OSCR

A high-throughput, quantitative platform using 2D dissociated human cerebral organoids to model neuroinflammation in Alzheimer's disease.

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] § Methods › Flow cytometry data analyses ↔ Flow_Cytometry/Gating_Summary_Figures.ipynb, lines 1–58 · score 0.72 · Alexa Fluor, Pacific Blue, Flow cytometry, singlets, FSC, SSC
  2. [2] § Methods › Odds ratio calculations from flow cytometry data ↔ Flow_Cytometry/Gating_Summary_Figures.ipynb, lines 1–58 · score 0.63 · Alexa Fluor, Pacific Blue, quadrants, gating, ratios, cytometry
  3. [3] § Methods › Flow cytometry data analyses ↔ Flow_Cytometry/Gating_Summary_Figures.ipynb, lines 64–121 · score 0.58 · Alexa Fluor, Pacific Blue, gates, cytometry, Flow
  4. [4] § Methods › ELISA assays and analyses ↔ ELISA/elisa_analyses_updated.R, lines 1–42 · score 0.55 · heat inactivated, UV inactivated, ELISA, ACV

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Jupyter notebook · 234 lines · 6.7 KB · no license · 3 matches

  1. # %%
  2. import matplotlib.pyplot as plt
  3. import matplotlib.gridspec as gridspec
  4. import matplotlib.image as mpimg
  5. import string
  6. import glob
  7. import os
  8. import gc # Import garbage collector
  10. for folder in glob.glob('HSV1_Results/*/*/'):
  11. _,marker, rep,_ = folder.split('/')
  12. print(folder)
  14. # Define image file paths
  15. fsc = f'{folder}/FSC-A_gate.png'
  16. ssc = f'{folder}/SSC-A_gate.png'
  17. singlet = f'{folder}/singlet_gate.png'
  18. pb = f'{folder}/Pacific Blue-A_autogate.png'
  19. live_488 = f'{folder}/Alexa Fluor 488-A_autogate.png'
  20. live_647 = f'{folder}/Alexa Fluor 647-A_autogate.png'
  21. quads = f'{folder}/Live/quadrants_scatterplot.png'
  22. image_files = [fsc, ssc, singlet, pb, live_488, live_647, quads]
  24. # Create the figure
  25. fig = plt.figure(figsize=(12, 8))
  27. # Define the GridSpec with refined height ratios
  28. gs = gridspec.GridSpec(4, 2, hspace=0, wspace=0)
  30. # Create axes
  31. axes = [fig.add_subplot(gs[i, j]) for i in range(4) for j in range(2)]
  33. # Read and plot images
  34. images = [] # Store references to images
  35. for ax, img_path in zip(axes, image_files):
  36. img = mpimg.imread(img_path) # Read image
  37. images.append(img) # Store reference (helps with garbage collection)
  38. ax.imshow(img) # Display image
  39. ax.axis("off") # Hide axes for a clean look
  40. axes[-1].axis("off")
  41. plt.savefig(f'{folder}/gating_summary.png', dpi=1000, bbox_inches='tight')
  42. plt.show()
  44. # **Clean up memory properly**
  45. for ax in axes:
  46. ax.clear() # Clear axes
  47. fig.clf() # Clear figure
  48. plt.close(fig) # Close figure
  50. # Explicitly delete objects
  51. del images
  52. del image_files
  53. del fig
  54. del gs
  55. del axes
  57. # **Force garbage collection**
  58. gc.collect()
  61. # %%
  64. # %%
  65. import matplotlib.pyplot as plt
  66. import matplotlib.gridspec as gridspec
  67. import matplotlib.image as mpimg
  68. import string
  69. import glob
  70. import os
  71. import gc # Import garbage collector
  73. for folder in glob.glob('IAV_Results/*/*/'):
  74. _,marker, rep,_ = folder.split('/')
  75. print(folder)
  77. # Define image file paths
  78. fsc = f'{folder}/FSC-A_gate.png'
  79. ssc = f'{folder}/SSC-A_gate.png'
  80. singlet = f'{folder}/singlet_gate.png'
  81. pb = f'{folder}/Pacific Blue-A_autogate.png'
  82. live_488 = f'{folder}/Alexa Fluor 488-A_autogate.png'
  83. live_647 = f'{folder}/Alexa Fluor 647-A_autogate.png'
  84. quads = f'{folder}/Live/quadrants_scatterplot.png'
  85. image_files = [fsc, ssc, singlet, pb, live_488, live_647, quads]
  87. # Create the figure
  88. fig = plt.figure(figsize=(8, 8))
  90. # Define the GridSpec with refined height ratios
  91. gs = gridspec.GridSpec(4, 2, hspace=0, wspace=0)
  93. # Create axes
  94. axes = [fig.add_subplot(gs[i, j]) for i in range(4) for j in range(2)]
  96. # Read and plot images
  97. images = [] # Store references to images
  98. for ax, img_path in zip(axes, image_files):
  99. img = mpimg.imread(img_path) # Read image
  100. images.append(img) # Store reference (helps with garbage collection)
