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In situ graphene-seq: spatial transcriptomics and chronic electrophysiological characterization of tissue microenvironments.

Code ↔ Paper

11 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 11 matches
  1. [1] § Results › Capturing electrophysiological and molecular heterogeneity within spatially heterogeneous tissue ↔ notebooks/Supplementary_Figure9.ipynb, lines 27–129 · score 0.85 · cm s1, cm s2, cm s3, CM states, HAND2, ITGB5
  2. [2] § Methods › Data analysis ↔ notebooks/Supplementary_Figure8.ipynb, lines 18–32 · score 0.81 · COL1A1, COL3A1, EOMES, SOX2, ZFP42, CDH5
  3. [3] § Methods › Data analysis ↔ notebooks/Supplementary_Figure10.ipynb, lines 35–76 · score 0.78 · random forest classifier, molecular model, split, stratified, accuracy, trained
  4. [4] § Results › Capturing electrophysiological and molecular heterogeneity within spatially heterogeneous tissue ↔ notebooks/Supplementary_Figure8.ipynb, lines 18–32 · score 0.67 · COL1A1, COL3A1, CDH5, PECAM1, ACTN2, MYH6
  5. [5] § Results › Capturing electrophysiological and molecular heterogeneity within spatially heterogeneous tissue ↔ notebooks/Supplementary_Figure9.ipynb, lines 27–129 · score 0.64 · cm s1, cm s2, cm s3, CM state, MYL7, cells
  6. [6] § Results › Integrative analysis of electrophysiological and molecular characteristics in spatially resolved cell niches ↔ notebooks/Supplementary_Figure10.ipynb, lines 108–138 · score 0.58 · random forest classifier, predicted niche, accuracy, cross, trained, cell
  7. [7] § Results › Electrical and optical performance on cultured tissues ↔ notebooks/Figure4.ipynb, lines 235–282 · score 0.58 · field potential duration, spike amplitude, FPD, dt, waveforms
  8. [8] § Results › Integrative analysis of electrophysiological and molecular characteristics in spatially resolved cell niches ↔ notebooks/Figure4.ipynb, lines 235–282 · score 0.58 · field potential duration, spike amplitude, FPD, Scatter, dt, PC1
  9. [9] § Results › Capturing electrophysiological and molecular heterogeneity within spatially heterogeneous tissue ↔ notebooks/Figure3.ipynb, lines 190–213 · score 0.56 · cm s1, cm s2, cm s3, CM states, Bar, Figure 3
  10. [10] § Results › Capturing electrophysiological and molecular heterogeneity within spatially heterogeneous tissue ↔ notebooks/Figure3.ipynb, lines 257–297 · score 0.55 · cm s1, cm s2, cm s3, pseudotime, transcriptomic, cell
  11. [11] § Methods › Electrophysiological signal processing and feature extraction ↔ notebooks/Figure3.ipynb, lines 120–128 · score 0.54 · Scanpy, neighbor, pp, tl, UMAP, PCA

