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<i>CACNA1C</i> Genetic Variants Differentially Affect Neuronal Networks Through Divergent Pathways.

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The authors' code

R · 218 lines · 10 KB · CC0-1.0

  1. setwd(choose.dir()) #set the environment
  4. library(tidyverse)
  5. library(dplyr)
  6. library(pracma)
  7. library(ggforce)
  8. library(purrr)
  10. #### Load file (Spike List) - only set to do one file at a time ####
  12. MEAdata <- read.csv(choose.files(default = "", caption = "Select files",
  13. multi = TRUE, filters = Filters,
  14. index = nrow(Filters)))
  16. Filename <- MEAdata[29,2] #Row location may vary with Axion updates
  17. Filename <- gsub("\\(000\\)\\.raw", "", Filename)
  18. Barcode <- MEAdata[19,2] #Row location may vary with Axion updates
  20. MEAdata <- MEAdata %>%
  21. select(3:5) %>%
  22. dplyr::mutate(Filename = Filename, Barcode = Barcode)
  24. colnames(MEAdata)<- c("Time_s", "Electrode", "Amplitude_mV", "Filename", "Barcode")
  26. tmax = 600 #time of recording in seconds
  28. #Upload Platemap info
  30. wellinfo<- read.csv(choose.files(default = "", caption = "Select files",
  31. multi = TRUE, filters = Filters,
  32. index = nrow(Filters)))#####add in well inf
  35. ##################Get just spikes data#########################
  37. MEAdata$Amplitude_mV<-as.numeric(MEAdata$Amplitude_mV)
  38. MEAdata$Time_s<-as.numeric(MEAdata$Time_s)
  41. MEAdata <- MEAdata%>%
  42. filter(Amplitude_mV!= "NA") %>%
  43. separate(Electrode, into = c("Well", "Electrode"), sep= "_")
  45. MEAdata <- merge(MEAdata, wellinfo, by.x = "Well", by.y = "Well", all = FALSE, all.x = TRUE, all.y = TRUE)%>%
  46. filter(Active == "TRUE")
  48. MEAdata<-arrange(MEAdata, Well, Time_s)
  51. Spike_Stats <- MEAdata %>%
  52. dplyr::group_by(Filename,Treatment, Well, Electrode)%>%
  53. dplyr::mutate(TotalSpikesE = n()) %>%
  54. dplyr::ungroup()%>%
  55. dplyr::group_by(Filename,Well)%>%
  56. dplyr::mutate(TotalSpikesWell = n(), MedSpikesE = median(TotalSpikesE),
  57. SpikeRateE = TotalSpikesE/tmax, MedSpikeRateE = median(SpikeRateE),
  58. maxSpikerateE= max(SpikeRateE), SpikeRateWell = TotalSpikesWell/tmax, ActiveElec = n_distinct(Electrode), medAmplitude_mV = median (Amplitude_mV), meanAmplitude_mV = mean(Amplitude_mV))
  60. Spike_Stats2<-Spike_Stats%>%
  61. select(-Time_s, -Electrode, -Amplitude_mV, -Well.Coloring) %>%
  62. distinct(Filename, Treatment, Well, .keep_all=TRUE)
  64. csv_name <- paste(Barcode,"_", Filename, "_Spike_stats.csv", sep = "")
  66. write.csv(Spike_Stats2, csv_name)
  68. Total_Spikes <- Spike_Stats %>%
  69. select(Filename,Treatment, Well, TotalSpikesWell, ActiveElec) %>%
  70. distinct(Filename, Treatment, Well, .keep_all=TRUE)
  72. ##### Bin spikes, find max ASDR and SB threshold #####
  74. # Step 1: Create a complete set of bins for each Filename and Well
  75. complete_bins <- MEAdata %>%
  76. dplyr::group_by(Filename, Treatment, Well) %>%
  77. tidyr::expand(bin = cut(seq(0, 600, 0.2), seq(0, 600, 0.2))) %>%
  78. dplyr::ungroup() %>%
  79. dplyr::mutate(bin = as.character(bin))
  81. # Step 2: Create your actual binned spike counts
  82. Binned_Spikes <- MEAdata %>%
  83. dplyr::group_by(Filename, Treatment, Well) %>%
  84. dplyr::mutate(bin = cut(Time_s, seq(0, 600, 0.2))) %>%
  85. dplyr::ungroup() %>%
  86. dplyr::group_by(Filename, Treatment, Well, bin) %>%
  87. dplyr::summarize(count = n()) %>%
  88. dplyr::ungroup() %>%
  89. dplyr::mutate(bin = as.character(bin))
