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SynaptoTagMe, a toolkit for in vivo mapping and modulating neurotransmission at single-cell resolution.

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Paper

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The authors' code

R Markdown · 74 lines · 3.1 KB · MIT

  1. ``` {r}
  2. #Track-modifying function. Nothing changes until a worm track first appears, but then each subsequent gap in the data is filled by duplicating the last observed value. One consequence is that a worm “freezes” at the boundary once it leaves the arena.
  3. fill_last_value <- function(x) {
  4. for(i in 2:length(x)) {
  5. if(is.na(x[i])) {
  6. x[i] <- x[i - 1]
  7. }
  8. }
  9. return(x)
  10. }
  12. #Function for obtaining mean worm distance over a given time period
  13. mean_dist_time_window_analysis <- function(peaks, frame_rate, minute_start, minute_end, ...) {
  14. runs <- list(...)
  15. min_frames <- min(sapply(runs, nrow))
  16. runs <- lapply(runs, function(df) df[1:min_frames, ])
  17. number_of_runs <- sum(sapply(runs, is.data.frame))
  19. #Removing frame and time columns, assuming same number of frames for each run
  20. for (i in 1:number_of_runs) {
  21. runs[[i]] <- runs[[i]][, -c(1, 2)]
  22. #Filling empty values
  23. for (j in seq_along(runs[[i]])) {
  24. runs[[i]][,j] <- fill_last_value(runs[[i]][,j])
  25. }
  26. }
  28. #Creating empty list of #_of_frames * (ncol/2) data frames for each input
  29. distances <- lapply(runs, function(df) {
  30. data.frame(matrix(NA, nrow = min_frames, ncol = ncol(df)/2))
  31. })
  33. #Getting distances from peak
  34. for (i in 1:number_of_runs) {
  35. for (j in 1:ncol(distances[[i]])) {
  36. distances[[i]][,j] <- sqrt((runs[[i]][,2*j-1]-peaks[2*i-1])^2 + (runs[[i]][,2*j]-peaks[2*i])^2)
  37. }
  38. }
  39. distances <- do.call(cbind, distances)
  41. #Extracting values in specified time window
  42. frame_start = 1 + (minute_start * frame_rate * 60)
  43. frame_end = minute_end * frame_rate * 60
  44. distances_time_window <- distances[frame_start:frame_end, ]
  46. #Calculating mean for each worm and scaling from um to mm
  47. mean_distances <- colMeans(distances_time_window, na.rm = TRUE) / (10^3)
  49. #Creating output file
  50. distances_file_name <- paste(as.character(substitute(list(...)))[-1][1], "_", minute_start, "-", minute_end, "_mean_distances.txt", sep = "")
  51. write(mean_distances, file = distances_file_name, ncolumns = 1)
  52. }
  53. ```
  55. ``` {r}
  56. #Before running code, specify data set locations and names, copy peak locations into vector, then update the parameter names in the single line of code at bottom.
  57. #Notes: requires a constant number of frames; requires two lines of WormLab formatting to be removed from the top of each CSV.
  58. N2_peaks <- c(x1, y1, x2, y2, x3, y3, x4, y4, x5, y5, x6, y6)
  60. N2_1 <- read.csv("N2_1.csv")
  61. N2_2 <- read.csv("N2_2.csv")
  62. N2_3 <- read.csv("N2_3.csv")
  63. N2_4 <- read.csv("N2_4.csv")
  64. N2_5 <- read.csv("N2_5.csv")
  65. N2_6 <- read.csv("N2_6.csv")
  67. #Duplicate this line for each analysis being performed. Parameter meanings:
  68. # N2_peaks: replace with any vector containing coordinates for the salt peak of each assay being analyzed
  69. # 3.75: replace with the acquisition frame rate (fps)
  70. # 6: replace with "start minute" for the analysis time window
  71. # 7: replace with "end minute" for the analysis time window
  72. # N2_1...N2_6: replace with an arbitrary number of assays; here, 6 are used, requiring 6 pairs of salt peak coordinates in the N2_peaks vector.
  73. mean_dist_time_window_results <- mean_dist_time_window_analysis(N2_peaks, 3.75, 6, 7, N2_1, N2_2, N2_3, N2_4, N2_5, N2_6)
  74. ```

analysis_chemotaxis.Rmd at commit 074abc7, under MIT · at the source

Overview

Authors: Andrea Cuentas-Condori1, Patricia Chanabá-López1, Matthew Thomas1, Likui Feng2, Aaron Wolfe1, Peter Agoba1, Matthew L Schwartz3, Maximillian Brown2, Margaret S Ebert2, Erik Jorgensen3, Cornelia I Bargmann2, Daniel A Colón-Ramos1,4,5
ORCID iDs: Andrea Cuentas-Condori, Patricia Chanabá-López, Matthew Thomas, Likui Feng, Erik Jorgensen, Cornelia I Bargmann, Daniel A Colón-Ramos
  1. Department of Neuroscience and Department of Cell Biology, Yale University School of Medicine New Haven United States
  2. Lulu and Anthony Wang Laboratory of Neural Circuits and Behavior, The Rockefeller University New York United States
  3. Howard Hughes Medical Institute and School of Biological Sciences, University of Utah Salt Lake City United States
  4. Wu Tsai Institute, Yale University New Haven United States
  5. Instituto de Neurobiología, Recinto de Ciencias Médicas, Universidad de Puerto Rico San Juan Puerto Rico
Institutions: Yale University (United States); Rockefeller University (United States); University of Utah (United States); University of Puerto Rico System (Puerto Rico)
Journal: eLife, volume 14, article RP108675
Dates: published online 25 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.108675 · PMID 42345381 · PMCID PMC13299628 · OpenAlex W4415963202
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: C. elegans (organism), cellular / molecular (subfield)
Methods: Statistics, Preprocessing, Evoked potentials
Keywords: synaptic vesicle transporter, neurotransmitters, co-transmission, synaptic terminals, monoamines, C. elegans
MeSH: Caenorhabditis elegans*, Single-Cell Analysis*, Synaptic Transmission*, Synaptotagmins*, Animals, Neurons, Neurotransmitter Agents, Synaptic Vesicles (* major topic)
Journal subjects: Neuroscience
Topic: Genetics, Aging, and Longevity in Model Organisms (Aging, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institutes of Health (R35NS132156, R01NS076558, K99AG083129, F32GM133139, R01NS034307, R01 GM095817); Pew Charitable Trusts (AWD0006561); Jane Coffin Childs Memorial Fund for Medical Research (AWD0006564); Howard Hughes Medical Institute (GT15993); Chan Zuckerberg Initiative (United States)
Citations: not cited yet (Europe PMC); 95 references in the paper

