Impact of Fluoxetine Exposure on Lymnaea stagnalis and Its Developing Eggs: Integrating Untargeted Lipidomics, Targeted Metabolomics, and Classical Risk Assessment
Creators: name: Ilyaskina, Diana
Publisher: name: Zenodo
Year: 2025

description: for .xlsx

 

1. T5_lipid_ID

This dataset contains three sheets corresponding to different biological matrices of Lymnaea stagnalis:

• CNS → Central nervous system.

• AlbG → Albumen gland.

• Eggs → Eggs.

The data correspond to lipid feature ID (alignment_4_NOREVA) and the full lipid feature name (Metabolite name).

 

2. T5_CNS_L_NEG_data

3. T5_CNS_L_POS_data

4. T5_AlbG_L_NEG_data

5. T5_AlbG_L_POS_data

6. T5_Eggs_L_NEG_data

7. T5_Eggs_L_POS_data

Tables 2–7 contain raw lipidomics data, including all steps conducted for data handling, structured across multiple sheets.

The initial data processing was performed separately for lipid features detected in positive and negative ionization modes.
description: for .csv

These .csv files were used for data analysis in R and MetaboAnalyst (for lipidomics). Below is a detailed description of each dataset and its columns.

1. T5_eggsT1.csv

This dataset contains data obtained from the exposure of synchronized eggs to fluoxetine to generate a dose-response curve.

Column Descriptions:

• Scan_name → Unique identifier for each scan, used for matching with replicates.

• Scan_Number → Corresponding scan number for data matching.

• Day → All eggs were sampled after 10 days of exposure.

• Treatment → Fluoxetine treatment in nominal concentrations (µg fluoxetine L⁻¹).

• Replicate → Replicate number.

• Total_eggs → Total number of eggs counted.

• Developed_10_d → Total number of eggs that reached the developed stage after 10 days.

• Developed_perct → Percentage of developed eggs.

• Postmetamorphic_7_d → Total number of eggs that reached the postmetamorphic stage.

• Veliger_3-6_d → Total number of eggs that reached the veliger stage.

• Not_developed → Total number of non-developed eggs.

• Nd_v → Sum of veliger-stage and non-developed eggs.

 

2. T5_Fecundity.csv

This dataset includes data on the number of eggs laid by individual snails during exposure.

Column Descriptions:

• Snail_ID → Unique identifier for each snail (replicate per treatment).

• Day → Days when egg masses were collected (Days 3, 5, and 7). Empty cells indicate no eggs laid.

• Treatment (ng/mL) → Fluoxetine treatment in nominal concentrations (µg fluoxetine L⁻¹).

• Total_eggs → Total number of eggs laid by each snail.

 

3. T5_Hatching_Rate_noC11.csv

Dataset for hatching rate analysis of eggs from fluoxetine-exposed and non-exposed snails.

Column Descriptions:

• Scan_name → ID used in ImageJ analysis.

• Scan_Number → Matching ID for ImageJ.

• Day_adult_exposure → Duration of adult exposure (in days).

• Snail_ID → Unique identifier for adult snails (replicates).

• Treatment_Egg → Treatment in which eggs were incubated for 10 days.

• Snail_Treatment → Exposure conditions of adult snails.

• Treatment_Egg2 → Categorized egg incubation treatments: Control, Spiked Water, and Water.

• Total_eggs → Total number of eggs analyzed using ImageJ.

• Developed_10_d → Total number of eggs that developed within 10 days.

• Developed_10_d_% → Percentage of developed eggs.

• Postmetamorphic_7_d → Number of eggs reaching the postmetamorphic stage.

• Veliger_3-6_d → Number of eggs reaching the veliger stage.

• Not_developed → Total number of non-developed eggs.

• Veliger_Not_developed → Sum of veliger-stage and non-developed eggs.

 

4. T5_NT_AlbG_Res_LOD_filtered.csv

Dataset containing neurotransmitter and amino acid concentrations in the albumen gland of L. stagnalis.

Column Descriptions:

• ID → Sample identifier.

• Replicate → Replicate number.

• Treatment → Fluoxetine treatment, as described in the manuscript.