  91. # Step 3: Join complete bins with actual binned spikes and fill missing counts with zeros
  92. Binned_Spikes <- complete_bins %>%
  93. dplyr::left_join(Binned_Spikes, by = c("Filename", "Well", "Treatment", "bin")) %>%
  94. dplyr::mutate(count = replace_na(count, 0))
  96. # Step 4: Separate bin into start_time and end_time
  97. Binned_Spikes <- Binned_Spikes %>%
  98. tidyr::separate(bin, c("start_time", "end_time"), sep = ",", remove = FALSE)%>%
  99. dplyr::mutate(end_time = gsub("]", "", end_time)) %>%
  100. dplyr::mutate(start_time = gsub("(", "", start_time, fixed=TRUE))
  102. # Calculate Max ASDR and set 40% theshold
  103. Well_MaxASDR <- Binned_Spikes %>%
  104. dplyr::group_by(Filename, Treatment, Well) %>%
  105. dplyr::mutate(maxASDR = as.numeric(max(count))) %>%
  106. dplyr::mutate(threshold = maxASDR*0.4) ## Threshold set at 40% of MaxASDR
  108. #### ASDR plots ####
  110. Well_MaxASDR$end_time <- as.numeric(Well_MaxASDR$end_time)
  112. split_ASDR <- split(Well_MaxASDR, Well_MaxASDR$Well)
  114. ASDR_plot <- function(Well_MaxASDR) {
  115. p <- ggplot(Well_MaxASDR, aes(end_time, count)) +
  116. geom_line(stat = "identity", linejoin = "round", size = 0.1) +
  117. geom_hline(aes(yintercept = mean(threshold)), color = "red") +
  118. xlab("Time (s)")+
  119. ylab("Spike Count")+
  120. scale_x_continuous(limits = c(0, 600), breaks = seq(0, 600, 100))
  122. well_name <- unique(Well_MaxASDR$Well)
  123. Treatment <-unique(Well_MaxASDR$Treatment)
  124. ggsave(paste0(Barcode, "_",Filename, "_", well_name,"_",Treatment, "_ASDR_plot.tiff"), plot = p, height = 3, width = 10)
  125. }
  127. lapply(split_ASDR, ASDR_plot)
  130. #### Calculate Synchronised Bust Interval, duration and number of spikes per SB ####
  132. Sync_bursts <- Well_MaxASDR %>% ## Filter 200ms bins so that only the ones above 40% of maxASDR remain --> sync bursts ##
  133. filter(count>threshold
  134. )
  136. Sync_bursts$end_time<-as.numeric(Sync_bursts$end_time)
  137. Sync_bursts$start_time<-as.numeric(Sync_bursts$start_time)
  138. Sync_bursts$count<-as.numeric(Sync_bursts$count)
  141. SB_interval <- Sync_bursts %>% ## Calculate interval between sync bursts
  142. ungroup()%>%
  143. dplyr::group_by(Filename, Treatment, Well)%>%
  144. mutate(interval = start_time-lag(end_time))
  146. Grouped_SB <- SB_interval %>% ## will only group up to 5 bins (1 second)
  147. dplyr::group_by(Filename, Treatment, Well)%>%
  148. mutate(Spike_count = ifelse(interval < 0.4 & !is.na(interval), count + lag(count), count)) %>% ##if interval is < 0.4 sec add spike count from previous row
  149. mutate(Spike_count = ifelse(interval < 0.4 & !is.na(interval), count + lag(Spike_count), Spike_count)) %>%
  150. mutate(Spike_count = ifelse(interval < 0.4 & !is.na(interval), count + lag(Spike_count), Spike_count)) %>%
  151. mutate(Spike_count = ifelse(interval < 0.4 & !is.na(interval), count + lag(Spike_count), Spike_count)) %>% #repeat lag count so can get up to 5 bins
  152. mutate(Spike_count = ifelse(interval < 0.4 & !is.na(interval), count + lag(Spike_count), Spike_count)) %>% #combined together if interval is < 0.4
  153. mutate(start_time = ifelse(interval < 0.4 & !is.na(interval), lag(start_time), start_time)) %>% ##if interval is < 0.4 sec change start time to match that of previous row
  154. mutate(start_time = ifelse(interval < 0.4 & !is.na(interval), lag(start_time), start_time)) %>%
  155. mutate(start_time = ifelse(interval < 0.4 & !is.na(interval), lag(start_time), start_time)) %>%
  156. mutate(start_time = ifelse(interval < 0.4 & !is.na(interval), lag(start_time), start_time)) %>%
