Glycogen Assay - Glycogen-Glo Dilution Testing with M.gigas Ctenidia Homogenate

INTRO

This was the initial testing of the Glycogen-Glo Assay (Promega) (GitHub; PDF) using M.gigas (Pacific oyster) ctenidia homegenate from 20251210 (notebook entry).

The idea was to identify a range (i.e. a very rough min/max) of starting tissues amounts that will fall within the range of the Glycogen-Glo standard curves. Additionally, this was an initial walk-through of using this kit to help figure out a general protocol that identified any tips/tricks/pitfalls to aid in developing a Standard Operating Protocol (SOP) for the Roberts Lab Handbook.

The assay will assess both glycogen and glucose content. The way the assay works is to convert any glycogen to glucose. As such, glucose levels also need to be assessed for each sample, in order to subtract that background glucose and get accurate glycogen quantification.

Overall, this process did not go very smoothly, but did highlight several challenges that can be addressed in future runs. Granted, I feel like most of this was related to my personal screw ups, but these will still serve as good guidance on improving this protocol for others.

MATERIALS & METHODS

Sample Selection

Sample A2 was selected due to it’s large tissue amount (50mg). This would allow us to perform serial dilutions representing different starting tissue amounts (roughly).

Assay Preparation

Samples and Standard Curves

Per the manufacturer’s recommendations, all reagents were thawed. After thawing, the Glucoamylase Buffer and Luciferin Detection Solution were kept at room temperature, while the remaining components, including the A2 sample, were kept on ice.

Warning

The following dilutions SHOULD have been 1:10 serial dilutions (e.g. 10uL sample in 90uL of dilution buffer)! That is reflected in the equivalent tissue/standard curve concentration values.

HOWEVER, these were, in fact, INCORRECTLY performed as 1:100 dilutions!

The assay was set up to measure the following:

• A2 GLYCOGEN content in the following equivalent “tissue” concentrations:

  • Not diluted (ND), 25mg, 2.5mg, 0.25mg, 0.025mg, 0mg
  • 1:100 dilutions - 1uL sample in 99uL of dilution buffer.

• A2 GLUCOSE content in the following equivalent “tissue” concentrations:

  • Not diluted (ND), 25mg, 2.5mg, 0.25mg, 0.025mg, 0mg
  • 1:100 dilutions - 1uL sample in 99uL of dilution buffer.

• GLYCOGEN Standard curve:

  • 20ug/uL, 2ug/mL, 0.2ug/uL, 0.02ug/uL, 0ug/uL
  • 1:100 dilutions - 1uL sample in 99uL of dilution buffer.

• GLUCOSE Standard curve:

  • 100µM, 10µM, 1µM, 0.1µM, 0µM
  • 1:100 dilutions - 1uL sample in 99uL of dilution buffer.

All dilutions were performed using a 2:1:1 mixture of PBS, 0.3N HCl, and TRIS Buffer (the latter two are supplied with the kit).

The A2 dilutions were performed in duplicate; primarily due to my poor calculations on the amount of the dilution buffer needed. Ugh.

Warning

The 25mg tissue equivalents were partially skipped - meaning sample was added to a one or two of the 25mg wells - due to insufficient dilution buffer (bad calculations on my part, as well as potentially low stock volumes available).

For future sample handling, sample homogenate should be thawed, vortexed, and centrifuged prior to pipetting to avoid this issue.

The standard curves were each performed in triplicate.

Assay

Samples and standard curves were prepared according to the manufacturer’s instructions. Theh plate used was a Costar 96-well, white, opaque, flat-bottom.

Briefly, 25uL of sample/standard was added to wells (see the plate layout below). For those samples/standards being assessed for glycogen, 25uL of glucoamylase digestion solution was added. For those samples/standards being assessed for just glucose, 25uL of only Glucoamylase Buffer was added. The plate was loosely covered with aluminum foil to prevent light/debris from getting in the wells, gently shaken by hand for 60s, and incubated at room temp for 1hr. All wells were loaded individually.

