OSCR

Genetic inactivation of Translin/Trax RNase disrupts miRNAs, hippocampal synaptic plasticity, and memory.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches
  1. [1] § STAR★Methods › Quantification and statistical analysis › Cell type-specific gene expression data from Allen brain cell atlas ↔ createCellTypeGeneFigure.py, lines 179–189 · score 1.00 · CNU HYa GABA, CNU HYa Glut, CNU LGE GABA, CNU MGE GABA, CTX CGE GABA, CTX MGE GABA
  2. [2] § Results › miRNAs targeted by TN/TX RNase regulate the expression of genes involved in focal adhesion, integrin signaling, and extracellular matrix in the hippocampus ↔ visualizeAllenBrainCellAtlas.py, lines 23–33 · score 0.94 · Allen Brain Cell, Col4a5, Slc13a3, Bach1, Ctsc, Dram1
  3. [3] § Results › miRNAs targeted by TN/TX RNase regulate the expression of genes involved in focal adhesion, integrin signaling, and extracellular matrix in the hippocampus ↔ createCellTypeGeneFigure.py, lines 23–27 · score 0.89 · Col4a5, Slc13a3, Bach1, Ctsc, Dram1, Emp1
  4. [4] § STAR★Methods › Quantification and statistical analysis › Cell type-specific gene expression data from Allen brain cell atlas ↔ visualizeAllenBrainCellAtlas.py, lines 80–91 · score 0.69 · Allen Brain Cell, C57BL6J, Atlas, MERFISH, ABC

