Metadata Reporting Standards

MACSima

A cyclic, automated immunofluorescence imaging platform that enables spatial profiling of hundreds of protein targets at subcellular resolution across a single tissue section. In each imaging cycle, up to three fluorochrome-conjugated antibodies are applied, the specimen is imaged across multiple fields, and the fluorescent signals are chemically erased before the next cycle begins. By repeating this stain–image–erase workflow, MACSima builds a high-dimensional protein map of intact tissue without requiring spectral unmixing.

DOI: https://doi.org/10.35079/HBM673.QFPX.636

Published: September 1, 2026

Download (ZIP)

MD5 hash: 6f1e05b9cc15bc6f8c1c0e89f57669a1

?

Schema

Legend: Text field Allowable value Radio Numeric

* Required

Attribute Type Description Allowable Values
Parent sample ID * The unique identifier from HuBMAP or SenNet for the sample (such as a block, section, or suspension) used to perform the assay. For instance, in an RNAseq assay, the parent sample would be the suspension, while in imaging assays, it would be the tissue section. If the assay is derived from multiple parent samples, this field should contain a comma-separated list of identifiers. Example: HBM386.ZGKG.235, HBM672.MKPK.442  
Lab ID A locally assigned identifier provided by the data provider for the dataset. It is used to reference an external metadata record that may be maintained independently, enabling traceability and supporting provenance tracking. Example: Visium_9OLC_A4_S1  
Preparation protocol DOI * The DOI for the protocols.io page that details the assay or the procedures used for sample procurement and preparation. For example, in the case of an imaging assay, the protocol may start with tissue section staining and end with the generation of an OME-TIFF file. The documented protocol should also include any image processing steps involved in producing the final OME-TIFF. Example: https://dx.doi.org/10.17504/protocols.io.eq2lyno9qvx9/v1  
Dataset type * The specific type of dataset being produced. Example: RNAseq HiFi-Slide SNARE-seq2 COMET Visium (no probes) DESI Confocal Stereo-seq Visium (with probes) Molecular Cartography Virtual Histology DBiT-seq Seq-Scope CosMx Transcriptomics CyCIF Light Sheet iCLAP seqFISH ATACseq CosMx Proteomics Singular Genomics G4X Visium HD MERFISH 10X Multiome 4i PhenoCycler Second Harmonic Generation (SHG) Thick section Multiphoton MxIF MACSima CyTOF Illumina Spatial v0 Olink MIBI Auto-fluorescence FACS Xenium SIMS Cell DIVE CODEX GeoMx (NGS) MUSIC Pixel-seqV2 MALDI 2D Imaging Mass Cytometry Histology Enhanced Stimulated Raman Spectroscopy (SRS) DART-FISH Resolve RNAseq LC-MS nanoSPLITS GeoMx (nCounter) Raman Imaging RNAseq (with probes) MS Lipidomics STARmap MPLEx
Analyte class * The analyte class which is the target molecule that the assay is measuring. Example: DNA DNA + RNA Nucleic acid + protein Chromatin Lipid + metabolite + protein RNA + protein RNA Metabolite Unsaturated lipid Lipid + metabolite Saturated lipid Lipid Protein Fluorochrome Collagen DNA Peptide Polysaccharide Endogenous fluorophore
Is targeted? * Indicates whether a specific molecule or set of molecules is targeted for detection or measurement by the assay. Example: Yes Yes No
Acquisition instrument vendor * The company that manufactures or supplies the acquisition instrument. An acquisition instrument is a device equipped with signal detection hardware and signal processing software. It captures signals produced by assays, such as variations in light intensity or color, or signals corresponding to molecular mass. If the instrument was custom-built or developed internally, enter “In-House”. Example: Illumina BGI Genomics Cytiva Thermo Fisher Scientific Zeiss Microscopy Complete Genomics 3DHISTECH GE Healthcare Leica Microsystems Akoya Biosciences NanoString Element Biosciences Andor Huron Digital Pathology Illumina Ionpath Waters In-House Resolve Biosciences Singular Genomics Vizgen Standard BioTools (Fluidigm) Sciex Bruker Evident Scientific (Olympus) Keyence Miltenyi Biotec Leica Biosystems Revvity Cytek Biosciences 10x Genomics Microscopes International Hamamatsu Motic
