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TyrRu: A Tyramide-Amplified Multimodal Ruthenium Tag for Elemental Mass Spectrometry and Immunofluorescence Imaging.

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Paper

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The authors' code

Python · 135 lines · 4.8 KB · MIT

  1. import numpy as np
  2. import pandas as pd
  3. import os
  4. from scipy.ndimage import gaussian_filter
  6. # -------------------------
  7. # 1. SETUP & PARAMETERS
  8. # -------------------------
  9. Directory = r"C:\Users\bergi\OneDrive - UTS\Onedrive\OneDrive - UTS\Tyr-Ru paper\paper figures\SRR\new SRR"
  10. horz_file = os.path.join(Directory, "2. B1 1.1000 10min Vert.csv")
  11. vert_file = os.path.join(Directory, "3. B1 1.1000 10min Hort (TtB).csv")
  13. # Experimental Parameters (Define the Aspect Ratio)
  14. Speed = 20 # µm/sec (v)
  15. Scantime = 0.05 #1 / 200 # 0.005 sec (tsc)
  16. SpotSize = 1 # µm (D)
  17. Layers = 2
  19. # --- CALCULATIONS BASED ON PAPER'S FORMULAS ---
  21. # Lateral Sampling Interval (Δx)
  22. Delta_x = Speed * Scantime # 20 * 0.005 = 0.1 µm
  24. # Expansion Factor / Aspect Ratio (M)
  25. # M is the factor by which the raw image must be expanded for SRR: M = Spot Size / Δx
  26. M = SpotSize / Delta_x # 1.0 / 0.1 = 10.0
  27. M_int = int(M)
  29. # Shift Offset (N)
  30. # N is the shift required (half the magnitude of the spot size): N = M / 2
  31. N = M_int // 2 # 10 // 2 = 5 pixels
  32. print(M_int, N)
  34. # -------------------------
  35. # 2. LOAD RAW DATA (FIXED FOR PERSISTENT BOM ERROR)
  36. # -------------------------
  37. print("--- LOADING DATA ---")
  39. # Step 1: Read the file, ensuring the encoding is handled.
  40. # If 'utf-8' didn't work, try reading the file content directly and stripping the BOM
  41. with open(horz_file, 'r', encoding='utf-8') as f:
  42. content_H = f.read()
  44. # Manually strip the BOM character from the beginning of the file content
  45. if content_H.startswith('\ufeff'):
  46. content_H = content_H.lstrip('\ufeff')
  48. # Now, read the cleaned content using StringIO
  49. import io
  50. H_df = pd.read_csv(io.StringIO(content_H), sep=None, engine='python', header=None)
  52. # Repeat for the Vertical file (V)
  53. with open(vert_file, 'r', encoding='utf-8') as f:
  54. content_V = f.read()
  56. if content_V.startswith('\ufeff'):
  57. content_V = content_V.lstrip('\ufeff')
  59. V_df = pd.read_csv(io.StringIO(content_V), sep=None, engine='python', header=None)
  62. # Step 2: Convert to NumPy array
  63. # This step is now safer as the BOM has been stripped from the file start.
  64. H = H_df.values.astype(np.float64)
  65. V = V_df.values.astype(np.float64)
  67. print(f"Loaded H Shape: {H.shape}")
  68. print(f"Calculated Aspect Ratio (M): {M}")
  69. # print("--- LOADING DATA ---")
  70. # # Robust CSV loading (sep=None) is critical to prevent the '710x1' error
  71. # H = pd.read_csv(horz_file, sep=None, engine='python', header=None).values
  72. # V = pd.read_csv(vert_file, sep=None, engine='python', header=None).values
  73. # print(f"Loaded H Shape: {H.shape}")
  74. # print(f"Calculated Aspect Ratio (M): {M}")
  77. # -------------------------
  78. # 3. CORE PROCESSING FUNCTION (Kronecker, Upsample, Shift/Pad)
  79. # -------------------------
  80. def process_layer(data, M_factor, shift_pixels, is_vertical):
  81. # A. Kronecker Product (Expands width/columns by M)
  82. data_exp = np.kron(data, np.ones((1, M_factor)))
  84. # B. Upsample (Expands height/rows by M to create square pixels)
  85. data_upsampled = np.repeat(data_exp, M_factor, axis=0)
  87. # C. Shift and Pad (Aligns the orthogonal scans)
  88. pad_width = shift_pixels
  90. if is_vertical:
  91. # Pre-Shift (Top/Left) for the orthogonal layer
  92. pad = ((pad_width, 0), (pad_width, 0))
  93. else:
  94. # Post-Shift (Bottom/Right) for the primary layer (to match size)
  95. pad = ((0, pad_width), (0, pad_width))
  97. # Pad with NaNs (null values) for interpolation
  98. data_shifted = np.pad(data_upsampled, pad, mode='constant', constant_values=np.nan)
  100. return data_shifted
  103. # -------------------------
  104. # 4. EXECUTE RECONSTRUCTION (Full Image Processing)
  105. # -------------------------
  107. # 4a. Process Layers
  108. H_final = process_layer(H, M_int, N, is_vertical=False)
  109. V_final = process_layer(V, M_int, N, is_vertical=True)
  111. # 4b. Crop to Common Size (Handles 1-pixel differences from padding)
  112. min_r = min(H_final.shape[0], V_final.shape[0])
  113. min_c = min(H_final.shape[1], V_final.shape[1])
  114. H_final = H_final[:min_r, :min_c]
  115. V_final = V_final[:min_r, :min_c]
  117. # 4c. Stack and Sum (Creates the final 2D image)
  118. stack = np.dstack([H_final, V_final])
  119. SRR_Image = np.nansum(stack, axis=2)
  121. # 4d. Gaussian Smoothing (Mitigates square pixel artifacts)
  122. # Sigma derived from FWHM relationship with Spot Size (M)
  123. calculated_sigma = M / 2.355
  124. Gaussian_Image = gaussian_filter(SRR_Image, sigma=calculated_sigma)
  126. # -------------------------
  127. # 5. SAVE OUTPUTS
  128. # -------------------------
  129. print(f"Saving SRR Image (Shape: {SRR_Image.shape})...")
  131. # Use fixed-point formatting ('%.5f') for compatibility with Fiji/ImageJ
  132. np.savetxt(os.path.join(Directory, "SRR_2D.csv"), SRR_Image, delimiter=",", fmt='%.5f')
  133. np.savetxt(os.path.join(Directory, "SRR_2D_Gaussian.csv"), Gaussian_Image, delimiter=",", fmt='%.5f')
  135. print("✅ Full image processed and saved.")

