End of Surgery, whichever comes first)
 
SECTION: Calculate ΔAUC, error, and store data
2. Calculate ΔAUC

ΔAUC = AUCLT serum - AUCHealthy pooled serum

Notes:
Steps 2 and 2.1 can be performed in a single Excel sheet after running the "transpose" function.
Both AUC and ΔAUC can be tabulated in a single Prism worksheet (Grouped)
Store results in a new "Grouped" table in Prism (Mean ± SEM) named "AUC" with column labels:
AUC, 0-3h 
ΔAUCLT - Healthy, 0-3h


SECTION: Determine AUC from 0-3 hrs
1.7. From AUC Results --> Copy "Net Area" and "Std Error" to Excel.

Note: It is not necessary to copy/paste transpose each result individually. Copy the "Net Area" rows from the whole table and then use "=transpose()" function in Excel.
SECTION: Calculate ΔAUC, error, and store data
2.1. Standard error calculation for ΔAUC


Std ErrorΔAUC =  
Where:
dx = Std ErrorAUC, LT serum
dy = Std ErrorAUC, Healthy pooled serum

Standard errors are obtained from the "Net Area" section of the Results tab in Prism after an AUC analysis.
SECTION: Calculate ΔAUC, error, and store data
2.3. Upload ΔAUC0 to 3 h in REDCap for each patient, timepoint, and cell line.


REDCap variables for Cell Line #1 are named:
auc_mean_ln1_s1
auc_se_ln1_s1
auc_mean_ln1_s2
auc_se_ln1_s2
...
auc_mean_ln1_s5
auc_se_ln1_s5


... similarly for Cell Line #2:
auc_mean_ln2_s1
auc_se_ln2_s1
SECTION: Determine AUC from 0-3 hrs
1.5. Delete data points with tadjusted > 3 hrs
SECTION: Calculate ΔAUC, error, and store data
2.2. Plot average curves with error bars from 0-6 h:
 Healthy serum with positive controls {LPS, TNF, IL-1b} and negative control {Media} - one for each day of experiment.
Healthy serum with Liver serum {S1, S2, S3, S4, S5} - one for each patient.
Generally there are 3 replicates, occasionally 2.