  101. ax.imshow(img) # Display image
  102. ax.axis("off") # Hide axes for a clean look
  103. axes[-1].axis("off")
  104. plt.savefig(f'{folder}/gating_summary.png', dpi=1000, bbox_inches='tight')
  105. plt.show()
  106. #break
  107. # **Clean up memory properly**
  108. for ax in axes:
  109. ax.clear() # Clear axes
  110. fig.clf() # Clear figure
  111. plt.close(fig) # Close figure
  113. # Explicitly delete objects
  114. del images
  115. del image_files
  116. del fig
  117. del gs
  118. del axes
  120. # **Force garbage collection**
  121. gc.collect()
  125. # %%
  126. import math
  127. import matplotlib.pyplot as plt
  128. import matplotlib.image as mpimg
  129. import matplotlib.gridspec as gridspec
  130. import os
  131. import glob
  132. import gc
  133. from collections import defaultdict
  135. # Step 1: Collect all individual gating summary images
  136. all_summaries = glob.glob('HSV1_Results/*/*/gating_summary.png')
  138. # Step 2: Group image paths by marker
  139. marker_to_images = defaultdict(list)
  140. for path in all_summaries:
  141. parts = path.split('/')
  142. marker = parts[1]
  143. marker_to_images[marker].append(path)
  145. # Step 3: Generate combined figure per marker using GridSpec (2 columns), with cleanup
  146. for marker, image_paths in marker_to_images.items():
  147. image_paths = sorted(image_paths)
  148. n = len(image_paths)
  149. ncols = 2
  150. nrows = math.ceil(n / ncols)
  152. fig = plt.figure(figsize=(12, 4*nrows))
  153. gs = gridspec.GridSpec(nrows, ncols, hspace=0, wspace=0)
  155. axes = [fig.add_subplot(gs[i, j]) for i in range(nrows) for j in range(ncols)]
  157. images = [] # Store references to images
  158. for ax, img_path in zip(axes, image_paths):
  159. img = mpimg.imread(img_path) # Read image
  160. images.append(img) # Store reference (helps with garbage collection)
  161. ax.imshow(img) # Display image
  162. ax.axis("off") # Hide axes for a clean look
  163. if n % 2 != 0:
  164. axes[-1].axis("off")
  165. output_path = f'HSV1_Results/{marker}/gating_summary_combined.png'
  166. #plt.tight_layout()
  167. plt.savefig(output_path, dpi=600, bbox_inches='tight')
  168. plt.show()
  169. plt.close(fig)
  171. # ---------- Memory cleanup ----------
  172. for ax in axes:
  173. ax.clear()
  174. fig.clf()
  175. del fig, gs, axes, images
  176. gc.collect()
  179. # %%
  180. import math
  181. import matplotlib.pyplot as plt
  182. import matplotlib.image as mpimg
  183. import matplotlib.gridspec as gridspec
  184. import os
  185. import glob
  186. import gc
  187. from collections import defaultdict
  189. # Step 1: Collect all individual gating summary images
  190. all_summaries = glob.glob('IAV_Results/*/*/gating_summary.png')
  192. # Step 2: Group image paths by marker
  193. marker_to_images = defaultdict(list)
  194. for path in all_summaries:
  195. parts = path.split('/')
  196. marker = parts[1]
  197. marker_to_images[marker].append(path)
  199. # Step 3: Generate combined figure per marker using GridSpec (2 columns), with cleanup
  200. for marker, image_paths in marker_to_images.items():
  201. image_paths = sorted(image_paths)
  202. n = len(image_paths)
  203. ncols = 2
  204. nrows = math.ceil(n / ncols)
  206. fig = plt.figure(figsize=(8, 4*nrows))
  207. gs = gridspec.GridSpec(nrows, ncols, hspace=0, wspace=0)
  209. axes = [fig.add_subplot(gs[i, j]) for i in range(nrows) for j in range(ncols)]
  211. images = [] # Store references to images
  212. for ax, img_path in zip(axes, image_paths):
  213. img = mpimg.imread(img_path) # Read image
  214. images.append(img) # Store reference (helps with garbage collection)
  215. ax.imshow(img) # Display image
  216. ax.axis("off") # Hide axes for a clean look
  217. if n % 2 != 0:
  218. axes[-1].axis("off")
  219. output_path = f'IAV_Results/{marker}/gating_summary_combined.png'
  220. #plt.tight_layout()
  221. plt.savefig(output_path, dpi=600, bbox_inches='tight')
  222. plt.show()
  223. plt.close(fig)
  225. # ---------- Memory cleanup ----------
  226. for ax in axes:
  227. ax.clear()
  228. fig.clf()
  229. del fig, gs, axes, images
  230. gc.collect()
  233. # %%