Acquisition instrument model * The specific model of the acquisition instrument, as manufacturers often offer various versions with differing features or sensitivities. These differences may be relevant to the processing or interpretation of the data. If the instrument was custom-built or developed internally, enter “In-House”. If the model is unknown, enter “Unknown”. Example: HiSeq 4000 SCN400 STELLARIS 5 BZ-X710 Pannoramic MIDI II Digital Scanner Not applicable MoticEasyScan One EVOS M7000 NovaSeq X LSM 710 Confocal Microscope NanoZoomer 2.0-HT timsTOF Ultra 2 Lightsheet 7 Phenocycler-Fusion 1.0 DNBSEQ-T7 timsTOF Pro Unknown AVITI DMi8 Opera Phenix Plus HCS timsTOF Pro 2 Q Exactive UHMR Q Exactive timsTOF SCP Zyla 4.2 sCMOS Helios uScopeHXII-20 Orbitrap Fusion Tribrid Custom: Multiphoton QTRAP 5500 timsTOF Ultra BZ-X800 CyTOF 2 G4X Spatial Sequencer NextSeq 500 NanoZoomer S360 Hyperion Imaging System NovaSeq X Plus CyTOF XT NanoZoomer-SQ NextSeq 550 Axio Zoom.V16 Digital Spatial Profiler timsTOF FleX timsTOF FleX MALDI-2 NanoZoomer S210 BZ-X810 Axio Observer 7 Cytek Northern Lights Opera Phenix HCS Zeiss LightSheet Z.1 IN Cell Analyzer 2200 timsTOF HT PhenoImager Fusion DM6 B Phenocycler-Fusion 2.0 Aperio CS2 Orbitrap Fusion Lumos Tribrid Resolve Biosciences Molecular Cartography MALDI timsTOF Flex Prototype TissueScope LE Slide Scanner VS200 Slide Scanner Axio Observer 5 NanoZoomer 2.0-RS Axio Observer 3 HiSeq 2500 Orbitrap Eclipse Tribrid Cell DIVE MERSCOPE NextSeq 2000 NovaSeq 6000 In-House HiSeq 4000 solariX Panoramic 150 Digital Scanner Aperio AT2 MIBIscope SYNAPT G2-Si MACSima System Biomark HD NanoZoomer S60 CosMx Spatial Molecular Imager MERSCOPE Ultra Axio Scan.Z1 Juno System Q Exactive HF Xenium Analyzer
Source storage duration value * The length of time the sample was stored prior to processing it. For assays performed on tissue sections, this refers to how long the tissue section (e.g., slide) was stored before the assay began (e.g., imaging). For assays performed on suspensions, such as sequencing, it refers to how long the suspension was stored before library construction started. Example: 12  
Source storage duration unit * The unit of measurement used to specify the source storage duration value. Example: hour hour month year day minute
Time since acquisition instrument calibration value The length of time since the acquisition instrument was last serviced or calibrated. This provides a metric for assessing drift in data capture. Example: 10  
Time since acquisition instrument calibration unit The unit of measurement used to specify the time since acquisition instrument calibration value. Example: month month year day
Contributors path * The name of the file containing the ORCID IDs for all contributors to this dataset. Example: ./contributors.csv  
Data path * The top-level directory containing the raw and/or processed data. For a single dataset upload, this might be represented as “.”, whereas for a data upload containing multiple datasets, this would be the directory name for the respective dataset. For example, if the data is within a directory named “TEST001-RK”, use the syntax “./TEST001-RK” for this field. If there are multiple directory levels, use the format “./TEST001-RK/Run1/Pass2”, where “Pass2” is the subdirectory where the single dataset’s data is stored. This is an internal metadata field used solely for data ingestion. Example: ./TEST001-RK  
Antibodies path * The path to the antibodies.tsv file relative to the root directory of the upload structure. This path should start with “.” and is typically formatted as “./extras/antibodies.tsv”. Example: ./extras/antibodies.tsv  
Total run time value * The total run time, which is the duration the instrument takes to fully complete all imaging rounds on the loaded slide. Example: 24  
