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Sci Data. 2019 Dec 17;6(1):323. doi: 10.1038/s41597-019-0332-y.

Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer.

Rashid R1,2,3,4, Gaglia G1,2,3, Chen YA2,3, Lin JR2,3, Du Z1,2,3, Maliga Z2,3, Schapiro D2,5, Yapp C2, Muhlich J2, Sokolov A2,4, Sorger P6,7,8, Santagata S9,10,11,12.

Author information

1
Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
2
Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States.
3
Ludwig Center at Harvard, Harvard Medical School, Boston, MA, United States.
4
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
5
Broad Institute of MIT and Harvard, Cambridge, MA, United States.
6
Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States. peter_sorger@hms.harvard.edu.
7
Ludwig Center at Harvard, Harvard Medical School, Boston, MA, United States. peter_sorger@hms.harvard.edu.
8
Department of Systems Biology, Harvard Medical School, Boston, MA, United States. peter_sorger@hms.harvard.edu.
9
Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States. ssantagata@bics.bwh.harvard.edu.
10
Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States. ssantagata@bics.bwh.harvard.edu.
11
Ludwig Center at Harvard, Harvard Medical School, Boston, MA, United States. ssantagata@bics.bwh.harvard.edu.
12
Department of Oncologic Pathology, Dana Farber Cancer Institute, Boston, MA, United States. ssantagata@bics.bwh.harvard.edu.

Abstract

In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.

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