Gating_Summary_Figures.ipynb at commit a85307c, no license · at the source

Overview

Authors: Meagan N Olson1,2,3,4, Nathaniel J Barton1,2,3,4, Luyao Feng1,2,3,4, Samantha M Chigas1,2,3,4,5, Khanh Tran1,2,3,4,6, Adrian R Orszulak1,2,3,4,7, Jafira M Johnson8, Pepper Dawes1,2,3,4, Chandani Shrestha1,2,3,4, Vishali R Umaiyalan1,2,3,4, Yen-Hsiang Huang1,2,3,4, Jonathan Sundstrom1,2,3,4, Liam F Murray1,2,3,4, Qi Wang9, Hyung Suk Oh10, Megan H Orzalli8, David M Knipe10, Benjamin Readhead9, Yingleong Chan3,4, Elaine T Lim1,2,3,4
ORCID iDs: Jonathan Sundstrom
  1. Department of Medicine, Division of Innate Immunity, University of Massachusetts Chan Medical School, Worcester, MA USA
  2. Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA USA
  3. Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA USA
  4. NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA USA
  5. Graduate Program in Neuroscience, University of Massachusetts Chan Medical School, Worcester, MA USA
  6. Graduate Program in Biochemistry & Molecular Biotechnology, University of Massachusetts Chan Medical School, Worcester, MA USA
  7. Graduate Program in Immunology and Microbiology, University of Massachusetts Chan Medical School, Worcester, MA USA
  8. Department of Medicine, Division of Infectious Diseases and Immunology, University of Massachusetts Chan Medical School, Worcester, MA USA
  9. ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA
  10. Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA USA
Institutions: University of Massachusetts Chan Medical School (United States); Arizona State University (United States); Harvard University (United States)
Journal: NPJ dementia, volume 2, issue 1, article 20
Dates: received 18 September 2025; accepted 4 February 2026; published online 9 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44400-026-00066-y · PMID 41816609 · PMCID PMC12971488 · OpenAlex W7134262727
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Evoked potentials
Keywords: Diseases, Immunology, Neurology, Neuroscience
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIAID NIH HHS (T32 AI095213); National Institute on Aging (R01AG083881, U01AG061835, U01AG088673); National Institute of Allergy and Infectious Diseases (P01AI09681, T32AI007349)
Citations: cited by 5 papers (Europe PMC); 146 references in the paper

Abstract

Neuroinflammation is a key process associated with Alzheimer’s disease (AD). There is interest in developing New Approach Methodologies (NAMs) by using human in-vitro complex systems such as brain organoids, combined with machine learning and computational approaches, to reproducibly and robustly evaluate monoclonal antibodies and other therapeutic modalities on these human-derived systems. Herpesviruses such as herpes simplex virus 1 (HSV-1) had been shown to be associated with AD risk and molecular pathology. Building on top of previously reported work, we used herpes simplex virus 1 (HSV-1) infection in 2D dissociated cells from human cerebral organoids (dcOrgs) to recapitulate AD-associated molecular readouts, such as high co-abundance of intracellular beta amyloid (Aβ) and phosphorylated tau (pTau) with HSV-1. Secreted Aβ42/40 ratios in conditioned media were lower from HSV-1-infected dcOrgs, compared to mock dcOrgs. Differentially expressed transcripts from bulk and single-cell RNA sequence data in HSV-1-infected dcOrgs were enriched for AD-associated GWAS genes. Our high-throughput, quantitative framework represents a comprehensive approach to harness on the strengths of 2D dcOrgs for high-throughput applications such as therapeutic screens and can complement the 3D brain organoids and animal models for neuroinflammation in AD.

