{"data":{"id":"10.5061/dryad.98sf7m0z9","type":"dois","attributes":{"doi":"10.5061/dryad.98sf7m0z9","prefix":"10.5061","suffix":"dryad.98sf7m0z9","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Dawson, Miranda","nameType":"Personal","givenName":"Miranda","familyName":"Dawson","affiliation":["Massachusetts Institute of Technology"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3226-9122","nameIdentifierScheme":"ORCID"}]},{"name":"Simon, Jacob","nameType":"Personal","givenName":"Jacob","familyName":"Simon","affiliation":["Stanford University School of Medicine"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7683-9839","nameIdentifierScheme":"ORCID"}]},{"name":"Das, Sayani","nameType":"Personal","givenName":"Sayani","familyName":"Das","affiliation":["Merck \u0026 Co., Inc., Rahway, NJ, USA (United States)"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0206-9223","nameIdentifierScheme":"ORCID"}]},{"name":"Jasanoff, Alan","nameType":"Personal","givenName":"Alan","familyName":"Jasanoff","affiliation":["Massachusetts Institute of Technology"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2834-6359","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Liposomal nanoprobes actuated by engineered peptide water channels for sensitive MRI detection of molecular targets"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"FOS: Nano-technology","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Neuroscience","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Magnetic resonance imaging","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Liposomes","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2026-03-03T16:04:37Z","dateType":"Created"},{"date":"2026-03-03T16:05:30Z","dateType":"Submitted"},{"date":"2026-04-07T00:00:00Z","dateType":"Issued"},{"date":"2026-04-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1038/s41551-026-01683-x","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["838132486 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This submission contains in vitro and in vivo MRI data for the article\n entitled \"Liposomal nanoprobes actuated by engineered peptide water\n channels for sensitive MRI detection of molecular targets,\" by Das,\n Simon et al., in Nature Biomedical Engineering. Data included comprise raw\n Bruker-format MRI time series obtained from the experiments of Figures 3\n and 4 in the paper, which focus on testing the functionality of a new type\n of MRI contrast agent in rodents. Methods details contained in the\n manuscript supplement metadata supplied here.","descriptionType":"Abstract"},{"description":"# README: Liposomal nanoprobes actuated by engineered peptide water\n channels for sensitive MRI detection of molecular targets Dataset DOI:\n 10.5061/dryad.98sf7m0z9 ## Description of the data and file structure This\n repository contains raw MRI data collected by Miranda Dawson for four in\n vivo experiments published in \"Liposomal nanoprobes actuated by\n engineered peptide water channels for sensitive MRI detection of molecular\n targets.\" Each .zip archive contains raw MRI data acquired using\n Bruker ParaVision software. Data are organized into one subfolder per\n animal, named rat_1, rat_2, etc. The number of animal subfolders varies by\n experiment (see individual file descriptions below). --- ## Software\n requirements and data access **Important: The MRI data in this repository\n are stored in a proprietary binary format (Bruker ParaVision) and cannot\n be opened directly. Users must use one of the methods below to access\n them.** ### Recommended: MATLAB with the provided loadbruker2.m script The\n recommended way to access these data is using MATLAB with the\n `loadbruker2.m` script provided in this repository. MATLAB is a commercial\n product; a free 30-day trial is available at\n [mathworks.com](https://www.mathworks.com). General usage: ```matlab A =\n loadbruker2(filename, [x y z t]) ``` * `filename`: path to the 2dseq file\n (e.g., `'rat_1/pdata/1/2dseq'`) * `x, y`: image dimensions in\n pixels * `z`: number of slices * `t`: number of time points Matrix\n dimensions for each dataset are listed in the file descriptions below.\n They can also be confirmed from the corresponding `method` file (look for\n `PVM_Matrix`, `PVM_SPackArrNSlices`, and `PVM_NRepetitions` parameters).