Abstract

Understanding the organization and regulation of neurotransmission at the level of individual neurons and synapses requires tools that can track and manipulate transmitter-specific vesicles in vivo. Here, we present SynaptoTagMe, a suite of genetic tools in Caenorhabditis elegans to fluorescently label and conditionally ablate the vesicular transporters for glutamate, GABA, acetylcholine, and monoamines. Using a structure-guided approach informed by protein topology and evolutionary conservation, we engineered endogenously tagged versions for each transporter that maintain their physiological function while allowing for cell-specific, bright, and stable visualization. We also developed conditional knockout strains that enable targeted disruption of neurotransmitter synthesis or packaging in single neurons. We applied this toolkit to map co-expression of vesicular transporters across the C. elegans nervous system, revealing that over 10% of neurons exhibit co-transmission. Using the ADF sensory neuron as a case study, we demonstrate that serotonin and acetylcholine are trafficked in partially distinct vesicle pools. Our approach provides a powerful platform for mapping, monitoring, and manipulating neurotransmitter identity and use in vivo. The molecular strategies described here are likely applicable across species, offering a generalizable approach to dissect synaptic communication in vivo.

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

Repository

Its files are read in the Code ↔ Paper reader above.

colonramoslab/Cuentas-Condori-et-al.-2025-Toolkit-

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 074abc72cd44573a0a6509ead69abef7bf1496a6, 22 June 2026
Languages: R (1)
Size: 3 files, 1 script
Software Heritage: not archived
Found in: the text, “Chemotaxis assay”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
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/colonramoslab/Cuentas-Condori-et-al.-2025-Toolkit-

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The source data file contains all numerical data used to generate Figure 2C, Figure 2 - Supplement 1, Figure 3E, Figure 3 - Supplement 1, Figure 3 - Supplement 2, Figure 4C, Figure 4 - Supplement 1, Figure 4 - Supplement 2, Figure 5D, and Figure 5E. All raw datasets used for three-dimensional electron microscopy reconstructions were previously generated and are available through WormAtlas (https://wormatlas.org/MoW_built0.92/MoW.html; White et al., 1986). Electron microscopy reconstructions of C. elegans are also accessible via the NeuroSC platform (https://neurosc.net/).

The following previously published dataset was used:

TaylorSR SantpereG WeinrebA BarrettA ReillyMB XuC VarolE OikonomouP GlenwinkelL McWhirterR PoffA BasavarajuM RafiI YeminiE CookSJ AbramsA VidalB CrosC TavazoieS SestanN HammarlundM HobertO MillerDM 2019Molecular topography of an entire nervous systemNCBI Gene Expression OmnibusGSE13604910.1016/j.cell.2021.06.023PMC871013034237253

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 12 authors, 6 keywords, 8 MeSH terms, 5 funders, 92 references.

Cite

This paper

Cuentas-Condori, A., Chanabá-López, P., Thomas, M., Feng, L., Wolfe, A., Agoba, P., Schwartz, M. L., Brown, M., Ebert, M. S., Jorgensen, E., Bargmann, C. I., & Colón-Ramos, D. A. (2026). SynaptoTagMe, a toolkit for in vivo mapping and modulating neurotransmission at single-cell resolution. eLife, 14, RP108675. https://doi.org/10.7554/elife.108675

BibTeX

@article{cuentascondori2026synaptotagme,
author = {Cuentas-Condori, Andrea and Chanabá-López, Patricia and Thomas, Matthew and Feng, Likui and Wolfe, Aaron and Agoba, Peter and Schwartz, Matthew L and Brown, Maximillian and Ebert, Margaret S and Jorgensen, Erik and Bargmann, Cornelia I and Colón-Ramos, Daniel A},
title = {{SynaptoTagMe, a toolkit for in vivo mapping and modulating neurotransmission at single-cell resolution}},
journal = {eLife},
year = {2026},
month = jun,
volume = {14},
pages = {RP108675},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.108675},
url = {https://doi.org/10.7554/elife.108675},
pmid = {42345381},
pmcid = {PMC13299628}
}

RIS

TY - JOUR
AU - Cuentas-Condori, Andrea
AU - Chanabá-López, Patricia
AU - Thomas, Matthew
AU - Feng, Likui
AU - Wolfe, Aaron
AU - Agoba, Peter
AU - Schwartz, Matthew L
AU - Brown, Maximillian
AU - Ebert, Margaret S
AU - Jorgensen, Erik
AU - Bargmann, Cornelia I
AU - Colón-Ramos, Daniel A
TI - SynaptoTagMe, a toolkit for in vivo mapping and modulating neurotransmission at single-cell resolution
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/06/25
VL - 14
SP - RP108675
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.108675
UR - https://doi.org/10.7554/elife.108675
LA - en
ER -

CSL-JSON

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