After the initial incubation period, 50uL of glucose detection reagent was added to ALL wells using an Eppendorf repeater pipette (Repeater M4). The plate was loosely covered with aluminum foil to prevent light/debris from getting in the wells, gently shaken by hand for 60s, and incubated at room temp for 1hr.

After incubating, luminescence was measured on the Roberts Lab Synergy HTX (Agilent) with the Gen5 software (Agilent).

Plate layout

blank = Blank measurements (buffer only - no sample/standard added)

SPL1 = Glycogen measurements of A2 homogenate.

SPL2 = Glucose measurements of A2 homogenate.

STDGly = Glycogen standard curve measurement.

STDGlu = Glucose standard curve measurement.

	1	2	3	4	5	6	7	8	9	10	11	12
A	SPL1:1	SPL1:2	SPL1:3	SPL1:4	SPL1:5	blank							Well ID
	50	25	2.5	0.25	0.025								Conc/Dil
B	SPL1:1	SPL1:2	SPL1:3	SPL1:4	SPL1:5	blank							Well ID
	50	25	2.5	0.25	0.025								Conc/Dil
C	SPL2:1	SPL2:2	SPL2:3	SPL2:4	SPL2:5	blank							Well ID
	50	25	2.5	0.25	0.025								Conc/Dil
D	SPL2:1	SPL2:2	SPL2:3	SPL2:4	SPL2:5	blank							Well ID
	50	25	2.5	0.25	0.025								Conc/Dil
E	STDGly1	STDGly2	STDGly3	STDGly4	STDGly1			STDGlu1	STDGlu2	STDGlu3	STDGlu4	STDGlu5	Well ID
	20	2	0.2	0.02	0			100	10	1	0.1	0	Conc/Dil
F	STDGly1	STDGly2	STDGly3	STDGly4	STDGly1			STDGlu1	STDGlu2	STDGlu3	STDGlu4	STDGlu5	Well ID
	20	2	0.2	0.02	0			100	10	1	0.1	0	Conc/Dil
G	STDGly1	STDGly2	STDGly3	STDGly4	STDGly1			STDGlu1	STDGlu2	STDGlu3	STDGlu4	STDGlu5	Well ID
	20	2	0.2	0.02	0			100	10	1	0.1	0	Conc/Dil
H													Well ID
													Conc/Dil

RESULTS

Raw fluorescence

	1	2	3	4	5	6	7	8	9	10	11	12
A	185099	174562	61687	7654	1762	813
B	166410	181069	83790	19910	10704	3187
C	843	744	421	237	227	689
D	396	230	261	257	221	234
E	107660	9963	1968	1277	1164			117932	12389	1703	673	606
F	110174	10220	1875	1256	737			122103	12418	1748	638	499
G	102674	10001	1815	1126	963			121056	12415	1629	480	356
H

Glycogen Standard Curve

library(ggplot2)

# Glycogen standard curve data from rows E, F, G, columns 1-5
# Concentrations in ug/uL
concentrations <- c(20, 2, 0.2, 0.02, 0)

# Luminescence values for each replicate
row_E <- c(107660, 9963, 1968, 1277, 1164)
row_F <- c(110174, 10220, 1875, 1256, 737)
row_G <- c(102674, 10001, 1815, 1126, 963)

# Calculate mean and standard error for each concentration
means <- numeric(5)
std_errors <- numeric(5)
std_devs <- numeric(5)

for (i in 1:5) {
  values <- c(row_E[i], row_F[i], row_G[i])
  means[i] <- mean(values)
  std_devs[i] <- sd(values)
  std_errors[i] <- sd(values) / sqrt(length(values))
}

# Create data frame for plotting
summary_data <- data.frame(
  concentration = concentrations,
  mean_luminescence = means,
  se = std_errors,
  sd = std_devs,
  cv = (std_devs / means) * 100
)

# Calculate linear regression and R-squared
lm_model <- lm(mean_luminescence ~ concentration, data = summary_data)
r_squared <- summary(lm_model)$r.squared
slope <- coef(lm_model)[2]
intercept <- coef(lm_model)[1]