Paper

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The authors' code

Python · 274 lines · 11 KB · no license · 2 matches

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. Created on Wed Oct 1 17:46:30 2025
  6. @author: zjpeters
  7. """
  8. import pandas as pd
  9. from pathlib import Path
  10. import numpy as np
  11. import anndata
  12. import time
  13. import matplotlib.pyplot as plt
  14. import SimpleITK as sitk
  15. import csv
  16. import os
  17. from abc_atlas_access.abc_atlas_cache.abc_project_cache import AbcProjectCache
  19. # location to store output images
  20. derivatives = os.path.join('/','media','zjpeters','Expansion','traxManuscript','derivatives')
  21. #%% Create lists of possible genes
  23. shortGeneList = ['Tnc','Itga5','Tln1','Pxn','Plaur','Itga10', 'Tsn']
  24. fullGeneList = ['Tsn', 'Dram1','Fgr','Ifih1','Sp100','Map4k4','Itga5','Tnc','Tln1','Cd84','Cd33','Pxn','Ctsc','Mtmr10',
  25. 'Lyn','Tapbp','Il10ra','Ctsa','Slc13a3','Gpsm3','Ptbp1','Stxbp2','Efs','Arhgef1','Adam17',
  26. 'Pcolce2','P2ry2','Ret','Arhgap9','Ptafr','Trpv4','Tmed7','Plaur','Isg20','Mpeg1','Bach1',
  27. 'Sulf1','Sfrp1','Emp1','Myrf','Hpgds','Col4a5','Itga10','Gbp6']
  28. #%% prepare allen brain cell atlas environment
  31. def create_expression_dataframe(ad, gf):
  32. """
  33. this code is taken from:
  34. https://alleninstitute.github.io/abc_atlas_access/notebooks/merfish_tutorial_part_1.html
  35. """
  36. gdata = ad[:, gf.index].to_df()
  37. gdata.columns = gf.gene_symbol
  38. joined = section.join(gdata)
  39. return joined
  41. # importing data
  42. dataLocation = Path('/media/zjpeters/Expansion/allenBrainCellAtlas/sourcedata/abc_atlas')
  43. abc_cache = AbcProjectCache.from_cache_dir(dataLocation)
  45. abc_cache.current_manifest
  46. # cell information
  47. cell = abc_cache.get_metadata_dataframe(
  48. directory='MERFISH-C57BL6J-638850',
  49. file_name='cell_metadata',
  50. dtype={"cell_label": str}
  51. )
  52. cell.set_index('cell_label', inplace=True)
  54. # cluster/taxonomy information
  55. cluster_details = abc_cache.get_metadata_dataframe(
  56. directory='WMB-taxonomy',
  57. file_name='cluster_to_cluster_annotation_membership_pivoted',
  58. keep_default_na=False
  59. )
  60. cluster_details.set_index('cluster_alias', inplace=True)
  62. cluster_colors = abc_cache.get_metadata_dataframe(directory='WMB-taxonomy', file_name='cluster_to_cluster_annotation_membership_color')
  63. cluster_colors.set_index('cluster_alias', inplace=True)
  65. cell_extended = cell.join(cluster_details, on='cluster_alias')
  66. cell_extended = cell_extended.join(cluster_colors, on='cluster_alias')
  68. # gene information
  69. gene = abc_cache.get_metadata_dataframe(directory='MERFISH-C57BL6J-638850', file_name='gene')
  70. gene.set_index('gene_identifier', inplace=True)
  72. # # load h5ad file
  73. # abc_cache.list_data_files('MERFISH-C57BL6J-638850')
  74. # file = abc_cache.get_data_path(directory='MERFISH-C57BL6J-638850', file_name='C57BL6J-638850/log2')
  76. #### NOTE: the imputed file below is 50.2gb
  77. imputed_h5ad_path = abc_cache.get_data_path('MERFISH-C57BL6J-638850-imputed', 'C57BL6J-638850-imputed/log2')
  79. adata = anndata.read_h5ad(imputed_h5ad_path, backed='r')
  81. # select for a single section (59 sections in this dataset)
  82. pred = (cell_extended['brain_section_label'] == 'C57BL6J-638850.38')
  83. section = cell_extended[pred]
  85. # create list of potential class types and neurotransmitters
  86. classTypes = np.unique(section['class'])
  87. neurotransmitter_types = np.unique(section['neurotransmitter'])
  88. # del section
  89. #%% look for genes from list in the data
  91. pred = [x in fullGeneList for x in adata.var.gene_symbol]
  92. gene_filtered = adata.var[pred]
  93. del pred
  94. asubset = adata[:, gene_filtered.index].to_memory()
  95. del adata
  97. #%% reorder gene list so Tsn is first
