Liposomal nanoprobes actuated by engineered peptide water channels for sensitive MRI detection of molecular targets
Data files
Apr 07, 2026 version files 838.13 MB
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Figure_3_a-d.zip
3.46 MB
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Figure_3_f-h.zip
138.10 MB
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Figure_4_b-d.zip
12.10 MB
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Figure_4_h-i.zip
684.46 MB
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loadbruker2.m
367 B
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README.md
5.42 KB
Abstract
This submission contains in vitro and in vivo MRI data for the article entitled "Liposomal nanoprobes actuated by engineered peptide water channels for sensitive MRI detection of molecular targets," by Das, Simon et al., in Nature Biomedical Engineering. Data included comprise raw Bruker-format MRI time series obtained from the experiments of Figures 3 and 4 in the paper, which focus on testing the functionality of a new type of MRI contrast agent in rodents. Methods details contained in the manuscript supplement metadata supplied here.
Dataset DOI: 10.5061/dryad.98sf7m0z9
Description of the data and file structure
This repository contains raw MRI data collected by Miranda Dawson for four in vivo experiments published in "Liposomal nanoprobes actuated by engineered peptide water channels for sensitive MRI detection of molecular targets."
Each .zip archive contains raw MRI data acquired using Bruker ParaVision software. Data are organized into one subfolder per animal, named rat_1, rat_2, etc. The number of animal subfolders varies by experiment (see individual file descriptions below).
Software requirements and data access
Important: The MRI data in this repository are stored in a proprietary binary format (Bruker ParaVision) and cannot be opened directly. Users must use one of the methods below to access them.
Recommended: MATLAB with the provided loadbruker2.m script
The recommended way to access these data is using MATLAB with the loadbruker2.m script provided in this repository. MATLAB is a commercial product; a free 30-day trial is available at mathworks.com.
General usage:
A = loadbruker2(filename, [x y z t])
filename: path to the 2dseq file (e.g.,'rat_1/pdata/1/2dseq')x, y: image dimensions in pixelsz: number of slicest: number of time points
Matrix dimensions for each dataset are listed in the file descriptions below. They can also be confirmed from the corresponding method file (look for PVM_Matrix, PVM_SPackArrNSlices, and PVM_NRepetitions parameters).
Alternative: Python (open-source)
Users without MATLAB access can use the open-source brukerapi Python library. Documentation is available at bruker-api.readthedocs.io.
Alternative: Bruker ParaVision
The proprietary acquisition software used to generate these files. Institutional access may be available through MRI core facilities.
Bruker directory structure
Each rat_N subfolder follows the standard Bruker ParaVision directory structure. Users only need two items to load and interpret the data:
method— A plain-text file containing scan acquisition parameters (e.g., matrix size, number of slices, TR, TE). Required to interpret the binary image data. Open with any text editor.pdata/1/2dseq— A binary file containing the reconstructed image data. Load using theloadbruker2.mscript (see above) or the Python brukerapi library.
All other files and subfolders in the Bruker directory are generated automatically by ParaVision and are not needed for data access.
Files and variables
loadbruker2.m
A MATLAB script for loading Bruker 2dseq binary files. Provided as a standalone script in the repository (not inside the .zip archives).
Figure_3_a-d.zip
Experiment: Intracranial biotin (or vehicle) infusion following intracranial Bt-LisNR injection.
Contents: 7 subfolders (rat_1 through rat_7), one per animal.
Sequence: RAREVTR (variable repetition time RARE). Each animal folder contains a single static image (t=1) acquired across 9 axial slices.
Image dimensions: [128 64 9 1] (x, y, z, t)
Loading example (MATLAB):
A = loadbruker2('rat_1/pdata/1/2dseq', [128 64 9 1]);
Figure_3_f-h.zip
Experiment: Peripheral biotin injection following intracranial Bt-LisNR injection.
Contents: 4 subfolders (rat_1, rat_2, rat_3, rat_4), one per animal.
Sequence: RAREVTR (variable repetition time RARE), used here for dynamic contrast-enhanced imaging. Each animal folder contains a time series of 84 volumes acquired across 9 axial slices.
Image dimensions: [128 64 9 84] (x, y, z, t)
Loading example (MATLAB):
A = loadbruker2('rat_1/pdata/1/2dseq', [128 64 9 84]);
Figure_4_b-d.zip
Experiment: Carotid artery injection of paramagnetic liposomes with or without LPA to enhance brain uptake.
Contents: 6 subfolders, one per animal. Subfolders are named by condition: LPA_1, LPA_2, LPA_3 (animals receiving LPA) and Control_1, Control_2, Control_3 (vehicle controls).
Sequence: RAREVTR (variable repetition time RARE), used here for T1 mapping. Each animal folder contains a single acquisition with multiple echo images (z=5) across multiple TR values (t=8).
Image dimensions: [150 75 5 8] (x, y, z=echo images, t=TR values)
Loading example (MATLAB):
A = loadbruker2('LPA_1/pdata/1/2dseq', [150 75 5 8]);
Figure_4_h-i.zip
Experiment: Paramagnetic liposome uptake into kidney, leg muscle, aorta, and bladder after intraperitoneal injection of liposomes and LPA.
Contents: 4 subfolders (liposome_mouse_01, liposome_mouse_02, liposome_mouse_03, liposome_mouse_04), one per animal.
Sequence: FcFLASH (fast gradient echo), used here for dynamic contrast-enhanced imaging. Each animal folder contains a time series of 175 volumes acquired across 14 coronal slices.
Image dimensions: [128 128 14 175] (x, y, z, t)
Loading example (MATLAB):
A = loadbruker2('liposome_mouse_01/pdata/1/2dseq', [128 128 14 175]);
Access information
Other publicly accessible locations of the data: N/A
Data was derived from the following sources: N/A