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Locally synthesized glycyl aminoacyl-tRNA synthetase is important for local translation in neurons.

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  1. [1] § Results › GARS1 protein is in proximity to tRNAGly in neurites ↔ probability density_v2_6_26_2025.py, lines 15–114 · score 0.60 · 0–20 um, neurite length, segments, proximal, distal, NS

Paper

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The authors' code

Python · 126 lines · 5.3 KB · no license · 1 match

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Thu Jun 26 11:27:51 2025
  4. @author: tyler
  5. """
  6. # -*- coding: utf-8 -*-
  7. """
  8. Modified on Thu Jun 26 2025
  9. @author: tyler (edited by assistant)
  10. """
  11. import pandas as pd
  12. import numpy as np
  13. import os
  15. def analyze_spots_by_distance(csv_file_path):
  16. """
  17. Analyze spot distributions along neurites in three biologically-relevant zones:
  18. • 0–20 µm from the soma (proximal)
  19. • middle segment
  20. • final 20 µm of the neurite (distal)
  21. For each neurite (distance column) the script reports:
  22. • total neurite length (µm)
  23. • counts of spots in the three zones
  24. • basic summary statistics
  25. • per-column CSV + global text summary
  26. """
  27. # ---------- 1. Load data ----------
  28. try:
  29. df = pd.read_csv(csv_file_path)
  30. print("CSV file loaded successfully!")
  31. except FileNotFoundError:
  32. print(f"Error: File '{csv_file_path}' not found.")
  33. return
  34. except Exception as e:
  35. print(f"Error reading CSV file: {e}")
  36. return
  37. print(f"Data shape: {df.shape}")
  38. print("Columns:", df.columns.tolist()[:10], "...\n")
  39. # ---------- 2. Ask for scale ----------
  40. pixels_per_micron = 11.7559791404
  41. # Heuristically locate distance columns
  42. distance_columns = [c for c in df.columns
  43. if ('x' in c.lower() or 'distance' in c.lower()) and df[c].dtype != object]
  44. if not distance_columns:
  45. col_name = input("Enter the column name that holds the x-distance values: ")
  46. if col_name in df.columns:
  47. distance_columns = [col_name]
  48. else:
  49. print(f"Column '{col_name}' not found.")
  50. return
  51. # ---------- 3. Prepare summary containers ----------
  52. summary_lines = [
  53. "SPOT DISTANCE ANALYSIS SUMMARY",
  54. "=" * 55,
  55. f"Analysis date: {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')}",
  56. f"Input file : {csv_file_path}",
  57. f"Pixels / µm : {pixels_per_micron}",
  58. f"Distance cols: {', '.join(distance_columns)}",
  59. ""
  60. ]
  61. # Get directory and base name of input file for output naming
  62. input_dir = os.path.dirname(csv_file_path)
  63. base_name = os.path.splitext(os.path.basename(csv_file_path))[0]
  64. # ---------- 4. Process each neurite ----------
  65. for col in distance_columns:
  66. print("\n" + "="*40 + f"\nAnalyzing '{col}'")
  67. # Convert to microns and drop NaNs
  68. dist_um = df[col].astype(float) / pixels_per_micron
  69. dist_um = dist_um.dropna()
  70. neurite_len = dist_um.max() # total length measured (µm)
  71. total_spots = dist_um.size
  72. # ----- 4a. Define the three zones -----
  73. prox_mask = dist_um <= 20
  74. dist_thresh = max(neurite_len - 20, 20) # prevents negative/overlap
  75. dist_mask = dist_um >= dist_thresh
  76. mid_mask = (~prox_mask) & (~dist_mask)
  77. counts = {
  78. "0–20 µm (proximal)" : prox_mask.sum(),
  79. "middle segment" : mid_mask.sum(),
  80. f"last 20 µm ({neurite_len - 20:.1f}–{neurite_len:.1f} µm)" : dist_mask.sum()
  81. }
  82. # ----- 4b. Print results -----
  83. print(f"Neurite length : {neurite_len:.2f} µm")
  84. print(f"Total spots : {total_spots}")
  85. for zone, n in counts.items():
  86. print(f"{zone:25s}: {n}")
  87. mean_d, med_d = dist_um.mean(), dist_um.median()
  88. print(f"Mean distance : {mean_d:.2f} µm")
  89. print(f"Median distance: {med_d:.2f} µm")
  90. # ----- 4c. Save per-neurite CSV -----
  91. out_df = (pd.Series(counts, name="Number_of_Spots")
  92. .reset_index()
  93. .rename(columns={"index": "Distance_Zone"}))
  94. out_df.insert(1, "Neurite_Length_µm", neurite_len)
  95. csv_out = os.path.join(input_dir, f"{base_name}_{col.replace(' ', '_')}_probability_distribution.csv")
  96. out_df.to_csv(csv_out, index=False)
  97. print(f"Saved zone counts to '{csv_out}'")
  98. # ----- 4d. Append to global summary -----
  99. summary_lines.extend([
  100. f"COLUMN: {col}",
  101. f" Neurite length : {neurite_len:.2f} µm",
  102. f" Total spots : {total_spots}",
  103. f" Spots 0–20 µm (prox) : {counts['0–20 µm (proximal)']}",
  104. f" Spots middle segment : {counts['middle segment']}",
  105. f" Spots last 20 µm : {counts[list(counts.keys())[2]]}",
  106. f" Mean distance : {mean_d:.2f} µm",
  107. f" Median distance : {med_d:.2f} µm",
  108. "-" * 55
  109. ])
  110. # ---------- 5. Write summary file ----------
  111. summary_txt = os.path.join(input_dir, f"{base_name}_probability_distribution_summary.txt")
  112. with open(summary_txt, "w", encoding="utf-8") as f:
  113. f.write("\n".join(summary_lines))
  114. print(f"\nFull summary saved to '{summary_txt}'")
  116. def main():
  117. print("Spot Distance Analysis Tool")
  118. print("="*32)
  119. # Hard-coded for convenience; replace or prompt as needed
  120. csv_path = r"C:\Users\tyler\OneDrive - Technion\Technion\Journal\9_2025\9_15_2025_qPCR and imaging\Gars Cy5_Tubb3 Cy3_Tubb 488\rep10_tubb_good\rep10_tubb rna_neurite7_filtered.csv"
  121. analyze_spots_by_distance(csv_path)
  123. if __name__ == "__main__":
  124. main()
  126. print("Current working directory:", os.getcwd())