• Neurotransmitter → Name of detected neurotransmitters and amino acids.

• ng → Measured concentration of the metabolite (ng).

• S/N-PP → Signal-to-noise ratio.

• Protein_conc → Protein concentration in each sample (mg), used for data normalization.

• ng_prot → Metabolite concentration normalized to protein content (ng metabolite mg⁻¹ protein).

 

5. T5_NT_CNS_Res_LOD_filtered.csv

Dataset containing neurotransmitter and amino acid concentrations in the central nervous system (CNS) of L. stagnalis.

Column Descriptions:

• ID → Sample identifier.

• Replicate → Replicate number.

• Treatment → Fluoxetine treatment, as described in the manuscript.

• Neurotransmitter → Name of detected neurotransmitters and amino acids.

• ng → Measured concentration of the metabolite (ng).

• S/N-PP → Signal-to-noise ratio.

• Protein_conc → Protein concentration in each sample (mg), used for data normalization.

• ng_prot → Metabolite concentration normalized to protein content (ng metabolite mg⁻¹ protein).

 

6. T5_NT_Eggs_Res_LOD_filtered.csv

Dataset containing neurotransmitter and amino acid concentrations in L. stagnalis eggs.

Column Descriptions:

• ID → Sample identifier.

• Replicate → Replicate number.

• Treatment → Fluoxetine treatment, as described in the manuscript.

• Neurotransmitter → Name of detected neurotransmitters and amino acids (pooled sample from 10 eggs).

• ng → Measured concentration of the metabolite (ng).

• S/N-PP → Signal-to-noise ratio.

• Protein_conc → Protein concentration in each sample (mg), used for data normalization.

• ng_prot → Metabolite concentration normalized to protein content (ng metabolite mg⁻¹ protein).

 

7. CNS_L_Norm_Data

8. AlbG_L_Norm_Data

9. Eggs_L_Norm_Data

These datasets contain lipid feature data after transformation and normalization using NOREVA.

Column Descriptions:

• Sample ID → Unique identifier for each replicate.

• Label → Exposure levels and control groups (water and ethanol).

• Full lipid names → Listed in the first column.

----------------------------

Important Notes:

• Fluoxetine concentrations in the tables are nominal. The actual measured concentrations are reported in the manuscript.

• Further details on data processing and analysis can be found in the manuscript and supplementary materials.

 
description: for .R

 

This R script, titled Ilyaskina_R_script_data_analysis.R, contains the code used for the statistical and visual data analysis presented in the manuscript titled “Impact of Fluoxetine Exposure on Lymnaea stagnalis and Its Developing Eggs: Integrating Untargeted Lipidomics, Targeted Metabolomics, and Classical Risk Assessment.”

 

All tests are labeled as chapters with the name (follow the original manuscript to see which analysis decribe in what chapter). All the commnents regarding the .cvs files are listed in separate description window.
description: The updated R script for Combined GLM analysis for eggs tests where: (1) eggs laid by the exposed snails and incubated in the same spiked water as their mother (exposure), and (2) eggs laid by exposed snails and incubated in clean water (recovery). 

This R script works with the same dataset as uploaded "Ilyaskina_R_script_data_analysis.R", where this analysis can be pasted and run all together.

R script: 

data <- read.csv('T5_Hatching_Rate_noC11.csv')str(data)data[is.na(data)] <- 0

# combined GLM analysis for exposed eggs and recovery test #Load required librarieslibrary(multcomp)  # For post hoc testslibrary(ggplot2)   # For visualizationlibrary(emmeans)

# New factor for combined treatmentdata$Combined_Treatment <- ifelse(data$Treatment_Egg2 == "Control", "Control",                                  ifelse(data$Treatment_Egg2 == "Water", "Recovery",                                         ifelse(data$Treatment_Egg2 == "Spiked", "Exposed", NA)))