\n ### Alternative: Python (open-source) Users without MATLAB access can use\n the open-source `brukerapi` Python library. Documentation is available at\n [bruker-api.readthedocs.io](https://bruker-api.readthedocs.io). ###\n Alternative: Bruker ParaVision The proprietary acquisition software used\n to generate these files. Institutional access may be available through MRI\n core facilities. --- ## Bruker directory structure Each `rat_N` subfolder\n follows the standard Bruker ParaVision directory structure. Users only\n need two items to load and interpret the data: * **`method`** — A\n plain-text file containing scan acquisition parameters (e.g., matrix size,\n number of slices, TR, TE). Required to interpret the binary image data.\n Open with any text editor. * **`pdata/1/2dseq`** — A binary file\n containing the reconstructed image data. Load using the `loadbruker2.m`\n script (see above) or the Python brukerapi library. All other files and\n subfolders in the Bruker directory are generated automatically by\n ParaVision and are not needed for data access. --- ## Files and variables\n ### loadbruker2.m A MATLAB script for loading Bruker 2dseq binary files.\n Provided as a standalone script in the repository (not inside the .zip\n archives). --- ### Figure_3_a-d.zip **Experiment:** Intracranial biotin\n (or vehicle) infusion following intracranial Bt-LisNR injection.\n **Contents:** 7 subfolders (rat_1 through rat_7), one per animal.\n **Sequence:** RAREVTR (variable repetition time RARE). Each animal folder\n contains a single static image (t=1) acquired across 9 axial slices.\n **Image dimensions:** [128 64 9 1] (x, y, z, t) **Loading example\n (MATLAB):** ```matlab A = loadbruker2('rat_1/pdata/1/2dseq',\n [128 64 9 1]); ``` --- ### Figure_3_f-h.zip **Experiment:** Peripheral\n biotin injection following intracranial Bt-LisNR injection. **Contents:**\n 4 subfolders (rat_1, rat_2, rat_3, rat_4), one per animal. **Sequence:**\n RAREVTR (variable repetition time RARE), used here for dynamic\n contrast-enhanced imaging. Each animal folder contains a time series of 84\n volumes acquired across 9 axial slices. **Image dimensions:** [128 64 9\n 84] (x, y, z, t) **Loading example (MATLAB):** ```matlab A =\n loadbruker2('rat_1/pdata/1/2dseq', [128 64 9 84]); ``` --- ###\n Figure_4_b-d.zip **Experiment:** Carotid artery injection of paramagnetic\n liposomes with or without LPA to enhance brain uptake. **Contents:** 6\n subfolders, one per animal. Subfolders are named by condition: LPA_1,\n LPA_2, LPA_3 (animals receiving LPA) and Control_1, Control_2, Control_3\n (vehicle controls). **Sequence:** RAREVTR (variable repetition time RARE),\n used here for T1 mapping. Each animal folder contains a single acquisition\n with multiple echo images (z=5) across multiple TR values (t=8). **Image\n dimensions:** [150 75 5 8] (x, y, z=echo images, t=TR values) **Loading\n example (MATLAB):** ```matlab A =\n loadbruker2('LPA_1/pdata/1/2dseq', [150 75 5 8]); ``` --- ###\n Figure_4_h-i.zip **Experiment:** Paramagnetic liposome uptake into kidney,\n leg muscle, aorta, and bladder after intraperitoneal injection of\n liposomes and LPA. **Contents:** 4 subfolders (liposome_mouse_01,\n liposome_mouse_02, liposome_mouse_03, liposome_mouse_04), one per animal.\n **Sequence:** FcFLASH (fast gradient echo), used here for dynamic\n contrast-enhanced imaging. Each animal folder contains a time series of\n 175 volumes acquired across 14 coronal slices. **Image dimensions:** [128\n 128 14 175] (x, y, z, t) **Loading example (MATLAB):** ```matlab A =\n loadbruker2('liposome_mouse_01/pdata/1/2dseq', [128 128 14\n 175]); ``` --- ## Access information Other publicly accessible locations\n of the data: N/A Data was derived from the following sources: N/A","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"awardUri":"https://reporter.nih.gov/project-details/9404176","schemeUri":"https://ror.org","awardTitle":"Nanoprobes for neurotransmitter-sensitive molecular fMRI in addiction research","funderName":"National Institute on Drug Abuse","awardNumber":"1R21DA044748","funderIdentifier":"https://ror.org/00fq5cm18","funderIdentifierType":"ROR"},{"awardUri":"https://reporter.nih.gov/project-details/10154138","schemeUri":"https://ror.org","awardTitle":"Nanosensors for sensitive brain-wide neurochemical imaging","funderName":"National Institute of Neurological Disorders and Stroke","awardNumber":"1R01NS120592-01","funderIdentifier":"https://ror.org/01s5ya894","funderIdentifierType":"ROR"}],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.5061/DRYAD.98SF7M0Z9</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Dawson, Miranda</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3226-9122</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/042nb2s44" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Massachusetts