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Dissociable perfusion chip (DPC): perfusable microfluidic chip for single-cell screening of anti-cancer drugs in live glioblastoma explants.

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The authors' code

Python · 105 lines · 4.5 KB · no license

  1. #! /usr/bin/python
  2. import numpy as np
  3. import umap
  4. import matplotlib as mpl
  5. mpl.use('Agg')
  6. import matplotlib.pyplot as plt
  7. from matplotlib.backends.backend_pdf import PdfPages
  8. plt.rcParams['pdf.fonttype'] = 42
  9. plt.rcParams['ps.fonttype'] =42
  10. from scipy.stats import rankdata
  11. import argparse
  12. import seaborn as sns
  14. def parser_user_input():
  15. parser = argparse.ArgumentParser()
  16. parser.add_argument('-rm','--ref-matrix',required=True,help='Path to count matrix for reference.')
  17. parser.add_argument('-pm','--proj-matrices',nargs='+',required=True,help='Path to count matrices for query samples.')
  18. parser.add_argument('-p','--prefix',required=True,help='Prefix for output including path.')
  19. parser.add_argument('-m','--markers',required=True,help='Path to file with one-column list of marker gids (cell-cell similarity will be computed using these markers).')
  20. parser.add_argument('-k','-k',required=True,default=20,type=int,help='Integer value of k parameter for UMAP.')
  21. return parser
  23. # for loading molecular count matrix for a list of marker gids with format GID\tSYMBOL\tCTS_CELL1\tCTS_CELL2\t...
  24. # first column in marker_INFILE contains list of marker gids
  25. def load_marker_matrix(matrix_INFILE,marker_INFILE):
  26. gids = []
  27. genes = []
  28. matrix = []
  29. with open(marker_INFILE) as f:
  30. markers = set([line.split()[0] for line in f])
  31. with open(matrix_INFILE) as f:
  32. for line in f:
  33. llist = line.split()
  34. gid = llist[0]
  35. if gid in markers:
  36. gids.append(gid)
  37. genes.append(llist[1])
  38. try:
  39. matrix.append([int(pt) for pt in llist[2::]])
  40. except ValueError:
  41. matrix.append([float(pt) for pt in llist[2::]])
  42. gids = np.array(gids)
  43. ind = np.argsort(gids)
  44. gids = gids[ind]
  45. genes = np.array(genes)[ind]
  46. matrix = np.array(matrix)[ind]
  47. return gids, genes, matrix
  50. parser = parser_user_input()
  51. ui = parser.parse_args()
  53. print('Loading data...') # get reference count matrix (only marker genes)
  54. rgids,rgenes,rmatrix = load_marker_matrix(ui.ref_matrix,ui.markers)
  56. print('Computing model...')
  57. ref_rank = np.apply_along_axis(rankdata,0,rmatrix) # rank each gene in each cell by counts
  58. # compute UMAP embedding of reference using Spearman as a similarity metric (by computing Pearson of ranks)
  59. umap_model = umap.UMAP(n_neighbors=ui.k,random_state=42,metric='correlation').fit(ref_rank.T)
  60. umap_model_emb = umap_model.embedding_
  61. model_output = ui.prefix+'.ref_emb.umap.txt'
  62. np.savetxt(model_output,umap_model_emb,delimiter='\t',fmt='%f') # write reference embedding coordinates to file
  64. print('Computing projections...')
  65. projs = []
  66. for i,proj_matrix in enumerate(ui.proj_matrices):
  67. pgids,pgenes,pmatrix = load_marker_matrix(proj_matrix,ui.markers) # get query count matrix (only marker genes)
  68. if len(pgids) < len(rgids):
  69. print('Error: Some marker GIDS in the reference matrix are missing in the query matrix %(i)d.' % vars())
  70. exit()
  71. elif len(rgids) > len(pgids):
  72. print('Error: Some marker GIDS in query matrix %(i)d are missing from the reference matrix.' % vars())
  73. exit()
  74. prj_rank = np.apply_along_axis(rankdata,0,pmatrix) # rank each gene in each cell by counts
  75. umap_proj = umap_model.transform(prj_rank.T) # use transform function to project query into reference embedding
  76. proj_output = ui.prefix+'.proj.'+str(i)+'.umap.txt'
  77. np.savetxt(proj_output,umap_proj,delimiter='\t',fmt='%f') # write projection coordinates to file
  78. projs.append(umap_proj)
  80. print('Plotting output...')
  81. pdf_output = ui.prefix+'.pdf'
  82. with PdfPages(pdf_output) as pdf:
  83. fig,ax = plt.subplots()
  84. ax.scatter(umap_model_emb[:,0],umap_model_emb[:,1],s=3,c='k')
  85. ax.set_aspect('equal')
  86. ax.set_axis_off()
  87. ax.set_title('Reference Embedding')
  88. pdf.savefig()
  89. fig,ax = plt.subplots()
  90. ax.scatter(umap_model_emb[:,0],umap_model_emb[:,1],s=3,c='lightgrey')
  91. sns.kdeplot(umap_model_emb[:,0],umap_model_emb[:,1],cmap='binary_r',shade=False,gridsize=70,n_levels=14)
  92. ax.set_aspect('equal')
  93. ax.set_axis_off()
  94. ax.set_title('Reference Projected onto Reference Embedding')
  95. pdf.savefig()
  96. for i,proj in enumerate(projs):
  97. fig,ax = plt.subplots()
  98. ax.scatter(umap_model_emb[:,0],umap_model_emb[:,1],s=3,c='lightgrey')
  99. sns.kdeplot(proj[:,0],proj[:,1],cmap='binary_r',shade=False,gridsize=70,n_levels=14)
  100. ax.set_aspect('equal')
  101. ax.set_axis_off()
  102. ax.set_title('Query %(i)d Projected onto Reference Embedding' % vars())
  103. pdf.savefig()