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 4 matches between paragraphs and lines of code.

elimlab/hsv1-bulk

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: a85307ce6a5e8ee53577ed583b2698fc3a9adfd2, 30 December 2025
Languages: Jupyter (6), R (4), Python (1)
Size: 16 files, 11 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 6 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (1 file), Matplotlib (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
2 files
At the source: gitlab.com/elimlab/hsv1-bulk

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;
  • 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);
  • 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

Our scripts have been uploaded to the project GitHub page (https://gitlab.com/elimlab/hsv1-bulk). Our bulk transcriptomics raw data is being uploaded to https://www.synapse.org/Synapse:syn69762030.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 20 authors, 4 keywords, 3 funders, 140 references.

Cite

This paper

Olson, M. N., Barton, N. J., Feng, L., Chigas, S. M., Tran, K., Orszulak, A. R., Johnson, J. M., Dawes, P., Shrestha, C., Umaiyalan, V. R., Huang, Y.-H., Sundstrom, J., Murray, L. F., Wang, Q., Oh, H. S., Orzalli, M. H., Knipe, D. M., Readhead, B., Chan, Y., & Lim, E. T. (2026). A high-throughput, quantitative platform using 2D dissociated human cerebral organoids to model neuroinflammation in Alzheimer's disease. NPJ dementia, 2(1), 20. https://doi.org/10.1038/s44400-026-00066-y

BibTeX

@article{olson2026high,
author = {Olson, Meagan N and Barton, Nathaniel J and Feng, Luyao and Chigas, Samantha M and Tran, Khanh and Orszulak, Adrian R and Johnson, Jafira M and Dawes, Pepper and Shrestha, Chandani and Umaiyalan, Vishali R and Huang, Yen-Hsiang and Sundstrom, Jonathan and Murray, Liam F and Wang, Qi and Oh, Hyung Suk and Orzalli, Megan H and Knipe, David M and Readhead, Benjamin and Chan, Yingleong and Lim, Elaine T},
title = {{A high-throughput, quantitative platform using 2D dissociated human cerebral organoids to model neuroinflammation in Alzheimer's disease}},
journal = {NPJ dementia},
year = {2026},
month = mar,
volume = {2},
number = {1},
pages = {20},
publisher = {Springer Science+Business Media},
issn = {3005-1940},
doi = {10.1038/s44400-026-00066-y},
url = {https://doi.org/10.1038/s44400-026-00066-y},
pmid = {41816609},
pmcid = {PMC12971488}
}

RIS

TY - JOUR
AU - Olson, Meagan N
AU - Barton, Nathaniel J
AU - Feng, Luyao
AU - Chigas, Samantha M
AU - Tran, Khanh
AU - Orszulak, Adrian R
AU - Johnson, Jafira M
AU - Dawes, Pepper
AU - Shrestha, Chandani
AU - Umaiyalan, Vishali R
AU - Huang, Yen-Hsiang
AU - Sundstrom, Jonathan
AU - Murray, Liam F
AU - Wang, Qi
AU - Oh, Hyung Suk
AU - Orzalli, Megan H
AU - Knipe, David M
AU - Readhead, Benjamin
AU - Chan, Yingleong
AU - Lim, Elaine T
TI - A high-throughput, quantitative platform using 2D dissociated human cerebral organoids to model neuroinflammation in Alzheimer's disease
T2 - NPJ dementia
J2 - NPJ Dement
PY - 2026
DA - 2026/03/09
VL - 2
IS - 1
SP - 20
SN - 3005-1940
PB - Springer Science+Business Media
DO - 10.1038/s44400-026-00066-y
UR - https://doi.org/10.1038/s44400-026-00066-y
LA - en
ER -

CSL-JSON

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"id": "10.1038/s44400-026-00066-y",
"type": "article-journal",
"title": "A high-throughput, quantitative platform using 2D dissociated human cerebral organoids to model neuroinflammation in Alzheimer's disease",
"container-title": "NPJ dementia",
"author": [
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