Paper

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The authors' code

Jupyter notebook · 300 lines · 7.8 KB · Apache-2.0 · 3 matches

  1. # %%
  2. import numpy as np
  3. import os
  4. import matplotlib.pyplot as plt
  5. import seaborn as sns
  6. import anndata as an
  7. import scanpy as sc
  8. import pandas as pd
  10. # %%
  11. #Set path
  12. data_path = '../data/'
  13. fig3_data_path = '../data/Figure3/'
  15. # %% [markdown]
  16. # # Plot waveforms (Figure3c)
  18. # %%
  19. loaded_data = np.load(fig3_data_path + "Figure3c_waveforms.npz", allow_pickle=True)
  21. mean_waveforms = loaded_data["mean_waveforms"].item()
  22. std_waveforms = loaded_data["std_waveforms"].item()
  23. channels = loaded_data["channels"]
  24. regions = loaded_data["regions"]
  26. cm_ec_channels = []
  27. cm_channels = []
  29. for i, (channel, region) in enumerate(regions):
  30. if region == "CM_EC":
  31. cm_ec_channels.append(channel)
  32. elif region == "CM":
  33. cm_channels.append(channel)
  35. print(f"CM_EC channels: {len(cm_ec_channels)}")
  36. print(f"CM channels: {len(cm_channels)}")
  38. a = 4 # number of rows
  39. b = 8 # number of columns
  41. c = 1
  42. fig = plt.figure(figsize=(8, 5))
  43. plt.suptitle("CM_EC Channels", fontsize=16)
  45. for ch in cm_ec_channels:
  46. if c > a*b:
  47. break
  49. plt.subplot(a, b, c)
  50. plt.title(f'{ch}', fontsize=8)
  52. plt.plot(mean_waveforms[ch], color='#ff7c0e', linewidth=2)
  53. plt.fill_between(np.arange(len(mean_waveforms[ch])),
  54. mean_waveforms[ch] - std_waveforms[ch],
  55. mean_waveforms[ch] + std_waveforms[ch],
  56. color='#ff7c0e', alpha=0.3)
  58. ax = plt.gca()
  59. ax.spines['top'].set_visible(False)
  60. ax.spines['right'].set_visible(False)
  61. ax.spines['left'].set_visible(False)
  62. ax.spines['bottom'].set_visible(False)
  63. ax.set_xticks([])
  64. ax.set_xticklabels([])
  65. ax.set_yticks([])
  66. ax.set_yticklabels([])
  68. scale_y = [np.min(mean_waveforms[ch]), np.min(mean_waveforms[ch]) + 20]
  69. scale_x = [10000, 10000]
  70. ax.plot(scale_x, scale_y, 'k-', linewidth=1)
  72. c = c + 1
  74. plt.tight_layout(rect=[0, 0, 1, 0.95])
  75. plt.show()
  77. c = 1
  78. fig = plt.figure(figsize=(8, 5))
  79. plt.suptitle("CM Channels", fontsize=16)
  81. for ch in cm_channels:
  82. if c > a*b:
  83. break
  85. plt.subplot(a, b, c)
  86. plt.title(f'{ch}', fontsize=8)
  88. plt.plot(mean_waveforms[ch], color='blue', linewidth=2)
  89. plt.fill_between(np.arange(len(mean_waveforms[ch])),
  90. mean_waveforms[ch] - std_waveforms[ch],
  91. mean_waveforms[ch] + std_waveforms[ch],
  92. color='blue', alpha=0.3)
  94. ax = plt.gca()
  95. ax.spines['top'].set_visible(False)
  96. ax.spines['right'].set_visible(False)
  97. ax.spines['left'].set_visible(False)
  98. ax.spines['bottom'].set_visible(False)
  99. ax.set_xticks([])
  100. ax.set_xticklabels([])
  101. ax.set_yticks([])
  102. ax.set_yticklabels([])
  104. scale_y = [np.min(mean_waveforms[ch]), np.min(mean_waveforms[ch]) + 20]
  105. scale_x = [10000, 10000]
  106. ax.plot(scale_x, scale_y, 'k-', linewidth=1)
  108. c = c + 1
  110. plt.tight_layout(rect=[0, 0, 1, 0.95])
  111. plt.show()
  113. # %% [markdown]
  114. # # Load Anndata
  116. # %%
  117. waveform_mean_denoised = sc.read_h5ad(fig3_data_path + 'Figure3_waveforms_mean_denoised.h5ad')
  118. waveform_mean_denoised.obs_names_make_unique()
  120. # %% [markdown]
  121. # # UMAP (Figure 3d)
  123. # %%
  124. sc.tl.pca(waveform_mean_denoised, svd_solver='arpack')
  125. sc.pp.neighbors(waveform_mean_denoised)
  126. sc.tl.umap(waveform_mean_denoised)
  128. waveform_mean_denoised.obsm['X_umap'].shape
  130. # %%
  131. fig, ax = plt.subplots(figsize=(5, 5))
  132. sc.pl.umap(waveform_mean_denoised, color=['cell_type'], size=300, ax=ax, show=False)
  133. plt.show()
  136. # %% [markdown]
  137. # # Read Gene Dataset
  139. # %%
  140. # All transcriptomic datset
  141. st_ad = sc.read_h5ad(data_path + 'st_ad_all.h5ad')
  143. cell_type_dict = {
  144. 'Unidentified': '#1f77b4',
  145. 'CM_s1': '#ff7f0e',
  146. 'CM_s2': '#2ca02c',
  147. 'CM_s3': '#d62728',
  148. 'Endothelial': '#9467bd',
  149. 'Fibroblast': '#8c564b'
  150. }
  152. niche_type_dict = {
  153. 'Niche_1': '#17becf',
  154. 'Niche_2': '#bcbd22',
  155. 'Niche_3': '#ff9896'
  156. }
  158. # %% [markdown]