  98. # first find index of Tsn
  99. tsn_idx = gene_filtered[gene_filtered['gene_symbol'] == 'Tsn'].index[0]
  100. gene_filtered_idxs = gene_filtered.index
  101. # create new list where remaining genes are in same order, but Tsn is first
  102. new_gene_filtered_idxs = [tsn_idx]
  103. for i in gene_filtered_idxs:
  104. if i != tsn_idx:
  105. new_gene_filtered_idxs.append(i)
  106. # perform reindexing of data
  107. gene_filtered = gene_filtered.reindex(new_gene_filtered_idxs)
  108. #%% load ccf registered coordinates for all cellsannotation
  110. cell = abc_cache.get_metadata_dataframe(directory='MERFISH-C57BL6J-638850', file_name='cell_metadata_with_cluster_annotation')
  111. cell.rename(columns={'x': 'x_section',
  112. 'y': 'y_section',
  113. 'z': 'z_section'},
  114. inplace=True)
  115. cell.set_index('cell_label', inplace=True)
  117. # #%%
  118. # reconstructed_coords = abc_cache.get_metadata_dataframe(
  119. # directory='MERFISH-C57BL6J-638850-CCF',
  120. # file_name='reconstructed_coordinates',
  121. # dtype={"cell_label": str}
  122. # )
  123. # reconstructed_coords.rename(columns={'x': 'x_reconstructed',
  124. # 'y': 'y_reconstructed',
  125. # 'z': 'z_reconstructed'},
  126. # inplace=True)
  127. # reconstructed_coords.set_index('cell_label', inplace=True)
  129. #%% import ccf coordinates
  130. ccf_coords = abc_cache.get_metadata_dataframe(
  131. directory='MERFISH-C57BL6J-638850-CCF',
  132. file_name='ccf_coordinates',
  133. dtype={"cell_label": str}
  134. )
  135. ccf_coords.rename(columns={'x': 'x_ccf',
  136. 'y': 'y_ccf',
  137. 'z': 'z_ccf'},
  138. inplace=True)
  139. # ccf_coords.drop(['parcellation_index'], axis=1, inplace=True)
  140. ccf_coords.set_index('cell_label', inplace=True)
  142. #%% import parcellation information
  144. parcellation_term = abc_cache.get_metadata_dataframe(directory='Allen-CCF-2020', file_name='parcellation_term')
  145. parcellation_term.set_index('label', inplace=True)
  147. parcellation_annotation = abc_cache.get_metadata_dataframe(directory='Allen-CCF-2020',
  148. file_name='parcellation_to_parcellation_term_membership_acronym')
  149. parcellation_annotation.set_index('parcellation_index', inplace=True)
  150. parcellation_annotation.columns = ['parcellation_%s'% x for x in parcellation_annotation.columns]
  152. #%% need to restrict original data based on cells contained in ccf
  153. # loop through ccf coordinates and find which are present in merfish data
  154. ccf_cells_idx = [x in ccf_coords.index for x in cell.index]
  155. adata_ccf_idx = [x in ccf_coords.index for x in asubset.obs.index]
  157. #%% mask cells
  158. ccf_cells = cell_extended[ccf_cells_idx]
  159. ccf_cells = ccf_cells.join(ccf_coords)
  161. asubset_ccf = asubset[adata_ccf_idx,:]
  163. section_ccf = ccf_cells[ccf_cells['brain_section_label'] == 'C57BL6J-638850.38']
  164. del ccf_cells_idx
  165. del adata_ccf_idx
  166. #%% use the parcellation_annotation to display only data from certain regions
  168. hpfAnnot = parcellation_annotation[parcellation_annotation['parcellation_division'] == 'HPF']
  169. hpfIdxs = hpfAnnot.index
  170. del hpfAnnot
  171. #%% create subset of only hippocampal formation
  172. hpfMask = [ x in hpfIdxs for x in section_ccf['parcellation_index']]
  173. section_hpf = section_ccf[hpfMask]
  175. adata_hpf_idx = [x in section_hpf.index for x in asubset_ccf.obs.index]
  176. asubset_hpf = asubset_ccf[adata_hpf_idx]
  177. del section_hpf
  179. #%% create figure that includes the three genes present from the short list
  180. # include the genes as columns and four cell types (exc, inh, astro, vascular) as rows
  181. cellTypesOfInterest = {'Excitatory': ['01 IT-ET Glut', '02 NP-CT-L6b Glut', '03 OB-CR Glut',
  182. '04 DG-IMN Glut', '13 CNU-HYa Glut', '14 HY Glut', '15 HY Gnrh1 Glut',
  183. '16 HY MM Glut', '17 MH-LH Glut', '18 TH Glut', '19 MB Glut',
  184. '23 P Glut', '24 MY Glut'],