Total run time unit * The unit of measurement for the total run time value. If the total run time is not specified, this field may be left blank. Example: hour hour minute
Number of antibodies * The number of antibodies used in the assay. If no antibodies were utilized, enter 0. Example: 5  
Number of channels * The number of fluorescent channels that are imaged during each cycle. Example: 3  
Number of biomarker imaging rounds * The number of imaging rounds required to capture the tagged biomarkers. For CODEX, a biomarker imaging round includes steps such as (1) oligo application, (2) fluor application, and (3) washes. For Cell DIVE, it involves (1) the staining of a biomarker via secondary detection or direct conjugate, followed by (2) dye inactivation. Example: 3  
Number of total imaging rounds * The total number of imaging rounds performed using a microscope to collect either autofluorescence/background or stained signals, such as those used in histological analysis. Example: 5  
Slide ID The unique identifier assigned to each slide, enabling users to determine which tissue sections were processed together on the same slide. It is recommended that data providers prefix the ID with the center name to prevent overlapping values across different centers. Example: VAN0071-PA-1-1_AF  
Cell boundary marker or stain * The name of the marker or stain used to identify all cell boundaries in the tissue. This name must exactly match the antibody-targeted molecule marker or non-antibody targeted molecule stain as found in the imaging data. For example, in the case of using the PhenoCycler, ensure the name corresponds to the value in the XPD output file. If multiple markers or stains are employed, list them in a comma-separated format. Example: Pan-Cytokeratin, E-Cadherin  
Nuclear marker or stain * The nuclear marker or stain used, which can be an antibody-targeted molecule present in or around the cell nucleus. For protein targets, use the protein or gene symbol that identifies the antibody target, ensuring it matches the antibody target from the panel used or custom panels. Preferably, if using a custom antibody marker, this symbol should be the HGNC symbol (https://www.genenames.org/). For non-protein targets, provide the stain name (e.g., DAPI) and, when applicable, include the associated staining kit and vendor. For the PhenoCycler, ensure the symbol matches the value found in the XPD output file. Example: DAPI  
Non global files Specifies a semicolon-separated list of non-global files that are to be included in the dataset. The file paths assume that the files are located in the “TOP/non-global/” directory. For instance, if the file is located at TOP/non-global/lab_processed/images/1-tissue-boundary.geojson, the value for this field would be “./lab_processed/images/1-tissue-boundary.geojson”. Once ingested, these files will be copied to their appropriate locations within the respective dataset directory tree. This field is intended for internal HuBMAP processing. Examples for GeoMx and PhenoCycler are provided in the File Locations documentation: https://docs.google.com/document/d/1n2McSs9geA9Eli4QWQaB3c9R3wo5d5U1Xd57DWQfN5Q/edit#heading=h.1u82i4axggee Example: ./lab_processed/images/1-tissue-boundary.geojson  
Signal quenching method * An automated step in cyclic imaging workflows that removes fluorescent signals from a sample to enable subsequent rounds of staining. This process is critical for high-multiplex imaging, allowing the detection of numerous markers on a single sample while preserving tissue integrity, as demonstrated by technologies such as MACSima Imaging Cyclic Staining (MICS). Example: Photobleaching Enzymatic cleavage Photobleaching
Metadata schema ID * The unique string identifier for the metadata specification version, which is easily interpretable by computers for purposes of data validation and processing. Example: 22bc762a-5020-419d-b170-24253ed9e8d9  
CEDAR OpenView Documentation CEDAR Validator