  159. # # UMAPS (Figure 3 h,i)
  161. # %%
  162. with plt.rc_context({"figure.figsize": (5, 5), "figure.dpi": 100}):
  163. ax = sc.pl.umap(st_ad, color=['cell_type'], s=100, show=False)
  164. plt.show()
  166. # %%
  167. with plt.rc_context({"figure.figsize": (5, 5), "figure.dpi": 100}):
  168. ax = sc.pl.umap(st_ad, color=['niche'], s=100, palette=niche_type_dict, show=False)
  169. plt.show()
  171. # %% [markdown]
  172. # # Niche composition (Figure 3j)
  174. # %%
  175. cell_type_niche_composition_df = pd.read_csv(fig3_data_path + 'Figure3l_cell_type_niche_composition_df.csv', index_col=0)
  176. cell_type_niche_composition_norm_df = cell_type_niche_composition_df.div(cell_type_niche_composition_df.sum(axis=1), axis=0)
  178. with plt.rc_context({"figure.figsize": (5, 5), "figure.dpi": 100}):
  179. ax = cell_type_niche_composition_norm_df.plot(kind='bar', stacked=True, color=cell_type_dict)
  180. ax.set_ylabel('Proportion')
  181. ax.set_xlabel('Niche')
  183. ax.set_yticks([0.0, 0.5, 1.0])
  185. ax.legend(bbox_to_anchor=(1, 0.5), loc="center right", bbox_transform=plt.gcf().transFigure)
  187. plt.tight_layout()
  188. plt.show()
  190. # %% [markdown]
  191. # # CM states vs Niche (Figure 3k)
  193. # %%
  194. st_ad_electrogenic = st_ad[st_ad.obs['cell_type'].isin(['CM_s1', 'CM_s2', 'CM_s3'])]
  195. data_i = st_ad_electrogenic
  197. contingency_table = pd.crosstab(data_i.obs['cell_type'], data_i.obs['niche'])
  198. proportions = contingency_table.div(contingency_table.sum(axis=1), axis=0)
  200. with plt.rc_context({"figure.figsize": (5, 5), "figure.dpi": 100}):
  201. ax = proportions.plot(kind='bar', stacked=True, color=[niche_type_dict[str(i)] for i in proportions.columns])
  203. ax.set_ylabel('Proportion', fontsize=14)
  204. ax.set_xlabel('Cell Type', fontsize=14)
  205. ax.tick_params(axis='x', rotation=0)
  206. ax.tick_params(axis='y', labelsize=12)
  208. ax.set_yticks([0.0, 0.5, 1.0])
  210. ax.legend(title="Niche", loc="upper right", fontsize=12, title_fontsize=12, frameon=True)
  212. plt.tight_layout()
  213. plt.show()
  215. # %% [markdown]
  216. # # Tissue region vs CM states (Figure 3l)
  218. # %%
  219. contingency_table = pd.crosstab(data_i.obs['condition'], data_i.obs['cell_type'])
  220. proportions = contingency_table.div(contingency_table.sum(axis=1), axis=0)
  222. with plt.rc_context({"figure.figsize": (3, 5), "figure.dpi": 100}):
  223. ax = proportions.plot(kind='bar', stacked=True, color=[cell_type_dict[str(i)] for i in proportions.columns])
  225. ax.set_ylabel('Proportion', fontsize=14)
  226. ax.set_xlabel('Condition', fontsize=14)
  227. ax.tick_params(axis='x', rotation=0)
  228. ax.tick_params(axis='y', labelsize=12)
  230. ax.set_yticks([0.0, 0.5, 1.0])
  232. ax.legend(title="Cell_type", loc="upper right", fontsize=12, title_fontsize=12, frameon=True)
  234. plt.tight_layout()
  235. plt.show()
  237. # %% [markdown]
  238. # # Pseudotime analysis
  240. # %%
  241. sc_ad_recorded = sc.read_h5ad(data_path + 'st_ad_recorded_filtered.h5ad')
  243. # %% [markdown]
  244. # # Plot
  246. # %%
  247. palette = {'CM_s1': '#ff7f0e', 'CM_s2': '#2ca02c', 'CM_s3': '#d62728'}
  249. fig, ax = plt.subplots(ncols=2, nrows=1, figsize=(12, 5))
  251. axs1 = sc.pl.umap(sc_ad_recorded, color=['cell_type'], size=300, palette= palette, ax=ax[0], show=False)
  252. axs2 = sc.pl.umap(sc_ad_recorded, color=['slingshot_pseudotime'], size=300, ax=ax[1], show=False)
  254. plt.tight_layout()
  255. plt.show()
  257. # %%
  258. plot_data = pd.DataFrame({
  259. 'pseudotime': sc_ad_recorded.obs['slingshot_pseudotime'],
  260. 'cell_type': sc_ad_recorded.obs['cell_type']
  261. })
  263. palette_cell_type = {
  264. 'CM_s1': '#ff7f0e',
  265. 'CM_s2': '#2ca02c',
  266. 'CM_s3': '#d62728',
  267. }
  269. order = ['CM_s1', 'CM_s2', 'CM_s3']
  271. plt.figure(figsize=(6, 5.5))
  273. sns.boxplot(
  274. data=plot_data,
  275. x='cell_type',
  276. y='pseudotime',
  277. palette=palette_cell_type,
  278. order=order,
  279. showfliers=False,
  280. whis=(0, 100)
  281. )
  283. sns.stripplot(
  284. data=plot_data,
  285. x='cell_type',
  286. y='pseudotime',
  287. order=order,
  288. color='black',
  289. jitter=0.1,
  290. size=8.5
  291. )
  293. plt.xlabel('Cell_type')
  294. plt.ylabel('Transcriptional Pseudotime')
  296. plt.tight_layout()
  297. plt.show()
  299. # %%