umap_transform.py at commit 5c5c646, no license · at the source

Overview

Authors: Darragh G Kennedy1, Wenting Zhao2, Terry L Chern1, Michael Yang1, Nicolas Acosta1, Siddarth Arumugam1, Pavan Upadhyayula3, Julia Furnari3, Athanassios Dovas4, Jeffrey N Bruce3, Peter Canoll3,4, Samuel K Sia1, Peter A Sims2,5
ORCID iDs: Darragh G Kennedy, Samuel K Sia
  1. Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
  2. Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
  3. Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY 10032, USA
  4. Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
  5. Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY 10032, USA
Institutions: Columbia University (United States); Columbia University Irving Medical Center (United States)
Journal: Lab on a chip, volume 26, issue 7, pages 2266-2280
Dates: received 30 November 2025; accepted 12 February 2026; published online 4 March 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1039/d5lc01105a · PMID 41778300 · PMCID PMC12958118 · OpenAlex W7133522183
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), human (organism), mouse (organism), other condition (population), clinical / translational (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning
MeSH: Antineoplastic Agents*, Brain Neoplasms*, Glioblastoma*, Lab-On-A-Chip Devices*, Microfluidic Analytical Techniques*, Single-Cell Analysis*, Animals, Cell Survival, Drug Screening Assays, Antitumor, Humans, Mice, Perfusion (* major topic)
Topic: 3D Printing in Biomedical Research (Biomedical Engineering, Engineering), according to OpenAlex
Funding: Columbia University Pilot Project (UR011736); NCI NIH HHS (P30 CA013696); National Institutes of Health (P30CA013696); Columbia University (UR011736)
Citations: cited by 2 papers (Europe PMC); 64 references in the paper

Abstract

New approaches are needed to screen anti-cancer drugs that can target specific subpopulations of tumor cells. This study presents a microfluidic chip that enables parallel culture and drug perturbation of five thick tissue slices from human GBM resections, and removes the slices non-destructively for downstream single-cell RNA sequencing (scRNA-seq). Importantly, in contrast to methods relying on chemical attachment of tissue to chip, mechanical clamping of layers allows for positive-pressure perfusion of 3D slices and nondisruptive dissociation of tissue slices from the microfluidic chip. We ran the dissociable perfusion chip (DPC) on slice cultures freshly resected from human glioblastoma (within 1 h of surgery), one of the deadliest forms of malignant brain tumor which exhibits profound intra-tumoral heterogeneity that is challenging to recapitulate even with patient-derived models. DPC maintained similar fluidic conditions between channels and high cell viability in slices, and enabled downstream scRNA-seq to confirm that a topoisomerase inhibitor targets a subpopulation of proliferating tumor cells. Tissues run on DPC showed oxidative stress levels more similar to uncultured GBM slices compared to Transwell culture, as demonstrated by scRNA-seq, fluorometric assessment on a separate human patient sample, and assessment of long-term viability in mouse GBM samples under low and high oxygen tension. Overall, this proof-of-concept work suggests that combining DPC with off-chip scRNA-seq enables rapid, high-resolution identification of cell type-specific drug responses directly in GBM tissue from individual patients. Future work will aim to use this approach for screening of multiple drugs and further validation on additional fresh human GBM slices.