HuBMAP Datasets

The HuBMAP Data Portal is an open platform to discover, visualize, and download standardized healthy single-cell and spatial tissue data.


View HuBMAP Datasets

Citation

Fisher SA, Hardi J, Morgan R, Nordgren E, Kant PM, Honick B, Rosario J, O'Connor MJ, Turner ML, DCWG Members, Gehlenborg N, Blood PD, Silverstein JC, Musen MA. 2026. The HuBMAP Framework for Advancing Data FAIRness. submitted. https://doi.org/10.64898/2026.06.01.728946


Reuse

This standard may be reused, expanded, or referenced by external repositories. It represents version 2.0 of the metadata schema and version 2.0 of the file directory structure. If you are uploading data, the latest versions of both are available at the "Latest Versions" link below. If you are querying or using HuBMAP or SenNet metadata, a harmonized view of the metadata specification is available at the "Harmonized Version" link below.


Latest Versions    Harmonized Version

Contributors

Below is the information for the individuals who contributed to the HuBMAP and SenNet metadata reporting standards.

For questions about this standard, email HuBMAP Helpdesk. You can alternatively reach out to the individuals listed below, either via the email address listed in the table or via contact information provided on their ORCID profile page.

Name Affiliation Contact ORCID
Stephen A Fisher University of Pennsylvania, Philadelphia PA, USA safisher@upenn.edu 0000-0001-8034-7685
Josef Hardi Stanford University, Stanford, CA, USA johardi@stanford.edu 0000-0002-2533-6681
Richard Morgan University of Pittsburgh, Pittsburgh, PA, USA rsm66@pitt.edu 0009-0003-1800-8545
Mark A Musen Stanford University, Stanford, CA, USA musen@stanford.edu 0000-0003-3325-793X
Jonathan C Silverstein University of Pittsburgh, Pittsburgh, PA, USA j.c.s@pitt.edu 0000-0002-9252-6039
Erik Nordgren University of Pennsylvania, Philadelphia PA, USA   0000-0002-5024-0278
Peter M Kant University of Pittsburgh, Pittsburgh, PA, USA; Currently - Otsuka Precision Health, Princeton, NJ, USA   0009-0002-6510-5041
Brendan John Honick Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, USA   0000-0001-6128-9854
Martin J O’Connor Stanford University, Stanford, CA, USA   0000-0002-2256-2421
Jean G Rosario University of Pennsylvania, Philadelphia PA, USA   0000-0002-6116-5058
Nils Gehlenborg Harvard Medical School, Boston, MA, USA   0000-0003-0327-8297
Philip D Blood Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, USA   0000-0002-9129-1223
Kyung Jin Ahn Children’s Hospital of Philadelphia, Philadelphia, PA, USA   0000-0002-4184-482X
Christopher R Anderton Pacific Northwest National Laboratory, Richland, WA, USA   0000-0002-6170-1033
Shovik Bandyopadhyay Children’s Hospital of Philadelphia, Philadelphia, PA, USA; Brigham and Women’s Hospital, Boston, MA, USA   0000-0003-3919-3914
Kenneth C Bedi University of Pennsylvania, Philadelphia PA, USA   0000-0003-3588-9324
Maigan Brusko University of Florida, Gainesville, FL, USA   0000-0002-4331-2202
Martha Campbell-Thompson University of Florida, Gainesville, FL, USA   0000-0001-6878-1235
James Carson The University of Texas at Austin, Austin, TX, USA   0000-0001-9009-5645
Chase M Carver Mayo Clinic, Rochester, MN, USA   0000-0003-4002-2418
Jing Chen University of Florida, Gainesville, FL, USA   0000-0001-8008-8062
Anthony M Corbett University of Rochester Medical Center, Rochester, NY, USA   0000-0001-9545-0853
Alexandra E Cuaycal University of Florida, Gainesville, FL, USA   0000-0002-9060-6326
Penny Cuda Carnegie Mellon University, Pittsburgh, PA, USA   0009-0002-6547-2650
Dinh Diep University of California, San Diego, CA, USA; Currently - Altos Labs, San Diego, CA, USA   0000-0001-6057-4119
Sergii Domanskyi The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA   0000-0002-6847-6019
Sean Donahue Carnegie Mellon University, Pittsburgh, PA, USA   0000-0002-4072-2046
Michael P Duffy University of Pennsylvania, Philadelphia PA, USA   0000-0001-5325-3683
Michael T Eadon Indiana University School of Medicine, Indianapolis, IN, USA   0000-0003-3066-2876
Jean Fan Johns Hopkins University, Baltimore, MD, USA   0000-0002-0212-5451
Melissa A Farrow Vanderbilt University, Nashville, TN, USA   0000-0002-1602-2082
Kathleen M Fisch University of California San Diego, La Jolla, CA, USA   0000-0002-0117-7444
William F Flynn The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA   0000-0001-6533-0340
James M Fulcher Pacific Northwest National Laboratory, Richland, WA, USA   0000-0001-9033-3623
