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Multimodal FRACTAL-MSI and Image Fusion Improves Detection and Resolution of Biomolecule Imaging.

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Paper

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The authors' code

Python · 149 lines · 3.9 KB · MIT

  1. import numpy as np
  4. def affine_transform(
  5. coords: np.ndarray, matrix: np.ndarray, inverse: bool = False
  6. ) -> np.ndarray:
  7. """Perform an affine transform of coordinates.
  9. Args:
  10. coords: array of points with shape (3, ...)
  11. matrix: the transformation matrix
  12. inverse: do an inverse transformation
  13. Returns:
  14. transformed coords
  15. """
  16. if inverse:
  17. matrix = np.linalg.inv(matrix)
  18. # swap x,y (numpy array index) to y,x (coords) and back
  19. return np.dot(coords[:, [1, 0, 2]], matrix)[:, [1, 0, 2]]
  22. def pixel_centers(
  23. shape: np.ndarray | tuple[int, int], px: float, py: float | None = None
  24. ) -> np.ndarray:
  25. """Gets center coordinate of pixels.
  27. Args:
  28. shape: array or array shape
  29. px: pixel width
  30. py: pixel height (defaults to px)
  32. Returns:
  33. array of (x, y, z) coordinates
  34. """
  35. if isinstance(shape, np.ndarray):
  36. shape = shape.shape[:2]
  37. if py is None:
  38. py = px
  40. xs = np.arange(shape[0]) * px + px / 2.0
  41. ys = np.arange(shape[1]) * py + py / 2.0
  43. X, Y = np.meshgrid(xs, ys)
  44. return np.stack((X.flat, Y.flat, np.ones(Y.size)), axis=1)
  47. def pixel_indicies(shape: np.ndarray | tuple[int, ...]) -> np.ndarray:
  48. """Indicies of pixels.
  50. Args:
  51. shape: array or array shape
  53. Returns:
  54. array of (x, y) indicices
  55. """
  56. if isinstance(shape, np.ndarray):
  57. shape = shape.shape
  58. shape = shape[:2]
  60. X, Y = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]))
  61. return np.stack((X.flat, Y.flat), axis=1)
  64. def pixel_indicies_from_centers(
  65. centers: np.ndarray,
  66. px: float,
  67. py: float | None = None,
  68. ) -> np.ndarray:
  69. """Gets the index for each pixel center.
  71. Args:
  72. centers: centers from pixel_centers (shape (N, 3))
  73. px: pixel width
  74. py: pixel height (defaults to px)
  76. Returns:
  77. indices of pixels
  78. """
  79. if py is None:
  80. py = px
  82. xi = (centers[:, 0] / px).astype(int)
  83. yi = (centers[:, 1] / py).astype(int)
  85. return np.stack((xi, yi), axis=1)
  88. def valid_indicies(
  89. indicies: np.ndarray, shape: np.ndarray | tuple[int, int]
  90. ) -> np.ndarray:
  91. """Creates a mask for indicies valid for shape.
  93. Args:
  94. indicies: from pixel_indicies_from_centers
  95. shape: array or array shape
  97. Returns:
  98. mask of valid indicies
  99. """
  100. if isinstance(shape, np.ndarray):
  101. shape = shape.shape[:2]
  103. return np.logical_and(
  104. np.logical_and(indicies[:, 0] >= 0, indicies[:, 1] >= 0),
  105. np.logical_and(indicies[:, 0] < shape[0], indicies[:, 1] < shape[1]),
  106. )
  109. def map_transformed_image(
  110. from_array: np.ndarray,
  111. from_pixel_size: tuple[float, float],
  112. to_array: np.ndarray,
  113. to_pixel_size: tuple[float, float],
  114. transform: np.ndarray,
  115. inverse: bool = False,
  116. ) -> tuple[np.ndarray, np.ndarray]:
  117. """Maps from_array indicies to ro_array using the given transform.
  119. Args:
  120. from_array: array map from
  121. from_pixel_size: size of pixels in from_array
  122. to_array: array map to
  123. to_pixel_size: size of pixels in to_array
  124. transform: affine transform from from_array to to_array
  125. inverse: transform is from to_array to from_array
  127. Returns:
  128. idx of from_array, idx mapped to to_array
  129. """
  131. from_idx = pixel_indicies(from_array.shape)
  133. if isinstance(from_pixel_size, float):
  134. centers = pixel_centers(from_array.shape, from_pixel_size)
  135. else:
  136. centers = pixel_centers(
  137. from_array.shape, from_pixel_size[0], from_pixel_size[1]
  138. )
  140. remapped = affine_transform(centers, transform, inverse=inverse)
  142. if isinstance(to_pixel_size, float):
  143. to_idx = pixel_indicies_from_centers(remapped, to_pixel_size)
  144. else:
  145. to_idx = pixel_indicies_from_centers(
  146. remapped, to_pixel_size[0], to_pixel_size[1]
  147. )
  149. return from_idx, to_idx