  185. 'Inhibitory': ['05 OB-IMN GABA','06 CTX-CGE GABA', '07 CTX-MGE GABA',
  186. '08 CNU-MGE GABA', '09 CNU-LGE GABA', '11 CNU-HYa GABA',
  187. '12 HY GABA', '20 MB GABA', '27 MY GABA'],
  188. 'Astrocytes': ['30 Astro-Epen'],
  189. 'Vascular': ['33 Vascular']}
  191. #%% limit data points to only those within the hippocampal formation
  192. def restrictToHPF(inputDataFrame, hemispheres='single'):
  193. """
  194. Generates a subset of a dataframe restricted to the coordinates included
  195. in one hemisphere of the mouse hippocampal formation
  197. Parameters
  198. ----------
  199. inputDataFrame : pandas dataframe
  200. A pandas dataframe from ABC data.
  202. Returns
  203. -------
  204. hpfDataFrame : pandas dataframe
  205. A pandas dataframe formatted for ABC data restricted using coordinates.
  207. """
  208. hpfDataFrame = inputDataFrame[inputDataFrame['x'] > 2.0]
  209. if hemispheres == 'single':
  210. hpfDataFrame = hpfDataFrame[hpfDataFrame['x'] < 5.0]
  211. elif hemispheres == 'double':
  212. hpfDataFrame = hpfDataFrame[hpfDataFrame['x'] < 9.0]
  213. hpfDataFrame = hpfDataFrame[hpfDataFrame['y'] > 2.5]
  214. hpfDataFrame = hpfDataFrame[hpfDataFrame['y'] < 5.0]
  215. return hpfDataFrame
  217. #%% find maximum expression value across genes
  219. for i in range(len(gene_filtered.gene_symbol)):
  220. geneName = gene_filtered.gene_symbol[i]
  221. print(geneName)
  222. gf = asubset.var[asubset.var.gene_symbol == geneName]
  223. geneDataFrame = create_expression_dataframe(asubset_hpf, gf)
  224. geneDataFrame = restrictToHPF(geneDataFrame)
  225. print(np.max(geneDataFrame[geneName]))
  227. #%% plotting to multiple figures
  228. # for a set of 25 genes, breaking it into 5 figures of 5 genes each,
  229. # plotted with genes along y-axis, cell types along x-axis
  231. # don't need to repeat the following line since it's run above
  232. section_hpf = restrictToHPF(section_ccf, hemispheres='single')
  234. plt.close('all')
  235. # , figsize=(8.5, 9.5)
  237. fig,ax = plt.subplots(5, len(cellTypesOfInterest), figsize=(9.5, 9.5))
  238. figNumber = 1
  239. geneNumber = 0
  240. for i in range(len(gene_filtered.gene_symbol)):
  241. geneName = gene_filtered.gene_symbol[i]
  242. print(geneName)
  243. gf = asubset.var[asubset.var.gene_symbol == geneName]
  244. geneDataFrame = create_expression_dataframe(asubset_hpf, gf)
  245. geneDataFrame = restrictToHPF(geneDataFrame)
  246. # loop over the cell type groups
  247. for j in enumerate(cellTypesOfInterest):
  248. ax[0, j[0]].set_title(j[1])
  249. cellTypeMask = [x in cellTypesOfInterest[j[1]] for x in geneDataFrame['class']]
  250. ax[geneNumber, 0].set_ylabel(geneName, rotation='horizontal', horizontalalignment='right')
  251. cellTypeDataFrame = geneDataFrame[cellTypeMask]
  252. ax[geneNumber, j[0]].scatter(section_hpf['x'], section_hpf['y'], c='tab:grey', s=3, alpha=0.1)
  253. sc = ax[geneNumber, j[0]].scatter(cellTypeDataFrame['x'], cellTypeDataFrame['y'], c=cellTypeDataFrame[geneName], s=1, cmap='Reds', vmin=0, vmax=8)
  254. ax[geneNumber, j[0]].yaxis.set_inverted(True)
  255. ax[geneNumber, j[0]].set_aspect('equal')
  256. ax[geneNumber, j[0]].tick_params(
  257. axis='both', # changes apply to the x-axis
  258. which='both', # both major and minor ticks are affected
  259. bottom=False, # ticks along the bottom edge are off
  260. top=False, # ticks along the top edge are off
  261. labelbottom=False,
  262. left=False,
  263. labelleft=False)
  264. if geneNumber == 4:
  265. cbar_ax = fig.add_axes([0.9, 0.15, 0.02, 0.7])
  266. fig.colorbar(sc, cax=cbar_ax, fraction=0.015, pad=0.04)
  267. plt.show()
  268. plt.savefig(os.path.join(derivatives, f'threeGeneFourCellTypes_vertical_{figNumber}.png'), bbox_inches='tight', dpi=300)
  269. fig,ax = plt.subplots(5, len(cellTypesOfInterest), figsize=(9.5, 9.5))
  270. geneNumber = 0
  271. figNumber +=1
  272. else:
  273. geneNumber += 1