# Sample data from row A (columns 1, 3, 4, 5, 6)
sample_data <- data.frame(
  tissue_mg = c(50, 2.5, 0.25, 0.025, 0),
  luminescence = c(185099, 61687, 7654, 1762, 813),
  label = c("50 mg", "2.5 mg", "0.25 mg", "0.025 mg", "0 mg")
)

# Create the plot
plot <- ggplot(summary_data, aes(x = concentration, y = mean_luminescence)) +
  geom_smooth(method = "lm", se = FALSE, linetype = "dashed", 
              color = "coral", linewidth = 1) +
  geom_errorbar(aes(ymin = mean_luminescence - se, ymax = mean_luminescence + se),
                width = 0.1, linewidth = 1, color = "darkblue") +
  geom_line(color = "steelblue", linewidth = 1) +
  geom_point(aes(color = "Standard Curve", shape = "Standard Curve"), size = 4) +
  geom_point(data = sample_data, aes(x = tissue_mg, y = luminescence,
             color = label, shape = label), size = 4) +
  scale_color_manual(name = "",
                     values = c("Standard Curve" = "steelblue",
                                "50 mg" = "darkred",
                                "2.5 mg" = "darkorange",
                                "0.25 mg" = "darkgreen",
                                "0.025 mg" = "purple",
                                "0 mg" = "brown")) +
  scale_shape_manual(name = "",
                     values = c("Standard Curve" = 16,
                                "50 mg" = 17,
                                "2.5 mg" = 15,
                                "0.25 mg" = 18,
                                "0.025 mg" = 8,
                                "0 mg" = 4)) +
  annotate("label", x = max(summary_data$concentration) * 1.2, 
           y = max(summary_data$mean_luminescence) * 0.15,
           label = sprintf("y = %.2fx + %.2f\nR² = %.4f", slope, intercept, r_squared),
           size = 3.5, fontface = "bold", fill = "white", 
           color = "coral", label.padding = unit(0.3, "lines")) +
  labs(
    title = "Glycogen Standard Curve",
    x = "Glycogen Concentration (µg/µL)",
    y = "Luminescence",
    caption = "Error bars represent standard error of the mean (SEM)"
  ) +
  scale_x_continuous(breaks = concentrations) +
  theme_bw() +
  theme(
    plot.title = element_text(size = 14, face = "bold"),
    axis.title = element_text(size = 12, face = "bold"),
    panel.grid.minor = element_line(linetype = "dashed", color = "grey70")
  )

# Save the plot
ggsave("glycogen_standard_curve.png", plot, width = 10, height = 6, dpi = 300)

`geom_smooth()` using formula = 'y ~ x'

# Display the plot
plot

`geom_smooth()` using formula = 'y ~ x'

# Print summary statistics
cat("Glycogen Standard Curve Summary:\n")
cat(rep("=", 50), "\n", sep = "")
for (i in 1:nrow(summary_data)) {
  cat(sprintf("Concentration: %g µg/µL\n", summary_data$concentration[i]))
  cat(sprintf("  Mean Luminescence: %.2f\n", summary_data$mean_luminescence[i]))
  cat(sprintf("  Standard Error: %.2f\n", summary_data$se[i]))
  cat(sprintf("  CV%%: %.2f%%\n\n", summary_data$cv[i]))
}

Glycogen Standard Curve Summary:
==================================================
Concentration: 20 µg/µL
  Mean Luminescence: 106836.00
  Standard Error: 2203.92
  CV%: 3.57%

Concentration: 2 µg/µL
  Mean Luminescence: 10061.33
  Standard Error: 80.09
  CV%: 1.38%

Concentration: 0.2 µg/µL
  Mean Luminescence: 1886.00
  Standard Error: 44.51
  CV%: 4.09%

Concentration: 0.02 µg/µL
  Mean Luminescence: 1219.67
  Standard Error: 47.22
  CV%: 6.71%

Concentration: 0 µg/µL
  Mean Luminescence: 954.67
  Standard Error: 123.33
  CV%: 22.38%