  157. mutate(start_time = ifelse(interval < 0.4 & !is.na(interval), lag(start_time), start_time)) %>% ## repeat lag 5 times so up to 4 bins can have same start time
  158. mutate(interval = ifelse(interval < 0.4, lag(interval), interval)) %>% ##if interval is < 0.4 sec change it so it matches previous row
  159. mutate(interval = ifelse(interval < 0.4, lag(interval), interval)) %>%
  160. mutate(interval = ifelse(interval < 0.4, lag(interval), interval)) %>%
  161. mutate(interval = ifelse(interval < 0.4, lag(interval), interval)) %>%
  162. mutate(interval = ifelse(interval < 0.4, lag(interval), interval)) ## repeat lag 5 times
  164. Grouped_SB2 <- Grouped_SB %>%
  165. dplyr::group_by(Filename, Treatment, Well)%>%
  166. filter(start_time != lead(start_time)) %>% ## if start time matches next row, delete row
  167. drop_na(start_time) %>%
  168. mutate(SB_duration = end_time - start_time) %>%
  169. mutate(medianSBinterval = median(interval, na.rm = TRUE), meanSBinterval = mean(interval, na.rm = TRUE),
  170. sd_SBinterval = sd(interval, na.rm = TRUE), SBinterval_CoV = sd_SBinterval/meanSBinterval) %>% ## average SB interval
  171. mutate(medianSBduration = median(SB_duration, na.rm = TRUE), meanSBduration = mean(SB_duration, na.rm = TRUE), ## average SB duration
  172. no_SB = n(), median_spike_SB = median(Spike_count, na.rm = TRUE), mean_spike_SB = mean(Spike_count, na.rm = TRUE), ##no of SBs, average no of spikes per SB
  173. max_spike_SB = max(Spike_count, na.rm = TRUE), ## max no of spikes per SB - like max ASDR but combined from multiple 200ms bins
  174. total_spike_SB = sum(Spike_count, na.rm = TRUE)) ## total spikes in SBs - can be used to calculate % of spikes in SBs
  177. ## Simplify data frame to just wells, add total spikes data and calculate % spikes in bursts
  178. ## and save output measures as csv
  180. SB_stats <- Grouped_SB2 %>%
  181. select(-bin, -start_time, -end_time, -count, -interval, -Spike_count) %>%
  182. distinct(Filename, Treatment, Well, .keep_all=TRUE)
  184. SB_stats <- SB_stats %>%
  185. left_join(Total_Spikes, by = join_by("Filename", "Treatment", "Well") ) %>%
  186. mutate(percent_spike_SB = total_spike_SB/TotalSpikesWell * 100)
  188. csv_name <- paste(Barcode,"_", Filename, "_SB_stats.csv", sep = "")
  190. write.csv(SB_stats, csv_name)
  192. #### Spike Plots ####
  194. MEAdata$Electrode <- factor(MEAdata$Electrode, levels = c("11", "12", "13", "14", "21", "22","23", "24", "31", "32", "33", "34", "41", "42", "43", "44"))
  196. Split_Spike <- split(MEAdata, MEAdata$Well)
  198. Spike_plot <- function(MEAdata) {
  199. p <- ggplot(MEAdata, aes(x = Time_s)) +
  200. geom_vline(aes(xintercept = Time_s)) +
  201. coord_cartesian(ylim = c(0,1))+
  202. facet_wrap(~Electrode, ncol = 1, strip.position = 'left', drop = FALSE) +
  203. scale_y_discrete(limits = c("11", "12", "13", "14", "21", "22","23", "24", "31", "32", "33", "34", "41", "42", "43", "44", "C", "B", "A")) +
  204. theme(axis.title.y = element_blank(),
  205. axis.text.y = element_blank(),
  206. axis.ticks.y = element_blank(),
  207. panel.grid.minor.y = element_blank(),
  208. panel.grid.major.y = element_blank())+
  209. xlab("Time (s)")+
  210. scale_x_continuous(limits = c(0, 600), breaks = seq(0, 600, 100))
  212. well_name <- unique(MEAdata$Well)
  213. Treatment <-unique(MEAdata$Treatment)
  215. ggsave(paste0(Barcode, "_", Filename, "_", well_name,"_",Treatment, "_Spike_plot.tiff"), plot = p, height = 3, width = 10)
  216. }
  218. lapply(Split_Spike, Spike_plot)