# Calculate hatching ratedata$hatching_rate <- data$Developed_10_d / data$Total_eggs

# Calculate the rate of postmetamorphic embryosdata$hatching_rate_7d <- data$Postmetamorphic_7_d / data$Total_eggs

# Calculate hatching_rate_6_0ddata$hatching_rate_6_0d <- data$Veliger_Not_developted / data$Total_eggs

# View the first few rows of the data to confirm the columnhead(data)

# Replace NA or infinite values with 0data$hatching_rate[is.na(data$hatching_rate) | is.infinite(data$hatching_rate)] <- 0data$hatching_rate_7d[is.na(data$hatching_rate_7d) | is.infinite(data$hatching_rate_7d)] <- 0data$hatching_rate_6_0d[is.na(data$hatching_rate_6_0d) | is.infinite(data$hatching_rate_6_0d)] <- 0

# Ensure Combined_Treatment is correctly factoreddata$Combined_Treatment <- factor(data$Combined_Treatment,                                   levels = c("Control", "Recovery", "Exposed"))

# Add a factor for stimulus treatment if not already presentdata$Stimulus_Treatment <- paste(data$Combined_Treatment, data$Snail_Treatment, sep = "_")

# Convert Snail_Treatment to a factordata$Snail_Treatment <- factor(data$Snail_Treatment)# Reorder Snail_Treatment levels to make "Water" the first leveldata$Snail_Treatment <- factor(data$Snail_Treatment,                                levels = c("Water", "EtOH", "30", "100", "300"))

# Confirm the new orderlevels(data$Snail_Treatment)

# Check the structure of the datastr(data)

# GLM using success/failure counts for developed eggs (10 days)# Fit the combined GLMcombined_glm <- glm(cbind(Developed_10_d, Total_eggs - Developed_10_d) ~                       Combined_Treatment * Snail_Treatment,                     family = binomial,                     data = data)

# Summary of the modelsummary(combined_glm)

# ANOVA for significance testinganova(combined_glm, test = "Chisq")

library(emmeans)

# Pairwise comparisons for Combined_Treatmentpost_hoc_combined <- emmeans(combined_glm, pairwise ~ Combined_Treatment | Snail_Treatment)

# View resultspost_hoc_combined

# GLM using success/failure counts for hatching_rate_7dglm_model_7d <- glm(cbind(Postmetamorphic_7_d, Total_eggs - Postmetamorphic_7_d) ~                       Combined_Treatment * Snail_Treatment,                     family = binomial(link = "logit"),                     data = data)

# ANOVA for significance testinganova(glm_model, test = "Chisq")

# Summary of the modelsummary(glm_model_7d)

# GLM using success/failure counts for hatching_rate_6_0dglm_model_6_0d <- glm(cbind(Veliger_Not_developted, Total_eggs - Veliger_Not_developted) ~                         Combined_Treatment * Snail_Treatment,                       family = binomial(link = "logit"),                       data = data)

# Summary of the modelsummary(glm_model_6_0d)# Analysis of Deviance (ANOVA)anova(model, test = "Chisq")

# GLM using success/failure counts for postmetamorphic embryos (7 days)# Fit the combined GLMpostmetamorphic_glm <- glm(cbind(Postmetamorphic_7_d, Total_eggs - Postmetamorphic_7_d) ~                              Combined_Treatment * Snail_Treatment,                            family = binomial,                            data = data)

# Summary of the modelsummary(postmetamorphic_glm)

# ANOVA for significance testinganova(postmetamorphic_glm, test = "Chisq")

# Pairwise comparisons for Combined_Treatmentpost_hoc_postmetamorphic <- emmeans(postmetamorphic_glm, pairwise ~ Combined_Treatment | Snail_Treatment)

# View resultspost_hoc_postmetamorphic

# GLM using success/failure counts for veliger + non-developed embryos# Fit the combined GLMveliger_glm <- glm(cbind(Veliger_Not_developted, Total_eggs - Veliger_Not_developted) ~                      Combined_Treatment * Snail_Treatment,                    family = binomial,                    data = data)

# Summary of the modelsummary(veliger_glm)

# ANOVA for significance testinganova(veliger_glm, test = "Chisq")

# Pairwise comparisons for Combined_Treatmentpost_hoc_veliger <- emmeans(veliger_glm, pairwise ~ Combined_Treatment | Snail_Treatment)

# View resultspost_hoc_veliger