Figure3.ipynb at commit 8961600, under Apache-2.0 · at the source

Overview

Authors: Jaeyong Lee1, Wenbo Wang1,2, Qiang Li1, Zuwan Lin1,2, Ren Liu1, Zefang Tang2, Junya Aoyama3, Richard T Lee3,4, Xiao Wang2,5, Jia Liu1
ORCID iDs: Jaeyong Lee, Wenbo Wang, Zuwan Lin, Zefang Tang, Richard T Lee, Xiao Wang, Jia Liu
  1. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA USA
  2. Broad Institute of MIT and Harvard, Cambridge, MA USA
  3. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA USA
  4. Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
  5. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA USA
Institutions: Harvard University (United States); Broad Institute (United States); Brigham and Women's Hospital (United States); Massachusetts Institute of Technology (United States)
Journal: Nature communications, volume 17, issue 1, article 8382
Dates: received 18 March 2025; accepted 22 May 2026; published online 19 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73883-7 · PMID 42321181 · PMCID PMC13473619 · OpenAlex W4408987240
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.03.25.645278
Status: code verified
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Connectivity, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials
Keywords: Bionanoelectronics, Electronic properties and devices, Biomedical engineering
MeSH: Cellular Microenvironment*, Electrophysiological Phenomena*, Graphite*, Transcriptome*, Coculture Techniques, Electrodes, Endothelial Cells, Humans, Induced Pluripotent Stem Cells, Myocytes, Cardiac, Polystyrenes, Single-Cell Analysis, Spatial Transcriptomics (* major topic)
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (5R01LM014465); U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (1DP1DK130673); NIDDK NIH HHS (DP1 DK130673); U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI) (1R33HL175683); U.S. Department of Health & Human Services | National Institutes of Health (NIH) (1RF1MH123948); U.S. Department of Health & Human Services | National Institutes of Health (1RF1MH123948); National Science Foundation (ECCS-2038603); U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) (1DP1DK130673); U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (1R33HL175683); U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM) (5R01LM014465); NIMH NIH HHS (RF1 MH123948); NHLBI NIH HHS (R33 HL175683); NLM NIH HHS (R01 LM014465)
Citations: cited by 1 paper (Europe PMC); 63 references in the paper