MEA-SB_stats_GitHub.R, under CC0-1.0 · at the source

Overview

Authors: Gemma Wilkinson1, Jamie Wood1, Nuppu Roivainen1,2, Josephine E Haddon1, Jack FG Underwood1,3, Jeremy Hall1,3,4, Adrian J Harwood1,2,5
ORCID iDs: Gemma Wilkinson, Jamie Wood, Nuppu Roivainen, Jack FG Underwood
  1. Neuroscience and Mental Health Innovation Institute, School of Medicine, Cardiff University, Cardiff, United Kingdom
  2. School of Biosciences, Cardiff University, Cardiff, United Kingdom
  3. School of Medicine, Cardiff University, Cardiff, United Kingdom
  4. Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
  5. Mental Health and Neuroscience Institute and Department of Translational Genomics, Maastricht University, Maastricht, the Netherlands
Institutions: Cardiff University (United Kingdom); Warneford Hospital (United Kingdom); University of Oxford (United Kingdom); Maastricht University (Netherlands)
Journal: Biological psychiatry global open science, volume 6, issue 6, article 100792
Dates: received 31 March 2026; accepted 15 July 2026; published online 22 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.bpsgos.2026.100792 · PMID 42733558 · PMCID PMC13571225 · OpenAlex W7170042096
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism)
Methods: Statistics, Single-unit activity, calcium imaging
Keywords: CACNA1C, iPSC, Network activity, Neurodevelopment, Neuron
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Waterloo Foundation; Medical Research Council; Medical Research Foundation
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Background: CACNA1C encodes the pore-forming subunit of the L-type calcium channel Cav1.2. Common variants in CACNA1C are associated with psychiatric disorders, whereas rare single nucleotide variants cause CACNA1C-related disorder, a multisystem disorder with symptoms that include autism spectrum disorder (ASD), intellectual disability, and seizures. However, the cellular mechanisms linking CACNA1C dysfunction to neurodevelopmental phenotypes remain poorly understood.

Methods: We generated isogenic CACNA1C loss-of-function induced pluripotent stem cell lines and reprogrammed a line from an individual carrying a novel predicted gain-of-function variant (p.Ala1521Pro) in CACNA1C. Neuronal activity was assessed using multielectrode arrays, pharmacological manipulation, and gene expression analysis. Early developmental phenotypes were examined using quantitative reverse transcriptase polymerase chain reaction, immunocytochemistry, and RNA sequencing.

Results: Neurons carrying CACNA1C variants displayed opposing alterations in network dynamics, depending on variant type. Pharmacological and molecular assays indicated that these network differences were associated with dysregulated GABAergic (gamma-aminobutyric acidergic) signaling. Early developmental analysis revealed that loss of CACNA1C altered rosette morphology, CREB (cAMP response element binding protein) phosphorylation, and transcriptional programs related to axonogenesis and synaptic signaling, indicating effects on neuronal differentiation. The patient line exhibited opposing effects on rosette morphology and CREB signaling, reflecting variant-specific effects.