Frac_MSI_IF_register.py at commit b9be015, under MIT · at the source

Overview

Authors: Mika T. Westerhausen1,2,3, Rosemary J. Bergin1,2, Connor R. Phillips2,4, Jake P. Violi5, Thomas E. Lockwood1,2,6, Reggie Ridlen4, David P. Bishop1,2
ORCID iDs: Rosemary J. Bergin, Thomas E. Lockwood, David P. Bishop
  1. School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
  2. Hyphenated Mass Spectrometry Laboratory, University of Technology Sydney, Ultimo NSW 2007, Australia
  3. Molecular Horizons, University of Wollongong, Wollongong, NSW 2500, Australia
  4. School of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia
  5. School of Chemistry, University of New South Wales, Sydney, NSW 2052, Australia
  6. NanoMicroLab, Institute of Chemistry,University of Graz, Graz 38010, Austria
Institutions: University of Technology Sydney (Australia); University of Wollongong (Australia); UNSW Sydney (Australia); University of Graz (Austria)
Journal: ACS central science, volume 12, issue 8, pages 1119-1131
Dates: received 9 March 2026; accepted 12 June 2026; published online 6 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1021/acscentsci.6c00422 · PMID 42666888 · PMCID PMC13523496 · OpenAlex W7167503785
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality)
Topic: Mass Spectrometry Techniques and Applications (Spectroscopy, Chemistry), according to OpenAlex
Funding: Australian Government (NA); Australian Research Council (DP230101740)
Citations: not cited yet (Europe PMC); 87 references in the paper

Abstract

Elemental mass spectrometry imaging (MSI) of biomolecules via metal-conjugated antibodies enables highly multiplexed, in situ quantitative analyses. However, the detection of low abundance analytes by elemental MSI and the spatial resolution of the images obtained are inhibited by detector sensitivity. To overcome this constraint, we introduce FRACTAL-MSI, a signal amplification method that adapts FluoRescent signal Amplification via Cyclic staining of TArget moLecules (FRACTAL) for multimodal laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and immunofluorescence (IF) imaging by alternating fluorescent- and lanthanide-tagged secondary antibodies on the same histological section. Using NeuN as a model neuronal marker, we demonstrate 23–80× amplification of IF and LA-ICP-MS signals in fresh-frozen murine brain, FFPE tissue, and SH-SY5Y cells. Two image fusion methods that did not require machine learning were then developed that exploited true probe colocalization to generate quantitative elemental images at IF resolution. The enhanced sensitivity permitted super-resolution reconstruction of LA-ICP-MS data to obtain subcellular images with a 250 nm resolution, or greater than that of the 40× IF obtained via image fusion. We also demonstrated selective target amplification within a 4-plex elemental panel. Together, FRACTAL-MSI provides a practical, broadly accessible strategy to improve detection, and therefore the resolution, of low-abundance biomolecules.

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

Repository

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

davepbish/FRACTAL-MSI

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b9be0158ff52fd439c14b33e79bba6479ac2b4ea, 20 February 2026
Languages: Python (2)
Size: 34 files, 2 scripts
Software Heritage: not archived
Found in: the availability statement
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), Matplotlib (1 file), Pillow (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/davepbish/FRACTAL-MSI

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;
  • 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

All code used can be found in the https://github.com/davepbish/FRACTAL-MSI

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 funders, 85 references.

Cite

This paper

Westerhausen, M. T., Bergin, R. J., Phillips, C. R., Violi, J. P., Lockwood, T. E., Ridlen, R., & Bishop, D. P. (2026). Multimodal FRACTAL-MSI and Image Fusion Improves Detection and Resolution of Biomolecule Imaging. ACS central science, 12(8), 1119-1131. https://doi.org/10.1021/acscentsci.6c00422

BibTeX

@article{westerhausen2026multimodal,
author = {Westerhausen, Mika T. and Bergin, Rosemary J. and Phillips, Connor R. and Violi, Jake P. and Lockwood, Thomas E. and Ridlen, Reggie and Bishop, David P.},
title = {{Multimodal FRACTAL-MSI and Image Fusion Improves Detection and Resolution of Biomolecule Imaging}},
journal = {ACS central science},
year = {2026},
month = jul,
volume = {12},
number = {8},
pages = {1119--1131},
publisher = {American Chemical Society},
issn = {2374-7943},
doi = {10.1021/acscentsci.6c00422},
url = {https://doi.org/10.1021/acscentsci.6c00422},
pmid = {42666888},
pmcid = {PMC13523496}
}

RIS

TY - JOUR
AU - Westerhausen, Mika T.
AU - Bergin, Rosemary J.
AU - Phillips, Connor R.
AU - Violi, Jake P.
AU - Lockwood, Thomas E.
AU - Ridlen, Reggie
AU - Bishop, David P.
TI - Multimodal FRACTAL-MSI and Image Fusion Improves Detection and Resolution of Biomolecule Imaging
T2 - ACS central science
J2 - ACS Cent Sci
PY - 2026
DA - 2026/07/06
VL - 12
IS - 8
SP - 1119
EP - 1131
SN - 2374-7943
PB - American Chemical Society
DO - 10.1021/acscentsci.6c00422
UR - https://doi.org/10.1021/acscentsci.6c00422
LA - en
ER -

CSL-JSON

{
"id": "10.1021/acscentsci.6c00422",
"type": "article-journal",
"title": "Multimodal FRACTAL-MSI and Image Fusion Improves Detection and Resolution of Biomolecule Imaging",
"container-title": "ACS central science",
"author": [
{
"family": "Westerhausen",
"given": "Mika T."
},
{
"family": "Bergin",
"given": "Rosemary J."
},
{
"family": "Phillips",
"given": "Connor R."
},
{
"family": "Violi",
"given": "Jake P."
},
{
"family": "Lockwood",
"given": "Thomas E."
},
{
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"given": "Reggie"
},
{
"family": "Bishop",
"given": "David P."
}
],
"container-title-short": "ACS Cent Sci",
"volume": "12",
"issue": "8",
"page": "1119-1131",
"DOI": "10.1021/acscentsci.6c00422",
"PMID": "42666888",
"PMCID": "PMC13523496",
"ISSN": "2374-7943",
"publisher": "American Chemical Society",
"URL": "https://doi.org/10.1021/acscentsci.6c00422",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
6
]
]
}
}

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