Glucose Standard Curve

library(ggplot2)

# Glucose standard curve data from rows E, F, G, columns 8-12
# Concentrations in µM
concentrations_glu <- c(100, 10, 1, 0.1, 0)

# Luminescence values for each replicate
row_E_glu <- c(117932, 12389, 1703, 673, 606)
row_F_glu <- c(122103, 12418, 1748, 638, 499)
row_G_glu <- c(121056, 12415, 1629, 480, 356)

# Calculate mean and standard error for each concentration
means_glu <- numeric(5)
std_errors_glu <- numeric(5)
std_devs_glu <- numeric(5)

for (i in 1:5) {
  values <- c(row_E_glu[i], row_F_glu[i], row_G_glu[i])
  means_glu[i] <- mean(values)
  std_devs_glu[i] <- sd(values)
  std_errors_glu[i] <- sd(values) / sqrt(length(values))
}

# Create data frame for plotting
summary_data_glu <- data.frame(
  concentration = concentrations_glu,
  mean_luminescence = means_glu,
  se = std_errors_glu,
  sd = std_devs_glu,
  cv = (std_devs_glu / means_glu) * 100
)

# Calculate linear regression and R-squared
lm_model_glu <- lm(mean_luminescence ~ concentration, data = summary_data_glu)
r_squared_glu <- summary(lm_model_glu)$r.squared
slope_glu <- coef(lm_model_glu)[2]
intercept_glu <- coef(lm_model_glu)[1]

# Sample data from row C (columns 1, 3, 4, 5, 6)
sample_data_glu <- data.frame(
  tissue_mg = c(50, 2.5, 0.25, 0.025, 0),
  luminescence = c(843, 421, 237, 227, 689),
  label = c("50 mg", "2.5 mg", "0.25 mg", "0.025 mg", "0 mg")
)

# Create the plot
plot_glu <- ggplot(summary_data_glu, aes(x = concentration, y = mean_luminescence)) +
  geom_smooth(method = "lm", se = FALSE, linetype = "dashed", 
              color = "coral", linewidth = 1) +
  geom_errorbar(aes(ymin = mean_luminescence - se, ymax = mean_luminescence + se),
                width = 0.1, linewidth = 1, color = "darkblue") +
  geom_line(color = "steelblue", linewidth = 1) +
  geom_point(aes(color = "Standard Curve", shape = "Standard Curve"), size = 4) +
  geom_point(data = sample_data_glu, aes(x = tissue_mg, y = luminescence,
             color = label, shape = label), size = 4) +
  scale_color_manual(name = "",
                     values = c("Standard Curve" = "steelblue",
                                "50 mg" = "darkred",
                                "2.5 mg" = "darkorange",
                                "0.25 mg" = "darkgreen",
                                "0.025 mg" = "purple",
                                "0 mg" = "brown")) +
  scale_shape_manual(name = "",
                     values = c("Standard Curve" = 16,
                                "50 mg" = 17,
                                "2.5 mg" = 15,
                                "0.25 mg" = 18,
                                "0.025 mg" = 8,
                                "0 mg" = 4)) +
  annotate("label", x = max(summary_data_glu$concentration) * 0.7, 
           y = max(summary_data_glu$mean_luminescence) * 0.2,
           label = sprintf("y = %.2fx + %.2f\nR² = %.4f", slope_glu, intercept_glu, r_squared_glu),
           size = 3.5, fontface = "bold", fill = "white", 
           color = "coral", label.padding = unit(0.3, "lines")) +
  labs(
    title = "Glucose Standard Curve",
    x = "Glucose Concentration (µM)",
    y = "Luminescence",
    caption = "Error bars represent standard error of the mean (SEM)"
  ) +
  scale_x_continuous(breaks = concentrations_glu) +
  theme_bw() +
  theme(
    plot.title = element_text(size = 14, face = "bold"),
    axis.title = element_text(size = 12, face = "bold"),
    panel.grid.minor = element_line(linetype = "dashed", color = "grey70")
  )