Conclusions: These findings demonstrate that Cav1.2 regulates excitatory-inhibitory balance, network organization, and aspects of neurodevelopment. Divergent effects of CACNA1C variants highlight how altered Cav1.2 signaling contributes to variable neurodevelopmental phenotypes, including ASD and epilepsy, and establish a framework for defining CACNA1C variant effects in human neurons.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Zenodo 21992966

License: CC0-1.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the acknowledgements
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
3 files
At the source: doi.org/10.5281/zenodo.21992966

ghwilkinson/mea-bursts

License: CC0-1.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0111e69345e1044f275851998d7247e79bbad7bc, 18 August 2026
Languages: R (1)
Size: 5 files, 1 script
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/GHWilkinson/MEA-bursts/tree/v1.0.0

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Version 2, 28 September 2026

  • Authors: added Gemma Wilkinson (0009-0006-5659-5981); Nuppu Roivainen (0009-0000-4222-5919); Jack FG Underwood (0000-0003-1731-6039); removed Gemma Wilkinson; Nuppu Roivainen; Jack FG Underwood
  • Funding: added Waterloo Foundation; Medical Research Council; Medical Research Foundation

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 53 references.

Cite

This paper

Wilkinson, G., Wood, J., Roivainen, N., Haddon, J. E., Underwood, J. F., Hall, J., & Harwood, A. J. (2026). &lt;i&gt;CACNA1C&lt;/i&gt; Genetic Variants Differentially Affect Neuronal Networks Through Divergent Pathways. Biological psychiatry global open science, 6(6), 100792. https://doi.org/10.1016/j.bpsgos.2026.100792

BibTeX

@article{wilkinson2026lt,
author = {Wilkinson, Gemma and Wood, Jamie and Roivainen, Nuppu and Haddon, Josephine E and Underwood, Jack FG and Hall, Jeremy and Harwood, Adrian J},
title = {{\&lt;i\&gt;CACNA1C\&lt;/i\&gt; Genetic Variants Differentially Affect Neuronal Networks Through Divergent Pathways}},
journal = {Biological psychiatry global open science},
year = {2026},
month = jul,
volume = {6},
number = {6},
pages = {100792},
publisher = {Elsevier},
issn = {2667-1743},
doi = {10.1016/j.bpsgos.2026.100792},
url = {https://doi.org/10.1016/j.bpsgos.2026.100792},
pmid = {42733558},
pmcid = {PMC13571225}
}

RIS

TY - JOUR
AU - Wilkinson, Gemma
AU - Wood, Jamie
AU - Roivainen, Nuppu
AU - Haddon, Josephine E
AU - Underwood, Jack FG
AU - Hall, Jeremy
AU - Harwood, Adrian J
TI - &lt;i&gt;CACNA1C&lt;/i&gt; Genetic Variants Differentially Affect Neuronal Networks Through Divergent Pathways
T2 - Biological psychiatry global open science
J2 - Biol Psychiatry Glob Open Sci
PY - 2026
DA - 2026/07/22
VL - 6
IS - 6
SP - 100792
SN - 2667-1743
PB - Elsevier
DO - 10.1016/j.bpsgos.2026.100792
UR - https://doi.org/10.1016/j.bpsgos.2026.100792
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.bpsgos.2026.100792",
"type": "article-journal",
"title": "&lt;i&gt;CACNA1C&lt;/i&gt; Genetic Variants Differentially Affect Neuronal Networks Through Divergent Pathways",
"container-title": "Biological psychiatry global open science",
"author": [
{
"family": "Wilkinson",
"given": "Gemma"
},
{
"family": "Wood",
"given": "Jamie"
},
{
"family": "Roivainen",
"given": "Nuppu"
},
{
"family": "Haddon",
"given": "Josephine E"
},
{
"family": "Underwood",
"given": "Jack FG"
},
{
"family": "Hall",
"given": "Jeremy"
},
{
"family": "Harwood",
"given": "Adrian J"
}
],
"container-title-short": "Biol Psychiatry Glob Open Sci",
"volume": "6",
"issue": "6",
"page": "100792",
"DOI": "10.1016/j.bpsgos.2026.100792",
"PMID": "42733558",
"PMCID": "PMC13571225",
"ISSN": "2667-1743",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.bpsgos.2026.100792",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
22
]
]
}
}

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