createCellTypeGeneFigure.py at commit ee5c97c, no license · at the source

Overview

Authors: Mahesh Shivarama Shetty1,2, Junko Kasuya1,2, Xiuping Fu3, Marisol Carmela Lauffer2, Satya Murthy Tadinada1,2, Tania Chatterjee Chowdhury1,2, Zeru Peterson2, Knute D Carter4, Jay M Baraban3, Ted Abel1,2
ORCID iDs: Marisol Carmela Lauffer, Ted Abel
  1. Department of Neuroscience and Pharmacology, Carver College of Medicine, University of Iowa, 51 Newton Rd, Iowa City, IA 52242, USA
  2. Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, 169 Newton Road, Iowa City, IA 52242, USA
  3. The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, 725 North Wolfe St, Baltimore, MD 21205, USA
  4. Department of Biostatistics, College of Public Health, University of Iowa, 145 N. Riverside Drive, Iowa City, IA 52242, USA
Institutions: University of Iowa (United States); Johns Hopkins University (United States); Johns Hopkins Medicine (United States)
Journal: iScience, volume 29, issue 6, article 116188
Dates: received 25 July 2025; accepted 14 May 2026; published online 4 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.116188 · PMID 42305625 · PMCID PMC13266039 · OpenAlex W7163542470
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Machine learning, Single-unit activity, calcium imaging
Keywords: genetics, molecular biology, neuroscience, omics
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: Roy J. Carver Charitable Trust; University of Iowa; National Institute of Mental Health (R01 MH 087463); National Institutes of Health
Citations: not cited yet (Europe PMC); 119 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

zjpeters/traxTranslinABCAnalysis

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ee5c97c3c003badf4172be39760051548049b86f, 4 November 2025
Languages: Python (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: anndata (2 files), Matplotlib (2 files), NumPy (2 files), pandas (2 files), SimpleITK (2 files)
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/zjpeters/traxTranslinABCAnalysis

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

Tracing map

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  • 2 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1016/j.isci.2026.116188.

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, issue, pages, dates, 10 authors, 4 keywords, 4 funders, 119 references.

Cite

This paper

Shetty, M. S., Kasuya, J., Fu, X., Lauffer, M. C., Tadinada, S. M., Chowdhury, T. C., Peterson, Z., Carter, K. D., Baraban, J. M., & Abel, T. (2026). Genetic inactivation of Translin/Trax RNase disrupts miRNAs, hippocampal synaptic plasticity, and memory. iScience, 29(6), 116188. https://doi.org/10.1016/j.isci.2026.116188

BibTeX

@article{shetty2026genetic,
author = {Shetty, Mahesh Shivarama and Kasuya, Junko and Fu, Xiuping and Lauffer, Marisol Carmela and Tadinada, Satya Murthy and Chowdhury, Tania Chatterjee and Peterson, Zeru and Carter, Knute D and Baraban, Jay M and Abel, Ted},
title = {{Genetic inactivation of Translin/Trax RNase disrupts miRNAs, hippocampal synaptic plasticity, and memory}},
journal = {iScience},
year = {2026},
month = jun,
volume = {29},
number = {6},
pages = {116188},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116188},
url = {https://doi.org/10.1016/j.isci.2026.116188},
pmid = {42305625},
pmcid = {PMC13266039}
}

RIS

TY - JOUR
AU - Shetty, Mahesh Shivarama
AU - Kasuya, Junko
AU - Fu, Xiuping
AU - Lauffer, Marisol Carmela
AU - Tadinada, Satya Murthy
AU - Chowdhury, Tania Chatterjee
AU - Peterson, Zeru
AU - Carter, Knute D
AU - Baraban, Jay M
AU - Abel, Ted
TI - Genetic inactivation of Translin/Trax RNase disrupts miRNAs, hippocampal synaptic plasticity, and memory
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/06/04
VL - 29
IS - 6
SP - 116188
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116188
UR - https://doi.org/10.1016/j.isci.2026.116188
LA - en
ER -

CSL-JSON

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"container-title": "iScience",
"author": [
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"family": "Shetty",
"given": "Mahesh Shivarama"
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{
"family": "Fu",
"given": "Xiuping"
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{
"family": "Lauffer",
"given": "Marisol Carmela"
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{
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{
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},
{
"family": "Peterson",
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{
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"page": "116188",
"DOI": "10.1016/j.isci.2026.116188",
"PMID": "42305625",
"PMCID": "PMC13266039",
"ISSN": "2589-0042",
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"issued": {
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Journal: Nature communications
In common: anndata, pandas, Matplotlib, 1 other tool, cellular / molecular, 2 references
[10] doi:10.1016/j.cell.2026.05.026 [code]
The critical role of the endogenous immune compartment after CAR T cell therapy in recurrent GBM.
Journal: Cell
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