probability density_v2_6_26_2025.py at commit 6b90b01, no license · at the source

Overview

Authors: Tyler Brent de Leon1, Adi Golani-Armon1, Bar Cohen1, Yoav S Arava1
ORCID iDs: Yoav S Arava
  1. Faculty of Biology, Technion – Israel Institute of Technology, Haifa, Israel
Institutions: Technion – Israel Institute of Technology (Israel)
Journal: Life science alliance, volume 9, issue 6, article e202603630
Dates: received 13 January 2026; accepted 27 March 2026; published online 15 April 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.26508/lsa.202603630 · PMID 41986236 · PMCID PMC13084160 · OpenAlex W7154528649
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Evoked potentials
MeSH: Glycine-tRNA Ligase*, Neurons*, Protein Biosynthesis*, Animals, Humans, Mice, Mitochondria, Neurites, Ribosomes, RNA, Messenger (* major topic)
Topic: RNA and protein synthesis mechanisms (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Israel Science Foundation (748/23 and 1349/23)
Citations: not cited yet (Europe PMC); 83 references in the paper

Abstract

Regulation of gene expression is essential for neuronal development and function. A prominent regulatory mechanism involves synthesis of proteins at their activity site. Such local protein synthesis enables neurons to respond rapidly and tightly to stimuli. Key components of the translation machinery, including mRNA and ribosomes, were identified in subcellular regions of neurons. Yet, the role of tRNAs and their charging enzymes, aminoacyl-tRNA synthetases (ARS), in this process remains largely unclear. Here, we demonstrate that glycyl-tRNA synthetase (Gars1) mRNA is abundant in neurites and undergoes local translation, producing GARS1 protein. Notably, Gars1 mRNA colocalizes with mitochondria in a translation-dependent manner, with its coding sequence (CDS) sufficient to direct this association. The localized GARS1 protein is in close proximity to tRNAGly, and disrupting their proximity impairs local protein synthesis in neurites. These findings establish the functional importance of GARS1 and tRNAGly in neuritic translation and highlight mitochondria as hubs for mRNA transport and translation.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

tylerbrent/gars1-

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 6b90b016099ccc61cb5c34aaf408a55394c52902, 2 March 2026
Languages: Python (3)
Size: 5 files, 3 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), pandas (3 files), tifffile (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
4 files
At the source: github.com/tylerbrent/Gars1-/tree/main

The paper's code and data availability statement is in the Data section.

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;
  • 3 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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

Datasets cited

Data Availability

Raw images are deposited in Zenodo (https://doi.org/10.6084/m9.figshare.31449445) and codes used for analysis in GitHub (https://github.com/tylerbrent/Gars1-/tree/main).

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 10 MeSH terms, 1 funder, 82 references.

Cite

This paper

de Leon, T. B., Golani-Armon, A., Cohen, B., & Arava, Y. S. (2026). Locally synthesized glycyl aminoacyl-tRNA synthetase is important for local translation in neurons. Life science alliance, 9(6), e202603630. https://doi.org/10.26508/lsa.202603630

BibTeX

@article{deleon2026locally,
author = {de Leon, Tyler Brent and Golani-Armon, Adi and Cohen, Bar and Arava, Yoav S},
title = {{Locally synthesized glycyl aminoacyl-tRNA synthetase is important for local translation in neurons}},
journal = {Life science alliance},
year = {2026},
month = apr,
volume = {9},
number = {6},
pages = {e202603630},
publisher = {Life Science Alliance LLC},
issn = {2575-1077},
doi = {10.26508/lsa.202603630},
url = {https://doi.org/10.26508/lsa.202603630},
pmid = {41986236},
pmcid = {PMC13084160}
}

RIS

TY - JOUR
AU - de Leon, Tyler Brent
AU - Golani-Armon, Adi
AU - Cohen, Bar
AU - Arava, Yoav S
TI - Locally synthesized glycyl aminoacyl-tRNA synthetase is important for local translation in neurons
T2 - Life science alliance
J2 - Life Sci Alliance
PY - 2026
DA - 2026/04/15
VL - 9
IS - 6
SP - e202603630
SN - 2575-1077
PB - Life Science Alliance LLC
DO - 10.26508/lsa.202603630
UR - https://doi.org/10.26508/lsa.202603630
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13084160",
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