SRR_TyrRu.py at commit 21d1c14, under MIT · at the source

Overview

Authors: Rosemary J. Bergin1,2, Ioannis Kohilas1, Thomas E. Lockwood1,2,3, Andrew M. McDonagh1, Mika T. Westerhausen1,2,4, David P. Bishop1,2
ORCID iDs: Rosemary J. Bergin, Ioannis Kohilas, Thomas E. Lockwood, Andrew M. McDonagh, David P. Bishop
  1. School of Mathematical and Physical Sciences, The University of Technology Sydney, Sydney, NSW 2007, Australia
  2. Hyphenated Mass Spectrometry Laboratory, University of Technology Sydney, Sydney, NSW 2007, Australia
  3. NanoMicroLab, Institute of Chemistry, University of Graz, Graz 8010, Austria
  4. Molecular Horizons, University of Wollongong, Wollongong 2522, Australia
Institutions: University of Technology Sydney (Australia); University of Graz (Austria); University of Wollongong (Australia)
Journal: Chemical & biomedical imaging, volume 4, issue 8, pages 1805-1815
Dates: received 7 January 2026; accepted 26 February 2026; published online 5 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1021/cbmi.6c00007 · PMID 42657241 · PMCID PMC13508111 · OpenAlex W7133905099
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), histology / microscopy (modality)
Keywords: TyrRu, tyramide signal amplification, multimodal imaging, immunofluorescence, LA-ICP-MS, subcellular imaging
Topic: Advanced Fluorescence Microscopy Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Australian Government (NA); Australian Research Council (DP230101740)
Citations: not cited yet (Europe PMC); 55 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.