Abstract

Biological systems comprise diverse, interconnected cell types whose functional dynamics and molecular identities are tightly coupled, yet difficult to capture simultaneously at high spatiotemporal resolution. Electrophysiology provides real-time measurements of cellular activity but with limited molecular context, whereas transcriptomics profiles gene expression without dynamic physiological readouts. Here, we introduce in situ graphene-sequencing, a platform that integrates chronic electrophysiology with imaging-based, spatially resolved transcriptomics (STARmap). The system combines stretchable mesh nanoelectronics for long-term, single-cell-level interfacing with transparent graphene/poly(3,4-ethylenedioxythiophene) polystyrene sulfonate electrodes, enabling seamless integration of electrical recording and optical imaging. By coupling electrophysiology with STARmap, the platform enables multimodal analysis of heterogeneous tissue microenvironments. We demonstrate in situ graphene-sequencing by charting multimodal profiles of human-induced pluripotent stem cell-derived cardiomyocyte and endothelial cell co-cultures, examining how spatial heterogeneity is associated with electrophysiological activity and gene expression. This approach provides an integrative framework for studying how tissue microenvironments shape cell behavior and molecular states.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.

LiuLab-Bioelectronics-Harvard/Graphene_seq

License: Apache-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8961600c6c2e643e1468eab5cc73cdc3a3e505c2, 20 April 2026
Languages: Jupyter (9), Python (2)
Size: 56 files, 11 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt), 9 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (10 files), NumPy (10 files), pandas (9 files), Scanpy (8 files), seaborn (8 files), anndata (6 files), scikit-learn (3 files), SciPy (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files
At the source: github.com/LiuLab-Bioelectronics-Harvard/Graphene_seq

Zenodo 19520229

License: Apache-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source:

Code availability

The code used to perform the analyses and generate results in this study is publicly available and has been deposited in a GitHub repository at (http://github.com/LiuLab-Bioelectronics-Harvard/Graphene_seq), under the Apache-2.0 license. The specific version of the code associated with this publication is archived in Zenodo and is accessible via 10.5281/zenodo.1952022963.

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 11 scripts, each with its path and the digest of its content;
  • 11 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 Statement

Imaging data underlying the in situ sequencing experiments have been deposited in the BioImage Archive under accession number S-BIAD3370. Single-cell sequencing data used for data integration are available from the NCBI Gene Expression Omnibus (GEO) under accession code GSE210513 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE210513). Processed datasets used for analysis and figure generation are available on GitHub (http://github.com/LiuLab-Bioelectronics-Harvard/Graphene_seq) and archived on Zenodo (10.5281/zenodo.19520229). Source data are provided with this paper.

The code used to perform the analyses and generate results in this study is publicly available and has been deposited in a GitHub repository at (http://github.com/LiuLab-Bioelectronics-Harvard/Graphene_seq), under the Apache-2.0 license. The specific version of the code associated with this publication is archived in Zenodo and is accessible via 10.5281/zenodo.1952022963.

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, issue, pages, dates, 10 authors, 3 keywords, 13 MeSH terms, 13 funders, 56 references.

Cite

This paper

Lee, J., Wang, W., Li, Q., Lin, Z., Liu, R., Tang, Z., Aoyama, J., Lee, R. T., Wang, X., & Liu, J. (2026). In situ graphene-seq: spatial transcriptomics and chronic electrophysiological characterization of tissue microenvironments. Nature communications, 17(1), 8382. https://doi.org/10.1038/s41467-026-73883-7

BibTeX

@article{lee2026situ,
author = {Lee, Jaeyong and Wang, Wenbo and Li, Qiang and Lin, Zuwan and Liu, Ren and Tang, Zefang and Aoyama, Junya and Lee, Richard T and Wang, Xiao and Liu, Jia},
title = {{In situ graphene-seq: spatial transcriptomics and chronic electrophysiological characterization of tissue microenvironments}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {8382},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73883-7},
url = {https://doi.org/10.1038/s41467-026-73883-7},
pmid = {42321181},
pmcid = {PMC13473619}
}

RIS

TY - JOUR
AU - Lee, Jaeyong
AU - Wang, Wenbo
AU - Li, Qiang
AU - Lin, Zuwan
AU - Liu, Ren
AU - Tang, Zefang
AU - Aoyama, Junya
AU - Lee, Richard T
AU - Wang, Xiao
AU - Liu, Jia
TI - In situ graphene-seq: spatial transcriptomics and chronic electrophysiological characterization of tissue microenvironments
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/19
VL - 17
IS - 1
SP - 8382
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73883-7
UR - https://doi.org/10.1038/s41467-026-73883-7
LA - en
ER -

CSL-JSON

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}
}

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