Institute of Technology</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Simon, Jacob</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7683-9839</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/011pcwc98" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Stanford University School of Medicine</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Das, Sayani</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0206-9223</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/02891sr49" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Merck &amp; Co., Inc., Rahway, NJ, USA (United States)</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Jasanoff, Alan</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2834-6359</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/042nb2s44" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Massachusetts Institute of Technology</affiliation>
    </creator>
  </creators>
  <titles>
    <title>
      Liposomal nanoprobes actuated by engineered peptide water channels for
      sensitive MRI detection of molecular targets
    </title>
  </titles>
  <publisher publisherIdentifier="https://ror.org/00x6h5n95" publisherIdentifierScheme="ROR" schemeURI="https://ror.org/">Dryad</publisher>
  <resourceType resourceTypeGeneral="Dataset">dataset</resourceType>
  <publicationYear>2026</publicationYear>
  <subjects>
    <subject subjectScheme="fos" schemeURI="https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf">FOS: Nano-technology</subject>
    <subject subjectScheme="fos" schemeURI="https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf">FOS: Biological sciences</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Neuroscience</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Magnetic resonance imaging</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Liposomes</subject>
  </subjects>
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    <fundingReference>
      <funderName>National Institute on Drug Abuse</funderName>
      <funderIdentifier funderIdentifierType="ROR">https://ror.org/00fq5cm18</funderIdentifier>
      <awardNumber awardURI="https://reporter.nih.gov/project-details/9404176">1R21DA044748</awardNumber>
      <awardTitle>Nanoprobes for neurotransmitter-sensitive molecular fMRI in addiction research</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>National Institute of Neurological Disorders and Stroke</funderName>
      <funderIdentifier funderIdentifierType="ROR">https://ror.org/01s5ya894</funderIdentifier>
      <awardNumber awardURI="https://reporter.nih.gov/project-details/10154138">1R01NS120592-01</awardNumber>
      <awardTitle>Nanosensors for sensitive brain-wide neurochemical imaging</awardTitle>
    </fundingReference>
  </fundingReferences>
  <dates>
    <date dateType="Created">2026-03-03T16:04:37Z</date>
    <date dateType="Submitted">2026-03-03T16:05:30Z</date>
    <date dateType="Issued">2026-04-07T00:00:00Z</date>
    <date dateType="Available">2026-04-07T00:00:00Z</date>
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    <rights rightsURI="https://spdx.org/licenses/CC0-1.0.html">Creative Commons Zero v1.0 Universal</rights>
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  <descriptions>
    <description descriptionType="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.
    </description>
    <description descriptionType="TechnicalInfo">
      # README: Liposomal nanoprobes actuated by engineered peptide water
      channels for sensitive MRI detection of molecular targets 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](https://www.mathworks.com). General usage: ```matlab 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
      pixels * `z`: number of slices * `t`: 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](https://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 the `loadbruker2.m`
      script (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):** ```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):** ```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):** ```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):** ```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
    </description>
  </descriptions>
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