davepbish/TyrRu

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 21d1c14c13ee96eba24a654846e1cb2e3b5d31ef, 15 June 2026
Languages: Python (1)
Size: 4 files, 1 script
Software Heritage: not archived
Found in: the text, “Data Analysis”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
3 files
At the source: github.com/davepbish/TyrRu

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Data

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Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 6 keywords, 2 funders, 52 references.

Cite

This paper

Bergin, R. J., Kohilas, I., Lockwood, T. E., McDonagh, A. M., Westerhausen, M. T., & Bishop, D. P. (2026). TyrRu: A Tyramide-Amplified Multimodal Ruthenium Tag for Elemental Mass Spectrometry and Immunofluorescence Imaging. Chemical & biomedical imaging, 4(8), 1805-1815. https://doi.org/10.1021/cbmi.6c00007

BibTeX

@article{bergin2026tyrru,
author = {Bergin, Rosemary J. and Kohilas, Ioannis and Lockwood, Thomas E. and McDonagh, Andrew M. and Westerhausen, Mika T. and Bishop, David P.},
title = {{TyrRu: A Tyramide-Amplified Multimodal Ruthenium Tag for Elemental Mass Spectrometry and Immunofluorescence Imaging}},
journal = {Chemical \& biomedical imaging},
year = {2026},
month = mar,
volume = {4},
number = {8},
pages = {1805--1815},
publisher = {American Chemical Society},
issn = {2832-3637},
doi = {10.1021/cbmi.6c00007},
url = {https://doi.org/10.1021/cbmi.6c00007},
pmid = {42657241},
pmcid = {PMC13508111}
}

RIS

TY - JOUR
AU - Bergin, Rosemary J.
AU - Kohilas, Ioannis
AU - Lockwood, Thomas E.
AU - McDonagh, Andrew M.
AU - Westerhausen, Mika T.
AU - Bishop, David P.
TI - TyrRu: A Tyramide-Amplified Multimodal Ruthenium Tag for Elemental Mass Spectrometry and Immunofluorescence Imaging
T2 - Chemical & biomedical imaging
J2 - Chem Biomed Imaging
PY - 2026
DA - 2026/03/05
VL - 4
IS - 8
SP - 1805
EP - 1815
SN - 2832-3637
PB - American Chemical Society
DO - 10.1021/cbmi.6c00007
UR - https://doi.org/10.1021/cbmi.6c00007
LA - en
ER -

CSL-JSON

{
"id": "10.1021/cbmi.6c00007",
"type": "article-journal",
"title": "TyrRu: A Tyramide-Amplified Multimodal Ruthenium Tag for Elemental Mass Spectrometry and Immunofluorescence Imaging",
"container-title": "Chemical & biomedical imaging",
"author": [
{
"family": "Bergin",
"given": "Rosemary J."
},
{
"family": "Kohilas",
"given": "Ioannis"
},
{
"family": "Lockwood",
"given": "Thomas E."
},
{
"family": "McDonagh",
"given": "Andrew M."
},
{
"family": "Westerhausen",
"given": "Mika T."
},
{
"family": "Bishop",
"given": "David P."
}
],
"container-title-short": "Chem Biomed Imaging",
"volume": "4",
"issue": "8",
"page": "1805-1815",
"DOI": "10.1021/cbmi.6c00007",
"PMID": "42657241",
"PMCID": "PMC13508111",
"ISSN": "2832-3637",
"publisher": "American Chemical Society",
"URL": "https://doi.org/10.1021/cbmi.6c00007",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
5
]
]
}
}

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