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

Repository

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

simslab/umap_projection

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 5c5c646da89157e34f2d57fb60ca1a7f2a7809f3, 7 March 2020
Languages: Python (1)
Size: 3 files, 1 script
Software Heritage: archived
Found in: the text, “Unsupervised clustering, differential expression”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file), SciPy (1 file), seaborn (1 file), UMAP (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: github.com/simslab/umap_projection

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.

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  • 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 data supporting this article have been included as part of the main text and within supplementary information (SI). In addition, please refer to the methods section for the various detailed methods used to analyze the data.

Supplementary information is available. See DOI: https://doi.org/10.1039/d5lc01105a

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 12 MeSH terms, 4 funders, 64 references.

Cite

This paper

Kennedy, D. G., Zhao, W., Chern, T. L., Yang, M., Acosta, N., Arumugam, S., Upadhyayula, P., Furnari, J., Dovas, A., Bruce, J. N., Canoll, P., Sia, S. K., & Sims, P. A. (2026). Dissociable perfusion chip (DPC): perfusable microfluidic chip for single-cell screening of anti-cancer drugs in live glioblastoma explants. Lab on a chip, 26(7), 2266-2280. https://doi.org/10.1039/d5lc01105a

BibTeX

@article{kennedy2026dissociable,
author = {Kennedy, Darragh G and Zhao, Wenting and Chern, Terry L and Yang, Michael and Acosta, Nicolas and Arumugam, Siddarth and Upadhyayula, Pavan and Furnari, Julia and Dovas, Athanassios and Bruce, Jeffrey N and Canoll, Peter and Sia, Samuel K and Sims, Peter A},
title = {{Dissociable perfusion chip (DPC): perfusable microfluidic chip for single-cell screening of anti-cancer drugs in live glioblastoma explants}},
journal = {Lab on a chip},
year = {2026},
month = mar,
volume = {26},
number = {7},
pages = {2266--2280},
publisher = {Royal Society of Chemistry},
issn = {1473-0197},
doi = {10.1039/d5lc01105a},
url = {https://doi.org/10.1039/d5lc01105a},
pmid = {41778300},
pmcid = {PMC12958118}
}

RIS

TY - JOUR
AU - Kennedy, Darragh G
AU - Zhao, Wenting
AU - Chern, Terry L
AU - Yang, Michael
AU - Acosta, Nicolas
AU - Arumugam, Siddarth
AU - Upadhyayula, Pavan
AU - Furnari, Julia
AU - Dovas, Athanassios
AU - Bruce, Jeffrey N
AU - Canoll, Peter
AU - Sia, Samuel K
AU - Sims, Peter A
TI - Dissociable perfusion chip (DPC): perfusable microfluidic chip for single-cell screening of anti-cancer drugs in live glioblastoma explants
T2 - Lab on a chip
J2 - Lab Chip
PY - 2026
DA - 2026/03/31
VL - 26
IS - 7
SP - 2266
EP - 2280
SN - 1473-0197
PB - Royal Society of Chemistry
DO - 10.1039/d5lc01105a
UR - https://doi.org/10.1039/d5lc01105a
LA - en
ER -

CSL-JSON

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"id": "10.1039/d5lc01105a",
"type": "article-journal",
"title": "Dissociable perfusion chip (DPC): perfusable microfluidic chip for single-cell screening of anti-cancer drugs in live glioblastoma explants",
"container-title": "Lab on a chip",
"author": [
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"family": "Kennedy",
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"PMCID": "PMC12958118",
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"issued": {
"date-parts": [
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