Soumya Ghose GE HealthCare, Niskayuna, NY, USA   0000-0002-2730-1482
Fiona Ginty GE HealthCare, Niskayuna, NY, USA   0000-0001-6638-683X
Joana P Gonçalves Delft University of Technology, Delft, The Netherlands   0000-0001-6072-9627
Yongqun He University of Michigan, Ann Arbor, MI, USA   0000-0001-9189-9661
Po Hu Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA   0000-0003-2422-1652
Sanjay Jain Washington University School of Medicine, St. Louis, MO, USA   0000-0003-2804-127X
Thomas V Karathanos Stanford University, Stanford, CA, USA   0000-0003-1754-3872
Madhurima Kaushal Washington University School of Medicine   0000-0003-2760-0586
Angela RS Kruse Vanderbilt University, Nashville, TN, USA; Currently - The Ohio State University, Columbus, OH, USA   0000-0001-8776-2769
Yumi Kwon Pacific Northwest National Laboratory, Richland, WA, USA   0000-0003-0523-6197
Blue B Lake University of California San Diego, La Jolla, CA, USA; Currently - Altos Labs, San Diego, CA, USA   0000-0002-8637-9044
Roy Lardenoije Delft University of Technology, Delft, The Netherlands   0000-0002-9026-7870
Shin Lin Emory University, Atlanta, GA, USA   0000-0003-0118-0413
Yiing Lin Washington University, St. Louis, MO, USA   0000-0002-0317-7608
Scott A Lindsay University of California, San Diego, CA, USA   0000-0002-2929-7755
Peiran Lu Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA   0009-0001-5096-3046
Clayton Mathews University of Florida, Gainesville, FL, USA   0000-0002-8817-6355
Elizabeth McDonough GE HealthCare, Niskayuna, NY, USA   0000-0001-7524-8260
Ricardo Melo Ferreira Indiana University School of Medicine, Indianapolis, IN, USA   0000-0003-2063-9744
Emma M Monte Stanford University, Stanford, CA, USA   0000-0003-2566-1967
Kathleen O’Neill University of Pennsylvania, Philadelphia PA, USA   0000-0003-1980-6840
Minxing Pang University of Pennsylvania, Philadelphia PA, USA   0000-0001-5208-5972
Mana Parast University of California San Diego, La Jolla, CA, USA   0000-0001-5963-2246
Liming Pei Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA   0000-0002-1924-0333
Samuel Peters University of Minnesota, Minneapolis, MN, USA   0000-0003-1479-8087
Ajay Pillai National Institute of Health, Bethesda, MD, USA   0000-0002-9789-7189
Gloria Pryhuber University of Rochester Medical Center, Rochester, NY, USA   0000-0002-9185-3994
Ling Qin University of Pennsylvania, Philadelphia PA, USA   0000-0002-2582-0078
Presha Rajbhandari Columbia University, NYC, NY, USA   0000-0003-2184-7238
Matthew M Ruffalo Carnegie Mellon University, Pittsburgh, PA, USA   0000-0003-2222-6169
Pinaki Sarder University of Florida, Gainesville, FL, USA   0000-0003-2450-5233
Diane C Saunders Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA; Northwestern University, Chicago, IL, USA   0000-0002-8849-6746
Kevin Schneider Buck Institute, Novato, CA, USA   0009-0001-8046-0167
Lingyan Shi University of California San Diego, La Jolla, CA, USA   0000-0003-1373-3206
Santhosh Sivajothi The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA   0000-0002-8854-4517
David Smith Children’s Hospital of Philadelphia, Philadelphia, PA, USA   0000-0001-7858-3785
Jeff M Spraggins Vanderbilt University, Nashville, TN, USA   0000-0001-9198-5498
Valentina Stanley University of California, San Diego, CA, USA   0000-0002-2212-7796
Kai Tan Children’s Hospital of Philadelphia, Philadelphia, PA, USA; University of Pennsylvania, Philadelphia, PA, USA   0000-0002-9104-5567
Anusha Thadi Children’s Hospital of Philadelphia, Philadelphia, PA, USA   0000-0002-1271-0398
Hua Tian Columbia University Medical Center, NYC, NY, USA   0000-0002-3598-0219
Morgan L Turner Harvard Medical School, Boston, MA, USA   0000-0002-1512-9742
Ioannis S Vlachos Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Cancer Center, Boston, MA, USA; Broad Institute of MIT and Harvard, Boston, MA, USA   0000-0002-8849-808X
Seth Winfree University of Nebraska Medical Center, Omaha, NE, USA; Currently - QCDx Inc, Farmington, CT, USA    
Pei-Hsun Wu Johns Hopkins University, Baltimore, MD, USA   0000-0002-7371-2960
Kevin J Zemaitis Pacific Northwest National Laboratory, Richland, WA, USA   0000-0002-3524-9776
Mowei Zhou Pacific Northwest National Laboratory, Richland, WA, USA; Currently - Zhejiang University, Hangzhou, Zhejiang 310058, China   0000-0003-3575-3224
Chenchen Zhu Department of Genetics, Stanford University, Stanford, CA, USA   0000-0003-2165-9456