# Save the plot
ggsave("glucose-std-curve.png", plot_glu, width = 10, height = 6, dpi = 300)

`geom_smooth()` using formula = 'y ~ x'

# Display the plot
plot_glu

`geom_smooth()` using formula = 'y ~ x'

# Print summary statistics
cat("Glucose Standard Curve Summary:\n")
cat(rep("=", 50), "\n", sep = "")
for (i in 1:nrow(summary_data_glu)) {
  cat(sprintf("Concentration: %g µM\n", summary_data_glu$concentration[i]))
  cat(sprintf("  Mean Luminescence: %.2f\n", summary_data_glu$mean_luminescence[i]))
  cat(sprintf("  Standard Error: %.2f\n", summary_data_glu$se[i]))
  cat(sprintf("  CV%%: %.2f%%\n\n", summary_data_glu$cv[i]))
}

Glucose Standard Curve Summary:
==================================================
Concentration: 100 µM
  Mean Luminescence: 120363.67
  Standard Error: 1252.84
  CV%: 1.80%

Concentration: 10 µM
  Mean Luminescence: 12407.33
  Standard Error: 9.21
  CV%: 0.13%

Concentration: 1 µM
  Mean Luminescence: 1693.33
  Standard Error: 34.69
  CV%: 3.55%

Concentration: 0.1 µM
  Mean Luminescence: 597.00
  Standard Error: 59.37
  CV%: 17.22%

Concentration: 0 µM
  Mean Luminescence: 487.00
  Standard Error: 72.42
  CV%: 25.76%

Sample A2 Quantification

# Function to calculate concentration from luminescence using linear model
# Returns concentration or "OUT OF RANGE" message
calculate_concentration <- function(luminescence, slope, intercept, min_conc, max_conc, units) {
  # Calculate concentration from y = mx + b -> x = (y - b) / m
  concentration <- (luminescence - intercept) / slope
  
  # Check if within range
  if (concentration < min_conc) {
    return("OUT OF RANGE - TOO LOW")
  } else if (concentration > max_conc) {
    return("OUT OF RANGE - TOO HIGH")
  } else {
    return(sprintf("%.4f %s", concentration, units))
  }
}

# Glycogen calculations
cat("GLYCOGEN ASSAY RESULTS (Sample A2):\n")
cat(rep("=", 70), "\n", sep = "")
cat(sprintf("%-15s %-20s %s\n", "Tissue Amount", "Luminescence", "Calculated Glycogen"))
cat(rep("-", 70), "\n", sep = "")

glycogen_min <- min(concentrations)
glycogen_max <- max(concentrations)

for (i in 1:nrow(sample_data)) {
  result <- calculate_concentration(
    sample_data$luminescence[i],
    slope,
    intercept,
    glycogen_min,
    glycogen_max,
    "µg/µL"
  )
  cat(sprintf("%-15s %-20.0f %s\n", 
              sample_data$label[i], 
              sample_data$luminescence[i], 
              result))
}

cat("\n\n")

# Glucose calculations
cat("GLUCOSE ASSAY RESULTS (Sample A2):\n")
cat(rep("=", 70), "\n", sep = "")
cat(sprintf("%-15s %-20s %s\n", "Tissue Amount", "Luminescence", "Calculated Glucose"))
cat(rep("-", 70), "\n", sep = "")

glucose_min <- min(concentrations_glu)
glucose_max <- max(concentrations_glu)

for (i in 1:nrow(sample_data_glu)) {
  result <- calculate_concentration(
    sample_data_glu$luminescence[i],
    slope_glu,
    intercept_glu,
    glucose_min,
    glucose_max,
    "µM"
  )
  cat(sprintf("%-15s %-20.0f %s\n", 
              sample_data_glu$label[i], 
              sample_data_glu$luminescence[i], 
              result))
}

GLYCOGEN ASSAY RESULTS (Sample A2):
======================================================================
Tissue Amount   Luminescence         Calculated Glycogen
----------------------------------------------------------------------
50 mg           185099               OUT OF RANGE - TOO HIGH
2.5 mg          61687                11.5114 µg/µL
0.25 mg         7654                 1.3269 µg/µL
0.025 mg        1762                 0.2163 µg/µL
0 mg            813                  0.0374 µg/µL


GLUCOSE ASSAY RESULTS (Sample A2):
======================================================================
Tissue Amount   Luminescence         Calculated Glucose
----------------------------------------------------------------------
50 mg           843                  0.3107 µM
2.5 mg          421                  OUT OF RANGE - TOO LOW
0.25 mg         237                  OUT OF RANGE - TOO LOW
0.025 mg        227                  OUT OF RANGE - TOO LOW
0 mg            689                  0.1822 µM

DISCUSSION

Recommended Tissue Amounts

# Calculate the luminescence range for the glycogen standard curve
std_curve_min_lum <- min(summary_data$mean_luminescence)
std_curve_max_lum <- max(summary_data$mean_luminescence)

cat("GLYCOGEN STANDARD CURVE RANGE:\n")
cat(rep("=", 60), "\n", sep = "")
cat(sprintf("Minimum luminescence: %.2f (at %g µg/µL glycogen)\n", 
            std_curve_min_lum, min(concentrations)))
cat(sprintf("Maximum luminescence: %.2f (at %g µg/µL glycogen)\n\n", 
            std_curve_max_lum, max(concentrations)))

# Use the linear regression to predict tissue amounts that would fall within the standard curve range
# Using the equation: tissue_amount = (luminescence - intercept) / slope
predicted_min_tissue <- (std_curve_min_lum - intercept) / slope
predicted_max_tissue <- (std_curve_max_lum - intercept) / slope

cat("PREDICTED TISSUE AMOUNTS USING STANDARD CURVE:\n")
cat(rep("=", 60), "\n", sep = "")
cat(sprintf("Equation: y = %.2fx + %.2f\n\n", slope, intercept))
cat(sprintf("Minimum tissue amount: %.4f mg\n", predicted_min_tissue))
cat(sprintf("  (produces luminescence: %.2f)\n\n", std_curve_min_lum))
cat(sprintf("Maximum tissue amount: %.2f mg\n", predicted_max_tissue))
cat(sprintf("  (produces luminescence: %.2f)\n\n", std_curve_max_lum))

cat(rep("=", 60), "\n", sep = "")
cat("RECOMMENDED TISSUE RANGE FOR FUTURE ASSAYS:\n")
cat(sprintf("%.4f mg to %.2f mg\n", predicted_min_tissue, predicted_max_tissue))
cat(rep("=", 60), "\n", sep = "")

# Create formatted values for inline use
max_tissue_formatted <- paste0("`", round(predicted_max_tissue, 2), "mg`")
min_tissue_formatted <- paste0("`", round(predicted_min_tissue, 4), "mg`")

GLYCOGEN STANDARD CURVE RANGE:
============================================================
Minimum luminescence: 954.67 (at 0 µg/µL glycogen)
Maximum luminescence: 106836.00 (at 20 µg/µL glycogen)

PREDICTED TISSUE AMOUNTS USING STANDARD CURVE:
============================================================
Equation: y = 5305.39x + 614.37

Minimum tissue amount: 0.0641 mg
  (produces luminescence: 954.67)

Maximum tissue amount: 20.02 mg
  (produces luminescence: 106836.00)

============================================================
RECOMMENDED TISSUE RANGE FOR FUTURE ASSAYS:
0.0641 mg to 20.02 mg
============================================================

Although this is far from an absolute determination, due to the lack of replicates and poor pipetting, we can still use these numbers as a rough guideline to determine whether or not a sample will likely need to be diluted before assaying or can be used directly without dilution.

SUMMARY

Things we’ve learned:

1. Pellet insoluble material before pipetting.

2. Glucose does not really appear to be present (detectable) in ctenidia tissue. This suggests that we can skip running this aspect of the assay going forward. However, some additional testing would be beneficial.

3. Roughly, the maximum tissue amount to use undiluted is: 20.02mg.

4. Roughly, the minimum tissue amount to detect glycogen is: 0.0641mg.